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# Exchange rate undervaluation and African surges: what do we learn from exported products?

Camille da Piedade, Luc Jacolin, Patrick Plane

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Camille da Piedade, Luc Jacolin, Patrick Plane. Exchange rate undervaluation and African surges: what do we learn from exported products?. 2024. hal-04840399

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# Exchange rate undervaluation and African surges: what do we learn from exported products?

Camille da Piedade¹, Luc Jacolin², Patrick Plane¹,³

1 : Université Clermont Auvergne, CNRS, IRD, CERDI, F-63000 CLERMONT FERRAND, France

2 : Banque de France, 31 Rue Croix des Petits Champs, 75001 Paris, France

3 : FERDI, 63 Bd François Mitterrand, 63000 Clermont-Ferrand, France

E-mail addresses : [camille.da_piedade@uca.fr](mailto:camille.da_piedade@uca.fr) (C. da Piedade) ; [luc.jacolin@banque-france.fr](mailto:luc.jacolin@banque-france.fr) (L. Jacolin) ; [patrick.plane@uca.fr](mailto:patrick.plane@uca.fr) (P. Plane)

## Acknowledgments:

We thank Martha Denisse Pierola for fruitful exchanges about the calculation of export surges. We are grateful to Simone Bertoli, Jean-Charles Bricongne, Bruno Cabrillac, Olivier de Bandt, Ibrahim Elbadawi, Ana Fernandes, Lionel Fontagné, Sylviane Guillaumont Jeanneney, Patrick Guillaumont, Mathilde Maurel, Jaime de Melo, Akiko Suwa-Eisenmann, Johannes Van Biesebroeck as well as the participants in the Ferdi-Banque de France research seminar for valuable comments on an earlier version of the paper. We are also indebted to Jean-Marc Atsebi, Joël Cariolle, Sosso Feindouno and Florian Léon for insightful discussions. We have a special thought for Olivier Cadot with whom we had stimulating talks on some points of the paper before he passed away. The paper has been presented at Oxford to the CSAE Conference, 19-21 March 2023: Economic Development in Africa.

**Disclosure statement:** No potential conflict of interest was reported by the author(s).

**Disclaimer:** The opinions expressed in this paper are those of the authors and should not be construed as representing the views of the institutions with which they are affiliated.

## Funding:

This research benefited from the financial support of the French government's Agence Nationale de la Recherche through the "Investissement d'Avenir" program (ANR-10-LABX-14-01) and the Banque de France - Ferdi partnership. This document reflects the views of the authors and does not express the views of the Banque de France.
Exchange rate undervaluation and African surges:
what do we learn from exported products?

Abstract

We study the role of undervaluation of currency exchange rates in triggering African product export surges. Over the period 1995-2017, 96 episodes are identified for 41 African countries from a basket of their primary and manufactured exported goods (149 products, 4-digit HS code). We compute country-product specific real exchange rate misalignments, that allow testing the hypothesis that undervaluation drives competitiveness and thus export surges. The complementary log-log model confirms that product-specific undervaluation promotes the occurrence of surge episodes. This effect proves robust to the way we define export episodes, the introduction of covariates in the model, and the use of the Relogit as an alternative estimator for rare events.

**Keywords:** Africa, export surges, exchange rate, undervaluation, competitiveness, trade.

JEL codes: F15, F41, F63, O11, O55.
Introduction:

A recent strand of the literature has shown that currency undervaluation can
be a driving force of surge episodes defined as a strong and sustained increase in the
growth of a performance variable. Hausmann, Prichett and Rodrik (2005), and Rodrik
(2008) reached this conclusion for the GDP growth, while Freund and Pierola (2012),
Eichengreen and Gupta (2013), Cadot et al (2014, 2016) showed a similar result for
sectoral exports. Given the relatively small size and openness of African economies,
exports are crucial, and competitiveness is a prerequisite for the structural
transformation and long-term economic growth. In this paper, we use the surge
identification methodology to examine African exports at the disaggregated level of
export products. Our study covers all African countries for which statistical data are
available, spans the period from 1995 to 2017, and focuses on for their main primary
and manufactured export products, a total of 149 products across 41 countries.

The contribution of this paper is on three levels. (i) First, we apply the surge literature to disaggregated exports (4-digit Harmonized System nomenclature or HS code). This method has similarities to the hypothetical-deductive framework of the double-difference method but is based on a backward reasoning. We start by identifying surges using a counterfactual. As in Freund and Pierola (2012), the performance over a seven-year period must be above the previous seven-year average, and higher than the growth rate of the world exports of the product (i.e., gain in market share). (ii) In a second stage, we identify what determines surge episodes. Our hypothesis is that production cost competitiveness, measured with respect to the main country exporters of a specific product, contributes to triggering surges.<fnref n="1" /> (iii) Third, the impact of the undervaluation is tested and controlled for concomitant determinants of surge episodes by using the complementary log-log model, which accounts for the fact that export surges are rare events.

All primary and manufactured export products with annual export flows
exceeding US$ 1 million for three consecutive years are included in our study. The set
of goods we consider excludes natural resource rents products-- i.e., those with an
average production cost significantly lower than the international price (raw minerals,
gas, and crude oil), and whose production or investment decisions are not sensitive to
the undervaluation of the currency.

On average, for the 41 African countries and over the 1995-2017, exported
products that we consider account for 78% for primary and for 51% for manufactured
products, respectively. We identify 96 export surges over the sample, with an
unconditional probability for a surge episode of 2.3%, slightly higher for primary
products. The cloglog estimator suggests that country product undervaluation of the

<footnote n="1">¹ In this article all references to undervaluation and real exchange rate misalignments are product-specific.</footnote>
currency preceded surges. A one standard deviation increase in the level of the undervaluation increases the probability of this rare event by respectively 1.6 percentage points for primary products and 0.7 percentage points for manufactured goods, or by 22 and 11 percent, respectively, in relative terms. This conclusion is robust to changes in the parameters defining surge episodes and to the use of the Rare events logistic regression (ReLogit) estimator as an alternative to the cloglog model.

Although it has no direct implications for exchange rate policy, this empirical study provides a diagnosis of competitiveness for each of the country-product pairs exported to the African continent. It identifies the sensitivity of country specializations to relative unit production costs, and the export surge that could result from undervaluing the product's real exchange rate. Changing the nominal exchange rate is one way of influencing relative unit costs, but it is not the easiest to implement, as each misalignment is linked to a specific country-product pair. Policies to stimulate export growth focus primarily on the productive behavior of exporting companies, on their ability to reduce domestic costs and improve product quality. This productive behavior contributes to variations in the real exchange rate and to the competitiveness of a country's firms.

The remainder of the paper is organized as follows. Section 1 briefly reviews the literature on the relationship between RER and surge episodes. We then define the way we calculate our country-product exchange rates. Section 2 presents the methodology for measuring export surges and analyzing their occurrence. Section 3 focuses on the regression model of surge episodes. We discuss the choice of the estimator and the covariates to be considered in addition to real exchange rate undervaluation. Section 4 discusses the empirical results. Section 5 proposes the above-mentioned robustness checks. Finally, Section 6 draws the main conclusions and sets out some implications for economic policy.

# I. Country-product real exchange rates, competitiveness, and export surges

## 1. Price-competitiveness and surge episodes

The competitiveness of export products depends on the intrinsic productivity of firms (i.e., the cost of primary factors of production), and on the price of their intermediate consumption. As shown by the World Bank's *Enterprise Surveys*, these intermediate consumptions often represent more than 60% of gross export values for most African firms (Chaffai *et al* 2012). In low-income economies with market failures and weak institutions, the higher the share of intermediate goods, the higher the transaction cost level. For example, African transport and logistics typically accounts for 15% to 20% of the value of an imported container at destination against 5% on
average in developed economies. Bribes and a poor institutional regulation add to the normal cost level of non-tradable transport services (Plane, 2021). Access to electricity is also unreliable and expensive. Low and medium voltage prices are above 10 US cents per kWh, twice as much as for Asian competitors for agricultural or manufactured products. Despite the average increase in broadband connectivity, access to Information and Communication Technologies (ICTs, thereafter) remains both limited and onerous (Chauvet and Jacolin, 2017). Given the costs of these intermediate inputs, measuring and monitoring competitiveness need to refer to a broad coverage of the prices of goods and services, including non-tradables, to proxy at best relative unit cost of production. This option is more relevant than a restrictive alternative measure focusing only on unit labor cost (Nouira et al. 2011; Gelb, 2016).<fnref n="2" /> The poor quality of the business environment favors the production of non-tradables whose extra costs can be passed on consumers in case of weak domestic competition and low-price elasticity of the demand. In this context, exchange rate adjustments can be used to remove price distortions, reduce transaction costs and resulting biases on the production structure. These adjustments act as implicit subsidies benefiting all tradables.

Hausmann et al. (2005) show that surge episodes of per capita GDP are positively correlated to the currency exchange rate undervaluation. Freund and Pierola (2012) analyze aggregate exports of manufactured products and find that both undervaluation and the reduction of the exchange rate volatility account for the occurrence of surges.<fnref n="3" /> Undervaluation of the currency improves national firms’ intensive and extensive margins -i.e., the emergence of new products and the openness of new export markets. Eichengreen and Gupta (2013) investigate export surge episodes for both merchandise and services. Regardless of the country's level of development, the real exchange rate stimulates all exports and is particularly effective for modern services. Palazzo and Rapetti (2017) investigate the long run export performance of Argentina (1980-2015). Their results show that the highest proportion of surge episodes occurred over the period 2003-2008, when Argentina maintained a stable and competitive exchange rate that benefited labor-intensive industries the most. Guzman et al. (2018) also argue that a Stable and Competitive Real Exchange Rate (SCRER) has a positive impact on the development of tradables. By reducing uncertainty, its spurs apprenticeships and help to cope with high fixed or sunk cost investments. More recently, Ebeke (2023) examines the determinants of surges in Global Value Chains (GVC) participation of developing countries. He finds that surges are rare in sub-Saharan Africa, typically preceded by strong FDI inflows and

<footnote n="2">² In some countries real wages may be high given the low productivity level or tight market conditions on some segments of the supply. Gelb et al. (2016) mention such a situation for specific middle manager jobs or skilled workers whose supply lags far behind productive sector needs.</footnote>

<footnote n="3">³ At an HS 6 level of disaggregation, Cadot et al. (2014) focus on export surges in bilateral trade flows for 8 developing countries over the period 1995-2012.</footnote>
good governance, while prolonged real exchange rate undervaluation facilitate their occurrence.

## 2. Measuring product-specific exchange rate undervaluation

The Purchasing Power Parity (hereafter PPP) criterion, adjusted for the country's productivity level, sheds light on the implicit profitability of exports. Assuming that the price of a 4-digit HS code product is the same for all producers (i.e., the law of one price), an undervaluation of the currency vis-à-vis the main world exporters of the product reflects more or less competitiveness, let say the difference in the relative unit cost of production of the exported product. We define RER as the ratio of the PPP conversion factor in domestic currency to the official exchange rate (ER) (i.e., number of national currency units per U.S. dollar).

$$RER_{ct} = \frac{PPP_{ct}}{ER_{ct}} \qquad (1)$$

We use ratios distributed over the 0-1 interval from the World Bank's *World Development Indicators* with the upper bound reflecting the value for the United States, the common reference for all countries. Misalignments are therefore benchmarked by the US dollar, implicitly supposed to be at the equilibrium exchange rate level.

For a country (c) a ratio lower than unity in year (t) means an undervaluation insofar as the domestic purchasing power of the currency turns out to be higher than what the official exchange rate indicates. These ratios are then adjusted for the impact of the productivity level of tradables on the price of non-tradables (Balassa - Samuelson effect). For this adjustment, RERs ratios are regressed on per capita GDP levels by considering a worldwide sample that includes high income level economies. Regression residuals provide by-country yearly misalignments that approximate the deviation to the long run international convergence rule of non-tradable good prices.

In absolute PPP terms, most African currencies are undervalued against the dollar, and remain so after adjusting non-tradable prices for productivity differences. Specialized in a limited number of products, often with a low level of processing, African economies do not compete with US exporters on most product markets. Therefore, by product (p) and for year (t), competitiveness is calculated by the difference between the misalignment of the currency (c) and that of the ten largest trading competitors (j) on the (p) product, each weighted by its average contribution into the product's world exports over the period 2009-2013 (ρj). The set of ten largest exporters of both primary and manufactured goods represents 72 percent on average, with a minimum of 52 and 42 percent respectively for each category. This coverage
appropriately reflects global conditions of production of the product (p). Let's briefly summarize the two steps involved in calculating exchange rate misalignments.

First, we conduct the regression below using OLS estimation over the largest country sample and estimate yearly country misalignment from its residuals ($u_{ct}$). We do not include country fixed effects, following Rodrik (2008), Couharde and Sallenave (2013) or Ribeiro et al (2020). This econometric estimation only aims to remove the long-run impact of productivity on the relative price of non-tradables from the RER. The joint introduction of country fixed effects and GDP per capita (GDPPC) would pose an identification problem since fixed effects are often viewed as a proxy for productivity (Cornwell, Schmidt and Sickles, 1990).

$$ \log (RER)_{ct} = \alpha + \beta \log(GDPPC)_{ct} + f_t + u_{ct} \quad (2) $$

With $f_t$, year-fixed effects; and $u_{ct} < 0$, undervaluation or $u_{ct} > 0$, overvaluation.

