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Pro-Poor Institutions for Development Effectiveness:
Cross-Country Empirical Analyses and a Case Study of Ethiopia○Yumeka HIRANO and Shigeru OTSUBO 1
Graduate School of International Development (GSID), Nagoya University[Email] hirano.yumeka@a.mbox.nagoya-u.ac.jp / sotsubo@gsid.nagoya-u.ac.jp
Abstract: This research revisits the discussions of development effectiveness with introducing a
new concept “pro-poor institutions” as the key factor. The results of cross-country empiricalanalyses (using the unbalanced panel data of 183 growth spells of 60 countries during the late
1990s-2000s) reveal that aid works more effectively in countries with positive pro-poor country
specific effects, i.e., those with pro-poor institutions. The case study of Ethiopia identifies thevarious pro-poor factors that are considered to influence development effectiveness. These include
strong leadership, ownership of government, decentralization of authority, and equitable public
service delivery. Not only growth-enhancing institutions, but also pro-poor institutions, should betaken into consideration for aid allocation, so that it will enhance development effectiveness.
Key Words: Development Effectiveness, Aid, Pro-Poor Institutions, Country Fixed Effects,
Ethiopia
1. Background
Effectiveness of development aid has been a controversial issue for decades in the field ofdevelopment economics. Aid is generally considered to have a positive impact on economic
growth, which is believed to be a vital force for poverty reduction. Although some studies haveconfirmed this positive relationship (Gulati, 1978; Hansen & Tarp, 2000; Clemens, Radelet,
Bhavnani. & Bazziz, 2004), others have argued that there is no significant impact by aid on
growth (Mosley, Hudson & Horrell, 1987; Bonne, 1996). Moreover, some studies suggested thatthe impact came with certain prerequisite conditions such as good policies (Burnside & Dollar,
2000; Collier & Dollar, 2001, 2002) and geography (Dalgaard, Hansen, & Tarp, 2004). The
current course of discussions asserts the importance of good policies (trade, monetary, and fiscal
policies) and institutions (such as ICRG and CPIA 2 ) for an effective aid in promoting growth. In
fact, these policies and institutions have been used as conditionality or a benchmark for the aid
allocation. However, our earlier study showed that institutions as measured by ICRG were
negatively associated with incomes of the poor, i.e. anti-poor, even though they tend to promote
growth and poverty reduction (through growth), i.e. pro-growth (Hirano & Otsubo, 2012). We
came up with the idea that if the country had pro-poor institutions, development effectivenesscould be increased. With this concern, this study aims: i) to investigate whether the pro-poor
country specific factors, i.e., pro-poor institutions, could be a key for development effectiveness,or not; and ii) to identify potential pro-poor institutions that determine development effectiveness.
2. Methods and Data
2.1 Cross-Country Empirical Analyses
First, we conducted a series of cross-country regression analyses for examining pro-poorness
or anti-poorness of institutions and other variables of our concern. We applied the models, which
Dollar and Kraay used (2002, Eqs. 1 & 4), to the Eqs. 1 and 2:a) Estimation by the level relationship for the average income of the poorest quintile.
1

Yumeka Hirano is a Ph.D. Candidate / Research Fellow of Japan Society for the Promotion of Science (JSPS)at GSID. Shigeru Otsubo is a professor and director of the Economic Development Policy & Management
Program at GSID. This research is supported by the JSPS Research Fellowships for Young Scientists (Research#:
24-3494, Research Title: Effective Development Aid in Reducing Poverty and Development Gaps,
Representative: Hirano) and the JSPS Scientific Research (A) & Challenging Exploratory Research (Research#:22252005/23653066, Title: Globalization and Development/Development Happiness, Representative: Otsubo).
[Address] GSID, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, JAPAN2
International Country Risk Guide (ICRG) from the PRS Group and Country Policy and Institutional
Assessment (CPIA) from the World Bank.

