Inception Report

Productivity Growth in Philippine Agriculture ProjectInception Report

INCEPTION REPORT

The Nature, Sources, and Causes of Productivity Growth in Philippine

Agriculture

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LIST OF FIGURES

Figure 1: Schematic on determinants of productivity growth.............................................6Figure 2: Project Organizational Chart..............................................................................11

LIST OF TABLES

Table 1: Program of Work.................................................................................................12Table 2: Sources of funds, in millions of pesos.................................................................13Table 3: Project budget, in millions of pesos....................................................................13

LIST OF ACRONYMS

DA-BAR Department of Agriculture – Bureau of Agricultural ResearchPhilRice Philippine Rice Research InstituteSEARCA Southeast Asian Regional Centre for Graduate Study and Research in

Agriculture TFP Total Factor Productivity

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1. INTRODUCTION

Philippine agriculture performed relatively well in the 1960’s and 70’s, but faltered in the following decades. Several trends portended its stagnation: production increases in almost all crops decelerated, expansion in cultivated area began to decline, real commodity prices dropped, and gains from the Green Revolution approached exhaustion. Policy-related factors may have also played a role, such as the sharp decline in public investments for agriculture. Cross-country studies suggest that total factor productivity (TFP) growth in the Philippine agriculture has been negligible to negative from the 1980s, in contrast with the good to impressive performance of other countries in the region (Table 1).

Table 1: Estimates of TFP growth for selected Asian countries, last two decades (%)

(1) (2) (3) (4)

Philippines -0.3 0.4 -1.3 0.1Afghanistan -0.1Iran 3.2Iraq -1.2Saudi Arabia 2.2Syria 1.9Turkey 0.1Yemen 2.4Bangladesh 1.3 1.1India 2.4 -1.1Nepal 2.1 1.2Pakistan 2.5 2.7Sri Lanka -0.9 -0.2Cambodia 2.0Indonesia -0.4 -1.1 1.5Laos 2.5Malaysia 1.4 1.5Thailand 1.1 1.4 0.9Vietnam 3.3 1.0China 4.8 3.6Mongolia -0.5

Notes and Sources: (1) Avila and Evenson (2004): 1981 - 2001

(2) FAO (2004): 1980-2000 (3) Cororaton and Caparas (1999): 1981 - 1996

(4) Mundlak, Larson, and Butzer (2002): Philippines 1980-1998; Indonesia 1981-1988; Thailand 1981-1995

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Nevertheless the critical role of agriculture in economic progress remains widely recognized. About 37% of the labor force is still engaged directly in the sector; over two-thirds of the poor depend, directly or indirectly, on agriculture for livelihood and sustenance. Hence, the country’s success or failure in winning the war against poverty would be decided by how household income from agriculture can increase. Given rapid population growth and the closure of the land frontier, such increases in income can originate only from productivity growth. This remains a critical challenge given the productivity slowdown in Philippine agriculture in recent decades.

Recently, a major research project on analyzing productivity growth in Philippine agriculture has been launched by SEARCA, DA-BAR, and PhilRice. The project addresses the following questions, which are key issues in defining a strategy for accelerating agricultural growth:

What explains the past performance and how do these influence the future sources of agricultural growth?

What are the key policy and investment levers needed to spur and sustain growth across commodities and geographic areas?

How do institutions and governance structures influence the patterns of agricultural growth?

How do emerging structures of global and regional trade, as well as communication and information technologies, influence the sources and patterns of agricultural growth?

How can agricultural growth be made more broadly based so that its impact on poverty reduction is enhanced?

The project meets two major challenges: the first is to quantify the determinants of productivity growth in such a way as to be instrumental for policy and investment choice. For example, while a standard productivity growth analysis may underscore the importance of total factor productivity, what is really useful for policymakers is the impact of an investment lever – say, funding for public agricultural research – on productivity growth. The second challenge, which is common to empirical work on Philippine agriculture, is unavailability and inadequacy of data. Addressing this requires a two-pronged approach: one is to devise data collection systems to address the remediable gaps; the other is to innovate analytical methods to maximize the information that could be generated from official surveys and administrative data.

