Philippines: Selected Issues

International Monetary Fund

doi:10.5089/9781451831375.002.A003

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Philippines: Selected Issues

Author: International Monetary Fund

Publication Date:: 22 May 2006

eISBN:: 9781451831375

Language:: English

Keywords:: ISCR; CR; Philippines; core inflation; RoPs; growth performance; Philippines dummy; East Asia economy; long-run growth record; economic policy variable; Total factor productivity; Public expenditure review; Capital accumulation; Foreign direct investment

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This Selected Issues paper on the Philippines reviews the diminished volatility of the Republic of Philippines bonds (RoPs), even through a period of extended turbulence cannot be attributed solely to the sanguine global factors currently prevailing across all credit markets. Considerable fiscal gains have already been achieved following the introduction of these reforms. The estimated magnitude of second-round effects for the Philippines is likely an upper bound, as greater competition, ongoing fiscal consolidation, and enhanced monetary policy credibility will likely reduce the scope for second-round effects over time.

Abstract

III. Explaining Economic Growth in the Philippines²¹

A. Introduction

1. To boost the performance of the economy, the Philippines has embarked on a comprehensive reform program since mid-2004. In the immediate aftermath of World War II, the Philippines had one of the highest per capita GDP in the East Asia region. However, the country’s growth performance has since been disappointing with per capita GDP slipping behind fast-growing East Asia economies (such as Korea, Malaysia, and Thailand) by 2003. The reform program, which is detailed in the Medium-Term Philippine Development Plan (MTPDP), covers a broad reform agenda including economic policy, infrastructure, and institutions. This chapter discusses the Philippines’ growth performance over the past few decades, and then examines the extent to which this can be explained using determinants suggested by recent growth theory.

B. Philippines’ Growth Performance

2. The Philippines has struggled to raise economic growth much above population growth over the past 30 years. With GDP growth lower and population growth higher, per capita GDP growth has lagged behind other developing economies, particularly fast-growing East Asia economies (Table 1). During the 1980s, the Philippines experienced a deep political and debt crisis which led to a large contraction in per capita GDP over the decade (Figure 1). In response, the authorities implemented major economic reforms, resulting in a more outward-oriented and liberalized economic system with less government intervention. In the 1990s, per capita growth rates picked up, but remain lower than other developing economies.²²

Table 1.

GDP and Population Growth, 1970-2003 ^1/

(Annual percent change)

Source: IMF, World Economic Outlook; and World Bank, World Development Indicators.

^{^1/}Regional data are averaged with purchasing power parity GDP weights.

^{^2/}Excludes China.

^{^3/}Includes China.

Table 1.

GDP and Population Growth, 1970-2003 ^1/

(Annual percent change)

			GDP	Population	GDP per capita
Philippines			3.6	2.5	1.1
Developing economies ^2/			4.3	2.1	2.2
	Selected East Asia		6.5	1.7	4.8
		Indonesia	5.8	1.9	3.9
		Korea	7.3	1.3	6.0
		Malaysia	6.7	2.5	4.1
		Thailand	6.3	1.7	4.5
Industrial economies			2.9	0.7	2.2
World ^3/			3.8	1.1	2.6

Source: IMF, World Economic Outlook; and World Bank, World Development Indicators.

^{^1/}Regional data are averaged with purchasing power parity GDP weights.

^{^2/}Excludes China.

^{^3/}Includes China.

Table 1.

GDP and Population Growth, 1970-2003 ^1/

(Annual percent change)

			GDP	Population	GDP per capita
Philippines			3.6	2.5	1.1
Developing economies ^2/			4.3	2.1	2.2
	Selected East Asia		6.5	1.7	4.8
		Indonesia	5.8	1.9	3.9
		Korea	7.3	1.3	6.0
		Malaysia	6.7	2.5	4.1
		Thailand	6.3	1.7	4.5
Industrial economies			2.9	0.7	2.2
World ^3/			3.8	1.1	2.6

Source: IMF, World Economic Outlook; and World Bank, World Development Indicators.

^{^1/}Regional data are averaged with purchasing power parity GDP weights.

^{^2/}Excludes China.

^{^3/}Includes China.

3. The Philippines’ growth pattern has been uneven (Figure 2). This is partly due to the country’s large vulnerability to external shocks, such as terms of trade shocks in the 1970s, global interest rate hikes in the early 1980s, power crises in 1992–93, as well as adverse political and natural shocks, including several coup attempts, the volcanic eruption of Mount Pinatubo (in 1991), and droughts caused by El Niño (particularly after 1998).

4. The sources of growth in the Philippines can be decomposed using standard growth accounting. The production process (Y) is assumed to be conventional Cobb-Douglas technology, which utilizes physical capital (K) and labor (L) as inputs, as well as total factor productivity (A):

Y_{t} = A_{t} K_{t}^{α} {(h_{t} L_{t})}^{1 - α}

where α represents the elasticity of output with respect to physical capital, t is the year, and h is the human capital stock per worker. Expressing all variables in per worker terms (denoted by small caps) and taking log derivatives with respect to time, yields (omitting time indices),

\frac{\overset{\cdot}{y}}{y} = \frac{\overset{\cdot}{A}}{A} + α \frac{\overset{\cdot}{k}}{k} + (1 - α) \frac{\overset{\cdot}{h}}{h}

where $\overset{\cdot}{x} / x$ represents growth of a variable x. Growth in output per worker can be decomposed into that due to changes in total factor productivity, physical capital per worker, and human capital per worker. Note that total factor productivity is measured as a residual, and a change in measured productivity might reflect not only technological innovation but also political and external shocks, changes in government policies and institutions, and a measurement error.²³

5. Growth accounting results suggest that the low growth in the Philippines is largely attributable to stagnant capital formation and low total factor productivity.²⁴ Physical capital in the Philippines grew at a slower pace than other developing economies in the 1980s and 1990s, while total factor productivity growth was negative or zero (Table 2). However, more recently, output per worker has increased, driven by a significant improvement in total factor productivity growth. Behind this improvement is a rise in productivity of the services sector, possibly reflecting the rapidly expanding telecommunications industry (Figure 3).

Table 2.