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The 24th JASID Annual Conference, 2013312ln y p ct =α+

国際開発学会第24回全国大会（大阪大学）

β ln y ct + δlnZ ct + θlnA ct + µ c + λ t + ε ct

(1)b) Estimation for the growth rates of average income of the poorest quintile (or QX).
(ln y p ct – ln y p c0 )/T = β(ln y ct – ln y c0 )/T + δlnZ ct + θlnA ct + λ t ’ + ε ct ’(2)c) Estimation with the possible convergence in inequality.
(lnGini ct – lnGini c0 )/T =α + β 1 lnGini c0 + β 2 [(ln y ct – ln y c0 )/T]+ δlnZ ct + θlnA ct + λ t ’ + ε ct

(3)where, c and t: countries and years, y: average per capita income of the county, y p : average
per capita income of the poorest quintile or other quintile groups, Z: important factors including
institutional quality and policies, A: period average aid (economic aid, social aid, and aggregateaid)/GDP, µ c : unobserved country fixed-effects, and λ + ε: a composite error term.

Second, by estimating Eq.1 in the fixed effects model, we obtained the pro-poor country fixed
effects, i.e., pro-poor institutions. We investigated the impact of aid by i) countries with positive
pro-poor country fixed effects and ii) countries with negative pro-poor country fixed effects.
2.2 Data for Empirical Analyses
The growth spells of medium terms are compiled from the available data points ofbottom-quintile income shares during 1978-2010. This contains 242 growth spells of the 5-9-year
period each with the average duration of 5.72 years from 99 countries during 1978-2010. The data
for sectoral aid analysis contains 183 growth spells for 60 countries during the late 1990s-2000s.
Most of the data comes from WDI, except ICRG from the PRS Group, and aid data from theOECD-DAC CRS database. For the index of institutional quality, we formed the “capacity”
sub-composite (ICRG-Capacity) by compiling the particular five components: (i) government
stability; (ii) investment profile; (iii) corruption; (iv) law and order; and (v) bureaucracy quality.
These five indicators denote the management ability of recipient countries.
2.3 A Case Study of Ethiopia
We singled out Ethiopia for our case study as she had the outstanding pro-poor country fixed
effects among our sample sets. We conducted a field survey in Ethiopia during July-August, 2013
in order to find out the country uniqueness and pro-poor institutions through the semi-structured
interviews with government officers, researchers, development practitioners, commodity exchange
agents, institutions for industrial/business development, and public/foreign companies.
3. Results of Cross-Country Empirical Analyses
3.1 Growth Enhancing Institutions Tend to be Anti-Poor
We conducted a country fixed effects regression with Eq.1 in order to examine the pro-/antipoor effects of institutions and other variables. We found that the coefficient of institutional
quality (ICRG-Capacity) was negative with significance (Table 1, Column 6). We also conducted
the quintile regressions with Eq.2 in order to estimate the systematic effects of institutions on
Quintile 1-5 groups (Table 2). We found some clear tendencies, despite the lack of statisticalsignificance that institutional quality (ICRG-Capacity) tends to benefit the richest quintile morethan any other income groups. On the other hand, the impact of institutional quality on the poor
was negative with moderate significance (Table 3a, Q1). The anti-poorness of institutions was also

