Local Spillovers from Foreign Direct Investment in the Ethiopian Manufacturing Sector
-
Upload
essp2 -
Category
Government & Nonprofit
-
view
25 -
download
1
Transcript of Local Spillovers from Foreign Direct Investment in the Ethiopian Manufacturing Sector
Local Spillovers from Foreign Direct Investment in the Ethiopian Manufacturing
Sector
Presentation of preliminary results from ongoing research. Major Research Grant No. 1386Project title: Ref 1386 "The Role of Asian Investment in Manufacturing in Catalyzing Economic Development in Africa" Friday, July 24, 2015.
EEA-Annual Meeting, Addis Ababa, Ethiopia
Maggie McMillan (Tufts-IFPRI, PI), Girum Abebe (EDRI), Deborah Brautigam (SAIS - Johns Hopkins), Iñigo Verduzco-Gallo (IFPRI), and Michel Serafinelli (University of Toronto)
Outline
• Motivation• FDI in Ethiopia• Data• Framework (1) • Preliminary Results• Framework (2)• Road Ahead
MotivationContributions (we hope)• Policy:
– Big push in Ethiopia to industrialize; to large extent, relying on FDI – Justification is that this will have significant spillover effects on the rest of economy
– Our paper aims to evaluate these claims and understand effects of FDI in Ethiopia
• Academic:– Contribute to agglomeration and FDI literature.– Little to no credible evidence on magnitude of FDI and agglomeration spillovers in manufacturing in less-developed countries (particularly in Africa)
– We have some data on potential mechanisms behind spillovers so we can look into what might drive the results
Significant inflows of FDI since 2000
Source: Own calculations with data from CSA
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
0
20
40
60
80
100
120
Stock of FDI capital inbillions of Birr
FDI stock 1992-2014
Source: EIC 2015
1995_96
1997_98
1998_99
1999_00
2000_01
2001_02
2002_03
2003_04
2004_05
2005_06
2006_07
2007_08
2008_09
2009_10
2010_11
2011_12
2013_14
0
50,000
100,000
150,000
200,000
250,000
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
Total employment in Manufacturing in Ethiopia
No. workers No. workers (FDI China)No. workers (FDI other)
Important role of Asian investorsLargest Operational Manufacturing ProjectsCountry of Origin Percent China 27.29India 9.28Turkey 5.57China/Ethiopia 4.91Sudan 2.95India/Ethiopia 2.51Saudi Arabia/Ethiopia 3.28Saudi Arabia 1.42Britain 1.42USA/Ethiopia 1.64Italy/Ethiopia 2.29USA 0.98
Source: Bloomberg Businessweek. http://www.businessweek.com/articles/2014-07-24/ethio
pia-vies-for-chinas-vanishing-factory-jobs#p2’Source: EIC 2015
FDI represents sizeable share of existing firms in main manuf. Sectors (2013/14)Top 10 sectors with FDI ISIC Activity (2-digit) All firms FDI firms FDI firms as
share of all firms
Manufacture of food products and beverages 464 54 12%Manufacture of other non-metallic mineral products 399 77 19%Manufacture of furniture; manufacturing n.e.c. 233 34 15%Tanning and dressing of leather; manufacture of luggage handbags saddlery harness and footwear 102 24 24%
Manufacture of fabricated metal products except machinery and equipment 101 18 18%
Manufacture of rubber and plastics products 85 21 25%Manufacture of chemicals and chemical products 84 23 27%
Publishing printing and reproduction of recorded media 73 10 14%
Manufacture of wood and of products of wood and cork except furniture; manufacture of articles of straw and plaiting materials
54 9 17%
Manufacture of textiles 47 16 34%Source: Own calculations with data from CSA
Data• EIC data is largely aggregated • We have been working with CSA to get more disaggregated information on FDI and linkages
• CSA manufacturing census data from 1995/96 to 2013/14• The latest round has module on FDI• Module asks about:
– Locational preferences for both local and foreign firms– Horizontal linkages– Vertical linkages
• Backward linkages• Forward linkages
Framework (1)• Following Aitken and Harrison (1999)
• is the log of output measured by sales revenue for plant operational in sector at time .
Framework (1)• Main interest is , the coefficient on the variable which captures the proportion of FDI in a given sector – The sign and magnitude indicates the nature of FDI spillover effect towards domestic firms in the same sector as the FDI.
𝐹𝐷𝐼𝑆𝑒𝑐𝑡𝑜𝑟 𝑗𝑡=∑𝑖
𝐹𝐷𝐼𝑃𝑙𝑎𝑛𝑡𝑖𝑗𝑡∗𝐸𝑚𝑝𝑡𝑖𝑗𝑡
∑𝑖
𝐸𝑚𝑝𝑡𝑖𝑗𝑡
Results (, OLS): Spillovers could be local National Region Zone TownShare of FDI in the firm () 0.01 0.02 0.06 0.04 [0.06] [0.065] [0.071] [0.064]Share of FDI in the sector ) 0.25 0.13 0.24 -0.48 [0.16] [0.18] [0.19] [0.353]
Share of FDI in the firm*Share of FDI in the sector(*) 0.005 0.006 0.006* 0.016*** [0.003] [0.004] [0.003] [0.005]Share of FDI in the sector at regional level 0.19 [0.13] Share of FDI in the firm*Share of FDI in the sector at regional level -0.002 [0.003] Share of FDI in the sector at zone level 0.033 [0.128] Share of FDI in the firm*Share of FDI in the sector at zone level -0.002 [0.002] Share of FDI in the sector at town level 0.813** [0.351]Share of FDI in the firm*Share of FDI in the sector at town level -0.012*** [0.005]Number of observations 13055 13055 13055 13055
Results (Difference; -)
First Diff. Second Diff. Third Diff. Fourth Diff.