Growth Accounting ^1/

(Annual percent changes)

Sources: Bosworth and Collins (2003); and Fund staff calculations.

^{^1/}Regional averages are simple average.

^{^2/}Indonesia, Korea, Malaysia, and Thailand.

Table 2.

Growth Accounting ^1/

(Annual percent changes)

			Contributions to output per worker growth
				TFP
		Output per worker	Physical capital per worker	Human Capital	With human capital adjustment	Without human capital adjustment
		1980-89 average
Philippines		-0.5	0.3	0.8	-1.6	-0.9
Developing economies		0.3	0.5	1.6	-1.8	-0.2
	East Asia ^2/	3.9	2.4	1.8	-0.2	1.5
Industrial economies		1.4	0.6	0.6	0.3	0.8
All economies		0.6	0.5	1.3	-1.2	0.1
		1990-99 average
Philippines		0.4	0.3	0.7	-0.7	0.0
Developing economies		1.0	0.4	1.1	-0.5	0.6
	East Asia	3.4	2.6	1.0	-0.2	0.8
Industrial economies		1.5	0.6	0.5	0.5	0.9
All economies		1.1	0.5	0.9	-0.2	0.7
		2000-04 average
Philippines		1.7	0.4	0.6	0.7	1.3

Sources: Bosworth and Collins (2003); and Fund staff calculations.

^{^1/}Regional averages are simple average.

^{^2/}Indonesia, Korea, Malaysia, and Thailand.

Table 2.

Growth Accounting ^1/

(Annual percent changes)

			Contributions to output per worker growth
				TFP
		Output per worker	Physical capital per worker	Human Capital	With human capital adjustment	Without human capital adjustment
		1980-89 average
Philippines		-0.5	0.3	0.8	-1.6	-0.9
Developing economies		0.3	0.5	1.6	-1.8	-0.2
	East Asia ^2/	3.9	2.4	1.8	-0.2	1.5
Industrial economies		1.4	0.6	0.6	0.3	0.8
All economies		0.6	0.5	1.3	-1.2	0.1
		1990-99 average
Philippines		0.4	0.3	0.7	-0.7	0.0
Developing economies		1.0	0.4	1.1	-0.5	0.6
	East Asia	3.4	2.6	1.0	-0.2	0.8
Industrial economies		1.5	0.6	0.5	0.5	0.9
All economies		1.1	0.5	0.9	-0.2	0.7
		2000-04 average
Philippines		1.7	0.4	0.6	0.7	1.3

Sources: Bosworth and Collins (2003); and Fund staff calculations.

^{^1/}Regional averages are simple average.

^{^2/}Indonesia, Korea, Malaysia, and Thailand.

C. Explaining Growth in the Philippines

6. Philippines’ growth performance can be analyzed in terms of potential growth determinants. Much of the recent growth literature (see Rodrik, Subramanian, and Trebbi, 2002 and Rodrik, 2003) emphasizes the role of geography, economic policies, and institutions as determinants of growth.²⁵ How the Philippines fares compared to other regional economies is examined in terms of each of these factors:

Geography

7. Geography sets a country’s advantages and disadvantages due to its physical location. Bloom, Sachs, Collier, and Udry (1998), Gallup, Sachs, and Mellinger (1999), and Sachs (2001) argued that geography influences growth through various channels. For example, geography would shape a large part of natural resource endowments, soil quality, and climate, which determine availability of marketable natural resources (such as oil), land productivity, and the public health environment. Geography is also an important determinant of trade and inward foreign direct investment from advanced economies, since a distant or landlocked country faces greater costs of transport.

8. For the Philippines, geography does not appear to be a disadvantage. Table 3 compares the Philippines with other economies in terms of major geographical indicators selected from Gallup et al (1999).²⁶ The Philippines is in an advantageous position in transport with all population having access to the sea, high population density in coastal regions, and shorter distance to world markets. However, the Philippines has a tropical climate and a relatively high malaria index.

Table 3.

Geography

Source: Gallup, Sachs, and Mellinger (1999).

^{^1/}The simple average of Indonesia, Korea, Malaysia, and Thailand.

Table 3.

Geography

		Philippines	East Asia ^1/	All developing economies
Indicators related to integration
	Proportion of the region’s population within 100 km of the coastline or ocean-navigable river	1.0	0.8	0.5
	Density of human settlement (population per km2) in the coastal region (within 100 km of the coastline)	230.3	215.0	144.6
	Density of human settlement (population per km2) in the interior (beyond 100 km from the coastline)	56.0	78.6	69.3
	Landlocked (Yes=1, No=0)	0.0	0.0	0.2
	Average distance by air of the economies within the region to the closest “core” capital-goods-supplying regions, such as the U.S., Western Europe, and Japan (in km)	3,010.0	4,237.5	5,050.0
Indicators related to natural resources and climate
	Proportion of land area within the geographical tropics	1.0	0.8	0.7
	Index of Malaria prevalence (from 0, low, to 1, high)	0.6	0.3	0.4

Source: Gallup, Sachs, and Mellinger (1999).

^{^1/}The simple average of Indonesia, Korea, Malaysia, and Thailand.

Table 3.

Geography

		Philippines	East Asia ^1/	All developing economies
Indicators related to integration
	Proportion of the region’s population within 100 km of the coastline or ocean-navigable river	1.0	0.8	0.5
	Density of human settlement (population per km2) in the coastal region (within 100 km of the coastline)	230.3	215.0	144.6
	Density of human settlement (population per km2) in the interior (beyond 100 km from the coastline)	56.0	78.6	69.3
	Landlocked (Yes=1, No=0)	0.0	0.0	0.2
	Average distance by air of the economies within the region to the closest “core” capital-goods-supplying regions, such as the U.S., Western Europe, and Japan (in km)	3,010.0	4,237.5	5,050.0
Indicators related to natural resources and climate
	Proportion of land area within the geographical tropics	1.0	0.8	0.7
	Index of Malaria prevalence (from 0, low, to 1, high)	0.6	0.3	0.4

Source: Gallup, Sachs, and Mellinger (1999).

^{^1/}The simple average of Indonesia, Korea, Malaysia, and Thailand.