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robust with Eq.3. These results indicate that growth-enhancing institutions (ICRG-Capacity) tend
to be anti-poor apart from its effects through growth.
3.2 Aid Works More Effectively in a Country with Pro-Poor InstitutionsFirst, the fixed effects were obtained from the C13 of Table 1. As listed in Table 3, we
identified Ethiopia, Tajikistan, Nepal, Pakistan, and Bangladesh, as the countries with the highpro-poor country fixed effects, i.e., pro-poor institutions. We also identified that Panama, Brazil,
Columbia, Seychelles, Argentine, and Bolivia were the countries with the least pro-poor fixed
effects. We examined the development effectiveness by different groups with the positive or
negative pro-poor country fixed effects. We found that the effectiveness of social and aggregate
aid was positive and more significant in countries with a positive pro-poor fixed effect (C3 &5)
while its significance decreased in countries with a negative pro-poor fixed effect (C4 &6). We
also found the significance of aggregate aid and social aid was much larger in comparison witheconomic aid (C1, 3, &5). In general, aggregate aid (which includes food assistance andhumanitarian assistance) and social aid are more targeted for poverty reduction. We could
interpret that the aid could not be extended to the poor, if there is no pro-poor institutions in adeveloping country. This study suggests providing aid to a country with pro-poor institutions
would increase development effectiveness.
4. Findings from the Case Study of Ethiopia
Ethiopia is one of the poorest countries in the world; nevertheless, she has achieved significant
progress in reducing poverty and inequality after the drastic reform in the early 1990s. The
government’s strong commitment and strategic measures for socioeconomic development pushed
the country forward with effectively utilizing foreign development aid. We found from the fieldsurvey that successful poverty reduction and high development effectiveness were greatly
influenced by the various pro-poor nature of the country, which were rarely captured in the current
measurement of institutions in the aid literatures. These include decisive and effective leadershipcommitted for poverty reduction, strong ownership of government, decentralization of authority
and service delivery, and pro-poor and equitable public expenditures.
5. Conclusion and Policy Recommendations
This study proposes a new perspective on development effectiveness. We argue the pro-poor
institutions could be the key determinants of development effectiveness, unlike the previous
literatures that focus on growth-enhancing policies and institutions. We suggest to the policy
makers and people in the development community to take into consideration not only
growth-enhancing policies and institutions, but also pro-poor institutions, in allocating aid, so thatdevelopment effectiveness will be further increased.
Main References:
Burnside, C. & Dollar, D. (2000). Aid, Policies, and Growth. American Economic Review, 90(4), 847-868.
Dollar, D., & Kraay, A. (2002), Growth is Good for the Poor, Journal of Economic Growth, 7(3), pp. 195-225.
Hirano. Y., & Otsubo, S. (2012). Poverty-Growth-Inequality Triangle under Globalization: Time Dimensions andthe Control Factors of the Impacts of Integration (GSID Discussion Paper No.191).

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The 24th JASID Annual Conference, 2013315

国際開発学会第24回全国大会（大阪大学）

Promoting Agroindustry-led Development under Globalization:
Simulations with a CGE model for the Democratic Republic of CongoOTCHIA Christian S.E.1
Graduate School of International Development, Nagoya University

Keywords: agroindustry, Free trade agreement, Productivity growth, CGE
1.

Introduction

Donor agencies and policy-makers agreed that agriculture and agro-industry are important value-adding businesssectors, with a highly positive development impact, and with a great contribution to developing countries’
economic growth. According to the World Bank, Africa's agriculture and food market is worth 310 billion US
Dollars and has the opportunity to grow to 1 trillion US dollars by 2030. Given this potential, many Africancountries made progress in improving agriculture productivity and strengthening technological effort to foster thedevelopment of agribusiness. In addition, African states have increased political commitment to increase
investment in agriculture and remove institutional constrain to agriculture and agribusiness. Despite of the progress
being made, the region is still a long way from taking full advantage of the opportunities to boost agribusiness, as
regional integration and regional industrial policy are not working well and domestic market are very small.
Therefore this paper explores how African countries can exploit local, regional and international demand to seize
opportunities for promoting agroindustry, using a computable general equilibrium model applied to the Democratic
Republic of Congo. The Congo is a special study case for two policy reasons: First, it has a huge potential for
agriculture and geo-location: 80 million hectares of arable land, diverse climatic conditions, abundant water
resources, and 8 neighbouring countries. Second, the Congo has signed a number of regional agreements, without,
however, implementing their free-trade protocols.
2.
Methodology and Data
This paper uses a global – national computable general equilibrium model to study how the Congo can use theforce of globalization to reinforce its industrial policy. Our methodological framework is built on three stages. First,
we use a national CGE model to simulate a policy package to increase productivity and output of agrofood industry.
This policy package comprises a joint simulation of productivity in processed food, a creation of trade promotion
unit which aims at reducing supply constrain to market, an investment of road infrastructure which reduce domestictransaction costs, an import licensing to protect infant industry, and an agriculture input reform. This joint
simulation is used as scenario 1 (base run) in this study. Further details of this simulation can be found in Otchia(2013). In the second stage, we use the GTAP model to simulate possible FTAs under which the DRC can promote
its industrial policy. In the last stage, we link the GTAP model to the national CGE to jointly simulate agrofood
scenario with FTA in order to assess under which FTA the agrofood scenario yields the greatest gain. We link thetwo models as follows: The changes in demand for exports, export prices, and import prices, as obtained from theGTAP model, are communicated to sectors of the Congo CGE model following Horridge and Zhai (2006).
The CGE of the DRC is fully documented in Otchia (2014a). The model is a single country, static CGE model
which contains 19 productive activities. Those activities combine intermediate commodities and primary factors
1[Address] Graduate School of International Development, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, JAPAN[Email] cotchia@gmail.com

