Papua New Guinea: Selected Issues and Statistical Appendix
Author: International Monetary Fund
Publication Date:
13 Mar 2006
eISBN:
9781451831719
Language:
English
Keywords:
ISCR; CR; GDP; mineral revenue; government; productivity growth; economic growth performance; time series; authorities' Medium-term Development Strategy; governance issue; Productivity; Foreign direct investment; Vector error correction models
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This Selected Issues paper on Papua New Guinea reports that although economic cycles have generally paralleled the many mineral sector booms and busts, the downward trend in growth rates may reflect other factors. Papua New Guinea’s economy is dominated by a large labor-intensive agricultural sector and a capital-intensive oil and minerals sector. The formal sector consists of enclave extractive industries, cash crop production, and a small, import-substituting manufacturing sector. The importance of the agriculture sector is about the same as at independence, reflecting structural impediments that have deterred more rapid growth.

Abstract

This Selected Issues paper on Papua New Guinea reports that although economic cycles have generally paralleled the many mineral sector booms and busts, the downward trend in growth rates may reflect other factors. Papua New Guinea’s economy is dominated by a large labor-intensive agricultural sector and a capital-intensive oil and minerals sector. The formal sector consists of enclave extractive industries, cash crop production, and a small, import-substituting manufacturing sector. The importance of the agriculture sector is about the same as at independence, reflecting structural impediments that have deterred more rapid growth.

II. Determinants of Productivity in Papua New Guinea1

A. Introduction

1. Papua New Guinea’s economic growth performance since independence has not been as strong as in comparator countries (see Chapter I, Table I.1). The preceding Chapter I discussed how the low growth rates experienced may be accounted for by both a significant slowing of capital inputs and falling total factor productivity (TFP) growth. Recent empirical studies suggest that a key distinguishing factor between high-growth and low- or negative-growth countries may be differences in TFP, which is affected by the quality of a country’s economic, social, and political institutions.2 Using time series analysis, this chapter investigates some determinants of productivity growth that may explain the poor performance of real GDP and TFP growth in Papua New Guinea. The second part of the paper then attempts to test for and evaluate some of these factors and their relationship to TFP.

B. Factors Affecting TFP Growth

2. The authorities’ Medium-term Development Strategy (MTDS) highlights the need to improve institutions to raise growth and targets measures to remove impediments that hinder achievement of that goal. Papua New Guinea has already made important efforts to remove structural impediments that hold back its potential for more rapid growth. The reforms of the early 1990s, including the floating of the kina, initially led to a recovery in investment and growth. More recent reforms undertaken include price and trade liberalization, tax reform, investment policy reform, improvements in public expenditure management, pension reform, privatization, financial sector reform, and decentralization of financial responsibilities from the national to the provincial and district levels—in addition to the restoration of macroeconomic stability. However, although a full analysis is beyond the scope of this paper, it is recognized that many of the reforms remain incomplete or have not been sufficiently profound. Therefore, as noted in the MTDS, additional reform is needed to bring growth rates up to the higher level required to sustain improvements in per capita GDP and to reduce poverty. The following discussion highlights several key areas for further reform.

Governance

3. Recent empirical studies have shown that poor governance deters investment, undermines competition, encourages rent-seeking behavior, and distorts public expenditure in an economy, and as a result, affects its productivity. Rent-seeking activities are reportedly high in many areas in Papua New Guinea, particularly in the resource-based sectors as in many other resource-rich countries. Political interference in the civil service and politicization of decision-making hampers effective public administration. In addition, an unstable political environment increases uncertainties for economic actors. No government has survived a full five-year term in office, although the current Somare government has a good chance of being the first government since independence to complete a full term in office. The short political cycles have increased incentives for rent-seeking behavior, while political uncertainty has made it difficult for firms to commit to long-term investment plans since the policy environment is generally viewed as fluid. Recent reforms of the political system, including the Organic Law on the Integrity of Political Parties, are expected to bring some stability to the political process in coming years. The following table ranks some political risks factors for Papua New Guinea relative to other countries in the region.

Table II.1.

Papua New Guinea: Political Risk by Component

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Source, ICRG, 2005. First 3 columns ranked 0-6, 2nd 3 columns ranked 0-12; the lower the number, the higher the risk.

