The Changing Structure of Africa s Economies

Transcription

1 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. 1

2 Outline 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 2

3 Motivation Most of Africa has been growing like gangbusters over the past decade. What is driving this growth? Commodity prices? Structural change? Something else? 3

4 Motivation: Commodity Prices? 250 Energy Agriculture Metals & Minerals 200 Africa GDP World GDP Index 2005= Source: World Bank, authors' calculations 4

5 Motivation: Structural Change? Employment shares of 3 broad sectors Comparing sample from Duarte and Restuccia (2010) and African countries (sample from Jan 2013) Agriculture Industry sehi_ag sehi_ind Log GDP per capita (1990 International $ Log GDP per capita (1990 International $ D&R sample Africa sample D&R sample Africa sample Services (with fitted values) sehi_srv Log GDP per capita (1990 International $ D&R sample Africa sample 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 Their data were accessed 07/24/2012 from Duarte's website GDP from Maddison (2010) 5

6 Motivation: Why Should You Care? 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 and only 9 countries in Africa 6

7 Structural Transformation in Africa Decomposition of productivity growth by country group within structural LAC LAC AFRICA AFRICA ASIA ASIA HI HI % change % change 7

8 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 9

9 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 10

10 Mauritius: Diversified Economy Log of Sectoral Productivity/Total Productivity Correlation Between Sectoral Productivity and Change in Employment Shares in Mauritius ( ) β = ; t-stat = 2.37 man agr min Change in Employment Share ( Emp. Share) ter 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 11

11 Nigeria: Resource Driven Log of Sectoral Productivity/Total Productivity Correlation Between Sectoral Productivity and Change in Employment Shares in Nigeria ( ) β = ; t-stat = 0.52 agr ter Change in Employment Share ( Emp. Share) min man 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) 12

12 Uganda: Emerging Economy Log of Sectoral Productivity/Total Productivity agr Correlation Between Sectoral Productivity and Change in Employment Shares in Uganda ( ) β = ; t-stat = 7.92 min man Change in Employment Share ( Emp. Share) ter 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 13

13 Malawi: Pre-Transition Log of Sectoral Productivity/Total Productivity Correlation Between Sectoral Productivity and Change in Employment Shares in Malawi ( ) β = ; t-stat = 0.49 agr ter Change in Employment Share ( Emp. Share) min man 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 14

14 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) 15

15 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. 16

16 Using DHS data to understand structural changes in Africa 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 over time and take into account the seasonality of agriculture. Will compare outcome variables including health and education across occupation categories and over time within occupations to assess whether observed occupational changes are welfare enhancing. 17

17 DHS regions Source: Günther and Harttgen

18 Changes in Occupational Structure Across Time 19

19 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 *** *** *** *** *** *** * *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Age *** *** ** *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Urban *** *** *** *** 0.173*** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Female *** *** *** *** *** *** *** 0.185*** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Log GDP per capita *** *** *** *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Polity IV score *** *** *** *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Observations R-squared 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 20

20 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 Agriculture self self employed) employed Agriculture employee Professional Clerical or sales or service Skilled manual Unskilled manual Not working No education *** *** *** *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Age *** *** *** *** *** *** *** 0.127*** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Urban *** *** *** *** 0.168*** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Log GDP per capita *** *** *** ** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Polity IV score *** *** *** *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Observations R-squared 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 21

21 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 Agriculture self self employed) employed Agriculture employee Professional Clerical or sales or service Skilled manual Unskilled manual Not working No education 0.189*** 0.137*** *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Age *** *** *** *** *** *** *** 0.133*** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Urban *** *** *** *** 0.190*** 0.143*** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Log GDP per capita *** ** *** *** *** *** 0.151*** (0.0130) (0.0128) ( ) ( ) (0.0116) (0.0105) ( ) (0.0109) Polity IV score *** *** *** *** ** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Observations R-squared 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 22

22 Socio-Economic Determinants of Occupational Structure: Rural VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) Rural Rural Rural Rural Rural Rural Rural Rural Agriculture (employee or Clerical or self Agriculture self Agriculture sales or Skilled Unskilled employed) employed employee Professional service manual manual Not working No education *** *** *** *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Age group *** *** *** *** *** 5.40e *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Female *** *** *** *** *** *** *** 0.186*** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Log GDP per capita *** *** *** *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Polity IV score *** *** *** *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Observations R-squared 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 23

23 Socio-Economic Determinants of Occupational Structure: Urban VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) Urban Urban Urban Urban Urban Urban Urban Urban Agriculture (employee or self employed) Agriculture self employed Agriculture employee Professional Clerical or sales or service Skilled manual Unskilled manual Not working No education *** *** *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Age group *** *** *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Female *** *** *** *** 0.106*** *** *** 0.189*** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Log GDP per capita * *** *** *** *** ** *** ( ) ( ) ( ) ( ) (0.0112) ( ) ( ) (0.0111) Polity IV score *** *** * *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Observations R-squared 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 24

24 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. 25