Second, from the misalignments as determined by the above regression, for a product (p) and a country (c), we calculate the misalignment in year (t) as follows, $\rho_{jp}$ being the share of the j-th trading competitor in worldwide exports of this specific product.

$$ Misalignment_{cpt} = [u_{ct} - \sum_{j=1}^{10} \rho_{jp} u_{jt}] \quad (3) $$

Misalignments are calculated under assumptions close to those used in price level PPP theory. (i) By assuming that the law of one price holds for each tradable, misalignments mainly result from non-tradable prices or differences in the taxation of imported goods. (ii) In the absence of any specific information, the country productivity for a given product is supposed equal to that of the average economy, as reflected by the per capita GDP. The concept of “misalignment” used in this study is product-specific and has no normative implication for the equilibrium exchange rate, which is based on macroeconomic performance as measured by the country’s internal and external balances (Plane and da Piedade, 2021). In other words, a country may experience undervaluation relative to some export products and an overvaluation for others, while keeping a global macroeconomic equilibrium. Each product-specific exchange rate misalignment depends on each country’s competitors in the world market for the product. Our analytical approach has therefore no direct implication on how appropriate the real exchange rate is for the economy as a whole.
## II. Measuring export surges and assessing their occurrence

### 1. The method for identifying surges

We define $v_{c,pt}$ as the value of exports of product $p$ at constant price for a country $c$ at time $t$, and the real growth rate of product exports: $g_{c,p,t} = \Delta\ln(v_{c,pt})$. To deflate nominal exports, we use the weighted unit export value of the product's ten largest exporting countries. Initiation is the first year of a seven-year period of a specific product export surge (i.e., Take-off phase). Let $g_{c,p,t}^1$ and $g_{c,p,t}^0$ denote, respectively be the average annual growth rate of the real export over the seven-year take-off, and the pre-acceleration growth rate in the previous seven-year period. Briefly, the identification of an export surge episode relies on the joint application of four criteria:

**Criterion 1.** The average real export growth of the product during a seven-year take-off phase must be strong and higher than the world average real growth.

$$g_{c,p,t} > \overline{g_p} \qquad (4)$$

Freund and Pierola (2012) deflate exports at current prices by the US consumer price index (CPI). This option is questionable at the sectoral level and even more so at the product level. Each tradable good has its own price evolution affecting the occurrence of a surge differently from what is assumed using the US consumer price index. Moreover, US retail prices depend on the productivity and the labor market conditions of this economy. To get values at constant prices, assuming the law of one price, the most relevant option is to use the specific export-product price. Accordingly, we deflate export value by the unit value index as determined from the ten major worldwide exporters of the given product over 2009-2013.

**Criterion 2.** The average real growth during the take-off phase must be 30% higher than that of the seven-year reference period ($\alpha = 1.3$), and at least three percentage points higher than that of a seven-year reference period ($\beta = 0.03$).

$$g_{c,p,t}^1 > \alpha \times g_{c,p,t}^0 \quad \text{And} \quad g_{c,p,t}^1 > g_{c,p,t}^0 + \beta \qquad (5)$$

**Criterion 3.** The minimum value of real exports during the take-off period should be higher than the maximum pre-take-off value in order to filter out surge episodes resulting from volatility phenomena of the export volume.

$$\min(v_{c,pt}, \dots, v_{c,p,t+6}) > \max(v_{c,pt-7}, \dots, v_{c,p,t-1}) \qquad (6)$$
**Criterion 4.** The average post-take-off growth calculated by eliminating the year of highest growth must be higher than the average pre-take-off growth (See criterion 2; $\alpha \times g_{c,p,t}^0$). This criterion eliminates accelerations that would depend on a single year of strong export growth.

$$g_{c,p,t}^1 \setminus \{\max(g_{c,p,t}, \dots, g_{c,p,t+6})\} > \alpha \times g_{c,p,t}^0 \quad (7)$$

This export surge measurement has three important features:

(i) First, the surge episode is by no means a single measure of the performance. A country that experiences a high but steady export growth on a product does not induce a surge although the growth can be far above that of the world economy. Onset surges are predicated on clear breaks from past export performance conditions.

(ii) Second, the number of exported products per country is set at a maximum of ten, respectively 5 primary and 5 manufactured products, the most important export products for each of the two categories. Including manufactured goods is justified not so much by their current, and limited, share in African exports but by their expected contribution to structural transformation (Rodrik, 2018). The composition of the basket of international partners is constant and the choice of products is based on the structure of exports over the period 2009-2013. Accordingly, we mainly center the performance on the intensive margin. Since we refer to the aggregate exports of the product on the world market, the extensive margin is only implicitly present through the potential opening of new markets.

(iii) Third, the limited number of selected products is appropriate for most African countries. It is only restrictive for a small number of large and more developed economies. The underestimation of export surges is likely for North African countries. This limitation avoids overrepresentation of some large middle-income economies within the sample.

## 2. The database and the occurrence of surges

We use the BACI database (CEPII), built from data reported to the United Nations Statistical Division (Comtrade) and include all African countries with available products exports data. Our sample is constituted of a three-dimensional panel of 41 African countries and 149 products (4-digit HS code) exported over the period 1995-2017. To be considered, a product must have been exported consecutively for at least three years during the period and represent an average export flow of at least one million dollars. We exclude some raw materials - i.e., crude oil, gas and minerals, as well as products of chapter 27 and chapters 84 and 86 to 89 of the HS 4
nomenclature. Export products of chapter 87 are considered for only 6 countries: Algeria, Egypt, Morocco, Nigeria, South Africa, and Tunisia. For the other African countries, these chapters account for either imported goods that are re-exported in neighboring countries (mirror data only provide an imperfect correction of such trade), or sales of secondhand products (e.g., helicopter reexports by Gabon, HS-8802).

By major African regions, Table A1 in Appendix 1 displays the contribution of the 5 selected products for primary and manufactured goods, respectively. Countries in brackets are those with the lowest and highest percentages in the regional subset considered. Products were selected on the basis of the export value in their sectoral category over the period 2009-2013. On average, the top 5 primary product exports, excluding crude oil, gas and minerals, account for 78% of the category over the 41 sampled African countries, ranging from 32% (Egypt) and 100% (Cabo Verde, Chad, Comoros, Central African Rep, Equatorial Guinea, Guinea-Bissau). Percentages vary across African sub-regions from 53% (Northern Africa) to 92% (Central Africa). For manufactured exports, the top five products account for 51% on average, ranging from 0,45% (Liberia) to 100% (Mauritania). The selected manufactured products typically represent a smaller fraction of exports than primary ones.

Applying the set of previously mentioned criteria to primary and manufacturing products yields 96 episodes of export accelerations over 2001-2011. Figure 1 shows when export surges occurred and Figure 2, their geographic distribution by product in Africa as well as their weight in the export basket (Appendix 2). The earliest surge can only occur in 2001 (1995+6 years) and the latest in 2011 (2017-6 years). In fact, the first event dates back to 2003. Among the 96 episodes, 55 fall under the category of primary products, and 41 to manufactured products. Export surges are more frequent before the subprime crisis (2008) and the post subprime profile proves better for primary products, in relation with the Chinese economic growth, at least until 2010. The years beyond were much less stimulating with a Chinese GDP growth that fell from 10.6% in 2010 to 6.9% in 2017. To put it another way, although African market shares are very small for most manufactured goods, when the world economy slows down, it proves difficult for a country to increase its market share, one of the conditions for a surge episode to occur (Criterion 1).

Figures 3 and 4 refer to export surges by product. For primary goods, cashew nuts (HS-0801) come first with 7 episodes. This economic specialization is present in ten African countries, especially in West Africa. Vietnam is the world's largest processor ahead of India. It imports a lot from Côte d'Ivoire but is promoting a policy of setting up operations in nearby countries. Cocoa beans (HS-1801) follow with 6 episodes. Ghana and Côte d'Ivoire are the most important worldwide producers, with
a combined market share of about 60 %. Natural rubber (HS-4001), and fruits (HS-0804) then come with 4 episodes each. Turning to manufactured products, 3 export surges are accounted for in both Portland cement (HS-2523) and transport articles (HS-3923). The first product reflects the African dynamics of investments in both infrastructure and housing. Both sectors have benefited from the so-called “super-cycle” of raw materials that fueled the economic growth of a wide range of African countries.

<figure><img src="image_2.png" /><figcaption>**Figure 1:** Africa and the timing of surge episodes (by category, number of products)</figcaption></figure>

Source: BACI, 2019 and Authors' calculations.

Table 1 lays out the unconditional probabilities of surges by type of products, per capita income levels, and regions. The occurrence of events is calculated over the total number of country-product-year observations for which an acceleration can potentially take place. As we focus on onset surges, the six years following the start of the episode are excluded. This is also the case for the years before 2001 and after 2011
<figure><img src="image_3.png" /><figcaption>Figure 2: Geographical distribution of Export surges
Number of surge episodes</figcaption></figure>

Percentage of product surges among all products considered for each category (%)

Source: Authors' calculations.
<figure><img src="image_4.png" /><figcaption>Figure 3: African export surges: primary products (2001-2011)</figcaption></figure>

Source: Authors' calculations

<figure><img src="image_5.png" /><figcaption>Figure 4: African export surges: manufactured goods (2001-2011)</figcaption></figure>

Source: Authors' calculations.
where no surges are possible due to the defined event time span. We breakdown the whole period into two sub-periods separated by the subprime crisis starting in end 2007 with the U.S. housing bubble and the global financial crisis.

<table><caption>Table 1: Unconditional probabilities of surges</caption><thead><tr><th></th><th>2001-2007</th><th>2008-2011</th><th>2001-2011</th></tr></thead><tbody><tr><td>Overall probability</td><td>2,5%</td><td>2,1%</td><td>2,3%</td></tr><tr><td colspan="4"><em>By type of products</em></td></tr><tr><td>Primary</td><td>2,6%</td><td>2,4%</td><td>2,5%</td></tr><tr><td>Manufactures</td><td>2,3%</td><td>1,7%</td><td>2,1%</td></tr><tr><td colspan="4"><em>By geographical regions</em></td></tr><tr><td>Southern Africa</td><td>0,5%</td><td>1,7%</td><td>0,9%</td></tr><tr><td>Northern Africa</td><td>3,9%</td><td>1,3%</td><td>3,0%</td></tr><tr><td>Central Africa</td><td>2,6%</td><td>0,4%</td><td>1,8%</td></tr><tr><td>East Africa</td><td>2,9%</td><td>1,4%</td><td>2,4%</td></tr><tr><td>West Africa</td><td>2,1%</td><td>3,5%</td><td>2,6%</td></tr><tr><td colspan="4"><em>By per capita income quintiles</em></td></tr><tr><td>1st quintile</td><td>0,6%</td><td>2,2%</td><td>1,1%</td></tr><tr><td>2nd quintile</td><td>1,4%</td><td>2,6%</td><td>1,7%</td></tr><tr><td>3rd quintile</td><td>3,5%</td><td>1,8%</td><td>2,8%</td></tr><tr><td>4th quintile</td><td>3,8%</td><td>2,3%</td><td>3,2%</td></tr><tr><td>5th quintile</td><td>3,7%</td><td>1,5%</td><td>2,8%</td></tr></tbody></table>

Source: Authors' calculations.

Over the whole period, the unconditional probability that an export surge occurs is 2.3%, with a higher probability for primary than manufactured products. An analysis by sub-regions shows that the occurrence of surges in North African countries is three times higher than in Southern Africa. Central Africa is particularly affected by the post 2007-2008 crisis, the unconditional probability of acceleration falling from 2.6% over 2001-2007 to 0.4% after 2008. The lower part of Table 2 classifies export surges by quintiles of the per-capita income level of sampled countries. The first and second quintiles are less likely to experience an export surge than those from the third to fifth quintiles. Cerra and Woldemichael (2017) evidence similar results for more aggregated exports. This may reflect the fact that high-income countries are more successful in promoting manufactured exports. The diversification process is more difficult for low-income African agricultural exporters. Given the concentration on primary products where African market shares are non-negligible, it is more difficult to initiate export accelerations in manufactures.

How does export performance fit with exchange rate misalignments? Descriptive statistics provide interesting preliminary information. In Figure 5 we compare all country-product pairs with an undervaluation according to whether or not they experienced export surges over the period 2001-2017. Firstly, undervaluation goes hand in hand with higher export growth for both primary and manufactured
products. Secondly, country-products surges provide higher export growth rates. Thirdly, the country-product export gains associated with export surges are significantly higher in the case of primary products. The average annual export growth rate over the 2001-2017 period rises from 3 % in the absence of export surges (with an average undervaluation of 6 %) to 11 % in the case of export surges (undervaluation of 10%).

<figure><img src="image_6.png" /><figcaption>**Figure 5:** Average variation in undervaluation and export growth: by product categories according to the presence or absence of surges episodes (Period 2001-2017, exports at constant prices, in %)</figcaption></figure>

N.B. Given the time span referenced in the surge definition (7 years), surges are only observable from 2001 onwards over the sample period (1995-2017). Average annual growth rate of exported products is unweighted.

Source: Authors using BACI 2019.

### III. Predicting surges and identifying their determinants

Given that an export surge is a rare event, our binary dependent variable model is characterized by a preponderance of zeros. The data then suffers from a class imbalance problem. In this case, standard probit and logit estimators provide biased results toward zero. To account for the structure of the sample, we use the complementary log-log (cloglog) model. The cloglog is related to the gompit model. It differs from the logit and the probit by its non-symmetric transformation, which
allows an appropriate treatment of rare events. The formal specification of the cloglog model is as follows:

$$Pr(ES_{c,p,t} = 1 | X_{c,p,t}) = F(\beta X_{c,p,t}) \quad (8)$$

Where $F(z) = 1 - \exp\{-\exp(z)\}$, $ES_{c,p,t}$ is a dummy variable that is equal to 1 in case of export surge (ES) for a country (c), product (p) at year (t), and zero otherwise. $X_{c,p,t}$ is a set of controls.

Taking up the approach of Hausmann (2005) and more recently Libman (2019), we rule out the uncertainty about the starting year of an export surge by centering the dependent variable over a three-year window around the initiation year ($t-1, t, t+1$). Given the way the filter parameters are defined (See section II.1), surge episodes cannot happen over the first and last seven-year spells. In addition, as a beginning of an acceleration is detected, observations from $t + 2$ to $t + 6$ are removed from the sample. By doing so we focus on the starting years of the country-product-episode. In other words, the sample only includes the three-year window for country-export-episodes, but the whole period in case of no country-export-product surge (control group).

Because of data limitations, this cross-sectional analysis does not control for all relevant export growth factors such as imported inputs. An exchange rate shock may make the firm more competitive due to local value-added content, but also increase the cost of imported inputs. This information however is not available. Other factors may also be not statistically significant over the whole country-product pairings. For example, the growth of cashew nut exports (HS 0801) from Africa may result from rising competitiveness, but also relates with rising climatic constraints in their Asian competitors.

Table 2 provides descriptive statistics for the main variables included in the regression analysis. The undervaluation level is the variable of interest. Over the period 2001-2017, its average level over the sample is 7.4%. To have an appropriate estimation of its coefficient we introduce a set of controls denoted $X_{c,p,t}$. These variables are measured as average over the seven-year period preceding the surge. Controls range into two categories: country related variables, generally with a limited variance over time, and specific country-product variables that are related either to the international or the domestic environment.
# 1. Country related variables

As is customary when conducting sectoral or aggregate export analyses, we introduce indicators reflecting time variant development levels (Fernandes et al., 2016). A higher *per capita GDP* goes hand in hand with better established and larger firms, which have a higher ability to seize international market opportunities. Real per capita GDP is expected to be positively correlated with export surges, probably more for manufactured than for primary products. We posit a potential non-linear effect that we simply test through a quadratic-type relationship as generally the case for aggregate exports (Klinger and Lederman 2006, Parteka 2007, Cadot, Carrère and Strauss-Kahn 2011).

