Success and failure of African Exporters

Olivier Cadot (World Bank)Leonardo Iacovone (World Bank)

Ferdinand Rauch (LSE)Martha Denisse Pierola (World Bank)

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Presentation overview

• Motivation• Research question• Literature review• Stylized facts• Regression results

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Motivation

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Why study survival rates of African exports?

• “… trade has a quantitatively large and robust, though only moderately statistically significant, positive effect on income.” This is a causal effect. (Frankel and Romer 1999)

• Key to export growth are survival rates of goods on export markets (Besedes and Prusa 2007): developing countries tend to have lower survival rates

• African countries in particular have a low level of exports and low survival rates

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Survival rates are especially low in Africa

Source: Brenton, Pierola and Uexküll (2008)

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Conditional on survival very high growth rates (e.g.

Tanzania)

2000 2001 2002 2003 2004 2005 2006 2007 2008 20090

200

400

600

800

1000

1200

1400

1600

1800

2000

Firms

2000 2001 2002 2003 2004 2005 2006 2007 2008 20090

200

400

600

800

1000

1200

1400

1600

products

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Research questions• What determines (first year) survival of African

exporters?

• What is the role of “spillovers” and “agglomeration”?

• What is the role of firm and product characteristics?

• Can we identify the mechanisms behind survival?

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Related literatureImportance of discovery - Rodrik and Hausmann (2003)• “Discovery”: Profitable export of a new good to a new destination

Uncertainty, diversification, survival - Albornez, Pardo, Corcos and Ornelas (2009)• Ex ante exporters don’t know their own ability• Firms have to learn it in nearby markets, before exporting more• Rauch and Watson (2003) starting small in “uncertain” market

Survival on exports markets – various papers• Freund and Pierola (2011) model with heterogenous firms and uncertainty

consistent with evidence across various countries• Besedes, Prusa (2007) analyze survival along various dimensions• Brenton, Saborowski, Uexkull (2009): Low survival rates, particularly in developing

countries, highlight “learning by doing”• Literature largely relies on customs data (Eaton, Kortum, Kramarz (2008)• Evenett and Venables (2002) show that selling existing products accounted for only

about one-third of export growth for 23 developing countries. Developing countries lower export performance due mostly to “lack of sustained export flows”

• World Bank (2009) - Breaking into new markets - “The analysis […] suggests that the larger the initial size of a new trade flow, the greater the chance that flow will survive

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Countries studied

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GNI per capita 2008 (PPP)

Country GNI (PPP) Global Rank (max: 210)

Mali 1,090 194

Malawi 830 198

Senegal 1,760 175

Tanzania 1,230 184

Sub-Saharan Africa 1,991

Middle East & North Africa

7,308

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Data: Two novel datasets

• Customs data from Malawi, Mali, Senegal and Tanzania• Collected by World Bank Export Survival Project from local

customs authorities• Advantage over existing data (comtrade): Detailed down to

product level information: Contains for each exported product of these countries: exporting firm, product classification, destination, quantity

1. Firm-level customs data

2. Exporters survey • We use results from an original World Bank survey of African exporters• Answers from around 100 randomly selected exporters (some of them

successful and other unsuccessful)

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Qualitative evidence: role of

“agglomeration/spillovers” and experience

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Role of “agglomeration/spillovers” for contacting buyers

First time exporters: How was first contact made?Exporters: How did the company approach its buyers

Research online

Third party contact

Competitors' network

Trade Fair

Export Promotion Agency

Exporters' Association

Another channel

0% 10%20%30%40%50%60%70%80%90%

Research online

Third party contact

Competitors' network

Trade Fair

Export Promotion Agency

Exporters' Association

Another channel

0% 10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

OverallTZASENGHA

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…still on the role of “agglomeration/spillovers” and experience to start exporting

An existing buyer approached the company

The company saw that the new export product was demanded in its buyers' domestic market

The company saw local competitors in the domestic market exporting the product successfully

The company was selling the product domestically and decided to offer it abroad

The company learned about this new export product success through a 3rd party

Any other type of opportunity?

0% 20% 40% 60% 80%

How did the opportunity to export a new product come about?