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using a Constant Elasticity of Substitution (CES) function to determine a level of output. Factors of production
include capital, urban high-skilled labour, urban semi-skilled, urban low-skilled, rural high-skilled labour, ruralsemi-skilled, and rural low-skilled. The model assumes that the choice of factors of production is governed by a
CES function, while the aggregate intermediate input is a Leontief function of disaggregated intermediate inputs.
The output is split into domestic goods and goods for foreign markets using a constant elasticity of transformation(CET) function. In the domestic market, domestic goods can be substituted with imports following the Armington
CES function. Domestic demanders are assumed to minimize the cost base on the relative prices of imports anddomestic goods. They demand the composite good according to the linear expenditure system derived from the
maximization of a Stone-Geary utility function subject to a household budget constraint.
In our experiments, we assume that tax rates are fixed, and real consumption expenditure is exogenously
determined. Therefore, fiscal deficit adjusts to ensure that government account balances. The rest of the world
balance is achieved by assuming flexible exchange rate maintains a fixed level of foreign savings, given the free
floating exchange rate system adopted in the Congo. This assumption has the following implications: governments
cannot simply pay for new imports by increasing foreign debt. Instead, they have to generate exports earnings to
pay for any additional imports. For savings-investment balance, the simulations assume that savings areinvestment-driven but with adjustment rule for government consumption. This closure assumes also that saving
rates of selected institutions are adjusted by an equal number of percentage points. In addition, we assume thatlabour is fully employed and mobile across sectors. Domestic price is the numéraire. The model is calibrated to a2005 SAM for the Democratic Republic of Congo by the author.
3.

Design of Policy Experiments

In this section, we examine how the Congo can exploit local, regional and international demand to seize
opportunities for promoting agrofood industry. A total of seven scenarios are simulated, of which one focuses on
the agrofood development (scen.1), and six on FTAs (Scen.2-7). Among the six FTA experiments, four are related
to the Tripartite COMESA-SADC-EAC (Scen.2-5), and two on the prospect African Continental Free Trade Area
(ACFTA). Scenario 2 explores the opportunity to promote agrofood industry under Tripartite COMESA-SADCEAC. Scenario 3 builds on scenario 2 and includes China, while scenario 4 and 5 include respectively BRICS(exclude China) and the EU15. On the other hand, scenario 6 assesses the strategies of promoting agrofood within
the prospective African Continental Free Trade Area. Similarly, scenario 7 builds on scenario 6 and adds China.
The simulations design is summarised in table 1.
4.
4.1.

Impact Analysis of the Experiments
Macro Impacts

Macro results of policy experiments described in the previous section are reported in table 2. As can be seen, theimpact of agrofood development is an increase in the size of the Congolese economy. Real GDP increases by 5.4
per cent. Government consumption, investment, and total welfare also increase. Imports and exports decrease but
those changes are very small. Table 2 further indicates that promoting agrofood industry under different FTAs
agreement will benefit to the Congo’s economy, except under FTA with EU15. In fact, GDP expands by 3.9%
when agrofood is promoted under Tripartite + EU15 while GDP would grow higher under Tripartite alone orwithout any trade agreement. Unexpectedly, Tripartite and ACFTA gives the same change in terms of GDP eventhough welfare effects are higher under ACFTA due to price changes. In all cases, FTAs with China result in a
relatively higher GDP growth and welfare gains. Imports grow to a lower extend than under FTAs with EU or
BRICS*.

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4.2.