Meeting these challenges divides the project into three stages. The first stage involves extensive preparatory work, which entails the following: evaluation of the statistical system and identification of critical data gaps; a review of the literature to synthesize the

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state-of-the-art in productivity analysis; and analytical work to formulate new empirical methods. The second stage involves application, which entails: piloting new data systems to test its value and feasibility for integration in regular systems; implementation of empirical techniques for in-depth study of the agricultural sector, major agricultural commodities, and cross-cutting themes. By the end of the second stage, substantive results and recommendations are already available for formulating policies and strategies. Lastly, the third stage covers the scientific and policy review process, in which the research is finalized for national and international publication and dissemination. The entire project is programmed for three years, with each stage running for one year.

The rest of this Inception Report discusses in detail the objectives (Section 2), the framework (Section 3), the components and activities (Section 4), and finally the institutional arrangements and funding (Section 5).

2. OBJECTIVES

As the title indicates, the project aims to determine the nature, sources and causes of productivity growth in Philippine agriculture. More specifically, the objectives of the project are:

i) To measure productivity growth in Philippine agriculture and disaggregate the sources of growth;

ii) To develop data collection systems and analytical approaches appropriate to Philippine conditions;

iii) To examine the determinants of productivity change over time; iv) To identify policy and investment levers relevant for formulating development

strategies for the agricultural sector.

The first objective is the straightforward starting point of the analysis. However, productivity estimation is constrained by availability of data, especially of the type and form that is uniform over long period of time. This problem has long hindered the application of existing models or tools. This leads to the second objective; the systems and techniques developed would lay the groundwork for a sustained and institutionalized system of gathering information and performing analysis that would establish and track the performance of Philippine agriculture over time.

Apart from measuring productivity change, the project also aims to examine the determinants of productivity change over time, both for the aggregate and sub-sector/commodity levels, accounting for Objective iii). Note that the measurement and estimation techniques should also account for the endogeneity of some parameters that explain technical change.

Analysis of these determinants leads to the fourth objective, which are specific policy and investment levers required to lift and sustain the productivity growth path of Philippine agriculture and its major sub-sectors. It focuses not only on proximate factors of productivity growth (such as R&D investment), but also to deeper determinants, including: policies, e.g. in trade and marketing; institutional change, e.g. agrarian and

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property rights reform; infrastructure development; changes in the environment, e.g. resource degradation in farmlands, uplands, and coastal areas; and finally, economic and technological trends, e.g. globalization, and the rise of information and communications technologies.

3. FRAMEWORK

3.1. Measuring productivity growth

The idea of “productivity growth” hinges on a simple distinction between various ways of increasing output. Output can be raised by increasing inputs; for instance in agriculture, more harvest can be obtained by expanding farm area and adding farm workers. A more interesting source of output increase though is improvements in productivity, that is getting more output from the same inputs. This is closely associated with better production techniques. As far back as Malthus, economists have understood that food supplies would run out in the face of a growing population, if farm output could only grow by adding more inputs. In fact the reason why the Malthusian scenario has failed to materialize is that output did manage to grow despite land constraints, owing to improvements in productivity.

Despite its conceptual appeal, productivity growth has posed major challenges to empirical measurement. The literature has addressed these challenges in various ways (see Annex 1 for details). One comprehensive approach is the estimation of production functions. However it requires strong assumptions on modeling and availability of data. Another, more parsimonious approach is “growth accounting”, which makes even stronger assumptions about market structure and technology. The choice of utilizing one particular approach is guided by the availability of data, area of analysis, dimension of the study, or simply analytical convenience.

For Philippine agriculture, productivity growth studies date as far back as Hooley, which covered the prewar period. Lawas (1965), Paris (1971), David and Barker (1979), David, Barker and Palacpac (1985) and Evenson and Sardido (1986) analyzed postwar agricultural data to identify subsequent changes in agricultural productivity growth. More recent evaluation of productivity performance include that of Patalinhug (2001) that reviewed the program components of the Medium term National Action Agenda for Productivity (MNAAP) which was formulated to promote globally competitive agriculture, industrial and services sectors. Teruel and Kuroda (2004) meanwhile used agricultural data set up to 2000 to empirically investigate the nature of Philippine agricultural productivity growth.

3.2. Determinants of productivity growth

Figure 1 represents a conceptual framework for understanding productivity growth (See Annex 1 for technical details). Consider the basic production function, where inputs are narrowly defined in the conventional manner, corresponding to land, labor, equipment, (primary inputs), as well as seed and chemicals (intermediate inputs). These inputs are used at the primary production stage to yield output, which is then converted into

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agricultural value added via a series of product market transactions along a supply chain. Other than conventional inputs, some determinants of the input-output production process are factor of production outside the immediate control of the producer (i.e. quasi-fixed factors). The former may include factors such as infrastructure, environmental or natural resource services, human capital, and so on. Transformation of inputs into outputs, and then into economic values through markets, allows one to measure productivity growth.

