Absorptive capacity and the impact of commodity terms of trade shocks in resource export-dependent economies
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Terada-Hagiwara, Akiko; Villaruel, Mai Lin C.; Edmonds, Christopher Working Paper Absorptive capacity and the impact of commodity terms of trade shocks in resource export-dependent economies ADB Economics Working Paper Series, No. 487 Provided in Cooperation with: Asian Development Bank (ADB), Manila Suggested Citation: Terada-Hagiwara, Akiko; Villaruel, Mai Lin C.; Edmonds, Christopher (2016) : Absorptive capacity and the impact of commodity terms of trade shocks in resource exportdependent economies, ADB Economics Working Paper Series, No. 487, Asian Development Bank (ADB), Manila This Version is available at: https://hdl.handle.net/10419/169318 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/3.0/igo/
ASIAN DEVELOPMENT BANK AsiAn Development BAnk 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org Absorptive Capacity and the Impact of Commodity Terms of Trade Shocks in Resource Export-Dependent Economies This paper investigates the role of “absorptive capacity” to manage unexpected shocks to the real economy, with a focus on small, open natural resource-dependent economies. Empirical investigation suggests that levels of absorptive capacity, or the ability to use resource windfalls effectively, and foreign reserves begin to matter when the sample is restricted to resource-dependent countries. Two case studies from Papua New Guinea and Timor-Leste support this claim, highlighting the challenges they face when confronted with a sudden influx of natural resource revenues and the capacity to effectively use fiscal revenues is limited. About the Asian Development Bank ADB’s vision is an Asia and Pacific region free of poverty. Its mission is to help its developing member countries reduce poverty and improve the quality of life of their people. Despite the region’s many successes, it remains home to the majority of the world’s poor. ADB is committed to reducing poverty through inclusive economic growth, environmentally sustainable growth, and regional integration. Based in Manila, ADB is owned by 67 members, including 48 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. ABSoRpTIvE CApACITy AnD ThE ImpACT of CommoDITy TERmS of TRADE ShoCkS In RESouRCE ExpoRT- DEpEnDEnT EConomIES Akiko Terada-Hagiwara, Mai Lin C. Villaruel, and Christopher Edmonds adb economics working paper series no. 487 June 2016
ADB Economics Working Paper Series Absorptive Capacity and the Impact of Commodity Terms of Trade Shocks in Resource Export-Dependent Economies Akiko Terada-Hagiwara, Mai Lin C. Villaruel, and Christopher Edmonds No. 487 | June 2016 Akiko Terada-Hagiwara ( [email protected]) is senior economist and Mai Lin C. Villaruel ([email protected]) is economics officer at the Economic Research and Regional Cooperation Department, Asian Development Bank (ADB). Christopher Edmonds ([email protected]) is senior economist at the Pacific Department, ADB. The analysis presented in this paper draws from research originally prepared for the Asian Development Outlook 2014 and the Pacific Economic Monitor 2014.
Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) © 2016 Asian Development Bank 6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, Philippines Tel +63 2 632 4444; Fax +63 2 636 2444 www.adb.org Some rights reserved. Published in 2016. Printed in the Philippines. ISSN 2313-6537 (Print), 2313-6545 (e-ISSN) Publication Stock No. WPS168164-2 Cataloging-In-Publication Data Asian Development Bank. Absorptive capacity and the impact of commodity terms of trade shocks in resource export-dependent economies. Mandaluyong City, Philippines: Asian Development Bank, 2016. 1. absorptive capacity. 2. economic growth. 3. natural resources. 4. real exchange rate. 5. terms of trade. I. Asian Development Bank. The views expressed in this publication are those of the authors and do not necessarily reflect the views and policies of the Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. The mention of specific companies or products of manufacturers does not imply that they are endorsed or recommended by ADB in preference to others of a similar nature that are not mentioned. By making any designation of or reference to a particular territory or geographic area, or by using the term “country” in this document, ADB does not intend to make any judgments as to the legal or other status of any territory or area. This work is available under the Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) https://creativecommons.org/licenses/by/3.0/igo/. By using the content of this publication, you agree to be bound by the terms of this license. This CC license does not apply to non-ADB copyright materials in this publication. If the material is attributed to another source, please contact the copyright owner or publisher of that source for permission to reproduce it. ADB cannot be held liable for any claims that arise as a result of your use of the material. Attribution—In acknowledging ADB as the source, please be sure to include all of the following information: Author. Year of publication. Title of the material. © Asian Development Bank [and/or Publisher]. URL. Available under a CC BY 3.0 IGO license. Translations—Any translations you create should carry the following disclaimer: Originally published by the Asian Development Bank in English under the title [title] © [Year of publication] Asian Development Bank. All rights reserved. The quality of this translation and its coherence with the original text is the sole responsibility of the [translator]. The