Company's website

Old clients of the company

Third-party contacts

Competitors' network

Trade Fair

Export Promotion Agency

Another channel

0% 20% 40% 60% 80% 100%

Overall

TZA

SEN

GHA

How did the buyers normally approach the company?

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Evidence from customs transactions

level data

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Customs transactions data

Firmf

Productf1 Productf2

Destinationf11 Destinationf12 Destinationf13

t1 t2 t2 t1 t2 t2 t1 t2

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Preliminary descriptive statistics

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Exports decomposition: Importance of extensive margin

Values (Tanzania)Numbers (Tanzania)

0.1

.2.3

.4

2004 2005 2006 2007 2008

Continued New ProductNew Firm New Destination

0.2

.4.6

.8

2004 2005 2006 2007 2008

Continued New ProductNew Firm New Destination

• Decompose number of firm product destinations into four mutually exclusive groups:• New firm (f)• New product (p)• New destination (d)• Continued firm-product-destination

• Numerically continued firm-product-destinations are less than 30 percent of all firms• They contribute to over 70 percent of the value of exports• A lot of action on the extensive margin, but on small scale

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firm Number of Survival with respect to previous year Survival with respect to first year

year 2004 2005 2006 2007 2008 2005 2006 2007 2008 2005 2006 2007 2008

2004 4202005 194 581 0.46 0.462006 118 219 653 0.61 0.38 0.28 0.382007 85 134 233 789 0.72 0.61 0.36 0.20 0.23 0.362008 75 95 135 281 870 0.88 0.71 0.58 0.36 0.18 0.16 0.21 0.36

product

2004 2,656

2005 4973,27

2 0.19 0.192006 200 559 3,618 0.40 0.17 0.08 0.172007 106 244 558 4,312 0.53 0.44 0.15 0.04 0.07 0.152008 71 145 241 707 5,337 0.67 0.59 0.43 0.16 0.03 0.04 0.07 0.16

product destination2004 4,908

2005 8375,58

0 0.17 0.172006 295 852 5,493 0.35 0.15 0.06 0.152007 167 395 869 6,355 0.57 0.46 0.16 0.03 0.07 0.162008 113 227 367 1,110 7,103 0.68 0.57 0.42 0.17 0.02 0.04 0.07 0.17

Tanzania: Low initial survival…….but increasing through time

See also Brooks (2004)

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NrSurvival with respect to previous year

Survival with respect to first year

firm200

1200

2200

3200

4200

5200

6200

7200

8200

2200

3200

4200

5200

6200

7200

8200

2200

3200

4200

5200

6200

7200

82001 2062002 84 236 0.41 0.412003 57 99 250 0.680.42 0.280.422004 40 67 100 256 0.700.680.40 0.190.280.402005 35 44 71 105 260 0.880.660.710.41 0.170.190.280.412006 29 32 52 63 85 292 0.830.730.730.600.33 0.140.140.210.250.332007 24 30 39 44 55 110 279 0.830.940.750.700.650.38 0.120.130.160.170.21 0.382008 21 25 33 39 45 77 111 2920.880.830.850.890.820.70 0.400.100.110.130.150.17 0.26 0.40

product

2001205

5

2002 449235

1 0.22 0.22

2003 192 508288

6 0.430.22 0.090.22

2004 117 275 696279

9 0.610.540.24 0.060.120.24

2005 94 186 376 634294

5 0.800.680.540.23 0.050.080.130.23

2006 78 144 257 309 578308

0 0.830.770.680.490.20 0.040.060.090.110.20

2007 61 115 203 204 272 648322

6 0.780.800.790.660.470.21 0.030.050.070.070.09 0.21

2008 54 91 158 140 184 333 633349

50.890.790.780.690.680.51 0.200.030.040.050.050.06 0.11 0.20Product-destinations

2001332

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2002 718374

1 0.22 0.22

2003 356 769464

2 0.500.21 0.110.21

2004 245 404113

7474

2 0.690.530.24 0.070.110.24

2005 167 262 623105

0458

0 0.680.650.550.22 0.050.070.130.22

2006 129 207 429 575 923475

6 0.770.790.690.550.20 0.040.060.090.120.20

2007 101 167 320 377 460 975515

4 0.780.810.750.660.500.21 0.030.040.070.080.10 0.21

2008 84 142 236 275 288 476 974548

60.830.850.740.730.630.49 0.190.030.040.050.060.06 0.10 0.19

Results also hold for Senegal….