国際開発学会第24回全国大会（大阪大学）

Impacts on Production

Table 3 summarizes the results of policy experiments on production structure. Looking on the column of scenario 1,
we observe that agrofood development scenario induces growth in agriculture sector and forestry sectors as they
provide inputs to agrofood industry. The value-added on agriculture increases by 10.6 per cent while forestry sector
expands by 2.7 per cent. The remaining sector also expands. Wood and non-metals grow by 5.4 per cent and 3.1 percent respectively, whereas utilities, trade, and hotel and catering grow by 5.2, 2.2, and 3.9 per cent. Under FTAs
scenarios, we can see that ACFTA will procure the higher rise in valued-added than under Tripartite. This implies
that policy in favour of greater integration within African can reinforce the impact of domestic industrial policies.
But FTAs should be negotiated case by case and be more dynamic as comparative advantages are moving. For
instance, we observe in table 3 that wood and mining value-added decreases significantly as we move from
Tripartite to ACFTA. Again FTAs with China seems to procure the highest rise in value-added for food processed.
Note that agriculture’s expansion is similar under Tripartite with China and ACFTA + China. This indicates that
agriculture capacity to expand might be limited. There is therefore a need to support sectors that add value addition
to agriculture products. It is worth mentioning that even if Tripartite with EU15 gives the lowest impact in many
sectors, this agreement provided the highest benefit for agriculture and construction.
4.3.

Impacts on factor market

With regards to factor income, table 4 indicates that agrofood-led development increases the return to all factors.
Rural highskilled and urban lowskilled benefit the most. All the FTAs with the exception of Tripartite + China
procures high benefit to urban workers. Under Tripartite with EU15, rural lowskilled and semiskilled factor income
decrease by 6.9 and 7.9 per cent respectively.
5.

Policy Implications

The experiments carried out highlight the potential of globalization (viewed as FTAs) in reinforcing the impact ofdomestic agrofood development policy. The results suggest that while FTAs may procure welfare gains and newmarkets opportunity for picked sectors, some infant industry may also lose from increased competition. Therefore,
FTAs should be very flexible to account for dynamic comparative advantage.
6.

Selected Reference

Horridge, M. and F. Zhai (2006). Chapter 3: Appendix Shocking a Single Country CGE Model with Export
Prices/Quantities from a Global Model. Poverty and the WTO : impacts of the Doha Development Agenda. T. W.
Hertel and L. A. Winters: pp.38-45.
Otchia, C. S. E. (2013). Institutions and Economic Policies for Pro-Poor Agribusiness Growth under Globalization:
Evidence from the Democratic Republic of Congo. International Conference on Economic Theory and Policy after
the Crisis. T. Yagi. Meiji University, Tokyo.
Otchia, C. S. E. (2014). Distributional and Poverty Effects of Agriculture Trade Liberalisation: The Case of the
Democratic Republic of Congo (Forthcoming). UNCATD VI Research Paper. Geneva, UNCTAD.

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Appendix

Table 1: Design of Policy Experiments

Scenarios
Scen. 1
Scen. 2
Scen. 3
Scen. 4
Scen. 5
Scen. 6
Scen. 5

Description
Agroindustry-led development
Agroindustry-led development under Tripartite (COMESA - SADC – EAC) FTA
Agroindustry-led development under Tripartite + China FTA
Agroindustry-led development under Tripartite + BRICS (without China) FTA
Agroindustry-led development under Tripartite + EU15 FTA
Agroindustry-led development under African Continental Free Trade Area (ACFTA) FTA
Agroindustry-led development under ACFTA + China FTA

Source: Author
Table 2: Impact on Macro variables
Scen.1:
Agrofood
Gross Domestic ProductGovernment consumption
Investment
Exports
Imports
Consumer price index
Exchange rate
Welfare (% of GDP)

5.4
3.2
2.5
-0.5
-0.5
0.0
2.8
5.2

Scen.2:
Scen.3:
Scen.4:
Scen.5:
Scen.6:
Scen.7:
Agrofood under Agrofood under Agrofood under Agrofood under Agrofood under Agrofood under
Tripartite
Tripartite + China Tripartite + Brics* Tripartite + EU
ACFTAACFTA + China
6.0
6.5
6.3
3.9
6.0
6.7
7.8
6.6
6.5
16.2
13.1
11.5
9.6
7.1
7.6
22.7
16.6
14.8
-6.3
-5.8
-5.8
-25.0
-13.7
-11.9
7.4
4.7
5.8
28.2
16.7
14.7
-2.0
-1.0
-1.6
-7.4
-4.5
-3.6
-15.1
-4.0
-10.5
-52.3
-33.8
-24.7
8.1
8.2
11.5
12.8
10.9
12.2