Economic policies

9. Positive correlations between policy reforms such as increasing trade openness and growth have been widely documented, as have the negative links between high inflation and growth. The Philippines has undertaken increasingly outward-oriented and market-based policies since the mid-1980s, including trade liberalization, privatization of government assets, strengthening of central bank independence, opening of sectors to foreign direct investment, and liberalization of domestic shipping, oil, and telecommunications. Kongsamut and Vamvakidis (2000) find that these policy shifts contributed to better economic performance in the Philippines in the 1990s.

10. These policies have been maintained to varying degrees in recent years (Figure 4). Since the 1990s, average inflation in the Philippines has declined, while the Philippines has also experienced a substantial increase in trade openness. However, the size of the fiscal deficit and external debt (in percent of GDP) in the Philippines has increased, much more so than in other East Asia economies. The size of government has also increased, while the total investment share to GDP has declined to the lowest in the sample period. After a significant improvement in the 1990s, net foreign direct investment (FDI) inflows have declined in recent years, as has privatization activity, although it should be noted that this has also declined in other East Asia economies. Public infrastructure quality in the Philippines has improved over the last decade, but remains below the East Asia average.

Institutions

11. The recent growth literature emphasizes the role that institutions play. In general, “good” institutions are considered to contribute to growth through two channels.²⁷ First, the quality of institutions affects the investment climate, and hence long-run growth (Hall and Jones, 1999; Acemoglu, Johnson, and Robinson, 2000). Second, good institutions strengthen the government’s ability to adjust policies to exogenous shocks (Rodrik, 1999).

12. Drawing from the recent literature, this chapter focuses on the following three measures of institutions: first, the quality of governance, including government effectiveness, regulatory quality, and rule of law; second, the effectiveness of the legal system and property rights; and third, political stability (see Annex 2 for details of the data).

The quality of governance matters for overall economic development as it determines government effectiveness and efficiency in utilizing or allocating public resources.²⁸ According to governance indicators, produced by Kaufmann, Kraay, and Masutruzzi (2005), the Philippines ranks poorly compared to the average developing and East Asia economies on all indicators but “voice and accountability” (Figure 5). In particular, “control of corruption,” and “rule of law” indicators are ranked much lower in the Philippines than in the other economies.
Legal system and property rights affect the incentives to invest and innovate as investors are concerned about the protection of their investment returns.²⁹ Legal system and property rights indicators, produced by the Cato Institute (2004), show that the Philippines fares worse than other economies in most components of the indicators (Figure 6).
Political stability is key to assure investors of the continuity of economic policies. According to “political risk index” provided by International Country Risk Guide (ICRG), the Philippines scores worse than Malaysia and Thailand, but better than Indonesia and the average for developing economies (Figure 7). Similarly, Gurr, Jaggers, and Marshall’s indicator on “constraints on executives” shows that the Philippines ranks slightly above the average developing economy.³⁰

D. Growth Regressions

13. Standard cross-country regression analysis is employed to examine the relative importance and validity of these explanatory factors. The following two questions are considered: first, how important are geographic factors in explaining the Philippines’ long-run growth record; and second, how much of the Philippines’ growth performance (total factor productivity and capital accumulation growth) can be explained by other factors, such as economic policy and institutions.

14. The first exercise investigates the importance of geography on growth, using cross-section regressions. In a sample of 79 advanced and developing economies, the model regresses the log level of per capita GDP in 2003 or the average annual growth rate of per capita GDP over the period 1965–2003 on geography variables. A Philippine dummy variable is included in each regression to examine whether the Philippines is unusual compared to the average across sample economies.

15. The estimation results confirm the close relationship between geography and economic development. As reported in Table A1, the level of per capita GDP or growth rates of per capita GDP is a positive function of the population density in coastal regions (Population 100km), and a negative function of tropical regions (Tropical area), transport costs (LDistance), and malaria prevalence (Malaria index in 1966 or 1994). The Philippines’ dummy is not significant, suggesting that the Philippines’ growth performance is just what one would expect for a country with the Philippines’ geographical conditions. This result contrasts with other Asian economies where both predicted levels and growth rates of per capita GDP are lower than actual levels, implying that those economies perform better than they would given their geography (Figure 8 and 9). More formally, in the per capita GDP growth regression, Korea, Malaysia, or Thailand dummies are found to be highly significant.³¹

16. The second exercise looks at correlations of growth with economic policy and institution variables. A sample of 81 advanced and developing economies is used to examine which explanatory variables are most strongly correlated with GDP per worker, physical capital per worker, or total factor productivity. Observations are averaged for three 10-year periods over 1970–99, thereby excluding cyclical components of GDP. The model is estimated by ordinary least squares (OLS) with a Philippines dummy, as well as feasible generalized least squares with random effects.³², ³³

17. The final results, reported in Table A2, suggest that:

Consistent with the results of the previous cross-section regressions, geography variables, particularly Tropical area, are significantly correlated with GDP per worker growth (model 1), although the significance of these variables reduces or disappears if economic policy and institution variables are added (models 2–8).
Most of the economic policy variables have the expected sign and are significant in the GDP per worker equations, although government fiscal balance becomes insignificant if institution variables are added (models 3 and 4). Economic policy variables appear to affect total factor productivity (models 5 and 6) more significantly than capital accumulation (models 7 and 8).
The institution variable is highly correlated with GDP per worker growth (models 3 and 4). It is also noteworthy that the variable appears to be more significant in capital accumulation equations (models 7 and 8) than in total factor productivity equations (models 5 and 6), suggesting that institutions are of particular importance for capital accumulation.
Philippine dummy variables are insignificant, implying that the Philippines’ growth performance is just the average of the sample economies conditional on the set of explanatory variables (Figure 10).
Growth performance in Korea, Malaysia, and Thailand can also be well explained by these regressions: none of the dummy variables for these economies are significant. This appears to suggest that over-performance of East Asia economies, which were not explained by the previous cross-section geography regressions, can be attributed to their better economic policy and institutions.