*The secondary education index* is incorporated in the regression to assess the role of human capital through the productivity level. A positive effect is expected for all types of products. This index is preferred to the primary enrollment rate which discriminates less across African countries.<fnref n="4" />

*The Exchange rate classification scheme.* In order to provide a broad picture of underlying impacts of exchange rate arrangements, we use three aggregated classes from the de facto exchange rate arrangement classification (Ilzetzki et al., 2019). The first category is for pegged (or related) exchange rate regimes. The second class is for intermediary systems based on all crawling peg options (codes 2 and 3). The third and last category gathers all exchange rate floating systems (codes 4 to 6). A pegged regime may provide more stability on expectations about the macroeconomic policy but may result in rigidities or foreign exchange shortages constraining the tradable sector. Intermediate and floating exchange rate systems may offer more flexibility, but possibly at the cost of more uncertainty and less credibility of macroeconomic policy. The independence of monetary and fiscal policy increases, but at the cost of the volatility of the nominal exchange rate finally creating a “fear of floating” (Calvo and Reinhart, 2002).

*Domestic conflicts.* Since the early 2000s, rebel and self-defense militias as well as various groups affiliated to Islamic jihad organizations erode North and West African states. For this region alone, Trémolières et al. (2020) counts no less than 3 700 violent events for 2018. Conflicts in the Lake Chad caused 68 000 victims since 2009. Civil wars in Sierra Leone (1991-2002) or Côte d'Ivoire (1999-2010) have had repeated negative impacts on cocoa exports and disincentivized investment in manufactured production. This bleak picture contrasts with the first decade of the 2000s, when African civil wars declined to half of their mid-1990s levels (Straus, 2012). We expect that the end of a conflict stimulates export catch-up phases whose effects may potentially correlate with real exchange rate changes. To identify the impact of domestic political disturbances or upheavals, we consider the weighted conflict index

<footnote n="4">⁴ SE Index is a subindex of the FERDI's Human Assets Index (HAI) retrospective series. See Feindouno and Goujon (2019).</footnote>
of the Cross-National Time Series (CNTS) from EUI (European University Institute). The CNTS combines 9 broad items using data coming from the *New York Times*. The dataset records occurrences of events and weights them according to gravity: general strikes, purges, government crises, riots, assassinations, anti-government demonstrations, as well as events with the highest weighted factors: guerilla warfare, and revolutions (Banks and Wilson, 2015). This conflict index ranges from zero (peaceful domestic environment), to 15.28 (highest intensity of conflict). We hypothesize that domestic conflicts are negatively correlated with export surges.

## 2. Country-product related variables

As mentioned earlier, the main innovation of this paper is to highlight the role that exchange rate undervaluation plays in product-specific export surges. An African cashew producer is indirectly affected by the macroeconomic exchange rate misalignment, but directly by its unit cost, vis-à-vis other cashew producer countries.

The misalignments are obtained from the regression below, based on the largest worldwide sample of countries to seize the long-run average impact of productivity on the price of non-tradable goods. Based on 176 countries that we consider over the period 1995-2017, this panel provides information about misalignments for all national currencies vis-à-vis the US dollar.

$$ \text{Log}(\overline{\text{RER}})_{ct} = \underset{(0.003)}{0.224}^{***} \text{log}(\text{Per capita GDP})_{ct} - \underset{(0.03)}{2.767}^{***} \quad (9) $$

$R^2 = 0.48, \quad n = 176 \text{ countries, } t = 1995 \text{ to } 2017. \text{ Standard error in parentheses}$

The coefficient on the regression of GDP per capita is quite close to that identified by Rodrik (2008). For a specific country-product pair, the exchange rate misalignment is measured by the difference between the country's residual and the weighted average of those of the ten largest competing countries on the product. From the distribution of real exchange rate misalignments, only observations with an undervaluation are retained, meaning that when this event does not occur, the observation is equal to zero. The underlying assumption is that, on average, overvaluation is unlikely to lead to export surges. The inclusion of overvaluation as an additional variable in the regressions proved to be statistically insignificant, with no impact on the specific coefficient of undervaluation.<fnref n="5" /> We now extend the information space to additional controls that combine country, product and time.

**Market shares.** These are measured on an annual basis by a country's exports of a specific product (p) in world exports of that product. The impact of this variable is ambiguous. In the case of primary agricultural products, African market shares can be large, as is the case for Ghanaian and Ivorian cocoa, which reduces the probability

<footnote n="5">⁵ Results incorporating the overvaluation variable are provided in Appendix 3.</footnote>
of export surges. Turning to manufactured products, a well-structured marketing and distribution network make it easier to increase export sales in a context where initial market shares are still low. When fixed and sunk costs are partly covered, mature countries have a better resilience to unforeseen events. Accordingly, within a sample restricted to African countries, we suppose that market shares are positively correlated to surge episodes of manufactured goods.

**The relative number of Internet users per 100 inhabitants.** Digital infrastructure is an efficient means to reducing transaction costs and informational issues. As such, the Internet enables productivity gains, supports business performance, and improves economic governance (Paunov and Rollo, 2015, 2016; Cariolle et al., 2019; Asongu and Nwachukwu, 2016). Hjort and Poulsen (2019) evidence the economic implications of the broadband Internet in African countries. Using data from the World Bank *Enterprise Surveys*, they show that this technological change has led to a rapid development of direct exports, in particular due to the greater fluidity of relations with foreign customers and the use of online communication between firms in customer-supplier relationships. Similarly, Cariolle and da Piedade (2023) show that increased digital connectedness leads to greater sophistication (complexity) of exported goods, particularly in Africa. Internet also enlarges the size of the formal sector and attracts foreign direct investments that are generally found to be a powerful channel to drive the diversification of exports (Freund and Weinhold, 2004, Jacolin et al., 2021). Access to ICTs is very uneven across Africa. For a country, it is assumed that the competitive position depends on the evolution of the gap with competing countries. So, we construct an indicator of the internet access for each country-product pair. We test the relative impact of this variable using the same subset of exporting competitors and weights as for exchange rate misalignments. In other words, as the set of competing countries is product-specific, this relative variable varies with country's export products. We posit a positive relationship between Internet access and export surges.

**Relative Corruption index.** Bribes and a weak governance can be akin to a tariff, discouraging international transactions by increasing costs (Anderson and Marcouiller, 2002). To investigate the role of this variable we compute the relative corruption index using Standaert (2015)'s Bayesian Corruption Index (BCI) and the same methodology as for the relative number of Internet users. By the relative corruption index, we compare the country's BCI to that of its competitors on the same export product. Thus, the resulting measure reflects the difference in the quality of institutions and the implications for transaction cost levels. An increase of this relative index means a less market-friendly environment.
<table><caption>Table 2 Descriptive statistics of export surge determinants (2001-2017)</caption><thead><tr><th>Variables</th><th>Obs</th><th>Mean</th><th>Standard deviation</th><th>Min</th><th>Max</th><th>Sources</th><th>Expected signs</th></tr></thead><tbody><tr><td>Export Surges (=1, dummy)</td><td>4090</td><td>0.07</td><td>0.256</td><td>0</td><td>1</td><td>BACI</td><td></td></tr><tr><td>Market share (in %)</td><td>6416</td><td>1.925</td><td>5.528</td><td>0</td><td>67.84</td><td>BACI</td><td>-/+</td></tr><tr><td>Undervaluation (in %)</td><td>6416</td><td>-7.4</td><td>16.4</td><td>-127.7</td><td>0</td><td>Authors</td><td>-</td></tr><tr><td>Overvaluation (in %)</td><td>6416</td><td>18.5</td><td>20.3</td><td>0</td><td>110.8</td><td>Authors</td><td>-</td></tr><tr><td>Log (per cap GDP)</td><td>5920</td><td>7.016</td><td>1.055</td><td>5.267</td><td>9.92</td><td>WDI</td><td>+</td></tr><tr><td>Log (per cap GDP) <sup>2</sup></td><td>5920</td><td>50.343</td><td>15.615</td><td>27.743</td><td>98.407</td><td>WDI</td><td>-</td></tr><tr><td>Relative Internet users</td><td>6416</td><td>-29.413</td><td>21.175</td><td>-82.322</td><td>24.725</td><td>WDI</td><td>+</td></tr><tr><td>Log (Secondary education)</td><td>5091</td><td>3.224</td><td>0.961</td><td>-2.323</td><td>4.605</td><td>HAI-FERDI</td><td>+</td></tr><tr><td>Relative Corruption index</td><td>6416</td><td>0.8</td><td>0.722</td><td>-.732</td><td>3.357</td><td>Standaert (2015)</td><td>-</td></tr><tr><td>Conflict index</td><td>5470</td><td>0.161</td><td>0.865</td><td>0</td><td>15.281</td><td>CNTS</td><td>-</td></tr><tr><td>Pegged regime</td><td>6048</td><td>0.406</td><td>0.491</td><td>0</td><td>1</td><td>Ilzetzki et al. (2019)</td><td>-/+</td></tr><tr><td>Intermediate regime</td><td>6048</td><td>0.561</td><td>0.496</td><td>0</td><td>1</td><td>Ilzetzki et al. (2019)</td><td>-/+</td></tr><tr><td>Floating regime</td><td>6048</td><td>0.033</td><td>0.179</td><td>0</td><td>1</td><td>Ilzetzki et al. (2019)</td><td>-/+</td></tr></tbody></table>

NB: for surge episodes, the dummy takes the value one. These descriptive statistics are given for the entire sample, including both primary and manufactured products. The correlation table is provided in Appendix 1. Source: Authors' calculations. HAI, Human Asset Index; WDI, World Development Indicators, World Bank. The BACI database is from CEPII.

# IV. Regression results

## 1. Determinants of export surges (take-off)

Table 3 provides a set of econometric relations between the onset of surges and currency undervaluation vis-à-vis major country-product competitors. Cases other than undervaluation have a value of zero. The coefficient of interest is controlled or not by regional dummies. Columns distinguish aggregated from by-sector results. The expected negative sign is obtained in all cases, which means that undervaluation, a negative value, stimulates export surges. Only in column 3 does the cloglog model fail to give statistical significance at the conventional confidence levels.
<table><caption>Table 3: Baseline regression: Onset of export surges (2001-2012)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr><tr><th></th><th>All</th><th>All</th><th>Primary</th><th>Primary</th><th>Manuf</th><th>Manuf</th></tr></thead><tbody><tr><td>Undervaluation</td><td>-0.047***<br>(0.018)</td><td>-0.070***<br>(0.018)</td><td>-0.044<br>(0.030)</td><td>-0.071***<br>(0.027)</td><td>-0.050**<br>(0.021)</td><td>-0.064***<br>(0.022)</td></tr><tr><td>Southern Africa</td><td></td><td>-0.086***<br>(0.023)</td><td></td><td>-0.108***<br>(0.039)</td><td></td><td>-0.066**<br>(0.026)</td></tr><tr><td>Central Africa</td><td></td><td>-0.023<br>(0.015)</td><td></td><td>-0.003<br>(0.020)</td><td></td><td>-0.043**<br>(0.021)</td></tr><tr><td>East Africa</td><td></td><td>0.001<br>(0.014)</td><td></td><td>-0.011<br>(0.020)</td><td></td><td>0.011<br>(0.017)</td></tr><tr><td>West Africa</td><td></td><td>0.005<br>(0.013)</td><td></td><td>0.033*<br>(0.017)</td><td></td><td>-0.028<br>(0.017)</td></tr><tr><td>Observations</td><td>4,090</td><td>4,090</td><td>2,150</td><td>2,150</td><td>1,940</td><td>1,940</td></tr><tr><td>Number of surges</td><td>96</td><td>96</td><td>55</td><td>55</td><td>41</td><td>41</td></tr></tbody></table>

Notes: Cloglog estimates. The regression coefficients are marginal effects. The dependent variable is the dummy variable equal to 1 over a 3-year window centered on the onset date of the surge. Standard errors are adjusted for clustering at the product-year level: *** p<0.01, ** p<0.05, * p<0.1.

In Table 4, these initial results prove robust to the introduction of the set of controls. Several of them are relevant with some variations depending on product categories. For primary products, the relative corruption and the secondary school enrollment are significant as well as the exchange rate regime. It appears that both pegged and intermediate exchange rate regimes outperform the floating regime, the reference category in the regressions. Manufactured goods prove sensitive to the number of Internet users. This expected result fits with what Huang et al. (2018) and Huang and Song (2019) or at the provincial level, Fernandes et al. (2019) found for Chinese firms' probability to export. It is also in line with Hjort and Poulsen (2019) who show, for Sub Saharan African countries, that access to a fast Internet connection has increased firm entry, productivity, and exports.

