The Changing Structure of Africas Economies Maggie McMillan - - PowerPoint PPT Presentation

The Changing Structure of Africa’s Economies

Maggie McMillan IFPRI/NBER/Tufts September 20, 2013

Based on joint work with Ken Harttgen, Dani Rodrik, Inigo Verduzco-Gallo and Sebastian Vollmer. Thanks to DFID/ESRC and the African Development Bank for financial support.

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• 1. Motivation
• 2. Structural Change in Africa: Recent Evidence
• McMillan&Rodrik 2011
• McMillan, Rodrik & Verduzco 2013
• McMillan 2013, Harttgen and Vollmer 2013 (AEO)
• 3. Structural Change in Africa: Using DHS Data
• Harttgen, McMillan and Vollmer in Progress
• 4. Summary and Directions for Future Research

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Outline

Most of Africa has been growing like gangbusters over the past decade. What is driving this growth? Commodity prices? Structural change? Something else?

Motivation

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Motivation: Commodity Prices?

50 100 150 200 250 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 Index 2005=100 Energy Agriculture Metals & Minerals Africa GDP World GDP

Source: World Bank, authors' calculations 4

Motivation: Structural Change?

0 .2 .4 .6 .8 1 sEhi_ag 6 7 8 9 10 Log GDP per capita (1990 International $ D&R sample Africa sample Agriculture 0 .2 .4 .6 .8 1 sEhi_ind 6 7 8 9 10 Log GDP per capita (1990 International $ D&R sample Africa sample Industry 0 .2 .4 .6 .8 1 sEhi_srv 6 7 8 9 10 Log GDP per capita (1990 International $ D&R sample Africa sample Services (with fitted values)

Note that Africa data measure sectoral share of total employment whereas D&R data measure share of total hours. Hours shares from Duarte and Restuccia (2010) cover 29 countries from 1950-2006 Their data were accessed 07/24/2012 from Duarte's website GDP from Maddison (2010) Comparing sample from Duarte and Restuccia (2010) and African countries (sample from Jan 2013)

Employment shares of 3 broad sectors

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Understanding what is driving Africa’s growth is important for understanding both its’ sustainability and the likely distributional implications of this growth McMillan & Rodrik (2011) found that structural change in Africa had been growth reducing, but they focused on the period 1990-2005 and only 9 countries in Africa Motivation: Why Should You Care?

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Structural Transformation in Africa

Decomposition of productivity growth by country group

• 1.00

0.00 1.00 2.00 3.00 4.00 HI ASIA AFRICA LAC % change within

• 1.00

0.00 1.00 2.00 3.00 4.00 HI ASIA AFRICA LAC % change structural

1990-99 2000-10

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Explaining the Reversal

• 1990s still going through adjustment
• Renewed commitment to agriculture

and increasing agricultural productivity

• Demographic trends – rural pop growth

rates coming down

• Political change – governments more

accountable

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Averages Hide Country Specific Heterogeneity

• Structural change in Mauritius, a diversified

economy, has been based on services.

• In Nigeria, a resource-driven economy, changes

in employment shares were tiny.

• In Uganda, an emerging economy, structural

change was significant and productivity grew in all sectors of the economy.

• There was very limited but positive structural

transformation in the pre-transition economy of Malawi

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Mauritius: Diversified Economy

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agr man min ter

• .4
• .2

.2 Log of Sectoral Productivity/Total Productivity

• .05

.05 .1 Change in Employment Share (∆Emp. Share) Fitted values

*Note: Size of circle represents employment share in 2000 **Note: β denotes coeff. of independent variable in regression equation: ln(p/P) = α + β∆Emp. Share Source: Authors' calculations with data from Mauritius' CSO and UN National Accounts Statistics

β = 2.5940; t-stat = 2.37

Correlation Between Sectoral Productivity and Change in Employment Shares in Mauritius (2000-2007)

Nigeria: Resource Driven

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agr man min ter

• 2

2 4 6 Log of Sectoral Productivity/Total Productivity

• .01
• .005

.005 .01 Change in Employment Share (∆Emp. Share) Fitted values

*Note: Size of circle represents employment share in 1999 **Note: β denotes coeff. of independent variable in regression equation: ln(p/P) = α + β∆Emp. Share Source: Authors' calculations with data from Adeyinka, Salau and Vollrath (2012)

β = 85.2651; t-stat = 0.52

Correlation Between Sectoral Productivity and Change in Employment Shares in Nigeria (1999-2009)