E. Conclusions

18. The main finding in this chapter is that the Philippines’ growth performance is well explained by geography, economic policies, and institutions. The empirical results support the authorities’ reform priorities in MTPDP. In particular, more sustainable fiscal policy, an improvement in infrastructure quality as well as institutions—that is to say a better investment climate—could significantly raise the Philippines’ growth potential.

19. The regression results permit a rough simulation of the benefits of reform. Although the regression results represent simple correlations (not causality), the predicted increase in growth on account of an improvement in explanatory factors can be calculated as an illustration (Table 4). For example, an improvement in “law and order” by about one rating (from 3.3 to 4.5, the average of Malaysia and Thailand) would be associated with a 0.3 percentage points increase in long-run growth of GDP per worker. These results underscore the likely importance of sound economic policy and better institutions for the long run economic development of the Philippines.

Table 4.

What are Predicted Increases in Growth Rates? ^1/

(Percentage points change)

^{^1/}Based on estimated parameters in Table A2 (4), (6), and (8).

^{^2/}Contributions to GDP per worker growth.

^{^3/}Korea, Indonesia, Malaysia, and Thailand.

Table 4.

What are Predicted Increases in Growth Rates? ^1/

(Percentage points change)

	GDP per worker growth	Of which: ^2/
	GDP per worker growth	TFP growth	Capital per worker
Improvement in “law and order” by one rating	0.3	0.2	0.1
Improvement in infrastructure to the level of the East Asia average ^3/	0.8	0.5	0.3
Reduction of external debts by 10 percentage points of GDP	0.4	0.2	0.2

^{^1/}Based on estimated parameters in Table A2 (4), (6), and (8).

^{^2/}Contributions to GDP per worker growth.

^{^3/}Korea, Indonesia, Malaysia, and Thailand.

Table 4.

What are Predicted Increases in Growth Rates? ^1/

(Percentage points change)

	GDP per worker growth	Of which: ^2/
	GDP per worker growth	TFP growth	Capital per worker
Improvement in “law and order” by one rating	0.3	0.2	0.1
Improvement in infrastructure to the level of the East Asia average ^3/	0.8	0.5	0.3
Reduction of external debts by 10 percentage points of GDP	0.4	0.2	0.2

^{^1/}Based on estimated parameters in Table A2 (4), (6), and (8).

^{^2/}Contributions to GDP per worker growth.

^{^3/}Korea, Indonesia, Malaysia, and Thailand.

ANNEX I The Regression Exercise

The first regression exercise: cross section regressions on growth and geography.

The following cross section equation is estimated by ordinary least squares (OLS) using the data of a sample of 79 advanced and developing economies.

$Y_{t} = μ_{t} + β [{intial per capita GDP]}_{t} + γ [geography var {iables]}_{t} + φ [Philippines {dummy]}_{t} + ε_{t},$ where i denotes an economy and $ε_{t} \sim I I D (o, σ_{i}^{2}) .$

The dependent variables, Y_i, are the logarithm of per capita GDP in U.S. dollars in 2003 or the average annual growth rates of per capita GDP over the period 1965–2003. Initial levels of per capita GDP are included for the per capita growth regression to capture catching-up effects. A set of geography variables are due to Gallup, et al. (1999) (see Annex 2 for details of data). A Philippine dummy variable (for the Philippines, value 1, otherwise, 0) is included to examine if the Philippines is unusual compared to the average across sample economies. The estimation results are:

Table A1.

Cross Section Regression ^1/

Source: Fund staff estimates.

^{^1/}T-statistics in parenthesis (***, **, and * indicate significant at 1, 5 and 10 percent, respectively).

Table A1.

Cross Section Regression ^1/

Dependent variable	Per capita GDP in 2003		Per capita GDP growth over 1965-2003
Dependent variable	(1)		(2)		(3)
Constant	5.63		4.25		5.27
	(14.73)	***	(2.28)	**	(2.80)	***
Per capita GDP in 1965	…		-0.52		-0.54
			(-1.49)		(-1.58)
Tropical area	-0.40		-1.11		…
	(-3.08)	***	(-2.66)	***
Population 100 km	0.37		1.41		0.87
	(2.68)	***	(-3.38)	***	(2.00)	**
LDistance	-0.57		-0.39		-0.64
	(-4.97)	***	(-0.97)		(-1.73)	*
Malaria index 1994	-0.70		…		…
	(-4.65)	***
Malaria index 1966	…		…		-1.37
					(-2.98)	***
Philippine dummy	-0.22		-0.80		-0.80
	(-0.57)		(-0.61)		(-0.62)
No. of observations	79		79		79
Adjusted R-squared	0.77		0.21		0.23

Source: Fund staff estimates.

^{^1/}T-statistics in parenthesis (***, **, and * indicate significant at 1, 5 and 10 percent, respectively).

Table A1.

Cross Section Regression ^1/

Dependent variable	Per capita GDP in 2003		Per capita GDP growth over 1965-2003
Dependent variable	(1)		(2)		(3)
Constant	5.63		4.25		5.27
	(14.73)	***	(2.28)	**	(2.80)	***
Per capita GDP in 1965	…		-0.52		-0.54
			(-1.49)		(-1.58)
Tropical area	-0.40		-1.11		…
	(-3.08)	***	(-2.66)	***
Population 100 km	0.37		1.41		0.87
	(2.68)	***	(-3.38)	***	(2.00)	**
LDistance	-0.57		-0.39		-0.64
	(-4.97)	***	(-0.97)		(-1.73)	*
Malaria index 1994	-0.70		…		…
	(-4.65)	***
Malaria index 1966	…		…		-1.37
					(-2.98)	***
Philippine dummy	-0.22		-0.80		-0.80
	(-0.57)		(-0.61)		(-0.62)
No. of observations	79		79		79
Adjusted R-squared	0.77		0.21		0.23

Source: Fund staff estimates.

^{^1/}T-statistics in parenthesis (***, **, and * indicate significant at 1, 5 and 10 percent, respectively).

The second regression exercise: panel regressions on growth and economic policies and institutions.

The following panel equation is estimated by OLS or random effects model to take account of country-specific random factors. In a sample of 81 advanced and developing economies, the data are averaged for three 10-year periods over 1970–99.