Table 3: Impact on production structure
Scen.1:
Sectors

Agrofood

Agriculture
Forestry
MiningProcessed foods
TextilesWood
Chemicals
Non-metals
Other manufacture
Utilities
Construction
Trade
Hotels and catering
Transportation
Education and health
Financial intermediation
Other services
Personal and other community services
Private households with employed persons

10.56
2.70
2.31
10.01
0.62
5.40
-0.95
3.06
1.94
5.17
2.41
2.16
3.86
-0.46
3.24
2.40
0.57
5.47
4.00

Scen.2:
Scen.3:
Scen.4:
Scen.5:
Scen.6:
Scen.7:
Agrofood under Agrofood underAgrofood under Agrofood under Agrofood under Agrofood under
Tripartite
Tripartite + China Tripartite + Brics* Tripartite + EU
ACFTAACFTA + China
10.76
10.81
10.77
10.90
10.80
10.81
3.45
2.66
-1.32
-0.05
3.67
3.32
-0.19
5.87
2.19
-24.43
-4.64
-1.02
10.17
10.29
10.19
9.58
10.47
10.74
2.32
2.06
2.20
3.62
3.62
3.65
-13.41-37.26-24.57-39.17-22.91-37.32
-1.08
-0.66
-1.05
-2.82
-1.32
-0.81
0.95
0.00
-0.18
0.02
1.37
1.25
4.41
4.63
4.70
5.15
5.66
7.10
5.67
8.70
2.03
-5.11
5.01
6.29
8.87
6.62
7.07
20.47
15.15
13.56
2.77
2.26
2.18
8.88
4.69
3.60
6.02
6.54
6.48
4.75
7.58
8.10
-0.27
-1.12
-0.83
-0.15
-0.77
-1.12
5.97
5.76
5.48
8.74
8.43
8.36
3.01
3.53
2.55
0.43
5.38
3.72
0.79
0.21
0.48
0.09
1.08
0.68
15.30
17.80
28.46
11.44
13.49
30.66
5.26
5.97
5.37
3.51
5.85
6.71

Table 4: Factor income
Scen.1:
Agrofood
Urban lowskilled
Urban semi-skilled
Urban highskilled
Rural lowskilled
Rural semi-skilled
Rural highskilled
Capital

7.00
5.61
5.22
6.95
6.92
7.25
5.11

Scen.2:Agrofood under
Tripartite
12.46
9.68
10.61
7.47
8.11
10.57
7.52

Scen.3:Agrofood under
Tripartite + China
11.19
10.45
11.50
11.50
13.89
13.58
7.51

Scen.4:
Agrofood under
Tripartite + Brics*
11.11
10.34
15.34
8.08
10.62
11.19
6.79