4. Governance issues are also reflected in the ease with which the private sector can conduct business.3 Surveys of the private sector in Papua New Guinea by the Institute of National Affairs (INA) revealed that employers rated crime, corruption, and political instability as the biggest impediments to doing business (Manning (1999), Levantis and Manning (2002)). The average company reported spending about 10 percent of its revenue on private security and losses from theft. The surveys conclude that irregular applications of law and regulations, sudden changes in public policy, and bribes to corrupt official were major costs drivers and significant obstacles to investment. The World Bank’s Doing Business survey reports that, although Papua New Guinea compares well to others in the region overall, on key factors that would deter the start-up of new activity, such as the environment for starting and closing a business, Papua New Guinea compares less poorly with its main trading partners and the region.

Table II.2.

Papua New Guinea: The Business Environment (2005)

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Source: World Bank - Doing Business Explore Economics.Website: http://www.doingbusiness.org/exploreeconomies/businessclimatesnapshot.

Human Capital

5. Access to and quality of education are major factors that impede productivity growth. Gross enrollment in Papua New Guinea at the primary school level is 69 percent, and at secondary schools is 11 percent (Table 3)—about the same levels as at independence. Retention rates are low and dropouts are widespread, with fewer than 60 percent of children completing grade 6. Access to education, particularly at higher levels, is constrained by long travel distances to school and a shortage of teachers in remote areas, and by the significant cost of education, especially at the secondary and tertiary level. The authorities’ MTDS targets an increase in the quantity and quality of basic health care, education, and other high-priority services, with a view to improving long-term growth and social indicators appreciably over the longer term.

Table II.3.

Papua New Guinea: School Enrollment Rates

(Percent)

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Source: World Development Indicators Database, World Bank.

Physical Infrastructure

6. An adequate supply of infrastructure services is an essential ingredient for productivity and growth.4 Poor economic infrastructure contributes to high production costs for businesses, which reduce TFP and the capacity to compete with countries in the Asia-Pacific region. Because of its mountainous and rugged terrain, Papua New Guinea suffers from a fragmented system of transportation, and large parts of the country are virtually isolated. It is estimated that 4 percent of roads and airport runways are paved (Table 4). The capital, Port Moresby, is accessible from the rest of the country only by sea or air, making it costly to distribute products and reach markets. In the center of the country, only the Highlands Highway links the port of Lae to major population centers in the Highlands. Papua New Guinea also lies off the major sea routes and generates little cargo itself, and as a result sea-freight costs to and from PNG are high and service to and from the rest of the world is infrequent.

Table II.4.

Papua New Guinea: Transport and Communications Indicators

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Source: Stephen Jones, OAP, Contribution of Infrastructure To Growth of Povery Reduction, Bali, June 28, 2004

7. An unreliable and costly supply of utilities is also a major impediment to TFP growth. Frequent power interruptions have sharply increased production costs through production stoppages and missed delivery dates. A large number of firms operate their own power generators. The telecommunications network is limited in rural areas and service quality is unreliable. As a result, costs are high, especially for international calls, and only about 2 percent of the population has access to residential service. This compares unfavorably with other countries in the region. Wireless services, including a digital GSM network, are restricted to the major centers of Port Moresby and Lae, limiting productivity gains from these technologies.

C. Empirical Estimation5

8. Many factors may affect TFP growth directly and indirectly. In this study, however, only those factors for which data are available are included in the empirical analysis. These include macroeconomic stability proxied by the inflation variable, technology transfer, and governance (proxied by foreign direct investment), and enrollment rates to capture improvements in human capital.6 The methodology employed in this paper uses unit root and Johansen’s cointegration tests followed by a vector error correction model and variance decomposition to examine the dynamic relationships among variables. The first step requires that the unit root test be conducted in order to determine whether the series are non-stationary in levels and stationary in first differences, that is, integrated of order one. The second step is to use the cointegration test in order to determine whether those six non-stationary series have common long-run relationships. There are many possible tests for cointegration, the most general of them is the multivariate test based on the autoregressive representation discussed in Johansen (1988, 1991, 1995) and Johansen and Juselius (1990). The Johansen maximum likelihood method provides two different likelihood ratio tests, the trace test and the maximum eigenvalue test, in order to determine the number of cointegrating vectors. The finding of the presence of cointegration paves the way for using the vector error correction model.