Uganda: Emerging Economy

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agr man min ter

• 1
• .5

.5 1 Log of Sectoral Productivity/Total Productivity

• .1
• .05

.05 .1 Change in Employment Share (∆Emp. Share) Fitted values

*Note: Size of circle represents employment share in 1999 **Note: β denotes coeff. of independent variable in regression equation: ln(p/P) = α + β∆Emp. Share Source: Authors' calculations with data from Uganda's Bureau of Statistics, IMF, and UN National Accounts Statistics

β = 9.9173; t-stat = 7.92

Correlation Between Sectoral Productivity and Change in Employment Shares in Uganda (1999-2009)

Malawi: Pre-Transition

14 agr man min ter

• 1

1 2 3 4 Log of Sectoral Productivity/Total Productivity

• .02
• .01

.01 .02 Change in Employment Share (∆Emp. Share) Fitted values

*Note: Size of circle represents employment share in 1998 **Note: β denotes coeff. of independent variable in regression equation: ln(p/P) = α + β∆Emp. Share Source: Authors' calculations with data from Malawi's National Statistical Office, WDI 2010, and ILO's LABORSTA

β = 43.9572; t-stat = 0.49

Correlation Between Sectoral Productivity and Change in Employment Shares in Malawi (1998-2005)

Summarizing Results from Macro Data

• Roughly half of Africa’s recent growth can be

attributed to structural change

• The expansion in services is only sustainable if

commodity prices remain high

• High skilled services cannot (now) be engine of

growth in Africa – not enough skilled labor

• Manufacturing has potential but is still very much

lagging (Ethiopia shoes, Blue Skies Ghana)

• Natural resources can facilitate structural change

(Robinson, 2013)

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Limitations of Macro Data

• Differences in treatment of informality across countries (e.g. Kenya)
• Differences in treatment of agriculture across countries (e.g.

Botswana)

• Limited availability of employment shares data (DFID/ESRC grant)
• But even if national accounts data are perfect, the macro data

ignores the following:

– important within country heterogeneity in occupational structure and

• productivity. For example, across age groups (youth unemployment),

across gender, across levels of education and across geographic location. – Only measures one standard of welfare, income.

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Using DHS data to understand structural changes in Africa

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• Harttgen, McMillan, Vollmer use occupational information

from the Demographic and Health Surveys (DHS) to document levels and changes in occupations across countries and over time by socioeconomic characteristics.

• Occupations include: self-employed agriculture, agricultural

employee, sales, clerical, services, professional, skillled and unskilled manual labor and unemployed.

• Importantly, surveys are consistent across countries and
• ver time and take into account the seasonality of

agriculture.

• Will compare outcome variables including health and

education across occupation categories and over time within

• ccupations to assess whether observed occupational

changes are welfare enhancing.

DHS regions

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Source: Günther and Harttgen 2013..

Changes in Occupational Structure Across Time

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Socio-Economic Determinants of Occupational Structure: Full Sample

(1) (2) (3) (4) (5) (6) (7) (8) Total sample Total sample Total sample Total sample Total sample Total sample Total sample Total sample VARIABLES Agriculture (employee or self employed) Agriculture self employed Agriculture employee Professional Clerical or sales or service Skilled manual Unskilled manual Not working No education 0.0811*** 0.0643*** 0.0169***

• 0.0538***
• 0.0446***
• 0.0186***
• 0.000868*

0.0355*** (0.00128) (0.00121) (0.000660) (0.000542) (0.00112) (0.000720) (0.000465) (0.00127) Age 15-24

• 0.0490***
• 0.0478***
• 0.00124**
• 0.0348***
• 0.0477***
• 0.00608***

0.000171 0.130*** (0.00117) (0.00111) (0.000560) (0.000457) (0.00102) (0.000677) (0.000467) (0.00123) Urban

• 0.359***
• 0.301***
• 0.0585***

0.0468*** 0.173*** 0.0526*** 0.0300*** 0.0401*** (0.00107) (0.00102) (0.000507) (0.000691) (0.00123) (0.000824) (0.000590) (0.00124) Female

• 0.160***
• 0.100***
• 0.0593***
• 0.0314***

0.0903***

• 0.0656***
• 0.0210***

0.185*** (0.00139) (0.00133) (0.000778) (0.000748) (0.00117) (0.000978) (0.000660) (0.00113) Log GDP per capita 0.0157*** 0.0281***

• 0.0124***

0.0368***

• 0.0306***

0.0639*** 0.0194***

• 0.0763***

(0.00551) (0.00545) (0.00216) (0.00285) (0.00494) (0.00338) (0.00239) (0.00596) Polity IV score 0.00626*** 0.00406*** 0.00220*** 0.00177*** 0.00191*** 0.00116***