Surprisingly, the impact of the relative corruption index is weaker in the case of manufactured goods than for primary products, at odds with the “sand in the wheel” hypothesis of the corruption-trade linkage literature (Anderson and Marcouiller, 2002). In a context of acute external competition with low firm profit margins, corruption is an additional obstacle for export surges. One potential reason for this weak correlation is that the so-called impact is already captured by other controls, especially those reflecting the development level (per capita GDP, Secondary education, Internet users, etc).
<table><caption>Table 4: Regression results with covariates: onset of export surges (2001-2012)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr><tr><th></th><th>All</th><th>All</th><th>Primary</th><th>Primary</th><th>Manuf</th><th>Manuf</th></tr></thead><tbody><tr><td>Log (Per cap GDP)</td><td>0.031<br>(0.068)</td><td>0.084<br>(0.068)</td><td>-0.082<br>(0.100)</td><td>-0.062<br>(0.099)</td><td>0.163**<br>(0.082)</td><td>0.269***<br>(0.083)</td></tr><tr><td>Log (Per cap GDP) <sup>2</sup></td><td>-0.004<br>(0.005)</td><td>-0.008*<br>(0.005)</td><td>0.004<br>(0.007)</td><td>0.003<br>(0.006)</td><td>-0.013**<br>(0.006)</td><td>-0.021***<br>(0.006)</td></tr><tr><td>Log (Secondary education)</td><td>0.022***<br>(0.005)</td><td>0.023***<br>(0.006)</td><td>0.031***<br>(0.007)</td><td>0.030***<br>(0.010)</td><td>0.014**<br>(0.007)</td><td>0.011<br>(0.007)</td></tr><tr><td>Undervaluation</td><td>-0.072***<br>(0.020)</td><td>-0.073***<br>(0.018)</td><td>-0.078**<br>(0.038)</td><td>-0.090**<br>(0.035)</td><td>-0.054**<br>(0.022)</td><td>-0.040**<br>(0.020)</td></tr><tr><td>Market share</td><td>-0.001*<br>(0.001)</td><td>-0.001*<br>(0.001)</td><td>-0.001<br>(0.001)</td><td>-0.001<br>(0.001)</td><td>-0.002<br>(0.002)</td><td>-0.002<br>(0.002)</td></tr><tr><td>Relative Internet users</td><td>0.001**<br>(0.000)</td><td>0.001**<br>(0.000)</td><td>0.001<br>(0.001)</td><td>0.001<br>(0.001)</td><td>0.001**<br>(0.000)</td><td>0.001**<br>(0.000)</td></tr><tr><td>Relative Corruption index</td><td>-0.020***<br>(0.007)</td><td>-0.018**<br>(0.008)</td><td>-0.023**<br>(0.010)</td><td>-0.022**<br>(0.010)</td><td>-0.017*<br>(0.010)</td><td>-0.000<br>(0.011)</td></tr><tr><td>Conflict index</td><td>0.055<br>(0.053)</td><td>0.062<br>(0.055)</td><td>0.086<br>(0.090)</td><td>0.077<br>(0.089)</td><td>0.026<br>(0.057)</td><td>0.054<br>(0.057)</td></tr><tr><td>Pegged regime</td><td>0.713***<br>(0.087)</td><td>0.691***<br>(0.070)</td><td>0.723***<br>(0.087)</td><td>0.645***<br>(0.111)</td><td>0.607***<br>(0.080)</td><td>0.545***<br>(0.089)</td></tr><tr><td>Intermediate regime</td><td>0.703***<br>(0.084)</td><td>0.684***<br>(0.072)</td><td>0.699***<br>(0.083)</td><td>0.636***<br>(0.110)</td><td>0.611***<br>(0.079)</td><td>0.539***<br>(0.090)</td></tr><tr><td>Southern Africa</td><td></td><td>-0.019<br>(0.020)</td><td></td><td>-0.034<br>(0.031)</td><td></td><td>-0.020<br>(0.022)</td></tr><tr><td>Central Africa</td><td></td><td>0.028<br>(0.021)</td><td></td><td>0.041<br>(0.032)</td><td></td><td>0.004<br>(0.022)</td></tr><tr><td>East Africa</td><td></td><td>0.010<br>(0.015)</td><td></td><td>0.021<br>(0.022)</td><td></td><td>-0.001<br>(0.020)</td></tr><tr><td>West Africa</td><td></td><td>-0.008<br>(0.015)</td><td></td><td>0.032<br>(0.022)</td><td></td><td>-0.051***<br>(0.018)</td></tr><tr><td>Observations</td><td>2,815</td><td>2,815</td><td>1,478</td><td>1,478</td><td>1,337</td><td>1,337</td></tr><tr><td>Number of surges</td><td>66</td><td>66</td><td>36</td><td>36</td><td>30</td><td>30</td></tr></tbody></table>

Notes: Cloglog estimates. The Regression coefficients are marginal effects. The dependent variable is the dummy variable equal to 1 over a 3-year window centered on the onset date of the surge. Control variables are averages over the 7 year-period prior the surge. Standard errors are adjusted for clustering at the product-year level: *** p<0.01, ** p<0.05, * p<0.1.

The main finding is that undervaluation remains statistically significant. As with the set of simple regression analysis, the coefficient again varies by sector. It is higher for primary than for manufactured goods. The sensitivity to current undervaluation is therefore stronger for the triggering of export surges of traditional products which are less subject to international marketing uncertainties, less demanding in terms of investments and profitability horizon, more open to substitutions between crops, especially for those with an annual production cycle.
Over the sample, a one-standard-deviation of the undervaluation level increases the probability of an export acceleration by 1.31 percentage points, or a 19 percent increase in the probability of experiencing an export surge.<fnref n="6" /> When we decompose the sample, the effect is respectively 1.62 for primary products, and 0.72 percentage point for manufactured goods, respectively an increase of 22 and 11 percent in the occurrence of export surges. Countries with a better human capital are more likely to experience export surges while the reverse occurs with a higher level of the relative corruption index. Broadly speaking, the results we get for these product export surges are in line with what Freund and Pierola (2012) find at a more aggregated level. A regression with jointly integrated overvaluation and undervaluation levels, i.e., with all misalignment information, yields similar results (See Appendix 3).

For a fairly large number of products, the variation in the nominal effective exchange rate translated into a product-specific undervaluation contributed to export performance. In Egypt, for example, the six export surges were concentrated in the period 2003-2006, and were preceded by a depreciation of the Egyptian pound by more than half against the currencies of the main export competitors (2000-2004). For these six products, undervaluation rates have ranged from 36% to 81% (see Appendix 1e and Appendix 1f). Similarly, the depreciation of the Kenyan shilling (1998-2002) helped cut flower exports to accelerate after 2004 (HS 0603) through a 25% undervaluation rate of the currency, just as the depreciation of the cedi (1991-2002) supported Ghana's exports of cocoa beans and processed products (HS 1801, HS 1803; HS 1804) with product-specific undervaluation rates ranging from 25% to 49%.

In some cases, the real undervaluation stemmed from a process of upgrading and increasing the sophistication level of exported products, hence resulting in the build-up of new competitive advantage. In Morocco, for instance, the export surge in the automotive sector (HS 8703) from 2012 onwards was spurred by significant foreign direct investment (Tangier-Renault project) - an efficient source of technological transfer- and enhanced by the 2014 *Industrial Acceleration Plan*. The combination of industrial policy and proactive behavior of foreign investors translated into comparative advantage on an upscale in the technological content of exports, as shown by gains in the *complexity* ranking of the *Harvard Growth Lab* (Hausmann and Hidalgo, 2011), along with a product undervaluation of around 28% in the years

<footnote n="6">⁶ This result is obtained from column 2 of table 5 and determined as follows: -0.073×-0.18= 1.31, where -0.18 represents a standard deviation of undervaluation over the period 2001-2011. In order to determine what this means in the sample, the following procedure is adopted: 1.31/(3×2.3), where 1.31 is the marginal effect induced by a one standard deviation decrease in the level of undervaluation. 2.3 is the unconditional probability of observing an acceleration (see Table 2), which we multiply by 3 as onset surges are defined over a three years period (t-1, t, t+1).</footnote>
preceding the surge. Rather than nominal variations in the dirham exchange rate, which appreciated vis-à-vis the dollar and the euro in the years preceding the start of export surges, in 2007, more favorable relative costs relative to European competitors and successful industrialization policies were key.

In Gabon, such policies took the shape of a Special Economic Zones (notably in Nkok) and a drive to capture a larger share of the global value chain of wood products. Aside from the ban on exports of raw timber (which carries its share of economic distortions), such industrialization policies included a flexible application of the provisions of the labor code, availability of serviced industrial land with an access to a lower cost of electricity, and last but not least a better access to the port of Owendo. This leads us to conclude that export surges may arise in a variety of policy levers, including horizontal policies through product-specific undervaluation or vertical industrial policies.

# V. Robustness and sensitivity

The robustness checks are implemented in two ways. First, we provide alternative calculations of export surges using different definition parameters. Previous results were obtained from the scenario of Freund and Pierola (2012) with respect to the value of the parameters (α, β), and the duration of surges: at least 7 years. Of course, these are ad hoc empirical hypotheses. In a second step, we ask whether an alternative estimator (i.e., ReLogit) modifies the conclusions as regard the impact of the undervaluation of currencies.

## 1. Export surges and the choice of alternative parameters

The export surge has been defined with respect to a seven-year period. Do the results change if we extend the period to 8 years or restrict it to 6 years? As shown in Figure A4.1. in Appendix 4, export surges drop by almost half with the 8-year window, and only 25 episodes out of the 59 relate to manufactured products. When a 6-year window is considered, we get 117 exports surges with 66 episodes for primary products and 51 for manufactured.

The second robustness check consists in modifying criterion 2 (Cf., section II.1). The real average growth that has been considered so far for calculating export surges $g_{c,p,t}^1$ was 30% and at least 3 percentage points higher than that of the previous seven-year period ($\alpha = 1.3, \beta = 0.03$). What happens for alternative pairs of parameters: $\alpha = 1.1$ and $\beta = 0.02$; or $\alpha = 1.5$ and $\beta = 0.04$? Figure A4.2 in Appendix 4, depicts the number of export surges under these two modalities of measurement. Compared to the standard criterion, episodes moderately change and the proportion of surges across
categories of products is quite stable. We detect 100 episodes for the first option, respectively 56 and 44 for primary and manufactured products, against 89 episodes for the second option: 52 and 37, respectively.

Table 5 summarizes the results. To save space, only our interest variable- i.e., undervaluation, is reported. Full regressions are provided in the Appendix 4. At the onset of surges, regardless of the specification, undervaluation remains negative and statistically significant at the conventional statistical levels except in one case for manufactured goods (Horizon 6), which is only significant at 81%.

<table><caption>Table 5: Robustness checks with alternative filter parameters and time horizons</caption><thead><tr><th></th><th>(1)<br>All</th><th>(2)<br>Primary</th><th>(3)<br>Manuf</th></tr></thead><tbody><tr><td colspan="4"><em>Horizon 6 (a = 1.3 and β = 0.03; 6 years)</em></td></tr><tr><td>Undervaluation</td><td>-0.106***<br>(-0.026)</td><td>-0.092**<br>(-0.044)</td><td>-0.039<br>(-0.031)</td></tr><tr><td colspan="4"><em>Horizon 8 (a = 1.3 and β = 0.03; 8 years)</em></td></tr><tr><td>Undervaluation</td><td>-0.057***<br>(-0.017)</td><td>-0.060*<br>(-0.032)</td><td>-0.022*<br>(-0.011)</td></tr><tr><td colspan="4"><em>Lower criterion 2 (a = 1.1 and β = 0.02; 7 years)</em></td></tr><tr><td>Undervaluation</td><td>-0.084***<br>(-0.019)</td><td>-0.101***<br>(-0.035)</td><td>-0.048**<br>(-0.021)</td></tr><tr><td colspan="4"><em>Higher criterion 2 (a = 1.5 and β = 0.04; 7 years)</em></td></tr><tr><td>Undervaluation</td><td>-0.065***<br>(-0.017)</td><td>-0.075**<br>(-0.031)</td><td>-0.037**<br>(-0.016)</td></tr></tbody></table>

Notes: cloglog estimates. The coefficients reported are marginal effect evaluated at the sample means. The dependent variable is the dummy variable equal to 1 over a 3-year window centered on the onset date. All controls are averages over 7 years prior to the surge initiation. The Table only displays the coefficient of the variable of interest. Refer to Appendix 4 for all results. Standard errors are adjusted for clustering at the product-year level: *** p<0.01, ** p<0.05, * p<0.1.

## 2. Alternative estimation technique

As an alternative estimation method, we use the rare event logit (ReLogit) proposed by King and Zeng (2001). Unlike the cloglog, which follows an asymmetric distribution, the ReLogit follows a symmetric distribution since it is based on a modified logit specifically designed to better handle and mitigate the biases associated with rare events.

Regression results are presented in Table 6 for the period of the surge over the initial time horizon (7 years) and filter parameters (α = 1.3 and β = 0.03). With this standard scenario, in all empirical cases the coefficient of the undervaluation variable remains strongly statistically significant.
<table><caption>Table 6: Onset of export surges - ReLogit estimates (2001-2012)</caption><thead><tr><th></th><th>(1)<br>All</th><th>(2)<br>All</th><th>(3)<br>Primary</th><th>(4)<br>Primary</th><th>(5)<br>Manuf</th><th>(6)<br>Manuf</th></tr></thead><tbody><tr><td>Log (Per cap GDP)</td><td>-0.767***<br>(0.212)</td><td>-0.618***<br>(0.197)</td><td>-0.821<br>(1.243)</td><td>-0.309<br>(0.977)</td><td>-0.430***<br>(0.162)</td><td>-0.089<br>(0.237)</td></tr><tr><td>Log (Per cap GDP) <sup>2</sup></td><td>0.008<br>(0.019)</td><td>-0.008<br>(0.019)</td><td>0.019<br>(0.083)</td><td>-0.008<br>(0.063)</td><td>-0.019<br>(0.017)</td><td>-0.060**<br>(0.026)</td></tr><tr><td>Log (Secondary education)</td><td>0.483***<br>(0.105)</td><td>0.532***<br>(0.129)</td><td>0.573***<br>(0.140)</td><td>0.589***<br>(0.183)</td><td>0.368**<br>(0.152)</td><td>0.415**<br>(0.171)</td></tr><tr><td>Undervaluation</td><td>-1.642***<br>(0.412)</td><td>-1.710***<br>(0.406)</td><td>-1.396**<br>(0.712)</td><td>-1.713**<br>(0.679)</td><td>-1.782***<br>(0.499)</td><td>-1.511***<br>(0.554)</td></tr><tr><td>Market share</td><td>-0.017<br>(0.011)</td><td>-0.016<br>(0.012)</td><td>-0.017<br>(0.012)</td><td>-0.013<br>(0.011)</td><td>-0.011<br>(0.041)</td><td>-0.016<br>(0.051)</td></tr><tr><td>Relative Internet users</td><td>0.017**<br>(0.007)</td><td>0.018**<br>(0.007)</td><td>0.015<br>(0.010)</td><td>0.016<br>(0.010)</td><td>0.019**<br>(0.009)</td><td>0.023**<br>(0.010)</td></tr><tr><td>Relative Corruption index</td><td>-0.433***<br>(0.159)</td><td>-0.428***<br>(0.161)</td><td>-0.451**<br>(0.213)</td><td>-0.439**<br>(0.192)</td><td>-0.385*<br>(0.219)</td><td>-0.160<br>(0.260)</td></tr><tr><td>Conflict index</td><td>1.120<br>(1.081)</td><td>1.165<br>(1.114)</td><td>1.955<br>(1.626)</td><td>1.805<br>(1.610)</td><td>0.249<br>(1.398)</td><td>0.651<br>(1.519)</td></tr><tr><td>Pegged regime</td><td>1.366*<br>(0.700)</td><td>0.947<br>(0.609)</td><td>1.090<br>(4.533)</td><td>-1.910<br>(3.805)</td><td>0.633<br>(0.558)</td><td>0.889<br>(0.562)</td></tr><tr><td>Intermediate regime</td><td>1.081<br>(0.696)</td><td>0.754<br>(0.553)</td><td>0.657<br>(4.522)</td><td>-2.054<br>(3.757)</td><td>0.505<br>(0.492)</td><td>0.533<br>(0.453)</td></tr><tr><td>Southern Africa</td><td></td><td>-0.412<br>(0.409)</td><td></td><td>-0.475<br>(0.634)</td><td></td><td>-0.399<br>(0.557)</td></tr><tr><td>Central Africa</td><td></td><td>0.548<br>(0.428)</td><td></td><td>0.912<br>(0.624)</td><td></td><td>0.068<br>(0.600)</td></tr><tr><td>East Africa</td><td></td><td>0.012<br>(0.297)</td><td></td><td>0.515<br>(0.411)</td><td></td><td>-0.543<br>(0.496)</td></tr><tr><td>West Africa</td><td></td><td>-0.193<br>(0.305)</td><td></td><td>0.688*<br>(0.411)</td><td></td><td>-1.354***<br>(0.499)</td></tr><tr><td>Observations</td><td>2,815</td><td>2,815</td><td>1,478</td><td>1,478</td><td>1,337</td><td>1,337</td></tr><tr><td>Number of surges</td><td>66</td><td>66</td><td>36</td><td>36</td><td>30</td><td>30</td></tr></tbody></table>