Y_{i t} = μ_{t} + β [e x o g e n o u s var i a b l e s]_{i t} + δ [e c o n o m i c x p o l i c y var {i a b l e s]}_{i t} + γ {[i n s t i t u t i o n var i a b l e s]}_{u} + α_{t} + ε_{i t},

where i and t denote an economy and time, respectively; $α_{t} \sim I I D (o . σ_{α}^{2})$ and $ε_{i t} \sim I I D (o . σ_{ε}^{2})$

Dependent variables, $Y_{i t}$ , are the average growth rates of GDP per worker, total factor productivity, or physical capital per worker. Explanatory variables can be categorized into three: exogenous variables, such as Tropical area, Population 100km (both measures of geography), initial per capita GDP, and terms of trade volatility (a measure of external shock); economic policy variables such as inflation, government balance, openness, external debt, and infrastructure; and institution variables such as ICRG law and order, political risk, and Economic Freedom’s legal system and property rights (see Annex 2 for details of data).³⁴ We start with a broad range of potential explanatory variables and drop those that prove clearly insignificant. A Philippine dummy variable (for the Philippines, value 1, otherwise, 0) is included in the OLS models to examine if the Philippines is unusual compared to the average across sample economies. The estimation results are:

Table A2.

Panel Regression ^1/

Source: Fund staff estimates

^{^1/}The data set comprises 10-year average of each variable. T-statistics are in parenthesis (***, **, and * indicate significant at 1, 5 and 10 percent, respectively).

^{^2/}Without human capital adjustments. The main thrust of the results hold even for TFP growth with human capital adjustments.

Table A2.

Panel Regression ^1/

Dependent variable	GDP per worker growth								TFP growth ^2/				Capital per worker growth
	OLS		OLS		OLS		Random		OLS		Random		OLS		Random
(1)		(2)		(3)		(4)		(5)		(6)		(7)		(8)
Constant	5.40		7.19		7.06		7.01		4.06		4.03		8.59		8.51
	(6.36)	***	(8.86)	***	(8.77)	***	(8.57)	***	(5.99)		(6.09)		(7.36)	***	(6.95)
Initial per capita GDP ^2/	-1.14		-1.87		-2.15		-2.22		-1.23		-1.23		-2.63		-2.63
	(-4.68)	***	(-7.94)	***	(-8.23)	***	(-8.48)	***	(-5.60)	***	(-5.71)	***	(-6.94)	***	(-6.84)	***
Tropical area	-2.09		-1.06		-0.89		-0.97		-0.67		-0.68		-0.63		-0.88
	(-6.16)	***	(-3.35)	***	(-2.77)	***	(-2.85)	***	(-2.47)	**	(-2.58)	***	(-1.35)		(-1.61)
Population 100km	1.03		0.07		0.15		0.19		-0.23		-0.26		1.09		1.28
	(2.76)	***	(0.21)		(0.48)		(0.56)		(-0.84)		(-0.99)		(2.36)	**	(2.27)	**
Terms of trade	…		-0.03		-0.03		-0.02		-0.03		-0.03		0.0004		0.013
			(-2.99)	***	(-2.19)	**	(-1.57)		(-2.61)	***	(-2.61)	***	(0.02)		(0.72)
Inflation			-0.001		-0.001		-0.001		-0.001		-0.001		-0.001		-0.001
			(-1.99)	**	(-1.85)	*	(-1.71)	*	(-1.66)	*	(-1.68)	*	(-0.88)		(-0.84)
Government balance	…		0.06		0.04		0.04		0.04		0.04		0.003		-0.02
			(2.43)	**	(1.68)		(1.62)		(1.95)	*	(1.93)	*	(0.09)		(-0.45)
Openness	…		0.01		0.01		0.006		0.004		0.004		0.004		0.003
			(4.30)	***	(3.82)	***	(3.68)	***	(3.35)	***	(3.48)	***	(1.97)	**	(1.38)
External debt	…		-0.37		-0.39		-0.37		-0.16		-0.16		-0.66		-0.61
			(-6.03)	***	(-6.39)	***	(-6.15)	***	(-3.10)	***	(-3.20)	***	(-7.46)	***	(-7.15)	***
Infrastructure	…		3.82		3.70		3.69		2.26		2.23		4.09		3.69
			(4.05)	***	(3.95)	***	(3.87)	***	(2.88)	***	(2.91)	***	(3.02)	***	(2.60)	***
ICRG law and order	…		…		0.24		0.29		0.14		0.15		0.29		0.30
					(2.37)	**	(2.76)	***	(1.61)		(1.75)	*	(1.99)	**	(1.86)	*
Philippine dummy	-0.83		-1.22		-1.00		…		-0.67		…		-0.94		…
	(-0.70)		(-1.26)		(-1.03)				(-0.82)				(-0.67)
No. of observations	243		243		243		243		243		243		243		243
Adjusted R-squared	0.14		0.42		0.43		0.42		0.32		0.28		0.35		0.34
Sample periods	1970-1999

Source: Fund staff estimates

^{^1/}The data set comprises 10-year average of each variable. T-statistics are in parenthesis (***, **, and * indicate significant at 1, 5 and 10 percent, respectively).

^{^2/}Without human capital adjustments. The main thrust of the results hold even for TFP growth with human capital adjustments.

Table A2.

