The African Growth and Opportunity Act and growth in sub‐Saharan Africa: A local projection approach

• Published date: 01 January 2021
• Date: 01 January 2021
• Author: Jason C. Jones,Nathaniel P. S. Cook
• DOI: http://doi.org/10.1111/twec.12995

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wileyonlinelibrary.com/journal/twec World Econ. 2021;44:234–261.

© 2020 John Wiley & Sons Ltd

Received: 18 July 2019

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Revised: 21 May 2020

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Accepted: 5 June 2020

DOI: 10.1111/twec.12995

ORIGINAL ARTICLE

The African Growth and Opportunity Act and

growth in sub-Saharan Africa: A local projection

approach

Nathaniel P. S.Cook

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Jason C.Jones

Furman University, Greenville, South Carolina, USA

KEYWORDS

African Growth and Opportunity Act, African Trade, economic growth, local projection

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INTRODUCTION

The African Growth and Opportunity Act (AGOA) is a U.S. trade policy that provides eligible coun-

tries in sub-Saharan Africa with preferential duty-free treatment of exports of eligible products to the

United States. As the name of the Act indicates, an explicit goal of AGOA is to stimulate growth in

sub-Saharan Africa. The implication is obviously that there is a relationship between AGOA, exports

and economic growth. Perhaps surprisingly, although previous research has investigated relationships

between AGOA and exports (Frazer & van Biesebroeck,2010; Tadesse & Fayissa,2008), the rela-

tionship between AGOA and growth has received little attention. This paper explores the dynamic re-

lationship between AGOA and growth in sub-Saharan Africa by applying the local projection method

(Jordà, 2005) to estimate impulse responses.

Our approach to this question is novel in two respects. First, although there is an extensive litera-

ture on the relationship between international trade and economic growth, previous research has not

always carefully distinguished between trade policies and trade volumes. Existing research that has

focused on trade policies and growth has largely attempted to identify the extent to which a country's

own trade policies affect its growth. Our approach differs in that we explore the relationship between

AGOA (a U.S. trade policy) and the growth rates of the countries in sub-Saharan Africa that are eligi-

ble for AGOA preferences (not the U.S. growth rate).

Second, our approach is novel in that we explore the dynamics of the relationship between AGOA

and growth by estimating impulse responses. We construct impulse responses by applying the local

projection method (Jordà, 2005), estimating the effect of changes in AGOA eligibility at time t on the

year-on-year growth rate in year t+h. Previous research that has investigated the effects of trade pol-

icies on growth has not typically explored dynamic relationships (indeed, even identification is often

driven by cross-sectional variation in policies). To the extent that previous research has considered

dynamic relationships between trade and growth, it has looked primarily at trade volumes, not trade

policies.

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COOK and JOnES

The main empirical finding is that that after an initial period of adjustment, AGOA eligibility is

associated with significantly higher rates of future per-capita GDP growth in eligible countries in

sub-Saharan Africa. In short, AGOA does contribute to growth in sub-Saharan Africa, although not

necessarily immediately or permanently. This result is robust to a variety of empirical specifications.

The rest of the paper is organised as follows. Section2 reviews some of the existing research on

the relationships between trade, trade policy and growth. Section3 provides some background on the

African Growth and Opportunity Act (AGOA). Section4 outlines our empirical application of the

local projection method. Section5 describes our data. Sections 6 and 7 summarise our results and

explore various robustness checks. Section8 concludes.

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TRADE, TRADE POLICY AND GROWTH

An extensive literature, nicely summarised by Singh (2010), has explored the relationships between

international trade and economic growth. Several important papers in this literature have found a

positive relationship between trade (e.g., the share of exports in income) and per-capita income

using either cross-sectional or time-series data (Michaely,1977; Heller & Porter,1978; Greenaway

& Sapsford,1994; Frankel & Romer,1999; and Irwin & Terviö, 2002; among many others). More

recent papers in this literature have explored the relationship between trade and growth using panel

estimators and have found a similar positive relationship between trade and per-capita income growth

(Dollar & Kraay,2004; Easterly & Levine,2001; Felbermayr,2005; Houchet-Bourdon, Le Mouël, &

Vijil, 2018). Estimating panels of sub-Saharan African countries, Baliamoune-Lutz and Ndikumana

(2007) and Zahongo (2016) also find a positive relationship between trade and per-capita income.

However, in their important critique of a number of trade and growth papers, Rodríguez and Rodrik

(2001) write, ‘From an operational standpoint, it is clear that the relevant question is the one having

to do with the consequences of trade policies rather than trade volumes’ (p. 264). That is, even if a

higher volume of trade (or a higher share of trade in income) is positively associated with economic

growth, the policy implication is unclear. For example, Frankel and Romer (1999) acknowledge that

their results, ‘cannot be applied without qualification to the effects of trade policies’ (p. 395).

Policymakers in particular are probably more interested in the relationship between trade policy

and income. However, measuring trade policy in an empirically consistent way across time and ge-

ography has been a significant challenge in this literature. Dollar (1992) attempts to do this through

quantifying real exchange rate distortions and variability. Anderson and Neary (1996) propose a trade

restrictiveness index that is equal to the uniform tariff that would generate the same level of welfare

as a given tariff structure. Sachs and Warner (1995) create a binary variable for closed economies

based on 5 criteria including nontariff barriers, average tariff rates, black market premiums, socialist

economies and state monopolies. Wacziarg (2001) constructs an openness measure using the share of

import duties in total imports, the coverage ratio of nontariff barriers and an indicator of liberalisation.

Vamvakidis (2002) uses six proxies for trade policy. Harrison (1996) uses seven. Edwards (1992) uses

nine. To say that there is no consensus on how to measure trade policy in this literature would be an

understatement.

Despite this lack of consensus on the measurement of trade policy, all of the papers above find

positive relationships between more open trade policies and growth. Subsequent research has updated

and corroborated these findings. Using an updated version of the Anderson and Neary (1996) trade

restrictiveness index and controlling for geography and institutions, Manole and Spatareanu (2010)

find that lower trade restrictiveness is associated with higher income. In a panel setting using updated

data, Greenaway, Morgan, and Wright (2002) find that the Sachs–Warner measure impacts growth