Notes: ReLogit estimates. The coefficients reported are not marginal effect. The dependent variable is the dummy variable equal to 1 over a 3-year window centered on the onset date. All control variables are averages over 7 years prior to the surge initiation. Standard errors are adjusted for clustering at the product-year level: *** p<0.01, ** p<0.05, * p<0.1.
# VI. Conclusion

The role of product-specific currency undervaluation is explored to shed light on the performance of Africa's disaggregated exports. The surge episode identification methodology is applied to primary and manufactured products. Over the period 1995-2017, we identified 96 export surge episodes for a total of 149 primary and manufactured export products (4-digit HS code) of 41 African countries. The majority of episodes occurred in middle-income African countries. Country-product pairs of real exchange rate misalignments are calculated on an annual basis. For this variable of interest, the use of the cloglog estimator allows the identification of the impact while controlling for other determinants. On average, exchange rate undervaluation preceded export surges. A one standard deviation increase in the level of the country's product undervaluation increases the probability of a surge by 1.3 percentage points. When the sample is split into primary and manufactured goods, the effect is 1.6 and 0.7 percentage points, or a 22% and 11% increase in the occurrence of surges in the sample, respectively. These results are robust to changes in the filter parameters of the surges, to the time frame of their duration, and to the use of the ReLogit as an alternative to the cloglog estimator.

What does this empirical analysis tell us? Identifying a product-specific exchange rate misalignment - i.e., the difference in unit production costs relative to competitors - provides information about the ability of firms to export the product on a sustainable basis with a normal profit margin. While variations in the official exchange rate can play a role in achieving this competitiveness objective, it is unlikely that the nominal exchange rate, which is by definition unique, can meet a country's expectations for all the products that make up its core international specialization. Product-specific misalignments are therefore important to signal potential changes needed not only in exchange rate policy, but also in the production behavior of exporting firms and in the accompanying measures of public policies. A large number of works today call for a rebirth of industrial policies, in the broadest sense- i.e., including production activities based on the transformation of primary products and the appropriate promotion of tradable services. Such strategies call for country-specific combinations of horizontal and vertical public measures, with nominal exchange rate adjustments being just one policy dimension interacting with other economic policy instruments. As recently emphasized by Aiginger and Rodrik (2020), industrial policy must not conflict with other dimensions of public interventions, such as the competition or trade policy, as well as regional or tax policies. A successful industrial policy should include sectoral elements, identify the goods and sectors that are important to a country, and support horizontal activities that shape the business environment. One of the key challenges of structural transformation is therefore to design horizontal and vertical policies that are complementary rather than substitutes and that promote sectoral or firm-level cherry picking in a non-clientelist manner. Such policies could be crucial for fostering sustained economic growth in small yet open low-income and sub-Saharan economies.
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# Appendices:

## Appendix 1: Sample and data

### Appendix 1a: List of countries

Algeria, Angola, Benin, Burundi, Burkina Faso, Cabo Verde, Central African Rep, Cameroon, Chad, Cote D'Ivoire, Comoros, Congo Dem. Rep., Congo Rep. of, Egypt, Ethiopia, Equatorial Guinea, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Morocco, Mozambique, Niger, Nigeria, Rwanda, Senegal, Tanzania, Sierra Leone, South Africa, Togo, Tunisia, Uganda, Zambia.

### Appendix 1b: Correlation table

<table><thead><tr><th>Variables</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th><th>(11)</th><th>(12)</th><th>(13)</th></tr></thead><tbody><tr><td>(1) Export Surges (=1, dummy)</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(2) Market share (in %)</td><td>-0.008</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(3) Undervaluation (in %)</td><td>-0.048</td><td>-0.012</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(4) Overvaluation (in %)</td><td>-0.008</td><td>-0.110</td><td>0.413</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(5) Log(per cap GDP)</td><td>-0.031</td><td>0.078</td><td>-0.320</td><td>-0.311</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(6) Log(per cap GDP)<sup>2</sup></td><td>-0.036</td><td>0.071</td><td>-0.306</td><td>-0.288</td><td>0.997</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(7) Log(Secondary education)</td><td>0.024</td><td>0.119</td><td>-0.228</td><td>-0.214</td><td>0.537</td><td>0.520</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(8) Relative internet users</td><td>0.019</td><td>0.157</td><td>-0.052</td><td>-0.018</td><td>0.141</td><td>0.136</td><td>0.062</td><td>1.000</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(9) Relative Corruption index</td><td>-0.025</td><td>-0.156</td><td>0.168</td><td>0.300</td><td>-0.155</td><td>-0.137</td><td>-0.147</td><td>-0.357</td><td>1.000</td><td></td><td></td><td></td><td></td></tr><tr><td>(10) Conflict index</td><td>-0.007</td><td>0.037</td><td>-0.040</td><td>-0.023</td><td>0.063</td><td>0.057</td><td>0.080</td><td>0.031</td><td>-0.058</td><td>1.000</td><td></td><td></td><td></td></tr><tr><td>(11) Pegged regime</td><td>-0.026</td><td>-0.040</td><td>0.238</td><td>0.212</td><td>0.080</td><td>0.079</td><td>-0.091</td><td>-0.078</td><td>0.362</td><td>-0.090</td><td>1.000</td><td></td><td></td></tr><tr><td>(12) Intermediate regime</td><td>0.043</td><td>0.052</td><td>-0.218</td><td>-0.209</td><td>-0.071</td><td>-0.070</td><td>0.112</td><td>0.079</td><td>-0.389</td><td>0.052</td><td>-0.934</td><td>1.000</td><td></td></tr><tr><td>(13) Floating regime</td><td>-0.049</td><td>-0.037</td><td>-0.048</td><td>-0.002</td><td>-0.026</td><td>-0.028</td><td>-0.083</td><td>-0.004</td><td>0.087</td><td>0.127</td><td>-0.153</td><td>-0.209</td><td>1.000</td></tr></tbody></table>
<table><caption>Table A1: Selected products and regional contribution to primary and manufactured exports
(In percentages, excluding crude oil, gas and minerals)</caption><thead><tr><th></th><th>Mean</th><th>Standard deviation</th><th>Lower Share</th><th>Higher share</th></tr></thead><tbody><tr><td colspan="5"><strong>Central Africa</strong></td></tr><tr><td>Primary</td><td>92</td><td>8</td><td>80(CMR)</td><td>100 (CAF, GNQ, TCD)</td></tr><tr><td>Manufactured</td><td>59</td><td>38</td><td>5 (COG)</td><td>97 (TCD)</td></tr><tr><td colspan="5"><strong>East Africa</strong></td></tr><tr><td>Primary</td><td>75</td><td>16</td><td>52 (TZA)</td><td>100 (COM)</td></tr><tr><td>Manufactured</td><td>48</td><td>15</td><td>24 (KEN)</td><td>81 (MWI)</td></tr><tr><td colspan="5"><strong>Northern Africa</strong></td></tr><tr><td>Primary</td><td>53</td><td>18</td><td>32 (EGY)</td><td>81 (DZA)</td></tr><tr><td>Manufactured</td><td>43</td><td>17</td><td>23 (EGY)</td><td>65 (DZA)</td></tr><tr><td colspan="5"><strong>Southern Africa</strong></td></tr><tr><td>Primary</td><td>69</td><td>26</td><td>36 (ZAF)</td><td>97 (AGO)</td></tr><tr><td>Manufactured</td><td>31</td><td>15</td><td>11 (AGO)</td><td>43 (ZAF)</td></tr><tr><td colspan="5"><strong>West Africa</strong></td></tr><tr><td>Primary</td><td>84</td><td>14</td><td>51 (SEN)</td><td>100 (CPV, GNB)</td></tr><tr><td>Manufactured</td><td>56</td><td>29</td><td>0,4 (LBR)</td><td>100 (MRT)</td></tr><tr><td colspan="5"><strong>Africa (total)</strong></td></tr><tr><td>Primary</td><td>78</td><td>19</td><td>32</td><td>100</td></tr><tr><td>Manufactured</td><td>51</td><td>27</td><td>0</td><td>100</td></tr></tbody></table>

Source: Authors using BACI 2019. Countries are identified in the table by their ISO Alpha-3 code. Mean is the weighted average of the top 5 exported products over the period 2009-2013, excluding natural rent products and potential chapters for which we presume reexports or second-hand exports. The last two columns provide respectively regional lower and higher shares with the ISO three letter code of countries in parentheses.
Appendix 1c: List of primary products

<table><tr><td>1</td><td>HS-0303</td><td>Fish, frozen, excluding fish fillets</td></tr><tr><td>2</td><td>HS-0306</td><td>Crustaceans, whether in shell or not</td></tr><tr><td>3</td><td>HS-0901</td><td>Coffee, whether or not roasted or decaffe</td></tr><tr><td>4</td><td>HS-2301</td><td>Flours, meals and pellets, of meat or me</td></tr><tr><td>5</td><td>HS-4403</td><td>Wood in the rough, whether or not stripp</td></tr><tr><td>6</td><td>HS-0902</td><td>Tea, whether or not flavoured.</td></tr><tr><td>7</td><td>HS-2203</td><td>Beer made from malt.</td></tr><tr><td>8</td><td>HS-2402</td><td>Cigars, cheroots, cigarillos and cigaret</td></tr><tr><td>9</td><td>HS-5201</td><td>Cotton, not carded or combed.</td></tr><tr><td>10</td><td>HS-0207</td><td>Meat and edible offal, of the poultry of</td></tr><tr><td>11</td><td>HS-0801</td><td>Coconuts, Brazil nuts and cashew nuts</td></tr><tr><td>12</td><td>HS-1006</td><td>Rice.</td></tr><tr><td>13</td><td>HS-0804</td><td>Dates, figs, pineapples, avocados, guava</td></tr><tr><td>14</td><td>HS-1207</td><td>Other oil seeds and oleaginous fruits</td></tr><tr><td>15</td><td>HS-1515</td><td>Other fixed vegetable fats and oils</td></tr><tr><td>16</td><td>HS-1701</td><td>Cane or beet sugar and chemically pure</td></tr><tr><td>17</td><td>HS-4407</td><td>Wood sawn or chipped lengthwise, sliced</td></tr><tr><td>18</td><td>HS-1801</td><td>Cocoa beans, whole or broken, raw</td></tr><tr><td>19</td><td>HS-1803</td><td>Cocoa paste, whether or not defatted.</td></tr><tr><td>20</td><td>HS-1804</td><td>Cocoa butter, fat and oil.</td></tr><tr><td>21</td><td>HS-4001</td><td>Natural rubber, balata, gutta-percha</td></tr><tr><td>22</td><td>HS-0803</td><td>Bananas, including plantains, fresh or d</td></tr><tr><td>23</td><td>HS-1211</td><td>Plants and parts of plants</td></tr><tr><td>24</td><td>HS-2401</td><td>Unmanufactured tobacco; tobacco refuse.</td></tr><tr><td>25</td><td>HS-4401</td><td>Fuel wood, in logs, in billets, in twigs</td></tr><tr><td>26</td><td>HS-0905</td><td>Vanilla.</td></tr><tr><td>27</td><td>HS-0907</td><td>Cloves (whole fruit, cloves and stems).</td></tr><tr><td>28</td><td>HS-4402</td><td>Wood charcoal (including shell or nut</td></tr><tr><td>29</td><td>HS-1604</td><td>Prepared or preserved fish; caviar</td></tr><tr><td>30</td><td>HS-0709</td><td>Other vegetables, fresh or chilled.</td></tr><tr><td>31</td><td>HS-1001</td><td>Wheat and meslin.</td></tr><tr><td>32</td><td>HS-2202</td><td>Waters, including mineral waters</td></tr><tr><td>33</td><td>HS-0406</td><td>Cheese and curd.</td></tr></table>