9. Table 5 shows the results of the unit root tests for the TFP, technology transfer and governance, and macroeconomic variables. The levels of the series are nonstationary; however, differencing the data established that the variables are integrated of order one or are I (1) processes. Given that the variables in the model are I (1) and endogenous, we would expect that the TFP variable will be cointegrated with the other variables. The long-term relationship corresponds to the cointegrating relationship(s), while the short-term dynamics—i.e. the vector error correction model—return the variables to equilibrium after a shock. The maximum likelihood technique of Johansen and Juselius (1990) is used to determine the rank (r) and identify a long-run TFP relationship among the cointegrating vectors. The number of lags used in the VAR is based on the evidence provided by both likelihood ratio tests. The null hypothesis of no cointegration was rejected using both the λ-max (maximum Eigenvalue statistics) and trace tests, in favor of one cointegrating relationship. Both tests indicate one cointegrating vector at the 5 percent level. We then consider a dynamic vector error-correction model to capture the short-run dynamics of variables in the system. The results are presented in Table 6.

Table II.5.

Papua New Guinea: Unit Root Statistics

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Note: Each ADF tests uses a constant and no trend. The lag length has been chosen based on the Schwartz information criterion.P-values are from Mackinnon (1996).
Table II.6.

Papua New Guinea: Johansen (Trace) Cointegration Test

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Note: The cointegrating test uses an intercept but no trend in the cointegration equation. The minimum value of the Schwartz criterion suggests the optimal lag is equal to zero for the Johanson cointegration test. Trace test indicates 1 cointegrating equatio at the 0.05 level.

denotes rejection of the hypothesis at the 5 percent levels.

Variance Decomposition

10. The variance decomposition breaks down the variance of the forecast error for each variable into components that can be attributed to each of the endogenous variables. It provides a measure of the percentage of a variable’s forecast error variance that occurs because of a shock from a variable in the system. To calculate both the Variance Decomposition (VDC) and Impulse Response Functions (IRF), the ordering of the variables is important. This is because the orthogonalizing process requires a particular causal ordering of variables, as different ordering will yield different results. To overcome this problem, the common practice is to place the policy variables at the beginning of the list, and the target variable at the end of the list. The results of the VDCs are shown in Table 7—for responses over a 15-year period to a one-standard deviation shock in each variable.

Table II.7.

Papua New Guinea: Variance Decomposition of TFP

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Note: Cholesky Ordering: FDI, Education, Inflation, TFP.

11. The variance decomposition results indicate that about 50 percent of the variation in TFP is explained by its own innovations in the first year, while the influence to its own shock diminishes to under 46 percent after 15 periods. Education, inflation, and foreign direct investment explains 20, 2.5, and 32 percent of the TFP variation, respectively, after 15 years. The variation in the governance—proxied by foreign direct investment (FDI)—and human capital variables have increasing influence on TFP. However, surprisingly, changes in the variability of economic conditions (proxied by the inflation variable) seem to have a decreasing impact on TFP. This may reflect the high volatility of inflation during this period, which would suggest that additional tests using other proxies for macroeconomic conditions could be pursued in future studies.

Impulse Functions

12. The impulse response function can be thought of simply as a type of dynamic multiplier that shows the response of each variable in the system to a shock in one of the variables (Figure 1). By introducing a one-period standard deviation shock to one of the endogenous variables, the observable responses of the system to the shock can be determined by using the IRF. The size and characteristics of the effects (either a positive or a negative reaction) can be identified from the IRF. Since the targeted variable in this paper is TFP, the IRFs examine the effect of a change in the education, inflation, and FDI variables on the former. Both the results of the VDC and IRF analyses suggest an interesting policy implication that improving institutional governance such as bureaucratic quality may be a strong signal of favorable investment environment for many foreign investors. In this regard, economic and political stability are necessary conditions for Papua New Guinea to attract foreign investment and boost TFP growth. In this test, the response of TFPG to inflation is more in line with intuitive expectations.

Figure II.1.
Figure II.1.