There are various sources of productivity growth. First and most straightforward is an increase in quasi-fixed factors, leading to an increase in physical output given fixed conventional inputs. Second, with respect to aggregative analysis of agriculture as a whole, a structural change that alters sectoral composition can change the level of productivity. For instance, a shift in resources from low-productivity to high-productivity sectors, can raise aggregate productivity, even in absence of productivity growth in the sub-sectors. Third, lagged prices affect the choice of production technology, which could therefore affect productivity growth. Fourth, the translation of products to economic values is strongly affected by transaction costs. The latter can denote a tangible “friction”, such as transportation cost and spoilage. These costs drive a wedge between farm and market prices, both in the output and factor markets, hence affecting the translation of factors into value added. Fifth, the coordination of output and input decisions within the production process is strongly affected by uncertainty, risk, asymmetric information, and strategic behavior. Hence underlying the market process are institutional factors, including asset endowments (which affect investments in new technologies), as risk instruments (such as insurance), legal systems (for contract enforcement), and trust networks. The latter mitigate risk and reduce intangible forms of transaction cost.

Ultimately we wish to relate policy and investment levers on productivity growth. First, policies can more directly affect productivity growth through market channels. For instance, trade liberalization exposes domestic agriculture to competitive pressures to increase productivity; it also allows reduces the cost of inputs embodying the latest technologies (i.e. chemicals, seeds, machinery). Other, indirect channels is through the quasi-fixed factors and transaction costs discussed above.

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Figure 1: Schematic on determinants of productivity growth

MARKETS

- supply chain- composition

PRODUCTION

PRODUCTIVITY GROWTH

POLICY AND INVESTMENT

LEVERS

QUASI-FIXED FACTORS

TRANSACTION COST

OTHER CHANNELS

DIRECT CHANNEL

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The research would measure indicators along the transmission chain, as well as estimate the quantitative links between these indicators. Hence it would transcend traditional analysis that would identify the TFP as a component of growth, without attributing TFP to policy handles such as R & D investments. The estimation process should cover both productivity growth in agriculture as a whole, and productivity growth disaggregated by major commodity, with consistency imposed throughout. Some more technical issues that need to be resolved in the course of implementation are: the use of simultaneous equation systems or commodity-specific single equations; choice of functional form; reduced form versus structural estimation; possible imposition of a parsimonious parameter and functional structure; and other issues related to the estimation procedure.

4. ACTIVITIES AND OUTPUTS

4.1. Components

Achieving the study objectives requires the following activities, which structure the study into four components: 1) Development of empirical methods; 2) Case studies; 3) Commodity studies; 4) Cross-cutting issues.

Component 1: Development of empirical methods

This component focuses on the development of empirical method and approaches to understand productivity growth in a regime of imperfect data. The main sources of information on Philippine agriculture would be the Bureau of Agricultural Statistics data, as well as the Census of Agriculture. The former are generally area-based data, while the latter incorporates farm-level information. The latter may be a more reliable source, but is decadal (the last was conducted in 2000). There are also a number of farm-level surveys or area-based data sets scattered in the literature, which could be used for productivity analysis. Conducting agricultural productivity analysis under such data-sparse conditions is a problem to be tackled in general terms under this component. Some topics to be dealt with under this component include the following:

A survey of the theoretical and econometric literature on estimating productivity growth;

A review of the empirical literature on agricultural productivity growth in the Philippines;

A review of statistical system in the Philippines, its strengths and weaknesses, and recommendations for improving data collection, processing, and presentation.

Special topics for bridging gaps in the data, such as but not limited to: - Interpolation for missing years in time-series data; - Small area estimation for increasing the resolution of geo-referenced data

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Component 2: Case studies

The case studies would involve empirical work, i.e. collection and analysis of primary data, to test some of the innovative methods suggested under component 1. Data collection can take the form of small-scale or even provincial or regional surveys. The intention is to pilot new data systems to be institutionalized in the regular administrative and statistical systems towards a sustained generation of the indicators for tracking agricultural performance, productivity growth, and its sources.