<table><tr><td>34</td><td>HS-0701</td><td>Potatoes, fresh or chilled.</td></tr><tr><td>35</td><td>HS-0805</td><td>Citrus fruit, fresh or dried.</td></tr><tr><td>36</td><td>HS-0603</td><td>Cut flowers and flower buds of a kind</td></tr><tr><td>37</td><td>HS-0713</td><td>Dried leguminous vegetables, shelled</td></tr><tr><td>38</td><td>HS-2403</td><td>Other manufactured tobacco and manufactu</td></tr><tr><td>39</td><td>HS-4406</td><td>Railway or tramway sleepers (cross-ties)</td></tr><tr><td>40</td><td>HS-1202</td><td>Ground-nuts, not roasted or otherwise</td></tr><tr><td>41</td><td>HS-1508</td><td>Ground-nut oil and its fractions</td></tr><tr><td>42</td><td>HS-0708</td><td>Leguminous vegetables, shelled or unshelled</td></tr><tr><td>43</td><td>HS-4002</td><td>Synthetic rubber and factice derived fro</td></tr><tr><td>44</td><td>HS-0307</td><td>Molluscs, whether in shell or not, live,</td></tr><tr><td>45</td><td>HS-0702</td><td>Tomatoes, fresh or chilled.</td></tr><tr><td>46</td><td>HS-0810</td><td>Other fruit, fresh.</td></tr><tr><td>47</td><td>HS-0102</td><td>Live bovine animals.</td></tr><tr><td>48</td><td>HS-5203</td><td>Cotton, carded or combed.</td></tr><tr><td>49</td><td>HS-0302</td><td>Fish, fresh or chilled, excluding fish f</td></tr><tr><td>50</td><td>HS-1504</td><td>Fats and oils and their fractions, of fi</td></tr><tr><td>51</td><td>HS-0106</td><td>Other live animals.</td></tr><tr><td>52</td><td>HS-1703</td><td>Molasses resulting from the extraction o</td></tr><tr><td>53</td><td>HS-0104</td><td>Live sheep and goats.</td></tr><tr><td>54</td><td>HS-6309</td><td>Worn clothing and other worn articles.</td></tr><tr><td>55</td><td>HS-0401</td><td>Milk and cream, not concentrated nor con</td></tr><tr><td>56</td><td>HS-1101</td><td>Wheat or meslin flour.</td></tr><tr><td>57</td><td>HS-2104</td><td>Soups and broths and preparations</td></tr><tr><td>58</td><td>HS-1301</td><td>Lac; natural gums, resins, gum-resins an</td></tr><tr><td>59</td><td>HS-1509</td><td>Olive oil and its fractions, whether or</td></tr><tr><td>60</td><td>HS-0304</td><td>Fish fillets and other fish meat</td></tr><tr><td>61</td><td>HS-0806</td><td>Grapes, fresh or dried.</td></tr><tr><td>62</td><td>HS-0808</td><td>Apples, pears and quinces, fresh.</td></tr><tr><td>63</td><td>HS-2204</td><td>Wine of fresh grapes</td></tr><tr><td>64</td><td>HS-4702</td><td>Chemical wood pulp, dissolving grades.</td></tr><tr><td>65</td><td>HS-1005</td><td>Maize (corn).</td></tr><tr><td>66</td><td>HS-2208</td><td>Undenatured ethyl alcohol of an alcoholi</td></tr></table>
Appendix 1d: List of manufactured products

<table><tr><td>1</td><td>HS-2523</td><td>Portland cement, aluminous cement, slag</td></tr><tr><td>2</td><td>HS-2804</td><td>Hydrogen, rare gases and other non-metal</td></tr><tr><td>3</td><td>HS-2807</td><td>Sulphuric acid; oleum.</td></tr><tr><td>4</td><td>HS-2809</td><td>Diphosphorus pentaoxide; phosphoric acid</td></tr><tr><td>5</td><td>HS-2814</td><td>Ammonia, anhydrous or in aqueous solution</td></tr><tr><td>6</td><td>HS-2822</td><td>Cobalt oxides and hydroxides; commercial</td></tr><tr><td>7</td><td>HS-2836</td><td>Carbonates; peroxocarbonates</td></tr><tr><td>8</td><td>HS-2844</td><td>Radioactive chemical elements</td></tr><tr><td>9</td><td>HS-2901</td><td>Acyclic hydrocarbons.</td></tr><tr><td>10</td><td>HS-2905</td><td>Acyclic alcohols and their halogenated,</td></tr><tr><td>11</td><td>HS-3004</td><td>Medicaments (excluding goods of heading</td></tr><tr><td>12</td><td>HS-3102</td><td>Mineral or chemical fertilisers,</td></tr><tr><td>13</td><td>HS-3105</td><td>Mineral or chemical fertilisers</td></tr><tr><td>14</td><td>HS-3301</td><td>Essential oils (terpeneless or not)</td></tr><tr><td>15</td><td>HS-3304</td><td>Beauty or make-up preparations</td></tr><tr><td>16</td><td>HS-3401</td><td>Soap; organic surface-active products</td></tr><tr><td>17</td><td>HS-3507</td><td>Enzymes; prepared enzymes not elsewhere</td></tr><tr><td>18</td><td>HS-3802</td><td>Activated carbon; activated natural mine</td></tr><tr><td>19</td><td>HS-3811</td><td>Anti-knock preparations, oxidation</td></tr><tr><td>20</td><td>HS-3823</td><td>Industrial monocarboxylic fatty acids</td></tr><tr><td>21</td><td>HS-3902</td><td>Polymers of propylene or of other</td></tr><tr><td>22</td><td>HS-3923</td><td>Articles for the conveyance or packing</td></tr><tr><td>23</td><td>HS-3924</td><td>Tableware, kitchenware, other household</td></tr><tr><td>24</td><td>HS-4105</td><td>Tanned or crust skins of sheep or lambs,</td></tr><tr><td>25</td><td>HS-4106</td><td>Tanned or crust hides and skins of other</td></tr><tr><td>26</td><td>HS-4107</td><td>Leather further prepared after tanning</td></tr><tr><td>27</td><td>HS-4408</td><td>Sheets for veneering</td></tr><tr><td>28</td><td>HS-4412</td><td>Plywood, veneered panels</td></tr><tr><td>29</td><td>HS-4802</td><td>Uncoated paper and paperboard</td></tr><tr><td>30</td><td>HS-4804</td><td>Uncoated kraft paper and paperboard</td></tr><tr><td>31</td><td>HS-4819</td><td>Cartons, boxes, cases, bags</td></tr><tr><td>32</td><td>HS-4907</td><td>Unused postage, revenue or similar stamp</td></tr><tr><td>33</td><td>HS-5205</td><td>Cotton yarn (other than sewing thread)</td></tr><tr><td>34</td><td>HS-5208</td><td>Woven fabrics of cotton, containing 85 %</td></tr><tr><td>35</td><td>HS-5408</td><td>Woven fabrics of artificial filament yarn</td></tr><tr><td>36</td><td>HS-5701</td><td>Carpets and other textile floor covering</td></tr><tr><td>37</td><td>HS-6104</td><td>Women's or girls' suits, ensembles, jackets</td></tr><tr><td>38</td><td>HS-6107</td><td>Men's or boys' underpants, briefs, night</td></tr><tr><td>39</td><td>HS-6109</td><td>T-shirts, singlets and other vests, knit</td></tr><tr><td>40</td><td>HS-6110</td><td>Jerseys, pullovers, cardigans, waistcoat</td></tr><tr><td>41</td><td>HS-6203</td><td>Men's or boys' suits, ensembles, jackets</td></tr><tr><td>42</td><td>HS-6204</td><td>Women's or girls' suits, ensembles, jackets</td></tr></table>

<table><tr><td>43</td><td>HS-6205</td><td>Men's or boys' shirts.</td></tr><tr><td>44</td><td>HS-6206</td><td>Women's or girls' blouses, shirts</td></tr><tr><td>45</td><td>HS-6210</td><td>Garments, made up of fabrics of heading</td></tr><tr><td>46</td><td>HS-6304</td><td>Other furnishing articles</td></tr><tr><td>47</td><td>HS-6305</td><td>Sacks and bags</td></tr><tr><td>48</td><td>HS-6402</td><td>Other footwear with outer soles</td></tr><tr><td>49</td><td>HS-6403</td><td>Footwear with outer soles of rubber</td></tr><tr><td>50</td><td>HS-6405</td><td>Other footwear.</td></tr><tr><td>51</td><td>HS-6406</td><td>Parts of footwear (including uppers)</td></tr><tr><td>52</td><td>HS-6704</td><td>Wigs, false beards, eyebrows and eyelash</td></tr><tr><td>53</td><td>HS-6810</td><td>Articles of cement, of concrete</td></tr><tr><td>54</td><td>HS-7005</td><td>Float glass and surface ground or polish</td></tr><tr><td>55</td><td>HS-7010</td><td>Carboys, bottles, flasks, jars, pots</td></tr><tr><td>56</td><td>HS-7102</td><td>Diamonds, whether or not worked, but not</td></tr><tr><td>57</td><td>HS-7103</td><td>Precious stones (other than diamonds) an</td></tr><tr><td>58</td><td>HS-7113</td><td>Articles of jewellery and parts thereof,</td></tr><tr><td>59</td><td>HS-7202</td><td>Ferro-alloys.</td></tr><tr><td>60</td><td>HS-7208</td><td>Flat-rolled products of iron or non-alloy</td></tr><tr><td>61</td><td>HS-7210</td><td>Flat-rolled products of iron or non-alloy</td></tr><tr><td>62</td><td>HS-7213</td><td>Bars and rods, hot-rolled</td></tr><tr><td>63</td><td>HS-7214</td><td>Other bars and rods of iron or non-alloy</td></tr><tr><td>64</td><td>HS-7215</td><td>Other bars and rods of iron or non-alloy</td></tr><tr><td>65</td><td>HS-7225</td><td>Flat-rolled products of other alloy</td></tr><tr><td>66</td><td>HS-7304</td><td>Tubes, pipes and hollow profiles,</td></tr><tr><td>67</td><td>HS-7306</td><td>Other tubes, pipes and hollow profiles</td></tr><tr><td>68</td><td>HS-7308</td><td>Structures (excluding prefabricated built</td></tr><tr><td>69</td><td>HS-7326</td><td>Other articles of iron or steel.</td></tr><tr><td>70</td><td>HS-8207</td><td>Interchangeable tools for hand tools</td></tr><tr><td>71</td><td>HS-8502</td><td>Electric generating sets and rotary</td></tr><tr><td>72</td><td>HS-8504</td><td>Electrical transformers</td></tr><tr><td>73</td><td>HS-8517</td><td>Telephone sets, including telephones for</td></tr><tr><td>74</td><td>HS-8525</td><td>Transmission apparatus for radio</td></tr><tr><td>75</td><td>HS-8536</td><td>Electrical apparatus for switching</td></tr><tr><td>76</td><td>HS-8537</td><td>Boards, panels, consoles, desks, cabinet</td></tr><tr><td>77</td><td>HS-8544</td><td>Insulated (including enamelled</td></tr><tr><td>78</td><td>HS-8703</td><td>Motor cars and other motor vehicles</td></tr><tr><td>79</td><td>HS-8704</td><td>Motor vehicles for the transport of good</td></tr><tr><td>80</td><td>HS-8708</td><td>Parts and accessories of the motor</td></tr><tr><td>81</td><td>HS-9015</td><td>Surveying</td></tr><tr><td>82</td><td>HS-9401</td><td>Seats (other than those of heading 94.02</td></tr><tr><td>83</td><td>HS-9406</td><td>Prefabricated buildings.</td></tr></table>
Appendix 1e: Primary products - pre-surge misalignment and year of surge