Share of FDI in the firm ()
0.023 -0.055 -0.288*** -0.353***[0.102] [0.113] [0.103] [0.122]
Share of FDI in the sector )
0.189 0.490* 0.806** 1.02***[0.217] [0.276] [0.333] [0.385]
Share of FDI in the firm*Share of FDI in the sector(*)
0.008 0.004 0.005 0.007[0.005] [0.005] [0.007] [0.009]
Number of observations 7481 4866 3486 2556
Differencing helps remove fixed effects at the plant level; helps us observe spillover effects over longer time horizon
Results: Interpretation
• Evidence of positive spillover from FDI to domestic firms in sectors with FDI presence– First FDI seems to benefit nearby domestic firms
• Point estimate imply that increasing foreign equity by one percentage points is associated with a 0.8 % increase in productivity of domestic firms located in the same town and sector as the FDI firm
– Second, the differenced model shows that the spillover effects tend to become stronger over time• The effects seems to be felt beyond the locality where FDI is present
Result: Possible channels of spillover • We are working to parametrically identify mechanisms but already from the data we have evidence on – Vertical linkages ( Local suppliers) :
• 10% of firms report selling to FDI firms• Of firms receiving technology transfers about 25% did so from FDI firms–Most of these transfers (~60%) from FDI firms aimed at improving production processes and product design
– Horizontal linkages (Competition) :• In 15% of firms, competitive pressure from FDI has lead to Δ in prod. Techniques– Of these, 57% did so by observing/copying FDI firms– And most of them (76%) have not perceived FDI firms tried to prevent this
Result: Possible channels of spillover
– Horizontal linkages (labor channel) :– 9% of firms have hired employees who have worked in FDI firms before– These workers do seem to make positive contributions to productivity in local firms:» 62% report former FDI workers contributing to better production technologies» 13% to bringing export knowledge to the firm» 11% help improve managerial practices
Framework (2)• Likely that if spillover effects exist, they will have a local effect (as indicated in the previous results based on Framework (1)).
• FDI firms don’t choose location at random, rather they choose based on characteristics of locality (potentially unobservable)
• If unobservables are correlated with productivity then simple productivity differences between incumbent firms in non-FDI localities v.s. FDI localities would be biased.
• Better identification strategy following Greenstone, Hornbeck and Moretti (2010):
FDI locally concentrated
Source: Own calculations using CSA data.
Framework (2)• Failed attempts– Use localities (towns) where FDI firms “almost” located but, for exogenous reasons (administrative, budget) did not
– These “Losing” towns are potentially a good counterfactual for “Winning” towns
– We can further control for pre-existing trends, firm and industry idiosyncrasies as well as other differences.
• Govt. designation– If we know decision rule by govt. we can control for this
Framework (2)• Pair-wise comparison is methodologically superior to both the regression formulation and DID-matching based on observables.
• In our case, we have information about other localities (towns) FDI firms considered before opening the current plant
• We can use that information to link “treated” towns(i.e. had a FDI firm opening in towns) with “control” towns(those not selected by FDI firm)
• We also observe location and year when FDI firm started operations
Framework (2)
𝑙𝑛 (𝑌𝑝𝑖𝑗𝑡 )=𝛽1𝑙𝑛 (𝐿𝑝𝑖𝑗𝑡 )+𝛽2𝑙𝑛 (𝐾𝑝𝑖𝑗𝑡 )+𝛽3𝑙𝑛 (𝑀𝑝𝑖𝑗𝑡 )+𝛿1 (𝑊𝑖𝑛𝑛𝑒𝑟 )𝑝𝑗+𝜓𝑇𝑟𝑒𝑛𝑑 𝑗𝑡+Ω (𝑇𝑟𝑒𝑛𝑑×1 (𝑊𝑖𝑛𝑛𝑒𝑟 ) )𝑝𝑗𝑡+𝜅1 (𝜏 ≥0 ) 𝑗𝑡+𝛾 (𝑇𝑟𝑒𝑛𝑑×1 (𝜏≥0 ) ) 𝑗𝑡+𝜃1 ( 1(𝑊𝑖𝑛𝑛𝑒𝑟 )×1 (𝜏 ≥0 ) )𝑝𝑗𝑡+𝜃2 (𝑇𝑟𝑒𝑛𝑑×1(𝑊𝑖𝑛𝑛𝑒𝑟 )×1 (𝜏 ≥0 ) )𝑝𝑗𝑡+𝛼𝑝+𝜇𝑖𝑡+𝜆 𝑗+𝜀𝑝𝑖𝑗𝑡
Econometric specification
• Parameters of interest are:– which represents a mean shift in productivity of incumbent plants in a “treated” towns
– which measures the trend break in productivity for incumbent plants in a “treated” towns
• Specification also controls for a number of pre- and post-treatment levels, allows for trend breaks and controls for firm, industry, case and industry-year fixed effects.
Road ahead
• We are now trying to complement some information on “losing” locations in order to be able to follow our main identification strategy
• Cleaning the location variables to reduce measurement error
• Will expect results from the main model in two to three months time.
THANK YOU.