21See also Brooks (2004)

Mali

Firm NumbersSurvival with respect to first year

2005 2006 2007 2008 2006 2007 2008

2005 273

2006 159 121 0.582

2007 123 59 140 0.451 0.488

2008 103 33 60 141 0.377 0.273 0.429

Product

2005 1,047

2006 305 783 0.291

2007 166 176 785 0.159 0.225

2008 123 85 186 1,049 0.117 0.109 0.237

Proddest

2,005 1,391

2006 286 1,199 0.206

2007 122 232 1,155 0.088 0.193

2008 82 115 207 1,500 0.059 0.096 0.179

Firm NumbersSurvival with respect to first year

20052006 2007 2008 2006 2007 2008

2005 670

2006 217 639 0.324

2007 154 104 283 0.230 0.163

2008 126 57 71 282 0.188 0.089 0.251

Product

2005 3,322

2006 325 3,181 0.098

2007 174 213 1,843 0.052 0.067

2008 127 95 200 1,973 0.038 0.030 0.088

Proddeset

2005 3,828

2006 509 3,469 0.133

2007 316 271 2,280 0.083 0.078

2008 224 115 278 2,389 0.059 0.033 0.122

Malawi

…and for Mali and Malawi as well

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Econometric model• Data aggregated to unique origin-firm-product-destination-time units• All following regressions use the subsample of entrants to export

markets (new firm-product-destinations) only • Define survival:

o 1 if f-p-d present in t and t+1 and not t-1o 0 if f-p-d present in t and not in t+1 and not t-1

• Estimate: Probit: survivalfpdt = Xfpdt + µ1t + µ2d + µ3i + εfpdt

OLS: log_valuefpdt = Xfpdt + µ1t + µ2d + µ3i + εfpdt

• µ1t: Time fixed effect, µ2d: Destination fixed effect, µ3i: Industry fixed effect

• Xfpdt includes measures for firm experience, agglomeration and market attractiveness (they will be introduced one by one shortly)

• Robust standard errors are clustered at level of product-destinations

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Baseline results

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Baseline results – cont.

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Placebo test

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Robustness checks – extended network

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Robustness checks – extended network

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What is going on? Focus on mechanisms

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Focus on mechanisms• The presence of other companies exporting same

products to same destination mayo Provide information to the new exporter on preferences and other

“demand attributes” – through imitation or just because it easier to find buyer, third parties, that can provide valuable information

o Provide information to financial institutions about “profitability” of the export ventures

• If these “synergy” effects are due to “information spillovers” then they should matter more for those products for which information is more valuable or needed to survive higher quality heterogeneityo Proxy quality heterogeneity with UV dispersion from COMTRADE

(coefficient of variation of UV of all exporters at HS6 level in 2000)• If these “information spillovers” operate through financial

institutions (reduce information asymmetry and scope for moral hazard) should matter for sectors more sensitive (dependent) to external finance

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Mechanisms: information spillovers

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Mechanisms: information spillovers 2

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Mechanisms: information spillovers 3

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Additional robustness checks

• Drop cases where there is only 1 exporter per product-country pair – only focus on variation of sectors with more than 1 exporter

• Use count instead of log of the count• Include 6-digit product fixed effects instead of

just 2-digit product fixed effects • Re-estimate the model using a linear

probability model instead than a probit – when introducing interactions terms

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Summary• We document high rates of first year exit

among new exporters• “Agglomeration” helps to foster survival

probability • This effect appears to be driven by

“information spillovers” • At the same time, exit rates depend

significantly on the experience that the exporter has with the product and the

• Consistently with multi-product firms models core products show higher probability of survival

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Conclusions and policy questions

• Role of information, experience and networks in determining survivalo Importance of firm experience with a

market/product and importance of agglomeration effects

• What policy interventions possible to provide public goods that generate market knowledge and information?

• What markets could be developed for these “goods” and how to solve coordination failures?

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Thanks for your comments!