English original of this work is the only official version. Adaptations—Any adaptations you create should carry the following disclaimer: This is an adaptation of an original Work © Asian Development Bank [Year]. The views expressed here are those of the authors and do not necessarily reflect the views and policies of ADB or its Board of Governors or the governments they represent. ADB does not endorse this work or guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. Please contact [email protected] if you have questions or comments with respect to content, or if you wish to obtain copyright permission for your intended use that does not fall within these terms, or for permission to use the ADB logo. Notes: In this publication, “$” refers to US dollars. Corrigenda to ADB publications may be found at: http://www.adb.org/publications/corrigenda
CONTENTS FIGURES iv ABSTRACT v I. INTRODUCTION AND LITERATURE REVIEW 1 II. DATA SOURCES AND CONSTRUCTION 2 III. EMPIRICAL ANALYSIS AND RESULTS 5 A. Estimation Model 5 B. Estimation Results 6 IV. CASE STUDIES: PAPUA NEW GUINEA AND TIMOR-LESTE 9 V. CONCLUSION 13 APPENDIXES 15 REFERENCES 19
FIGURES 1 Average Commodity Terms of Trade 3 2 Composite Index of Absorptive Capacity of ADB’s Developing Member Countries 4 3 Impulse Response Function of Gross Domestic Product to Commodity Terms of Trade: All Countries 6 4 Impulse Response Function of Gross Domestic Product to Commodity Terms of Trade: By Exchange Rate Regime 7 5 Impulse Response Function of Gross Domestic Product to Commodity Terms of Trade: By Existence of Wealth Funds 8 6 Variance Decomposition by Absorptive Capacity Index and Resource Dependency Contribution of Commodity Terms of Trade 9 7 Gross Domestic Product Growth and Government Expenditure in Papua New Guinea and Timor-Leste 10 8 Planned and Actual Capital Expenditure in Papua New Guinea and Timor-Leste 11 9 Spending in Each Quarter as a Share of Total Annual Expenditure in Papua New Guinea and Timor-Leste 12
ABSTRACT This paper investigates the role of “absorptive capacity” to manage unexpected shocks to their real economy, with a focus on small, open, natural resource-dependent economies. A quarterly panel data series for 45 countries is constructed, including 23 developing Asian countries for empirical investigation. For the entire sample, the analysis finds that absorptive capacity, choice of exchange rate regime, presence of wealth funds, level of foreign reserves, or degree of resource dependency alone, does not matter when real shocks are introduced to output. However, levels of absorptive capacity or ability to use resource windfalls effectively, and foreign reserves begin to matter when the sample is restricted to resource-dependent countries. Case studies from Papua New Guinea and Timor-Leste support this claim highlighting the challenges they face with a sudden influx of natural resource revenues when capacity to effectively use fiscal revenues is limited. Keywords: absorptive capacity, economic growth, natural resources, real exchange rate, terms of trade JEL codes: F14, F43, H11
I. INTRODUCTION AND LITERATURE REVIEW Management of natural resource windfalls is pivotal to the short- to medium-run challenge for policy makers in resource-rich countries. The drastic changes in commodity prices before and after the global financial and economic crisis (2007 and 2009) and, more recently, with the low commodity prices that have prevailed since mid-2014, have highlighted, again, the importance and peculiarity of price movements. Since the resource windfalls tend to be large relative to the size of many small, open economies, they can generate macroeconomic pressures, mainly through fiscal policy. As fiscal policy is the main impulse through which windfalls influence economic activity, this paper relates to a series of studies on aid windfalls for the interaction of fiscal and reserve policy during aid inflows (Berg et al. 2010). In this line of studies, absorptive capacity of the government is the key to determine the magnitude of the impacts, which is the focus of this paper. Methodologically, this paper relates to Broda (2004) in investigating impacts of terms of trade (TOT) shocks on real factors, but differs in several aspects. First, it investigates the transmission of the shocks through commodity prices rather than export and import price movements, in general. This is because the commodity prices tend to be much more volatile than prices of manufacturing products, and commodities are particularly significant for many small, open economies. Since many of the commodity exporters in Asia are small, open economies, they can suffer from detrimental impacts from abrupt movements. Secondly, attention is paid to various factors that affect the degree of impacts arising from commodity price fluctuations. In addition to the exchange rate regime considered by Broda (2004), we look closely at other factors relevant to natural resource exporters in developing countries, such as the presence of wealth funds, level of foreign exchange reserves, and absorptive capacity. Studies such as Mendoza (1995), Barro (1996), and Kose (2002) also show that the TOT shocks explain a significant portion of the actual gross domestic product (GDP) variability, and it is particularly so in developing countries (Bems and Filho 2011). Similar findings in terms of impacts are in order for real effective exchange rates (REER). Aizenman et al. (2012) claim that relative price fluctuation of exports and imports is a major determinant of real exchange rate volatility.1 Further, they find that international reserves cushion the impact of TOT shocks and the REER