relates to other indexes of economic performance
There is now a heightened focus all across Africa, and indeed in the international development com-munity, on the need for econom-ic transformation. The ATI is an attempt to systematically specify and quantify economic transforma-tion and compare countries on it.
Unlike many indexes that measure economic performance, the ATI focuses only on outcome mea-sures of the economy, not on policy inputs or institutional forms that are believed to affect outcomes.
We start from a definite view of the important features one expects to observe in a transformed economy or in an economy undergoing eco-nomic transformation—the DEPTH indicators specified here. Although they do not cover every important economic feature, most policy-makers in Africa would consider them essential in their thinking about economic transformation.
They are the outcomes policy-makers care about, and policies and institutional reforms are means to achieving them.
Whether a country’s policies and institutional reforms are appropri-ate depends on the country’s prog-ress toward achieving its desired economic outcomes. Indeed, there is some agreement on a number of policies and institutions that in general matter for economic trans-formation, and chapter 2 touches on some of them. But country context matters. And trying to get policymakers to pursue particular standard policies or institution-al reforms, which is the implied
rationale of the input indexes, may not always be helpful.
The approach here is to use our DEPTH indicators, subindexes, and the overall index to generate information for policymakers on the outcomes they profess to care about. The expectation is that for policymakers keen on promoting economic transformation, poor per-formance on the desired outcomes would prompt them to ask ques-tions and begin to look for answers.
The questions asked and the answers generated would depend on a country’s circumstance—its current economic and institutional structures, and the priorities on its economic development agenda.
For any given period the package of policy and institutional reforms as well as the sequencing of required reforms may vary from one country to another. And for any particu-lar country what is required may change over time. In most cases policymakers may not know the right package from the beginning.
But as long as they are focused on the outcomes, aided by indexes such as ours, and are willing to seek improvements and make correc-tions as they learn, they are likely to make progress.
The ATI, as a purely economic and an outcome-oriented index, is in some ways similar and in others dif-ferent from some of the well known indexes, such as a GDP per capita index (purely economic and purely outcome-oriented), the United Nations Development Programme’s Human Development Index (HDI;
partly economic, partly social, and purely outcome-oriented), and the Africa Competitiveness Index (purely economic and a mixture of inputs and outcomes).
On the whole, the correlation between the ranking on ATI and the rankings on the other indexes is positive. But there are differences.
Some countries that do well on both the GDP per capita index and the HDI, do poorly on the ATI, and vice versa. The main reason is that, despite overlaps, the indexes are not trying to measure the same things. The ATI is focused exclusive-ly on economic transformation, as defined and specified in this annex.
Notes
1. An increasing share of modern and high-value services in produc-tion would also be an indicator of production diversification, in addition to increasing manufac-turing. But we do not have the required data. Although there are more sophisticated ways of measuring production diversity—
for example, as in the “product- space” approach of Hidalgo, Klinger, Barabasi, and Hausmann (2007)—we focus on the share of manufacturing value added in GDP due to data constraints and a desire to focus on simple and more recognizable measures.
2. Imbs and Wacziarg (2003) show that countries diversify their pro-duction base as they develop up to around $9,000 per capita. Then they begin to re-specialize.
3. Export competitiveness = (Country Exp/World Exp)/(Country GDP/World GDP) = (Country Exp/
Country GDP)/(World Exp/World GDP), where both exports and GDP exclude extractives for both country and world. We do not have actual data for extractive GDP so we subtract extractive exports from GDP to get non-extractive GDP. This has the drawback that GDP data are on a value-added basis and exports are on an output basis. But the trends in this ratio are essentially the same as those obtained when extractives are not subtracted from GDP.
4. Note that while the division by GDP reduces the bias against small economies, it could be biased against large economies, which tend to have low shares of exports relative to GDP. But among Sub- Saharan countries, this potential large economy bias is likely to be
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less of an issue than the potential small economy bias. Another way to reduce the effect of country size is to take the growth rate of the market share. But in the ATI we use only the levels, not the growth rates, of variables.