<table><thead><tr><td>Country</td><td>Product (HS-4)</td><td>Year</td><td>Weight*</td><td>Pre-surge misalignment</td></tr></thead><tbody><tr><td>Algeria</td><td>HS-0804 Dates, figs, pineapples, avocados,</td><td>2011</td><td>7.0%</td><td>-37.5%</td></tr><tr><td>Benin</td><td>HS-0801 Coconuts, Brazil nuts and cashew nuts</td><td>2008</td><td>27.0%</td><td>46.1%</td></tr><tr><td>Burkina Faso</td><td>HS-1515 Other fixed vegetable fats and oils</td><td>2011</td><td>1.3%</td><td>19.8%</td></tr><tr><td>Burkina Faso</td><td>HS-5201 Cotton, not carded or combed.</td><td>2003</td><td>74.5%</td><td>10.3%</td></tr><tr><td>Burkina Faso</td><td>HS-0801 Coconuts, Brazil nuts and cashew nuts</td><td>2011</td><td>4.7%</td><td>45.3%</td></tr><tr><td>Burkina Faso</td><td>HS-0804 Dates, figs, pineapples, avocados,</td><td>2003</td><td>1.5%</td><td>9.0%</td></tr><tr><td>Cabo Verde</td><td>HS-1604 Prepared or preserved fish; caviar</td><td>2009</td><td>46.7%</td><td>34.2%</td></tr><tr><td>Cameroon</td><td>HS-1801 Cocoa beans, whole or broken, raw</td><td>2004</td><td>26.0%</td><td>37.3%</td></tr><tr><td>Congo Dem. Rep. of</td><td>HS-1211 Plants and parts of plants</td><td>2006</td><td>4.3%</td><td>58.4%</td></tr><tr><td>Congo Dem. Rep. of</td><td>HS-4403 Wood in the rough, whether or not</td><td>2004</td><td>40.7%</td><td>46.5%</td></tr><tr><td>Congo Dem. Rep. of</td><td>HS-4407 Wood sawn or chipped lengthwise</td><td>2006</td><td>23.8%</td><td>45.1%</td></tr><tr><td>Congo Rep. of</td><td>HS-4001 Natural rubber, balata, gutta-perch</td><td>2005</td><td>4.7%</td><td>19.8%</td></tr><tr><td>Congo Rep. of</td><td>HS-4403 Wood in the rough, whether or not</td><td>2007</td><td>65.6%</td><td>-13.8%</td></tr><tr><td>Cote D' Ivoire</td><td>HS-4001 Natural rubber, balata, gutta-perch</td><td>2010</td><td>11.7%</td><td>59.6%</td></tr><tr><td>Cote D' Ivoire</td><td>HS-0801 Coconuts, Brazil nuts and cashew nuts</td><td>2004</td><td>4.5%</td><td>29.3%</td></tr><tr><td>Egypt</td><td>HS-0406 Cheese and curd.</td><td>2003</td><td>7.5%</td><td>-73.5%</td></tr><tr><td>Egypt</td><td>HS-0701 Potatoes, fresh or chilled.</td><td>2005</td><td>4.7%</td><td>-80.7%</td></tr><tr><td>Egypt</td><td>HS-0805 Citrus fruit, fresh or dried.</td><td>2003</td><td>11.4%</td><td>-52.9%</td></tr><tr><td>Ethiopia</td><td>HS-0603 Cut flowers and flower buds</td><td>2003</td><td>9.8%</td><td>16.0%</td></tr><tr><td>Ethiopia</td><td>HS-0709 Other vegetables, fresh or chilled.</td><td>2006</td><td>12.7%</td><td>-4.3%</td></tr><tr><td>Ethiopia</td><td>HS-0713 Dried leguminous vegetables</td><td>2010</td><td>7.3%</td><td>5.8%</td></tr><tr><td>Gabon</td><td>HS-4001 Natural rubber, balata, gutta-perch</td><td>2005</td><td>11.0%</td><td>50.5%</td></tr><tr><td>Gabon</td><td>HS-4407 Wood sawn or chipped lengthwise</td><td>2011</td><td>30.9%</td><td>26.5%</td></tr><tr><td>Ghana</td><td>HS-1801 Cocoa beans, whole or broken</td><td>2004</td><td>52.8%</td><td>-28.1%</td></tr><tr><td>Ghana</td><td>HS-1803 Cocoa paste, whether or not defatte</td><td>2004</td><td>9.0%</td><td>-48.7%</td></tr><tr><td>Ghana</td><td>HS-1804 Cocoa butter, fat and oil.</td><td>2009</td><td>4.5%</td><td>-24.8%</td></tr><tr><td>Ghana</td><td>HS-0801 Coconuts, Brazil nuts and cashew nuts</td><td>2011</td><td>4.2%</td><td>16.4%</td></tr><tr><td>Guinea Bissau</td><td>HS-0303 Fish, frozen, excluding fish fillet</td><td>2010</td><td>11.1%</td><td>14.7%</td></tr><tr><td>Guinea Bissau</td><td>HS-0801 Coconuts, Brazil nuts and cashew nuts</td><td>2011</td><td>84.4%</td><td>46.2%</td></tr><tr><td>Kenya</td><td>HS-0603 Cut flowers and flower buds</td><td>2004</td><td>18.5%</td><td>-25.4%</td></tr><tr><td>Liberia</td><td>HS-1801 Cocoa beans, whole or broken</td><td>2008</td><td>6.7%</td><td>37.1%</td></tr><tr><td>Malawi</td><td>HS-2401 Unmanufactured tobacco; tobacco</td><td>2005</td><td>65.8%</td><td>24.2%</td></tr><tr><td>Malawi</td><td>HS-5201 Cotton, not carded or combed.</td><td>2004</td><td>3.6%</td><td>13.2%</td></tr><tr><td>Malawi</td><td>HS-0713 Dried leguminous vegetables</td><td>2007</td><td>5.3%</td><td>19.1%</td></tr><tr><td>Mali</td><td>HS-1207 Other oil seeds and oleaginous</td><td>2006</td><td>6.5%</td><td>-0.5%</td></tr><tr><td>Mali</td><td>HS-0804 Dates, figs, pineapples, avocados,</td><td>2003</td><td>3.3%</td><td>-14.9%</td></tr><tr><td>Mauritania</td><td>HS-1504 Fats and oils and their fractions,</td><td>2009</td><td>1.2%</td><td>-35.4%</td></tr><tr><td>Mauritania</td><td>HS-2301 Flours, meals and pellets, of meat</td><td>2010</td><td>6.0%</td><td>-22.2%</td></tr><tr><td>Morocco</td><td>HS-0702 Tomatoes, fresh or chilled.</td><td>2007</td><td>11.2%</td><td>-15.8%</td></tr><tr><td>Mozambique</td><td>HS-4407 Wood sawn or chipped lengthwise</td><td>2007</td><td>6.9%</td><td>68.1%</td></tr><tr><td>Niger</td><td>HS-1701 Cane or beet sugar and chemically</td><td>2010</td><td>3.2%</td><td>48.7%</td></tr><tr><td>Niger</td><td>HS-6309 Worn clothing and other worn article</td><td>2005</td><td>7.4%</td><td>-6.6%</td></tr><tr><td>Nigeria</td><td>HS-1207 Other oil seeds and oleaginous</td><td>2005</td><td>6.3%</td><td>-33.0%</td></tr><tr><td>Nigeria</td><td>HS-1801 Cocoa beans, whole or broken</td><td>2003</td><td>31.0%</td><td>-18.7%</td></tr><tr><td>Nigeria</td><td>HS-4001 Natural rubber, balata, gutta-perch</td><td>2006</td><td>19.4%</td><td>23.4%</td></tr><tr><td>Nigeria</td><td>HS-0801 Coconuts, Brazil nuts and cashew nuts</td><td>2003</td><td>4.9%</td><td>-9.5%</td></tr><tr><td>Senegal</td><td>HS-0303 Fish, frozen, excluding fish fillet</td><td>2007</td><td>17.7%</td><td>3.5%</td></tr><tr><td>Sierra Leone</td><td>HS-1801 Cocoa beans, whole or broken</td><td>2004</td><td>67.0%</td><td>37.2%</td></tr><tr><td>South Africa</td><td>HS-4702 Chemical wood pulp, dissolving grad</td><td>2010</td><td>5.7%</td><td>-21.3%</td></tr><tr><td>Tanzania</td><td>HS-2401 Unmanufactured tobacco; tobacco ref</td><td>2005</td><td>15.9%</td><td>20.6%</td></tr><tr><td>Tanzania</td><td>HS-0801 Coconuts, Brazil nuts and cashew nuts</td><td>2009</td><td>8.8%</td><td>24.5%</td></tr><tr><td>The Gambia</td><td>HS-0303 Fish, frozen, excluding fish fillet</td><td>2008</td><td>4.6%</td><td>10.6%</td></tr><tr><td>Togo</td><td>HS-1801 Cocoa beans, whole or broken, raw o</td><td>2004</td><td>45.1%</td><td>48.8%</td></tr><tr><td>Tunisia</td><td>HS-0804 Dates, figs, pineapples, avocados,</td><td>2004</td><td>13.3%</td><td>-6.4%</td></tr><tr><td>Uganda</td><td>HS-1701 Cane or beet sugar and chemically</td><td>2005</td><td>4.6%</td><td>33.5%</td></tr></tbody></table>

<footnote>* The weight corresponds to the product's share of total exports in its category. Pre-surge misalignment is the average misalignment over the 7-year period prior to the surge.</footnote>
Appendix 1f: Manufactured products – pre-surge misalignment and year of surge

<table><thead><tr><td>Country</td><td>Product (HS-4)</td><td>Year</td><td>Weight*</td><td>Pre-surge misalignment</td></tr></thead><tbody><tr><td>Cote D' Ivoire</td><td>HS-3923 Articles for the conveyance or pack</td><td>2009</td><td>4.1%</td><td>-2.6%</td></tr><tr><td>Cameroon</td><td>HS-3401 Soap; organic surface-active produc</td><td>2005</td><td>8.2%</td><td>16.0%</td></tr><tr><td>Cameroon</td><td>HS-4819 Cartons, boxes, cases, bags</td><td>2005</td><td>3.9%</td><td>-6.4%</td></tr><tr><td>Congo Rep. of</td><td>HS-4408 Sheets for veneering</td><td>2007</td><td>1.2%</td><td>-16.2%</td></tr><tr><td>Congo Rep. of</td><td>HS-7326 Other articles of iron or steel.</td><td>2005</td><td>1.1%</td><td>-50.4%</td></tr><tr><td>Algeria</td><td>HS-8703 Motor cars and other motor vehicles</td><td>2005</td><td>11.8%</td><td>-93.8%</td></tr><tr><td>Egypt</td><td>HS-3102 Mineral or chemical fertilisers</td><td>2006</td><td>8.5%</td><td>-36.2%</td></tr><tr><td>Egypt</td><td>HS-6203 Men's or boys' suits, ensembles</td><td>2003</td><td>3.5%</td><td>-42.9%</td></tr><tr><td>Egypt</td><td>HS-6204 Women's or girls' suits, ensembles,</td><td>2004</td><td>2.6%</td><td>-44.0%</td></tr><tr><td>Ethiopia</td><td>HS-6109 T-shirts, singlets and other vests</td><td>2010</td><td>3.7%</td><td>24.7%</td></tr><tr><td>Ethiopia</td><td>HS-6403 Footwear with outer soles of rubber</td><td>2005</td><td>3.2%</td><td>24.1%</td></tr><tr><td>Ghana</td><td>HS-3304 Beauty or make-up preparations and</td><td>2011</td><td>4.8%</td><td>-30.8%</td></tr><tr><td>Kenya</td><td>HS-2523 Portland cement, aluminous cement,</td><td>2005</td><td>5.0%</td><td>-7.8%</td></tr><tr><td>Kenya</td><td>HS-2836 Carbonates; peroxocarbonates</td><td>2003</td><td>7.1%</td><td>-29.9%</td></tr><tr><td>Kenya</td><td>HS-3923 Articles for the conveyance or pack</td><td>2006</td><td>3.6%</td><td>-35.9%</td></tr><tr><td>Morocco</td><td>HS-3105 Mineral or chemical fertilisers</td><td>2010</td><td>10.8%</td><td>2.0%</td></tr><tr><td>Morocco</td><td>HS-8703 Motor cars and other motor vehicles</td><td>2007</td><td>4.5%</td><td>-28.1%</td></tr><tr><td>Madagascar</td><td>HS-3301 Essential oils (terpeneless or not)</td><td>2007</td><td>4.8%</td><td>-9.3%</td></tr><tr><td>Madagascar</td><td>HS-6205 Men's or boys' shirts.</td><td>2007</td><td>5.4%</td><td>1.5%</td></tr><tr><td>Mali</td><td>HS-4105 Tanned or crust skins of sheep</td><td>2010</td><td>7.1%</td><td>6.4%</td></tr><tr><td>Mali</td><td>HS-8517 Telephone sets, including telephone</td><td>2003</td><td>2.2%</td><td>-16.1%</td></tr><tr><td>Mozambique</td><td>HS-7103 Precious stones (other than diamond</td><td>2011</td><td>6.1%</td><td>65.8%</td></tr><tr><td>Mozambique</td><td>HS-7306 Other tubes, pipes and hollow</td><td>2010</td><td>6.8%</td><td>53.5%</td></tr><tr><td>Mauritius</td><td>HS-6104 Women's or girls' suits, ensembles</td><td>2009</td><td>4.3%</td><td>4.5%</td></tr><tr><td>Malawi</td><td>HS-4412 Plywood, veneered panels</td><td>2004</td><td>2.7%</td><td>40.8%</td></tr><tr><td>Niger</td><td>HS-2844 Radioactive chemical elements</td><td>2009</td><td>92.0%</td><td>22.3%</td></tr><tr><td>Niger</td><td>HS-8517 Telephone sets, including telephone</td><td>2009</td><td>0.5%</td><td>34.4%</td></tr><tr><td>Nigeria</td><td>HS-4105 Tanned or crust skins of sheep</td><td>2004</td><td>4.8%</td><td>-28.3%</td></tr><tr><td>Nigeria</td><td>HS-4106 Tanned or crust hides and skins</td><td>2003</td><td>14.9%</td><td>-24.7%</td></tr><tr><td>Nigeria</td><td>HS-6402 Other footwear with outer soles</td><td>2006</td><td>4.4%</td><td>-13.7%</td></tr><tr><td>Senegal</td><td>HS-7213 Bars and rods, hot-rolled</td><td>2007</td><td>3.3%</td><td>0.9%</td></tr><tr><td>Togo</td><td>HS-3923 Articles for the conveyance or pack</td><td>2003</td><td>5.5%</td><td>3.8%</td></tr><tr><td>Togo</td><td>HS-6704 Wigs, false beards, eyebrows</td><td>2009</td><td>2.5%</td><td>54.5%</td></tr><tr><td>Tunisia</td><td>HS-6403 Footwear with outer soles of rubber</td><td>2003</td><td>3.5%</td><td>6.8%</td></tr><tr><td>Tanzania</td><td>HS-3105 Mineral or chemical fertilisers</td><td>2005</td><td>6.1%</td><td>14.8%</td></tr><tr><td>Uganda</td><td>HS-2523 Portland cement, aluminous cement,</td><td>2004</td><td>17.6%</td><td>31.9%</td></tr><tr><td>Uganda</td><td>HS-3401 Soap; organic surface-active produc</td><td>2003</td><td>4.7%</td><td>31.7%</td></tr><tr><td>Uganda</td><td>HS-7210 Flat-rolled products of iron or non</td><td>2003</td><td>4.4%</td><td>4.6%</td></tr><tr><td>Uganda</td><td>HS-8525 Transmission apparatus for radio</td><td>2005</td><td>17.0%</td><td>15.4%</td></tr><tr><td>Zambia</td><td>HS-2523 Portland cement, aluminous cement,</td><td>2008</td><td>12.1%</td><td>-1.5%</td></tr><tr><td>Zambia</td><td>HS-2807 Sulphuric acid; oleum.</td><td>2007</td><td>10.9%</td><td>-44.2%</td></tr></tbody></table>

<footnote>* The weight corresponds to the product's share of total exports in its category. Pre-surge misalignment is the average misalignment over the 7-year period prior to the surge.</footnote>
# Appendix 2: Surge episodes and their contribution to product categories

<figure><img src="image_7.png" /><figcaption>## Appendix 2a: Number of export surges, by country among top 5 products of each category</figcaption></figure>

Source: BACI 2019

<figure><img src="image_8.png" /><figcaption>## Appendix 2b: Percentage of product surges among all products considered for each category (%)</figcaption></figure>

Source: BACI 2019
Appendix 3: Regressions with the overvaluation level as an additional control (Cloglog)