318

Scen.5:
Agrofood under
Tripartite + EU
25.51
10.85
5.28
-6.83
-7.82
5.63
8.75

Scen.6:
Agrofood under
ACFTA
18.51
12.10
10.69
5.86
6.45
11.79
9.48

Scen.7:
Agrofood under
ACFTA + China
17.91
14.19
18.08
9.17
11.36
15.16
9.46

The 24th JASID Annual Conference, 2013319

国際開発学会第24回全国大会（大阪大学）

Spatial Dimension of Innovation in Developing Countries:
The State of Innovation in the 17 Regions of the Philippines
Dumayas, Arianne Dela Rosa
Graduate School of Economics-Chuo University
E-mail: ariannedumayas@gmail.com
Keywords: innovation, spatial disparity, regional development, research and development, humancapital
1. Introduction
The Philippines economy is on the upbeat trend despite the global economic slowdown. It posted
7.6 percent growth during the first half of 2013. The Philippines have also achieved upgradedinvestment-grade credit from major credit rating agencies such as Fitch and Moody’s. The country
is also poised to attain the self-sufficiency in rice by the end of 2013. Despite the re markable
resiliency to worldwide economic decline, the country remains low in innovation, which is widely
accepted as a significant driver in economic growth. In the recent Global Innovation Index 2013,
the Philippines jumped places from 95 th to 90 th ; however, this ranking is comparable only with the
African countries of Uganda (89) and Botswana (91).
Many studies have been done to explain this dismal state of innovation in the Philippines; however,there are very few studies that take in to account the spatial dimension of the issue. This study
aims to fill this research gap by analyzing the state of innovation at the regiona l level as it adheresto the notion that innovation is spatially-bounded. This study will examine the state of innovation
at 17 regions of the country by constructing an Innovation Index and utilizing Principal
Component Analysis (PCA).
2. Data and Methodology
The Oslo Manual defines innovation as, “the implementation of a new or significantly improved
product (good or service), or process, a new marketing method, or a new organizational method inbusiness practices, workplace organization or external relations” (Oslo Manual, p.46). There aredifferent ways to measure innovation and the most of widely used are the Oslo and Bogota Manua l.
Unfortunately, many guidelines were not applicable to developing countries and availability ofdata is quite problematic. Thus, in order to capture the state of innovation in the developing
countries, improvisation is needed. This study used the followi ng indicators from Department of
Science and Technology (DOST), Commission on Higher Education (CHED), and Department ofTrade and Industry (DTI) to construct innovation index at the regional level. Each variable isstandardized but no assignment of weights was used.
Human Capital: R&D Personnel (Public, Private, and Non -profit), Higher Education Institutions

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国際開発学会第24回全国大会（大阪大学）(HEI) Enrollment, Percentage of Faculty with Advanced degrees (MS and PhD) in State
Universities and Colleges (SUC)
R&D Intensity: R&D Expenditure (Public, Private, Non-profit, and Industry)R&D Institutions: Number of micro, small, and medium enterprises (MSME), and Number of HEI(Public and Private)Principal Component Analysis (PCA) is used to discern the set of variables in two -dimensional
space. This representation could give a meaningful classification of regions. PCA is also used todetermine the correlation among the variables.
3. Results and Discussion
Based on the constructed innovation index, the state of innovation is quite high in three adjacentregions of National Capital Region (NCR), Region IV-A (Calabarzon), and Region III (Central
Luzon). Innovation is also relatively favorable in the neighboring regions of Region VI (Western
Visayas), and Region VII (Central Visayas). This observation implies the possibility of knowledge
spillovers between or among these regions. While the regions in Mindanao lag significantly interms of innovation, particularly, CARAGA and Autonomous Region of Muslim Mindanao(ARMM). This particular finding indicates that proximity matters in innovation.
Table 1. State of Innovation at Regional Level
Innovation
Human
R&D
Regions
Ranking
R&D Intensity
Index
CapitalInstitutions
NCR
2.348
2.437
2.630
1.976
1
(0.007)(0.333)(0.791)CAR-0.377
14
Region I(Ilocos)-0.178(0.007)(0.304)(0.222)
8
Region II (Cagayan Valley)-0.2490.176(0.302)(0.622)
10
Region III(Central Luzon)
0.353
0.176
(0.040)0.923
3Region IV-A(Calabarzon)
1.080
0.278
1.267
1.695
2
Region IV-B(MIMAROPA)-0.397(0.727)(0.221)(0.245)
15
Region V( Bicol)-0.122(0.112)(0.317)0.062
7Region VI (Western Visayas)
0.034(0.251)(0.151)0.503
5Region VII (Central Visayas)
0.209
0.546
(0.062)0.142
4Region VIII (Eastern Visayas)-0.309(0.267)(0.311)(0.349)
11
Region IX(Zamboanga Region)-0.332(0.543)(0.348)(0.105)
12
Region X(Northern Mindanao)-0.194(0.195)(0.239)(0.147)
9
Region XI(Davao Region)-0.1200.295(0.162)(0.492)
6(0.156)(0.313)(0.598)
Region XII(SOCCKSARAGEN)-0.356
13
CARAGA-0.465(0.150)(0.385)(0.860)16
ARMM-0.925(1.494)(0.410)(0.870)17source: Author's calculation based from the data from Department of Science and Technology(DOST), Commission
on Higher Education(CHED), and Department of Trade and Industry(DTI)