5. The export “sophistication”
measure of Lall, Weiss, and Zhang (2006) and Hausmann, Hwang, and Rodrik (2005) could also be used to show trends in export competitiveness. This measure is essentially the per capita income equivalent of a country’s export basket. Hausmann, Hwang, and Rodrik call their version EXPY.
A country has a high EXPY if its export basket includes a high share of products also exported by high-income countries. In that sense the country’s export basket could be said to be “sophisticat-ed,” since the country appears to be competing with higher income, and usually more developed, countries. We use the simpler measure because we think it has more direct policy implications for export competitiveness. But we report the EXPY values in annex table A1.1. Note that a country whose exports basket is dom-inated by a product that tends to be exported by high-income countries would necessarily have a high EXPY even if the product is unsophisticated. This is the case with Nigeria with about 90% of its exports from crude oil. And oil-exporting countries tend to have high incomes.
6. A limitation is that countries are not equally suited, in the agro-climatic sense, to cultivate cereals.
One could also argue for using value added per farm worker, rather than output (in kilograms) per hectare. But comparable data for the former measure are not available.
7. We classify all commodity exports, but we put agricultural exports and resource-based (that is, extractive) exports in separate categories, so the low, medium, and high categories refer only to manufactured exports. The full classification is therefore:
agriculture, resource-based, low- technology, medium- technology, and high- technology.
8. The classification schemes use International Standard Industrial Classification of All Economic Activities for manufacturing value added and Standard International Trade Classification for exports.
Another classification scheme that is sometimes used for exports is the “exports sophistication”
measure of Lall or, equivalently the EXPY of Hausmann and Rodrik.
This approach can also be adapted for manufacturing value added, as done in UNIDO (2009). We use Lall’s technology approach since it is more technology-focused than the sophistication approach, which is related more to per capita income levels. Data on EXPY for Sub- Saharan Africa, ACET 15, and the comparator countries are given in annex table A1.1.
9. The share of formal employment (F) in the labor force (L) can be decomposed as:
F/L = (F/E) × (E/L), where E is the level of employment. The first term on the right is the measure of “formality” in employment, and the second is the rate of overall employment.
10. A high level of employment in government (or public sector) would raise the share of formal employment in the labor force, but it may not necessarily reflect progress on economic transforma-tion. So perhaps a better measure would be formal employment in the private sector as a share of the labor force, but such data are not readily available.
11. The Lall decomposition of the technology of exports is in Lall (2000). UNIDO (2009) applies the decomposition to productions. We have modified the classifications a bit by making a clearer distinction among resource-based, agricultur-al, and low- technology products.
See www.africantransformation.
org for our classifications by SITC and SIC codes. The high level of aggregation (low digit level) reflects the lack of finely calibrat-ed data in Sub- Saharan Africa.
12. The rate of formal employment is derived as (rate of employ-ment) × (100 – rate of vulner-able employment). Data are from the International Labour
Organization’s Key Indicators of the Labour Market.
13. Due to the normalization proce-dure used in the construction of the indexes for the subindicators, a rise in a country’s index score on a subindicator (and therefore on the associated index) from one period to the next may not necessarily signify an improvement or deteri-oration in the country’s perfor-mance on that subindicator in an absolute sense. For example, if the underlying raw score for a subindi-cator stays the same for a country while the maximum and minimum values in the sample for the subindicator change, the country’s score on the subindicator could rise or fall, which would affect the country’s score on the associated index. So we have to compare the increase (or fall) in the country’s score with the corresponding changes in the scores of the other countries to determine how well the country has done—hence our focus on the rankings.
References
Hausmann, Ricardo, Jason Hwang, and Dani Rodrik. 2005. “What You Export Matters.” NBER Working Paper 11905, National Bureau of Economic Research, Cambridge, MA.
Hidalgo, Cesar A., Bailey Klinger, Albert-Laszlo Barabasi, and Ricardo Hausmann. 2007. “The Product Space Conditions the Development of Nations.”