• 0.00547***
• 0.00612***

(0.000289) (0.000284) (0.000118) (0.000128) (0.000240) (0.000166) (0.000150) (0.000294) Observations 791085 791085 791085 791085 791085 791085 791085 791085 R-squared 0.310 0.327 0.192 0.065 0.131 0.047 0.054 0.241 Country FE YES YES YES YES YES YES YES YES Year FE YES YES YES YES YES YES YES YES Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Socio-Economic Determinants of Occupational Structure: Women Only

(1) (2) (3) (4) (5) (6) (7) (8) Women sample Women sample Women sample Women sample Women sample Women sample Women sample Women sample VARIABLES Agriculture (employee or self employed) Agriculture self employed Agriculture employee Professional Clerical or sales or service Skilled manual Unskilled manual Not working No education 0.0619*** 0.0492*** 0.0128***

• 0.0470***
• 0.0497***
• 0.0151***
• 0.00218***

0.0497*** (0.00139) (0.00132) (0.000638) (0.000564) (0.00127) (0.000736) (0.000473) (0.00149) Age 15-24

• 0.0477***
• 0.0447***
• 0.00303***
• 0.0283***
• 0.0520***
• 0.00394***
• 0.00287***

0.127*** (0.00127) (0.00121) (0.000568) (0.000476) (0.00115) (0.000681) (0.000463) (0.00141) Urban

• 0.314***
• 0.266***
• 0.0476***

0.0349*** 0.168*** 0.0266*** 0.0229*** 0.0431*** (0.00118) (0.00112) (0.000510) (0.000699) (0.00140) (0.000798) (0.000585) (0.00149) Log GDP per capita 0.0538*** 0.0534*** 0.000375 0.0294***

• 0.0133**

0.0653*** 0.0317***

• 0.142***

(0.00628) (0.00621) (0.00227) (0.00299) (0.00574) (0.00322) (0.00229) (0.00722) Polity IV score 0.00784*** 0.00681*** 0.00103*** 0.00187*** 0.00170*** 0.00139***

• 0.00660***
• 0.00696***

(0.000312) (0.000302) (0.000121) (0.000133) (0.000277) (0.000170) (0.000167) (0.000359) Observations 616129 616129 616129 616129 616129 616129 616129 616129 R-squared 0.313 0.333 0.143 0.055 0.147 0.029 0.061 0.224 Country FE YES YES YES YES YES YES YES YES Year FE YES YES YES YES YES YES YES YES Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Socio-Economic Determinants of Occupational Structure: Men Only

(1) (2) (3) (4) (5) (6) (7) (8) Men sample Men sample Men sample Men sample Men sample Men sample Men sample Men sample VARIABLES Agriculture (employee or self employed) Agriculture self employed Agriculture employee Professional Clerical or sales or service Skilled manual Unskilled manual Not working No education 0.189*** 0.137*** 0.0520***

• 0.0806***
• 0.0397***
• 0.0380***

0.000914

• 0.0310***

(0.00288) (0.00275) (0.00170) (0.00150) (0.00225) (0.00202) (0.00133) (0.00191) Age 15-24

• 0.0383***
• 0.0447***

0.00640***

• 0.0573***
• 0.0351***
• 0.0174***

0.0114*** 0.133*** (0.00263) (0.00254) (0.00140) (0.00128) (0.00213) (0.00201) (0.00143) (0.00235) Urban

• 0.508***
• 0.419***
• 0.0881***

0.0804*** 0.190*** 0.143*** 0.0533*** 0.0292*** (0.00235) (0.00225) (0.00140) (0.00191) (0.00255) (0.00240) (0.00167) (0.00192) Log GDP per capita

• 0.206***

0.0281**

• 0.234***

0.0522*** 0.0110 0.0657***

• 0.0348***

0.151*** (0.0130) (0.0128) (0.00623) (0.00771) (0.0116) (0.0105) (0.00772) (0.0109) Polity IV score

• 0.000247
• 0.00708***

0.00684*** 0.00139*** 0.00380***

• 0.000943**
• 0.00222***
• 0.00131***

(0.000620) (0.000617) (0.000301) (0.000313) (0.000505) (0.000436) (0.000339) (0.000312) Observations 174956 174956 174956 174956 174956 174956 174956 174956 R-squared 0.372 0.396 0.431 0.115 0.105 0.078 0.058 0.120 Country FE YES YES YES YES YES YES YES YES Year FE YES YES YES YES YES YES YES YES Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Socio-Economic Determinants of Occupational Structure: Rural