Component 3: Commodity studies

This component covers commodity and sub-sectoral studies on productivity growth. The following are the major categories to be covered; italics denote the priority commodity types under each category.

1. Grains – rice, corn2. Traditional export crops – coconut, sugarcane; abaca, tobacco3. High value crops – banana, pineapple, mango; vegetables 4. Livestock and poultry – chicken, hogs5. Aquaculture – seaweed, shrimp, tilapia, milkfish

Each study would consist of: i) a commodity profile covering both production and the supply chain; ii) a complete set of commodity-specific data and indicators for conducting productivity growth analysis; iii) qualitative analysis of growth determinants; and possibly, iv) estimation of links between policies and productivity growth.

Component 4: Cross-cutting themes

Cross-cutting themes to be tackled under this component are as follows:

1.1. The research and extension system 1.2. Institutions and Social Capital 1.3. Infrastructure 1.4. Landscape/Environment 1.5. Human Capital 1.6. Transaction costs 1.7. Estimation of productivity growth for agriculture 1.8. Science frontiers and implications for agricultural productivity growth 1.9. Overview, synthesis, and prospects

Themes 1 to 6 are factors derived from the framework (Section 3.2). Each of these are to elaborate the following: i) components, profile, and trends; ii) transmission channels to productivity growth; iii) measurement issues; iv) indices for productivity analysis; v) evaluation and recommendations. Themes 7 to 9 examine agriculture as a whole: Theme 7 is the technical estimation, Theme 8 looks forward to future sources of growth, and Theme 9 summarizes the findings and recommendations of the project.

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4.2. Approaches and expected outputs

Based on the project components, the project will be conducted through literature reviews, compilation and analysis of secondary data, theory-building, collection of primary data, as well as statistical and econometric analysis. The study will actively involve young researchers, particularly faculty and graduate students, whose work would simultaneously build their capacity in productivity analysis and the economics of agricultural development. Peer review would be integrated into the work through workshops with both national and international experts, editorial work and refereeing within the project, as well as submission of the work to peer-reviewed journals.

The project aims to inform policy and investment decisions; hence, its findings would be would be disseminated to policymakers and other stakeholders through non-technical communication materials, such as policy briefs; these briefs will be disseminated at the conclusion of each Phase of the project (see Section 4.3). In sum the expected output of the projects are as follows:

1) Monographs on commodity studies and cross-cutting issues – to be published by SEARCA.

2) Articles in peer-reviewed journals. 3) Book on agricultural productivity – this volume synthesizes the project studies

and is intended for global and national distribution, in partnership with a local and foreign publisher. It will serve as a reference for both policymakers and researchers, as it would contain both policy evaluation and technical analysis of productivity growth.

4) Expanded pool of young researchers doing empirical work on agriculture. 5) Policy briefs and other communications –dissemination would be both through

printed matter and the internet.

4.3. Time frame

The project is expected to last 3 years, beginning March 2007 (with possible extension to 5 years.) Each year corresponds to a phase of the project, with activities described as follows:

Phase 1 (first year) - Benchmarking

This first phase involves literature review and two workshops, an inception and a planning workshop, to answer the questions “Where are we?”, “Where do we want to go”, and “How do we proceed?” The first wave of studies will cover data and data sources, including gaps and ways of enhancing information; review of literature; research/knowledge gaps in terms of productivity growth studies; and detailed work plans covering the remaining phases of the study. The monthly program is shown in Table 1. The planning workshop in the last row covers identification and programming of activities for Components 2 to 4, as well as Phases 2 and 3 of the project, hence a more detailed timetable would be developed then.

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Phase 2 (second year) – Research

The main body of the research will be conducted in the second phase. This entails model formulation, data collection, field work, statistical analysis, etc. The expected output of the second wave of studies will present the initial results of the empirical work.

Phase 3 (third year) - Finalization

The third phase involves finalization of the research, as well as synthesis of the research findings, that would identify the nature, sources, and causes of productivity growth of Philippine agriculture, along with policy and investment levers for accelerating productivity growth. Results will be reviewed and integrated in the final project conference workshop. Writing, editing, and publication of the expected project outputs will be completed in this stage.