Science 317 (5387): 482–487.
Imbs, Jean, and Romain Wacziarg. 2003.
“Stages of Diversification.” American Economic Review 93 (1): 63–86.
Lall, Sanjaya. 2000. “The Technological Structure and Performance of Devel-oping Country Manufactured Exports, 1985–98.” Oxford Development Studies 28 (3): 337–369.
Lall, Sanjaya, John Weiss, and Jinkang Zhang. 2006. “The ‘Sophistication’ of Exports: A New Trade Measure.” World Development 34 (2): 222–237.
UNIDO (United Nations Industrial Devel-opment Organization). 2009. Industrial Development Report: Breaking In and Moving Up: New Industrial Challenges for the Bottom Billion and the Middle-income Countries. Vienna.
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Table A1.1 African Transformation Index indicators
Indicator Country
groupings
Year
1970 1975 1980 1985 1990 1995 2000 2005 2010
(D1) Manufacturing value added (% of GDP)
SSA 10 11 10 11 12 11 11 10 10
ACET 15 12 14 13 13 15 12 12 11 10
COMP 19 20 23 21 22 22 25 25 22
(D2) Share of top 5 exports (%)
SSA — — 86 83 78 79 78 76 75
ACET 15 — — 80 82 66 76 72 69 70
COMP — — 63 54 43 40 43 42 43
(D3) Manufactured and services exports (% of total exports of goods and services)
SSA — 18 26 35 35 35 44 41 42
ACET 15 — 20 23 18 45 46 44 44 42
COMP — 33 38 38 55 63 64 62 57
(E) Nonextractive export-to-GDP share of country divided by nonextractive export-to-GDP share of the world
SSA — — 3 3 2 1 1 1 1
ACET 15 — — 2 4 2 1 1 1 1
COMP — — 14 5 5 3 3 4 3
(P1) Manufacturing value added per manufacturing worker (in 2005 US$)
SSA 13,217 13,996 16,301 16,383 18,184 19,371 19,371 30,911 41,949
ACET 15 11,630 11,560 11,371 12,229 13,492 14,389 19,274 25,202 28,861
COMP 13,075 12,034 14,858 16,942 19,719 26,346 31,580 35,896 44,121
(P2) Cereal yield (kilograms per hectare)
SSA 918 995 1,006 1,174 1,138 1,144 1,333 1,331 1,552
ACET 15 954 1,080 1,116 1,386 1,325 1,360 1,776 1,851 2,240
COMP 2,109 2,176 2,405 2,780 2,987 3,222 3,339 3,946 4,462
(T1) Medium- and high-technology manufactures
(% of total manufacturing output)
SSA 15 17 17 18 17 19 18 15 —
ACET 15 19 20 22 19 20 21 19 16 —
COMP 33 36 40 46 46 53 47 58 —
(T2) Medium- and high-technology commodity exports (% of total exports)
SSA — — 3 4 8 7 6 8 9
ACET 15 — — 4 2 8 5 5 9 8
COMP — — 14 17 27 37 39 37 34
(H1) GDP per capita (PPP 2005 international $)
SSA — — 2,383 2,325 2,369 2,319 2,663 3,186 3,584
ACET 15 — — 2,244 2,080 2,225 2,245 2,581 2,964 3,445
COMP — — 6,075 6,183 8,065 10,652 12,019 14,207 16,513
(H2) Ratio of formal employment to labor force (%)
SSA — — — — — 27 27 27 —
ACET 15 — — — — — 20 30 30 —
COMP — — — — — 53 55 55 —
Source: (D1) ACET staff calculations from undata.org. (D2) ACET staff calculations from UN Comtrade, Revision 2, Digit 3. (D3) World Bank Development Indicators (database); International Monetary Fund, Balance of Payments Statistics Yearbook and data files; World Bank staff estimates from UN Comtrade; World Trade Organization; World Bank national accounts data; OECD National Accounts data files. (E) World Development Indicators (database); UN Comtrade, Revision 2, Digit 3. (P1) UNIDO INDSTAT2, Revision 3, Digit 2, and undata.org. (P2) Food and Agriculture Organization, electronic files and web site (http://faostat3.fao.org/). (T1) ACET staff calculations from UNIDO INDSTAT2, Revision 3, Digit 2. (T2) ACET staff calculations from UN Comtrade, Revision 2, Digit 3. (H1) World Bank national accounts data; OECD National Accounts data files; World Development Indicators (database). (H2) ACET staff calculations from International Labour Organization, World Bank population estimates, and Key Indicators of the Labour Market (database).