Response to Cholesky One S.D. Innovations

Citation: IMF Staff Country Reports 2006, 105; 10.5089/9781451831719.002.A002

D. Conclusions

13. This chapter uses time series techniques to analyze some possible determinants of productivity growth in Papua New Guinea. Studies show that sound macroeconomic fundamentals, price stability, and opening up of the economy to foreign trade and investments are critical factors affecting TFP growth. The results of the empirical estimation conducted for PNG are consistent with these findings. In particular, the results suggest that factors that can positively influence real GDP and productivity growth in PNG include, among others, a stable macroeconomic environment and policies, higher levels of investment and technology transfer, and better public policies to reduce corruption and improve the quality of public institutions.

ANNEX II.1.

Appendix Table 1.

Papua New Guinea: Growth Accounting

(Factor Contribution to GDP Growth)

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Sources: National Statistical Office, IFS, and Author’s estimates.

ANNEX II.2.

Appendix Table 2.

Papua New Guinea: Growth Accounting

(Log Levels)

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Sources: National Statistical Office, IFS, and Author’s estimates.

References

  • Aschauer, D. 1989, ‘Is Public Expenditure Productive?’ Journal of Monetary Economics, Vol. 23, 177 –200.

  • Bank of Hawaii 2001, Papua New Guinea Economic Report, Honolulu, Hawaii.

  • Johansen, S., & Juselius, K. 1990, ‘The Full Information Likelihood Procedure for Inference on Cointegration—with applications,’ Oxford Bulletin of Statistics and Economics, 52, 169 –211.

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  • King, R. G. & Levine, R. 1993, ‘Finance, entrepreneurship and growth: theory and evidence’. Journal of Monetary Economics, 32: 513 -542.

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  • Kinoshita, Y. & Moody, A. 2001 ‘Private information for foreign investment decisions in emerging markets’, Canadian Journal of Economics 34: 448 -464.

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  • Levantis, T, & Manning, M. 2002. The Business and Investment Environment in PNG: The Private Sector Perspective, Institute of National Affairs, Port Moresby. Papua New Guinea.

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  • Manning, M, 1999. ‘Factors Contributing to the Lack of Investment in Papua New Guinea: A Private Sector Survey,’ Institute of National Affairs, Discussion paper, 74.

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  • Mauro, P. 1997, ‘Why Worry About Corruption?’ Economic Issues, 6, Washington, D.C, International Monetary Fund.

  • McGavin, P. A., 1991, ‘Wages and Productivity in Papua New Guinea,’ Institute of National Affairs, Discussion paper, 48.

  • Röller, L. & Waverman, L. 2001, ‘Telecommunications Infrastructure and Economic Development: A Simultaneous Approach,’ American Economic Review, 91: 909 –23.

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  • Tanzi, V., & Davoodi, H. 1997, ‘Corruption, Public Investment, and Growth,’ International Monetary Fund, Working Papers, WP/97/139.

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1

Prepared by Ebrima Faal.

3

An empirical analysis of transition economies in Eastern Europe and Central Asia showed that investment levels in countries with high levels of corruption were 6 percent lower on average than in countries with medium levels of corruption (21 percent and 27 percent respectively), see World Bank (2000). The same survey revealed that firms operating in environments with high levels of administrative corruption performed significantly more poorly than firms in countries with moderate levels of corruption did.

4

Aschauer (1989) finds that the stock of public infrastructure capital is a significant determinant of aggregate TFP. More recent empirical literature, mostly in a cross-country panel data context, has confirmed the significant output contribution of infrastructure. See for example Roller and Waverman (2001).

5

The dataset incorporates splicing of new and old data sources, and thus are an approximation, which the author believes provides a foundation for drawing general analytical conclusions. The policy implications, however, would need further empirical investigation given better data.

6

The foreign investment proxy may appear unorthodox, however Kinoshita and Campos (2001) and several researchers have found that bureaucratic efficiency—institutions— is a key factor in explaining foreign investors’ decisions. They find that corruption in a host country substantially deters inward FDI. See for example Kinoshita and Davidson (2004) and Wei (2000).

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Cover IMF Staff Country Reports
Papua new Guinea: Selected Issues and Statistical Appendix
Author: International Monetary Fund
Volume/Issue:
Volume 2006: Issue 105
Publisher:
International Monetary Fund
ISBN:
9781451831719
ISSN:
1934-7685
Pages:
62
DOI:
https://doi.org/10.5089/9781451831719.002

Response to Cholesky One S.D. Innovations