<table><caption>Baseline regression results. Onset of export surges: cloglog results (2001-2012)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr><tr><th></th><th>All</th><th>All</th><th>Primary</th><th>Primary</th><th>Manu</th><th>Manu</th></tr></thead><tbody><tr><td>Log(GDP per cap)</td><td>0.024<br>(0.067)</td><td>0.076<br>(0.069)</td><td>-0.082<br>(0.099)</td><td>-0.065<br>(0.098)</td><td>0.143*<br>(0.086)</td><td>0.253***<br>(0.084)</td></tr><tr><td>Log(GDP per cap)<sup>2</sup></td><td>-0.004<br>(0.005)</td><td>-0.008*<br>(0.005)</td><td>0.004<br>(0.007)</td><td>0.003<br>(0.006)</td><td>-0.012**<br>(0.006)</td><td>-0.020***<br>(0.006)</td></tr><tr><td>Log(Secondary education)</td><td>0.022***<br>(0.005)</td><td>0.024***<br>(0.006)</td><td>0.031***<br>(0.007)</td><td>0.030***<br>(0.009)</td><td>0.015**<br>(0.007)</td><td>0.012*<br>(0.007)</td></tr><tr><td>Undervaluation</td><td>-0.069***<br>(0.020)</td><td>-0.071***<br>(0.019)</td><td>-0.078**<br>(0.039)</td><td>-0.088**<br>(0.035)</td><td>-0.050**<br>(0.024)</td><td>-0.038*<br>(0.020)</td></tr><tr><td><strong>Overvaluation</strong></td><td><strong>-0.012<br>(0.028)</strong></td><td><strong>-0.014<br>(0.027)</strong></td><td><strong>0.000<br>(0.034)</strong></td><td><strong>-0.005<br>(0.032)</strong></td><td><strong>-0.034<br>(0.049)</strong></td><td><strong>-0.019<br>(0.041)</strong></td></tr><tr><td>Market share</td><td>-0.001*<br>(0.001)</td><td>-0.001*<br>(0.001)</td><td>-0.001<br>(0.001)</td><td>-0.001<br>(0.001)</td><td>-0.002<br>(0.002)</td><td>-0.002<br>(0.002)</td></tr><tr><td>Relative Internet users</td><td>0.001***<br>(0.000)</td><td>0.001***<br>(0.000)</td><td>0.001<br>(0.001)</td><td>0.001*<br>(0.001)</td><td>0.001**<br>(0.000)</td><td>0.001**<br>(0.000)</td></tr><tr><td>Relative Corruption index</td><td>-0.020***<br>(0.007)</td><td>-0.018**<br>(0.008)</td><td>-0.023**<br>(0.010)</td><td>-0.022**<br>(0.009)</td><td>-0.017<br>(0.010)</td><td>-0.000<br>(0.012)</td></tr><tr><td>Conflict index</td><td>0.055<br>(0.052)</td><td>0.063<br>(0.055)</td><td>0.086<br>(0.091)</td><td>0.077<br>(0.089)</td><td>0.020<br>(0.058)</td><td>0.051<br>(0.058)</td></tr><tr><td>Pegged regime</td><td>0.712***<br>(0.044)</td><td>0.637***<br>(0.068)</td><td>0.723***<br>(0.102)</td><td>0.645***<br>(0.086)</td><td>0.604***<br>(0.117)</td><td>0.545***<br>(0.071)</td></tr><tr><td>Intermediate regime</td><td>0.701***<br>(0.047)</td><td>0.630***<br>(0.069)</td><td>0.698***<br>(0.101)</td><td>0.636***<br>(0.083)</td><td>0.605***<br>(0.119)</td><td>0.538***<br>(0.070)</td></tr><tr><td>Southern Africa</td><td></td><td>-0.019<br>(0.020)</td><td></td><td>-0.034<br>(0.031)</td><td></td><td>-0.020<br>(0.022)</td></tr><tr><td>Central Africa</td><td></td><td>0.029<br>(0.021)</td><td></td><td>0.041<br>(0.032)</td><td></td><td>0.004<br>(0.022)</td></tr><tr><td>East Africa</td><td></td><td>0.009<br>(0.015)</td><td></td><td>0.021<br>(0.022)</td><td></td><td>-0.002<br>(0.020)</td></tr><tr><td>West Africa</td><td></td><td>-0.008<br>(0.015)</td><td></td><td>0.032<br>(0.022)</td><td></td><td>-0.051***<br>(0.017)</td></tr><tr><td>Observations</td><td>2,815</td><td>2,815</td><td>1,478</td><td>1,478</td><td>1,337</td><td>1,337</td></tr><tr><td>Number of surges</td><td>66</td><td>66</td><td>36</td><td>36</td><td>30</td><td>30</td></tr></tbody></table>

Notes: Cloglog estimates. The regression coefficients are marginal effects. The dependent variable is the dummy variable equal to 1 over a 3-year window centered on the onset date of the surge. Control variables are averages over the 7 year-period prior the surge. Standard errors are adjusted for clustering at the product-year level: *** p<0.01, ** p<0.05, * p<0.1.
# Appendix 4: Robustness - alternative parameters (Cloglog)

<figure><img src="image_9.png" /><figcaption>Figure A.4.1: Sensitivity of export surges to the time window</figcaption></figure>

Source: BACI 2019

<figure><img src="image_10.png" /><figcaption>Figure A.4.2: Modification of criterion 2 and export surges over the period</figcaption></figure>

Source: BACI 2019
<table><caption>Appendix 4a: Robustness about the onset surges: Horizon 6 and 8</caption><thead><tr><th></th><th>(1)<br>All</th><th>(2)<br>Primary</th><th>(3)<br>Manu</th><th>(4)<br>All</th><th>(5)<br>Primary</th><th>(5)<br>Manu</th></tr><tr><th></th><th colspan="3">Horizon 6<br>(α = 1.3 and β = 0.03; 6 years)</th><th colspan="3">Horizon 8<br>(α = 1.3 and β = 0.03; 8 years)</th></tr></thead><tbody><tr><td>Log (Per cap GDP)</td><td>0.069<br>(0.084)</td><td>0.067<br>(0.115)</td><td>0.114<br>(0.102)</td><td>0.023<br>(0.071)</td><td>-0.082<br>(0.113)</td><td>0.105**<br>(0.043)</td></tr><tr><td>Log (Per cap GDP)<sup>2</sup></td><td>-0.008<br>(0.006)</td><td>-0.006<br>(0.007)</td><td>-0.012*<br>(0.007)</td><td>-0.004<br>(0.005)</td><td>0.005<br>(0.007)</td><td>-0.009***<br>(0.003)</td></tr><tr><td>Log (Secondary education)</td><td>0.034***<br>(0.009)</td><td>0.019*<br>(0.011)</td><td>0.042***<br>(0.010)</td><td>0.016***<br>(0.006)</td><td>0.018**<br>(0.008)</td><td>0.007<br>(0.004)</td></tr><tr><td>Undervaluation</td><td>-0.106***<br>(0.026)</td><td>-0.092**<br>(0.044)</td><td>-0.039<br>(0.031)</td><td>-0.057***<br>(0.017)</td><td>-0.060*<br>(0.032)</td><td>-0.022*<br>(0.011)</td></tr><tr><td>Market share</td><td>-0.003***<br>(0.001)</td><td>-0.002**<br>(0.001)</td><td>-0.009<br>(0.008)</td><td>-0.001<br>(0.001)</td><td>-0.001<br>(0.001)</td><td>-0.000<br>(0.001)</td></tr><tr><td>Relative Internet users</td><td>0.000<br>(0.000)</td><td>0.001<br>(0.001)</td><td>0.000<br>(0.000)</td><td>0.000<br>(0.000)</td><td>0.000<br>(0.000)</td><td>0.000<br>(0.000)</td></tr><tr><td>Relative Corruption index</td><td>-0.030**<br>(0.013)</td><td>-0.022<br>(0.015)</td><td>-0.013<br>(0.014)</td><td>-0.012*<br>(0.007)</td><td>-0.005<br>(0.007)</td><td>-0.007<br>(0.006)</td></tr><tr><td>Conflict index</td><td>0.090<br>(0.082)</td><td>0.140<br>(0.102)</td><td>0.028<br>(0.090)</td><td>0.046<br>(0.048)</td><td>-0.006<br>(0.079)</td><td>0.063**<br>(0.028)</td></tr><tr><td>Pegged regime</td><td>0.097<br>(0.080)</td><td>0.890***<br>(0.122)</td><td>0.072<br>(0.059)</td><td>0.006<br>(0.043)</td><td>-0.059<br>(0.050)</td><td>0.188***<br>(0.054)</td></tr><tr><td>Intermediate regime</td><td>0.071<br>(0.080)</td><td>0.880***<br>(0.125)</td><td>0.027<br>(0.057)</td><td>0.027<br>(0.043)</td><td>-0.044<br>(0.049)</td><td>0.205***<br>(0.053)</td></tr><tr><td>Southern Africa</td><td>-0.033<br>(0.027)</td><td>-0.068<br>(0.042)</td><td>0.000<br>(0.027)</td><td>-0.004<br>(0.017)</td><td>-0.028<br>(0.031)</td><td>-0.005<br>(0.010)</td></tr><tr><td>Central Africa</td><td>0.027<br>(0.027)</td><td>0.041<br>(0.036)</td><td>-0.017<br>(0.029)</td><td>0.032*<br>(0.019)</td><td>0.012<br>(0.030)</td><td>0.020<br>(0.014)</td></tr><tr><td>East Africa</td><td>0.023<br>(0.022)</td><td>0.034<br>(0.026)</td><td>-0.002<br>(0.028)</td><td>-0.007<br>(0.014)</td><td>0.006<br>(0.022)</td><td>-0.016*<br>(0.009)</td></tr><tr><td>West Africa</td><td>-0.014<br>(0.021)</td><td>0.058**<br>(0.026)</td><td>-0.104***<br>(0.022)</td><td>-0.007<br>(0.014)</td><td>0.032<br>(0.021)</td><td>-0.029***<br>(0.011)</td></tr><tr><td>Observations</td><td colspan="3">2,686</td><td colspan="3">2,647</td></tr><tr><td>Number of surges</td><td colspan="3">84</td><td colspan="3">41</td></tr></tbody></table>

Notes: cloglog estimates. The coefficients reported are marginal effect evaluated at the sample means. The dependent variable is a dummy that is equal to 1 over a 3-year window centered on the onset date of the surge. Controls are averages over the 7 year-period prior the surge. Standard errors are adjusted for clustering at the product-year level: *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>Appendix 4b: Robustness about the onset surges: Lower criterion 2 and Higher criterion 2</caption><thead><tr><th></th><th>(1)<br>All</th><th>(2)<br>Primary</th><th>(3)<br>Manu</th><th>(4)<br>All</th><th>(5)<br>Primary</th><th>(6)<br>Manu</th></tr><tr><th></th><th colspan="3">Lower criterion<br>(α = 1.1 and β = 0.02; 7 years)</th><th colspan="3">Higher criterion<br>(α = 1.5 and β = 0.04; 7 years)</th></tr></thead><tbody><tr><td>Log(Per cap GDP)</td><td>0.078<br>(0.074)</td><td>-0.108<br>(0.112)</td><td>0.294***<br>(0.084)</td><td>0.078<br>(0.062)</td><td>-0.075<br>(0.085)</td><td>0.247***<br>(0.075)</td></tr><tr><td>Log(Per cap GDP)<sup>2</sup></td><td>-0.008<br>(0.005)</td><td>0.006<br>(0.007)</td><td>-0.022***<br>(0.006)</td><td>-0.007*<br>(0.004)</td><td>0.004<br>(0.005)</td><td>-0.019***<br>(0.005)</td></tr><tr><td>Log(Secondary education)</td><td>0.025***<br>(0.007)</td><td>0.030***<br>(0.009)</td><td>0.011<br>(0.007)</td><td>0.023***<br>(0.006)</td><td>0.026***<br>(0.008)</td><td>0.013**<br>(0.007)</td></tr><tr><td>Undervaluation</td><td>-0.084***<br>(0.019)</td><td>-0.101***<br>(0.035)</td><td>-0.048**<br>(0.021)</td><td>-0.065***<br>(0.017)</td><td>-0.075**<br>(0.031)</td><td>-0.037**<br>(0.016)</td></tr><tr><td>Market share</td><td>-0.001**<br>(0.001)</td><td>-0.001<br>(0.001)</td><td>-0.002<br>(0.002)</td><td>-0.001<br>(0.000)</td><td>-0.000<br>(0.001)</td><td>-0.001<br>(0.001)</td></tr><tr><td>Relative Internet users</td><td>0.001***<br>(0.000)</td><td>0.001*<br>(0.001)</td><td>0.001***<br>(0.000)</td><td>0.001**<br>(0.000)</td><td>0.001<br>(0.000)</td><td>0.001*<br>(0.000)</td></tr><tr><td>Relative Corruption index</td><td>-0.018**<br>(0.008)</td><td>-0.024**<br>(0.010)</td><td>0.008<br>(0.012)</td><td>-0.018**<br>(0.007)</td><td>-0.020**<br>(0.009)</td><td>-0.004<br>(0.011)</td></tr><tr><td>Conflict index</td><td>0.053<br>(0.059)</td><td>0.057<br>(0.093)</td><td>0.047<br>(0.062)</td><td>0.072<br>(0.048)</td><td>0.057<br>(0.074)</td><td>0.087*<br>(0.048)</td></tr><tr><td>Pegged regime</td><td>0.696***<br>(0.080)</td><td>0.688***<br>(0.104)</td><td>0.588***<br>(0.089)</td><td>0.597***<br>(0.069)</td><td>0.589***<br>(0.048)</td><td>0.459***<br>(0.077)</td></tr><tr><td>Intermediate regime</td><td>0.690***<br>(0.079)</td><td>0.678***<br>(0.104)</td><td>0.582***<br>(0.090)</td><td>0.601***<br>(0.068)</td><td>0.592***<br>(0.043)</td><td>0.461***<br>(0.077)</td></tr><tr><td>Southern Africa</td><td>-0.022<br>(0.021)</td><td>-0.039<br>(0.033)</td><td>-0.023<br>(0.024)</td><td>0.000<br>(0.018)</td><td>-0.012<br>(0.031)</td><td>-0.002<br>(0.019)</td></tr><tr><td>Central Africa</td><td>0.036*<br>(0.022)</td><td>0.037<br>(0.033)</td><td>0.013<br>(0.023)</td><td>0.052**<br>(0.021)</td><td>0.064**<br>(0.032)</td><td>0.025<br>(0.020)</td></tr><tr><td>East Africa</td><td>0.015<br>(0.016)</td><td>0.023<br>(0.024)</td><td>0.007<br>(0.020)</td><td>0.024*<br>(0.015)</td><td>0.035<br>(0.023)</td><td>0.013<br>(0.018)</td></tr><tr><td>West Africa</td><td>-0.005<br>(0.016)</td><td>0.036<br>(0.024)</td><td>-0.056***<br>(0.019)</td><td>0.007<br>(0.015)</td><td>0.049**<br>(0.022)</td><td>-0.036**<br>(0.016)</td></tr><tr><td>Observations</td><td>2,811</td><td>1,477</td><td>1,334</td><td>2,827</td><td>1,480</td><td>1,347</td></tr><tr><td>Number of surges</td><td>71</td><td>39</td><td>32</td><td>60</td><td>33</td><td>27</td></tr></tbody></table>

Notes: cloglog estimates. The coefficients reported are marginal effect evaluated at the sample means. The dependent variable is a dummy that is equal to 1 over a 3-year window centered on the onset date of the surge. Controls are averages over the 7 year-period prior the surge. Standard errors are adjusted for clustering at the product-year level: *** p<0.01, ** p<0.05, * p<0.1.