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Table A1.2 Other transformation indicators
Indicator Country
groupings
Year
1970 1975 1980 1985 1990 1995 2000 2005 2010
GDP per capita growth (annual %)
SSA — 1.94 0.83 –0.16 0.47 –0.76 1.96 2.00 2.42
ACET 15 — 2.31 1.67 –0.68 1.56 –0.22 2.23 3.11 3.41
Comp — 4.20 5.71 1.96 — 5.81 1.91 3.48 3.54
Manufactured exports (% of total exports of goods and services)
SSA — 7.02 5.23 6.42 11.27 9.46 18.05 17.24 14.28
ACET 15 — 9.33 7.20 3.50 19.44 16.70 18.86 18.48 17.82
Comp — 17.40 23.31 25.82 40.15 48.17 50.17 48.43 42.28
Commercial services exports (% of total exports of goods and services)
SSA 14.34 14.10 16.35 18.60 19.76 22.93 25.86 22.27 23.15
ACET 15 14.34 14.28 14.68 17.04 20.62 24.06 25.71 24.23 24.06
Comp — 15.29 14.47 12.36 14.74 14.99 14.11 13.74 13.93
EXPY (GDP, PPP 2005 international $)
SSA — — 1,901.66 995.67 1,883.17 3,002.77 5,304.79 5,606.83 3,690.13
ACET 15 — — 1,847.79 937.79 2,431.07 3,461.86 6,424.79 7,262.88 7,291.23
Comp — — 3,733.56 6,561.26 8,132.47 8,252.72 10,506.10 11,369.20 8,617.77
Gross fixed capital formation (% of GDP)
SSA 16.39 20.29 22.41 18.05 19.06 20.07 18.42 20.65 23.16
ACET 15 18.96 19.56 17.72 15.05 19.41 19.22 20.14 21.72 23.72
Comp 22.15 25.84 26.38 25.49 29.54 30.65 23.22 23.45 25.18
Gross domestic savings (% of GDP)
SSA 13.81 10.27 8.56 8.15 8.14 7.55 8.98 9.68 13.23
ACET 15 16.33 13.97 10.74 13.91 12.13 12.01 12.46 13.47 14.16
Comp 19.84 23.22 27.79 28.37 28.44 31.23 31.96 33.38 33.39
Poverty headcount ratio at PPP$1.25 a day (% of population)
SSA — — 76.06 51.02 59.39 57.51 48.49 43.44 60.70
ACET 15 — — 66.22 54.30 53.86 50.60 55.64 43.91 65.58
Comp — — 25.71 21.60 24.80 18.84 13.22 8.59 18.06
Source: GDP per capita growth: World Bank national accounts data; OECD National Accounts data files; World Development Indicators (database). Manufactured exports: ACET staff calculations from World Trade Organization, and World Bank staff estimates from UN Comtrade. Commercial services exports: International Monetary Fund, Balance of Payments Statistics Yearbook and data files. EXPY: ACET staff calculations from UN Comtrade, Revision 2, Digit 3, and World Development Indicators (database). Gross fixed capital formation: World Bank national accounts data; OECD National Accounts data files. Gross domestic savings: World Bank national accounts data; OECD National Accounts data files.
Poverty headcount ratio at PPP$1.25 a day: ACET staff calculations from World Bank Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study (database). For more information and methodology see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm), World Development Indicators (database), and Key Indicators of the Labour Market (database).
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