Panel Regression ^1/

Dependent variable	GDP per worker growth								TFP growth ^2/				Capital per worker growth
	OLS		OLS		OLS		Random		OLS		Random		OLS		Random
(1)		(2)		(3)		(4)		(5)		(6)		(7)		(8)
Constant	5.40		7.19		7.06		7.01		4.06		4.03		8.59		8.51
	(6.36)	***	(8.86)	***	(8.77)	***	(8.57)	***	(5.99)		(6.09)		(7.36)	***	(6.95)
Initial per capita GDP ^2/	-1.14		-1.87		-2.15		-2.22		-1.23		-1.23		-2.63		-2.63
	(-4.68)	***	(-7.94)	***	(-8.23)	***	(-8.48)	***	(-5.60)	***	(-5.71)	***	(-6.94)	***	(-6.84)	***
Tropical area	-2.09		-1.06		-0.89		-0.97		-0.67		-0.68		-0.63		-0.88
	(-6.16)	***	(-3.35)	***	(-2.77)	***	(-2.85)	***	(-2.47)	**	(-2.58)	***	(-1.35)		(-1.61)
Population 100km	1.03		0.07		0.15		0.19		-0.23		-0.26		1.09		1.28
	(2.76)	***	(0.21)		(0.48)		(0.56)		(-0.84)		(-0.99)		(2.36)	**	(2.27)	**
Terms of trade	…		-0.03		-0.03		-0.02		-0.03		-0.03		0.0004		0.013
			(-2.99)	***	(-2.19)	**	(-1.57)		(-2.61)	***	(-2.61)	***	(0.02)		(0.72)
Inflation			-0.001		-0.001		-0.001		-0.001		-0.001		-0.001		-0.001
			(-1.99)	**	(-1.85)	*	(-1.71)	*	(-1.66)	*	(-1.68)	*	(-0.88)		(-0.84)
Government balance	…		0.06		0.04		0.04		0.04		0.04		0.003		-0.02
			(2.43)	**	(1.68)		(1.62)		(1.95)	*	(1.93)	*	(0.09)		(-0.45)
Openness	…		0.01		0.01		0.006		0.004		0.004		0.004		0.003
			(4.30)	***	(3.82)	***	(3.68)	***	(3.35)	***	(3.48)	***	(1.97)	**	(1.38)
External debt	…		-0.37		-0.39		-0.37		-0.16		-0.16		-0.66		-0.61
			(-6.03)	***	(-6.39)	***	(-6.15)	***	(-3.10)	***	(-3.20)	***	(-7.46)	***	(-7.15)	***
Infrastructure	…		3.82		3.70		3.69		2.26		2.23		4.09		3.69
			(4.05)	***	(3.95)	***	(3.87)	***	(2.88)	***	(2.91)	***	(3.02)	***	(2.60)	***
ICRG law and order	…		…		0.24		0.29		0.14		0.15		0.29		0.30
					(2.37)	**	(2.76)	***	(1.61)		(1.75)	*	(1.99)	**	(1.86)	*
Philippine dummy	-0.83		-1.22		-1.00		…		-0.67		…		-0.94		…
	(-0.70)		(-1.26)		(-1.03)				(-0.82)				(-0.67)
No. of observations	243		243		243		243		243		243		243		243
Adjusted R-squared	0.14		0.42		0.43		0.42		0.32		0.28		0.35		0.34
Sample periods	1970-1999

Source: Fund staff estimates

^{^1/}The data set comprises 10-year average of each variable. T-statistics are in parenthesis (***, **, and * indicate significant at 1, 5 and 10 percent, respectively).

^{^2/}Without human capital adjustments. The main thrust of the results hold even for TFP growth with human capital adjustments.

ANNEX II Data used for the Regression Exercise

Per capita GDP in 1965 (or 2003): The logarithm of per capita GDP in U.S. dollars in 1965 (or 2003). Sources: International Monetary Fund, World Economic Outlook (WEO); and World Bank, World Development Indicators (WDI).

Per capita GDP growth over 1965–2003: The average annual growth rate (percent) of GDP per capita over the period 1965–2003. Sources: WEO; and WDI.

Tropical area: The proportion of the country’s land area within the geographical tropics. Data are calculated by Gallup, et al. (1999) using ArcWorld Supplement database. See http://www.cid.harvard.edu/ciddata/ciddata.html.

Population 100 km: The proportion of the population in 1994 within 100 km of the coastline. Data are calculated by Gallup, et al. (1999) using the GIS population dataset. See http://www.cid.harvard.edu/ciddata/ciddata.html.

LDistance: The logarithm of the minimum Great-Circle (air) distance in kilometers to one of the three capital-goods-supplying regions: the U.S., Western Europe, and Japan, specifically measured as distance from the country’s capital city to New York, Rotterdam, or Tokyo. Data are calculated by Gallup, et al. (1999). See http://www.cid.harvard.edu/ciddata/ciddata.html.

Malaria index in 1966 (or 1994): Index of malaria prevalence based on a global map of extent of malaria in 1966 and the fraction of falciparum malaria in 1966 (or 1994). Data are calculated by Gallup, et al. (1999) using World Health Organization database. The variable has a scale from 0 to 1, with a higher score indicating greater malaria prevalence. See http://www.cid.harvard.edu/ciddata/ciddata.html.

Data used mainly in second (panel) regression exercise

GDP per worker: Real GDP per worker in local currencies. For non-Philippine economies, calculated by Bosworth and Collins (2003) using WDI and OECD Statistical Compendium. For the Philippines, calculated by staff using WDI, CEIC, and International Labor Organization, Labor Statistics Database.

Human capital quality (used for the growth accounts): Average years of education of population 15 and older. Data are calculated by Bosworth and Collins using Barro and Lee (2000) and Cohen and Soto (2001).

Capital stock (used for the growth accounts): Estimated using a perpetual inventory model, where the depreciation rate equals 0.05. For non-Philippine economies, calculated by Bosworth and Collins, using Nehru and Dhareshwar (1993). For the Philippines, calculated by staff using the Philippine National Accounts.

Initial per capita GDP: Logarithms of GDP per capita in constant U.S. dollars in 1970, 1980, and 1990. Source: WDI.

Terms of Trade (volatility): Standard deviations of annual growth rate of terms of trade. Source: WDI.

Inflation: Average annual inflation of CPI. Sources: International Monetary Fund, International Financial Statistics and WEO; and WDI.

Government balance: Government fiscal balance as a percent of GDP. Government is either national or general government depending on the data availability. Source: WEO.

Openness: The sum of exports of goods and services and imports of goods and services divided by GDP. Source: WDI.

External debt: Data in Figure 4 are gross external debt. Data used in the panel regressions are net external debt, transformed to an index raging from 1 (low) to 8 (high), with “1” indicating net external debt GDP ratio below 40 percent and “8” above 100 percent. Sources: WDI and Lane and Milesi-Ferretti (forthcoming).