The study also found a strong correlation between region’s economic development and innovation.
Regions with higher gross regional domestic product (GRDP) per capita also exhibits highe r

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国際開発学会第24回全国大会（大阪大学）

innovation index. NCR has the highest GRDP per capita and also ranks number one in innovationindex. ARMM, which has the lowest GRDP per capita, ranks the lowest in the innovation index.
Figure 1. Innovation Index and GRDP Per Capita
2.5
NCR

Innovation Index2
1.5
Region IV-A(Calabarzon)

1
0.5
0
0
-0.5
-1

RegionIII(Central Luzon)
RegionVII (Central
Region
VI (Western
Visayas)Visayas)RegionX(Northern
RegionXI(Davao
Region) 200000
RegionV(
Bicol)
RegionI(Ilocos)
50000
100000150000250000Region(Eastern
RegionIX(Zamboanga
RegionIIVIII(Cagayan
Valley)RegionMindanao)
CAR
RegionIV-B(MIMAROPA)
Visayas)Region)XII(SOCCKSARAGEN)CARAGA

300000350000400000450000500000ARMM

GDRP per capita 2008

For the 17 regions, the first principal component (PC) accounts for 70.66 % of the total variancewith Eigen-value significantly larger than 1, the second principal components accounts for 16.51%
with Eigen-value higher than 1. These first two principal components explain 87.17 % of the total
variance. The first component emphasizes R&D expenditure and R&D Institutions and the second
component highlights human capital, particularly, the percentage of the faculty with advanced
degrees (MS and PhD) or the quality of human capital.
The higher the R&D expenditure and the number of R&D institutions, the higher the score of first
PC on the x-axis, and the regions which has these characteristics are found on the right part of thegraph. Meanwhile, the regions with higher percentage of faculty with advanced degrees
demonstrate a higher score in the second PC on y-axis. This representation yields a classificationof the regions in the Philippines: highly-innovative region (high supply and quality of humancapital, vibrant R&D and strong presence of R&D institutions), R&D intensiv e but relatively
deficient in quality of human capital regions, High in quality of human capital -rich but low in
R&D regions, and lagging regions (inadequate human capital, R&D, and R&D institutions). NCR
is the only region that falls under the category of highly-innovative regions; while the rest of theregions clustered in two categories which is either lacking in the quality of human capital or R&D
expenditure and R&D institutions. In order for the Philippines to sustain accelerated growth andachieve inclusive growth, massive effort should be undertaken to address this spatial disparity ininnovation.

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Figure 2. State of Innovation at Regional Level
6

Human Capital

4

2

RegionXI(Davao
Region)RegionVII(CentralRegionII (Cagayan
Valley)Region Visayas)
CARAGA
CAR
Region I(Ilocos)
XII(SOCCKSARAGEN)

NCR

RegionX(Northern
RegionV((Eastern
Bicol)
RegionVIII
Mindanao) Region III(Central Luzon)
Visayas)Region IX(Zamboanga
RegionIVRegion)
Region
VI (Western
B(MIMAROPA)Visayas)
Region IV-A(Calabarzon)

0

-2
ARMM
-4
-6

-4

-2

0

2

4

6

8

10

R&D Intensity and R&D Institutions

4. References

Audretsch, D.B & Feldman, M.P .(2004). Knowledge spillovers and the geography of innovation.
In J. V. Henderson& J.F Thisse(eds.), Handbook of Regional and Urban Economics, edition 1,
volume 4 (pp. 2713-2739). Elsevier.
Camagni, R. &Capello, R. (2009). Knowledge-based economy and knowledge creation: The Roleof Space. In U. Fratesi & L.Senn(eds.), Growth and innovation of competitive regions: the role ofinternal and external connections (pp. 145-165). Berlin: SpringerCrescenzi, R. & Rodriguez-Pose, A.(2009). Systems of innovation and regional growth in the EU:
endogenous vs. external innovative activities and socio -economic conditions. In U. Fratesi &L.Senn(eds.), Growth and innovation of competitive regions: the role of internal and external
connections (pp. 145-165). Berlin: SpringerFratesi, U.& Senn, L. (2009). Growth and Innovatiion of Competitive Regions: The Role ofInternal and External Connections. Berlin: Springer-322