5. IMPLEMENTATION

5.1. Institutional arrangements

Oversight of the project will be exercised by the Project Coordinator, the SEARCA Director, as well as a Steering Committee composed of the Directors of SEARCA, BAR, PhilRice. An internationally recognized expert in the field of agricultural productivity would serve as a Project Adviser, who will participate in the Planning and Final Workshop, as well in the review of selected aspects of the project. Project management will be exercised by the Project Manager based in SEARCA, who will be supported by a full-time research assistant. Additional administrative coordination will be exercised by the Research Services and Consulting Services Departments of SEARCA, represented by their respective managers. The organizational chart for the project is shown in Figure 2. Research will be carried out with partner institutions, with study leaders (either senior or junior researchers) based in these institutions. Preferred partners would be statistical agencies (BAS) and academic institutions with graduate students who can conduct parts of the research as part of their thesis.

5.2. Funding

Project cost is estimated at PhP 18.0 M for the three-year study period, from core contributions of BAR, PHILRICE and SEARCA (Table 2). Based on Table 1 and 2, the milestones are:

Inception Report draft – April 2007: First release“First Wave” studies – December 2008: Second releaseDraft of “Second wave” Studies – December 2008: Third release

The budget for the project, by activity and phase, is shown in Table 3. The travel and honorarium of the Project Adviser would be mobilized from external sources.

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Figure 2: Project Organizational Chart

Project managementProject Manager

Research assistant

Consulting Services Division

Research Services Division

Study leaders Study leaders Study leaders

Steering Committee

Project Adviser

Project CoordinatorSEARCA Director

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Table 2: Program of Work

Activity 2007 2008 2009 2010

q2 q3 q4 q1 q2 q3 q4 q1 q2 q3 q4 q1

Inception Report *

Inception workshop *

Component 1 reviews *

Submission of 1st wave studies X

Planning workshop *

Component 2, 3, 4 research * * * *

Submission of 1st drafts – 2nd wave studies X

Final workshop *

Revisions *

Submission of final drafts 2nd wave studies X

Publication * * *

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Table 3: Sources of funds, in millions of pesos

Year 1 Year 2 Year 3Total

BAR 2 3 3 8

PhilRice 1 2 2 5

SEARCA 1 2 2 5

Total 4 7 6 18

Table 4: Project budget, in millions of pesos

Total Year 1 Year 2 Year 3

General costs 3.8 1.1 1.4 1.3

Grants:

Component 1 1.0 1.0 0.0 0.0

Component 2 5.0 1.5 2.3 1.2

Component 3 5.0 1.5 2.3 1.2

Component 4 2.2 1.0 1.0 0.2

Total 18.0 6.1 7.0 3.9

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Annex: Measuring Productivity Growth

The first attempts to measure productivity growth relied on simple approaches, such as the productivity ratio (Kendrick, 1961). A more rigorous approach represents productivity in terms of production function (Solow, 1957), which leads to parametric estimation of given functional forms (Christensen, Jorgenson, and Lau, 1973; Lau and Yotopoulos, 1989; Boskin and Lau, 1992). Such estimation would require data on production, inputs, and (in dual approaches), input and output prices.

The production function became the basis of more general, nonparametric approaches represented by growth accounting, the third approach to measuring productivity growth. Consider a production function of the following form:

. (1)

Here i is a commodity index, the level of output, a vector of conventional input

quantities, a vector of quasi-fixed factors, a technology index (represented by shift-

neutral parameter), and a production function. Technology is represented by a Hicks shift-neutral parameter. Suppose the output price is normalized (to unity). Let the real factor prices be , where j is an factor index; let the share of factor j in output i be

denoted , and suppose factor prices equal the marginal products at equilibrium. Finally, denote growth rates of outputs, factors, and technology by corresponding lower-case notation. Totally differentiating (1), keeping quasi-fixed factors constant yields:

(2)

Hence, TFP growth can be measured as a residual after subtracting the weighted average of factor growth. However this residual is admittedly a “measure of ignorance” rather than a true productivity measure. It could include measurement error, omitted variables, and a stochastic disturbance term. Jorgenson and Grilliches (1967) and Denison (1972) implemented various techniques to adjust inputs for quality changes, to better isolate the productivity component of the residual.

When growth accounting is applied to aggregate output or sectors, one can analyze structural change (Jorgenson, 1988). Growth in measured productivity may be due in part to a change in composition from products with low TFP to those with high TFP. One way

to formalize this is to define total agricultural output as (Warr, 2006):

Let be the share of commodity i in total agricultural output. Total differentiation yields:

(5)

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That is, aggregate sectoral growth can be decomposed into a weighted average of TFP growth by commodity, plus a weighted average of production growth (sans the TFP component).