Infrastructure: Simple average of fixed line and mobile phone subscribers (per 100 people), paved roads (percent of total roads), irrigated land (percent of cropland), 100 minus electric power transmission and distribution losses (percent of output), and improved sanitation facilities (percent of population with access). Source: WDI.

ICRG law and order: A measure of the strength and impartiality of the legal system and the popular observance of the law. The variable has a scale from zero to six, with a higher score indicating better legal system and rule of law. Source: International Country Risk Guidance (ICRG). See http://www.icrgonline.com/.

ICRG political risk: A measure of assessing the political stability of a country by assessing risk factors including government stability, socioeconomic conditions, investment profile, internal conflict, external conflict, corruption, military in politics, religious tensions, law and order, ethnic tensions, democratic accountability, and bureaucracy quality. The variable has a scale from 0 to 100, with a higher score indicating lower risk. Source: ICRG. See http://www.icrgonline.com/.

Other data used in tables and figures.

Kaufmann, Kraay, and Masutruzzi (2005)’s governance indicators:

Control of corruption: Absence of use of public power for private gain or corruption.
Rule of law: the protection of persons and property against violence or theft, independent and effective judges, contract enforcement.
Regulatory burden: the relative absence of government controls on goods markets, banking system, and international trade.
Government effectiveness: the quality of public service delivery and competence of the civil service, including the degree of its politicization.
Voice and accountability: the extent to which citizens can choose their government, political rights, civil liberties, and independent press.

For more details, see http://www.worldbank.org/wbi/governance/govdata/.

The Cato Institute (2004)’s economic freedom indicators:

Law and order: Data are from ICRG.
Military in politics: Military interference in rule of law and the political process. Data are from ICRG.
Protection of intellectual property: Data are from World Economic Forum, Global Competitiveness Reports (GCR).
Impartial courts: A trusted legal framework exists for private businesses to challenge the legality of government actions or regulation. Data are from GCR.
Judicial independence: the judiciary is independent and not subject to interference by the government or parties in disputes. Data are from GCR.

For the regression analysis, a composite index of these variables is used. For more details, see http://www.cato.org/pubs/efw/.

Gurr, Jaggers, and Marshall’s Polity IV Project database

Constraints on executives: Operational (de facto) independence of chief executive of a state. The variable has a scale from 1 to 7, with a higher score indicating more constraints.

For more details, see http://www.cidcm.umd.edu/inscr/polity/.

References

Acemoglu, Daron, Simon Johnson, and James A. Robinson, 2001, “The Colonial Origins of Comparative Development: An Empirical Investigation.” American Economic Review, Vol. 91.
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Bloom, David E., Jeffrey D. Sachs , Paul Collier, and Christopher Udry, 1998, “Geography, Demography, and Economic Growth in Africa,” Brookings Papers on Economic Activity, Vol. 1998, No. 2.
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Bosworth, Barry P. and Susan M. Collins, 2003, “The Empirics of Growth: An Update,” Brookings Papers on Economic Activity, Vol. 2003, No. 2.
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Cohen, Daniel, and Marcelo Soto, 2001, “Growth and Human Capital: Good Data, Good Results,” OECD Technical Paper 179, September.
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Gwartney, James, Robert Lawson, and Erik Gartzke, 2004, “Economic Freedom of the World: 2005 Annual Report,” The Cato Institute.
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Kaufmann, Daniel, Aart Kraay, and Massimo Mastruzzi (2005), “Governance Matters IV: Governance Indicators for 1996–2004,” World Bank Policy Research Working Paper Series No. 3630, May.
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Kongsamut, Piyabha, and Athanasios Vamvakidis, 2003, “Philippines: Toward Sustainable and Rapid Growth—Recent Developments and the Agenda Ahead,” International Monetary fund, Occasional Paper 187.
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Nehru, Vikram, and Ashok Dhareshwar, 1993, “A New Database on Physical Capital Stock: Sources, Methodology, and Results,” Revista de Anălisis Econŏomico 8(1): 37–59.
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Prepared by Kotaro Ishi (kishi@imf.org).

For a comprehensive discussion about the Philippines’ growth performance, see Balisacan and Hill (2003).

Other shortcomings of growth accounting include the need to make a fairly arbitrary assumption about the production function form. There is an extensive discussion on growth accounting issues in Bosworth and Collins (2003).

Except for the Philippines, data are due to Bosworth and Collins (2003). The Philippine data are staff calculations.

The demographic profile of the Philippines may also have had an effect on growth performance. However, the empirical support for such an effect is inconclusive (see Kongsamut and Vamvakidis, 2003) and demographic variables are not included in this study.

Gallup, et al (1999) concluded that (i) tropical regions are hindered in development relative to temperate regions; (ii) coastal regions, and regions linked to coasts by ocean-navigable waterways, are strongly favored in development relative to the hinterlands; (iii) landlocked economies may be particularly disadvantaged by their lack of access to the sea; (iv) high population density in coastal regions would be favorable for growth, as evidenced by the fact that high growth in developing economies has often been achieved through labor intensive manufacturing exports that require good access to internal and international trade; (v) greater transport costs (measured in distance from core capital-goods-supplying regions, such as the U.S., Western Europe, and Japan) and the prevalence of infectious diseases are negatively correlated with growth.

North (1990), who is widely cited in the literature, defined institutions as the set of formal rules and informal conventions that provide the framework for human interaction and shape the incentives of members of society. See International Monetary Fund, World Economic Outlook (2003 and 2005) for more discussion about the role of institutions.

See Kaufmann, Kraay, and Zoido-Lobaton (1999), Philippine Senate Economic Planning Office (2004), and World Bank (2005).

See Accemoglu, Johnson, and Robinson (2001) and Dollar and Kraay (2002).

A society in which elites and politicians are effectively constrained is expected to experience less infighting between various groups to take control of the state, and to pursue more sustainable policies.

T statistics for Korea, Malaysia, and Thailand are 3.17 (p value, 0.01), 2.50 (0.03), 3.62 (0.00), respectively. In the GDP per capita level regression, dummy variables are not found to be significant.

As extensively discussed in other studies, economic policy and institution variables may be considered endogenous. However, it is not easy to find effective instruments for each endogenous variable (see for example, Bosworth and Collins, 2003). Hence, regression results without controlling endogeneity problem are presented, which are interpreted to show correlation and not causality.