(1) (2) (3) (4) (5) (6) (7) (8) Rural Rural Rural Rural Rural Rural Rural Rural VARIABLES Agriculture (employee or self employed) Agriculture self employed Agriculture employee Professional Clerical or sales or service Skilled manual Unskilled manual Not working No education 0.0860*** 0.0698*** 0.0162***

• 0.0364***
• 0.0563***
• 0.0191***
• 0.00411***

0.0315*** (0.00166) (0.00156) (0.000865) (0.000561) (0.00119) (0.000743) (0.000486) (0.00145) Age group

• 0.0526***
• 0.0517***
• 0.000904
• 0.0182***
• 0.0269***
• 0.00596***

5.40e-05 0.1000*** (0.00155) (0.00147) (0.000749) (0.000431) (0.00108) (0.000697) (0.000479) (0.00142) Female

• 0.217***
• 0.138***
• 0.0789***
• 0.0137***

0.0844***

• 0.0277***
• 0.0130***

0.186*** (0.00182) (0.00174) (0.000997) (0.000676) (0.00120) (0.000967) (0.000651) (0.00130) Log GDP per capita 0.0258*** 0.0565***

• 0.0307***

0.0286***

• 0.0145***

0.0596*** 0.0111***

• 0.0863***

(0.00752) (0.00746) (0.00316) (0.00269) (0.00511) (0.00335) (0.00258) (0.00710) Polity IV score 0.00718*** 0.00488*** 0.00230*** 0.00124*** 0.000695*** 0.00211*** -0.00435***

• 0.00730***

(0.000357) (0.000357) (0.000149) (0.000116) (0.000258) (0.000184) (0.000165) (0.000339) Observations 524419 524419 524419 524419 524419 524419 524419 524419 R-squared 0.225 0.285 0.242 0.037 0.085 0.026 0.028 0.263 Country FE YES YES YES YES YES YES YES YES Year FE YES YES YES YES YES YES YES YES Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Socio-Economic Determinants of Occupational Structure: Urban

(1) (2) (3) (4) (5) (6) (7) (8) Urban Urban Urban Urban Urban Urban Urban Urban VARIABLES Agriculture (employee or self employed) Agriculture self employed Agriculture employee Professional Clerical or sales or service Skilled manual Unskilled manual Not working No education 0.0878*** 0.0758*** 0.0120***

• 0.0995***
• 0.0174***
• 0.0230***

0.00131 0.0445*** (0.00168) (0.00157) (0.000683) (0.00117) (0.00255) (0.00168) (0.00106) (0.00254) Age group

• 0.0217***
• 0.0199***
• 0.00177***
• 0.0719***
• 0.107***
• 0.00637***
• 0.000596

0.193*** (0.00127) (0.00117) (0.000514) (0.00113) (0.00228) (0.00155) (0.00107) (0.00239) Female

• 0.0367***
• 0.0221***
• 0.0146***
• 0.0688***

0.106***

• 0.156***
• 0.0397***

0.189*** (0.00169) (0.00149) (0.000831) (0.00190) (0.00273) (0.00232) (0.00158) (0.00225) Log GDP per capita 0.00305

• 0.0106*

0.0136*** 0.0602***

• 0.0697***

0.0671*** 0.0133**

• 0.0463***

(0.00578) (0.00554) (0.00181) (0.00668) (0.0112) (0.00771) (0.00523) (0.0111) Polity IV score 0.00302*** 0.00271*** 0.000305* 0.00310*** 0.00325***

• 0.00182*** -0.00731***
• 0.000811

(0.000354) (0.000320) (0.000157) (0.000381) (0.000592) (0.000388) (0.000345) (0.000574) Observations 266666 266666 266666 266666 266666 266666 266666 266666 R-squared 0.087 0.099 0.028 0.080 0.114 0.074 0.102 0.218 Country FE YES YES YES YES YES YES YES YES Year FE YES YES YES YES YES YES YES YES Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Preliminary Results from DHS Data

• Broad patterns are consistent with macro data.
• Women much more likely to be unemployed and

much less likely to be employed in agriculture.

• Growth appears to be inclusive in so much as has

had quantitatively more important positive effects in rural areas (caveat, may be increasing rural urban migration).

• Youth much more likely to be unemployed across the

board but problem more severe in urban areas.

• Lots more to do: health&education by sector,

changes over time by sector. Commodity prices, more details on institutional changes.

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