A more general formalization follows Mundlak, Larsen, and Butzer (1997). This time let i denote production technique, such that we write ; let the output price for the ith technique; let the technique, output and factor price vectors be , p and w, where T is a set of feasible techniques. The Lagrangian equation for the optimization problem is:

(6)

subject to , , where T is a set of feasible technologies. Let S be a vector of state variables for the problem, such that . The Kuhn-Tucker conditions

allow us to write the implicit functions . Hence we can write or .

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References

Aigner, D. J., Knox Lovell, C. A. and Schmidt, P. 1977, “Formation and estimation of stochastic frontier production function models, ” Journal of Econometrics 6:21—37.

Avila, D.A. and R. Evenson. 2004. Total Factor Productivity Growth in Agriculture: The Role of Technological Capital. Economic Growth Center paper, Yale University, New Haven.

Boskin, M. And L. J. Lau..1992. “Capital, Technology, and Economic Growth,” in N. Rosenberg, R. Landau and D. Mowery (Ed.), Technology and the Wealth of Nations, Stanford University Press, Stanford, CA.

Christensen, Laurits R., Dale W. Jorgenson and Lawrence J. Lau, 1973, "Transcendental Logarithmic Production Frontiers," Review Economics and Statistics 55: 28-45.

Cororaton, C.B. and M.T. Caparas. 1999. Total Factor Productivity: Estimates for the Philippine Economy. Discussion Paper 99-06. Philippine Institute for Development Studies, Manila.

David, C.C. and R. Barker, 1979. “Agricultural Growth in the Philippines, 1948-1971,” in Y. Hayami, V.W. Ruttan and H.M. Southworth (eds.) Agricultural Growth in Japan, Taiwan, Korea, and the Philippines. University Press, Hawaii.

David, C.C., R. Barker, and A. Palacpac. 1985. “The Nature of Productivity Growth in Philippine Agriculture, 1948-1982.” Paper presented at the Symposium of Agricultural Productivity Measurement and Analysis, Asian Productivity Organization, Tokyo, Japan, October 1-8, 1985.

Denison, E., 1972. "Some Major Issues in Productivity Analysis: an Examination of the Estimates by Jorgenson and Griliches," Survey of Current Business, 49 (5, Part II): 1-27.

Evenson, R.E. and M.L. Sardido, 1986. “Regional Total Factor Productivity Change in Philippine Agriculture.” Journal of Philippine Development. 23(13): 40-61.

Food and Agriculture Organization (FAO). 2004. State of Food and Agriculture 2004. FAO, Rome.

Jorgenson, Dale W. and Zvi Griliches, 19767. "The Explanation of Productivity Change," Review of Economic Studies 34:349-83.

Hooley. R. 1985. Productivity and Growth in the Philippine Manufacturing: Retrospect and Future Prospects. PIDS Monograph Series No. 9. Makati City, Philippine Institute for Development Studies

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Lau, Lawrence J. & Yotopoulos, Pan A., 1989. "The meta-production function approach to technological change in world agriculture," Journal of Development Economics 31(2): 241-269.

Lawas, J. 1965. Output, Growth, Technical Change and Employment Resources in Philippine Agriculture: 1948-1975. Unpublished PhD Dissertation, Purdue University.

Kendrick, J., 1961. Productivity Trends in the United States. NationalBureau of Economic Research, New York.

Mundlak, Y., D. Larsen, R. Butzer, 1997. “The determinants of agricultural production: a cross-country analysis.” Policy Research Working Paper No. 1827. World Bank, Washington, D.C.

Paris, T. 1971. Outputs, Inputs, and Productivity of Philippine Agriculture. Unpublished Masteral Thesis. University of the Philippines.

Solow, R., 1957. "Technical Change and the Aggregate Production Function." Review of Economics and Statistics 39(3):312-320.

Patalinhug, E. 2001. A Review of the Components of the Medium Term National Action Agenda for Productivity (MNAAP) 2000-2004. PIDS Research Paper Series No. 2001-02, Makati City, Philippine Institute for Development Studies.

Teruel, R., and Y. Kuroda, 2005. “Public infrastructure and productivity growth in Philippine agriculture, 1974-2000.” Journal of Asian Economics 16: 555-576.

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