Random effects model is chosen, rather than fixed effects model, because the fixed effects model discards time-invariant country specific factors, which in the current case, are geography variables. The Hausman tests are employed to test the validity of the random effects, which proves inconclusive, because the result differs across models.

These institutions variables are chosen due to availability of time series data. However, even these variables lack the first period 1970–79, for which the same data for the second period 1980–89 are used. Furthermore, due to high correlations, only one institution variable was included in each regression. Although not reported, other institution variables, such as “Economic Freedom’s legal system and property rights” and “ICRG political risk” were found to be highly significant.

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Philippines: Selected Issues

Author: International Monetary Fund

Volume/Issue:: Volume 2006: Issue 181

Publisher:: International Monetary Fund

ISBN:: 9781451831375

ISSN:: 1934-7685

Pages:: 54

DOI:: https://doi.org/10.5089/9781451831375.002

Keywords
III. Explaining Economic Growth in the Philippines21
References

View raw image

Real Per Capita GDP Growth

(Average annual growth; in percent) 1/

View raw image

Philippines: GDP per capita

(Constant 2000 U.S. dollars)

View raw image

Philippines: Labor Productivity (GDP/Employment) Growth

(5-year backward average; contributions; in percent)

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Economic Policy Indicators 1/

(Period average)

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Economic Policy Indicators 1/

(Period average)

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Governance Indicators 1/

(2000-04 average)

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Legal System and Property rights Indicators (2003) 1/

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Political Risk and Constraints on Executives 1/

View raw image

Per Capita GDP Level: Actual vs. Prediction 1/

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Per Capita GDP Growth: Actual vs. Prediction 1/

View raw image

GDP per Worker Growth: Actual vs. Prediction

(1990-99 average) 1/

Table A2.

Panel Regression ^1/

Dependent variable	GDP per worker growth								TFP growth ^2/				Capital per worker growth
	OLS		OLS		OLS		Random		OLS		Random		OLS		Random
(1)		(2)		(3)		(4)		(5)		(6)		(7)		(8)
Constant	5.40		7.19		7.06		7.01		4.06		4.03		8.59		8.51
	(6.36)	***	(8.86)	***	(8.77)	***	(8.57)	***	(5.99)		(6.09)		(7.36)	***	(6.95)
Initial per capita GDP ^2/	-1.14		-1.87		-2.15		-2.22		-1.23		-1.23		-2.63		-2.63
	(-4.68)	***	(-7.94)	***	(-8.23)	***	(-8.48)	***	(-5.60)	***	(-5.71)	***	(-6.94)	***	(-6.84)	***
Tropical area	-2.09		-1.06		-0.89		-0.97		-0.67		-0.68		-0.63		-0.88
	(-6.16)	***	(-3.35)	***	(-2.77)	***	(-2.85)	***	(-2.47)	**	(-2.58)	***	(-1.35)		(-1.61)
Population 100km	1.03		0.07		0.15		0.19		-0.23		-0.26		1.09		1.28
	(2.76)	***	(0.21)		(0.48)		(0.56)		(-0.84)		(-0.99)		(2.36)	**	(2.27)	**
Terms of trade	…		-0.03		-0.03		-0.02		-0.03		-0.03		0.0004		0.013
			(-2.99)	***	(-2.19)	**	(-1.57)		(-2.61)	***	(-2.61)	***	(0.02)		(0.72)
Inflation			-0.001		-0.001		-0.001		-0.001		-0.001		-0.001		-0.001
			(-1.99)	**	(-1.85)	*	(-1.71)	*	(-1.66)	*	(-1.68)	*	(-0.88)		(-0.84)
Government balance	…		0.06		0.04		0.04		0.04		0.04		0.003		-0.02
			(2.43)	**	(1.68)		(1.62)		(1.95)	*	(1.93)	*	(0.09)		(-0.45)
Openness	…		0.01		0.01		0.006		0.004		0.004		0.004		0.003
			(4.30)	***	(3.82)	***	(3.68)	***	(3.35)	***	(3.48)	***	(1.97)	**	(1.38)
External debt	…		-0.37		-0.39		-0.37		-0.16		-0.16		-0.66		-0.61
			(-6.03)	***	(-6.39)	***	(-6.15)	***	(-3.10)	***	(-3.20)	***	(-7.46)	***	(-7.15)	***
Infrastructure	…		3.82		3.70		3.69		2.26		2.23		4.09		3.69
			(4.05)	***	(3.95)	***	(3.87)	***	(2.88)	***	(2.91)	***	(3.02)	***	(2.60)	***
ICRG law and order	…		…		0.24		0.29		0.14		0.15		0.29		0.30
					(2.37)	**	(2.76)	***	(1.61)		(1.75)	*	(1.99)	**	(1.86)	*
Philippine dummy	-0.83		-1.22		-1.00		…		-0.67		…		-0.94		…
	(-0.70)		(-1.26)		(-1.03)				(-0.82)				(-0.67)
No. of observations	243		243		243		243		243		243		243		243
Adjusted R-squared	0.14		0.42		0.43		0.42		0.32		0.28		0.35		0.34
Sample periods	1970-1999

Source: Fund staff estimates

^{^1/}The data set comprises 10-year average of each variable. T-statistics are in parenthesis (***, **, and * indicate significant at 1, 5 and 10 percent, respectively).

^{^2/}Without human capital adjustments. The main thrust of the results hold even for TFP growth with human capital adjustments.

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About

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Abstract

III. Explaining Economic Growth in the Philippines21

A. Introduction

B. Philippines’ Growth Performance

C. Explaining Growth in the Philippines

Geography

Economic policies

Institutions

D. Growth Regressions

E. Conclusions

ANNEX I The Regression Exercise

ANNEX II Data used for the Regression Exercise

Data used mainly in second (panel) regression exercise

Other data used in tables and figures.

References

Same Series

Other IMF Content

Other Publishers

Asian Development Bank

Inter-American Development Bank

International Labour Organization

The World Bank

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III. Explaining Economic Growth in the Philippines²¹