is cushioned by international reserves for developing countries but not for industrialized countries (e.g., Aizenman and Riera-Crichton 2008). Not only international reserves can buffer and stabilize the impact of real shocks but also flexible exchange rate arrangements help reduce the impact both in emerging and industrial economies (Edwards and Yeyati 2005). The objective of the paper is to examine the impacts of shocks in prices, particularly sharp on changes in non-oil commodity terms of trade (CTOT) on the macroeconomic performance of a number of small, open economies in Asia and the Pacific. Contributions of this paper are several folds. First, this paper sheds light on underinvestigated small economies in Asia and the Pacific region mainly due to lack of data especially in the Pacific island economies. We use quarterly data series on CTOT, output, REER, and real interest rates for 45 countries, including 23 countries in Asia and the Pacific. Using these data we analyze the relationship between the macroeconomic variables and consider how they influence the relationship between external shocks and economic growth in small, open, and natural resource-dependent economies. Second, this paper focuses and examines the role of institutions or the absorptive capacity of a country, in particular, as a factor amplifying in determining 1 Papers suggest that there are direct links between TOT shocks and the REER: Dornbusch 1983, Marion 1984, Ostry 1988, Edwards 1989, and Frenkel and Razin 1992.
2 | ADB Economics Working Paper Series No. 487 the impact of the shocks. Unlike widely examined factors, such as exchange rate regime or precautionary buffers, the absorptive capacity or the ability to use the natural resource windfalls has been overlooked in the formal analysis. We use composite index of absorptive capacity that measures absorptive capacities at a country level to see if this factor matters in transmitting the price shocks to real sectors. Finally, this paper provides case studies from Papua New Guinea and Timor-Leste to complement the empirical findings. The paper finds that commodity price shocks per se do not matter on real output, on the average, regardless of whether a country is natural resource dependent or not. Furthermore, our analysis suggests that the choice of exchange rate regime, presence of wealth funds, level of foreign reserves nor degree of resource and dependency alone, does not aggravate the impacts arising from the external shocks on either output or REER. The major finding of this paper is that the levels of absorptive capacity and foreign reserves begin to matter when the sample is restricted to resourcedependent countries. The remainder of this paper is organized as follows. Section II discusses the data used in the paper’s empirical analysis. Section III discusses the empirical approach. Case studies are considered in section IV and the final section offers some conclusions. II. DATA SOURCES AND CONSTRUCTION As booms in commodity prices do not necessarily translate directly into TOT, we construct a more appropriate measure to capture commodity price fluctuations in this paper, CTOT, defined as the ratio of weighted real commodity export prices to weighted real commodity import prices, which reflects changes in commodity prices but also the importance of commodities to the overall economy.2 CTOT is computed for country at time t, as follows: ⁄ ⁄ where are the individual commodity prices, is a manufacturing unit value index used as deflators, is the share of exports of commodity in a country , and is the share of imports of commodity in a country . The weights are defined in terms of GDP; hence, they take into account cross-country differences in both the composition of commodity exports and import baskets, and the importance of commodities to the overall economy. Figure 1 shows the computed CTOT of both resource-dependent and resource-independent country groups. A country is considered natural resource dependent if the share of merchandise exports to nominal GDP account for more than 20% share, the country is resource independent otherwise. The CTOT of the natural resource-dependent economies has been quite volatile, on the average in recent years. It improved sharply by 5% in 2007, deteriorated by more than 10% toward the end of 2008, and then surged by 6.6% by the end of 2011. On the other hand, the CTOT has been 2 The same methodology used by Deaton and Miller (1996); and Cashin, Cespedes, and Sahay (2004); and Spatafora and Tytell (2009).
Absorptive Capacity and the Impact of Commodity Terms of Trade Shocks in Resource Export-Dependent Economies | 9 We now turn our attention to the role of absorptive capacity more closely. The estimates find that the combination of resource dependence and low absorptive capacity increases an economy’s vulnerability to CTOT shocks. Looking at the full sample of countries (bars A and B in Figure 6), absorptive capacity alone does not make a significant difference, as commodity price shocks have similar output effects for high and low capacity countries. But absorptive capacity clearly matters when the sample is restricted to resource-dependent countries (bars C and D). In this case, CTOT shocks explain more than 16% of output variation, when the sample is restricted to resource-dependent economies that also display limited absorptive capacity. This explanatory power of CTOT is significantly higher than in the case of the high absorptive capacity economies. In other words, those that are less able to use windfall proceeds productively will be less able to shield real output from global commodity price shocks. Figure 6: Variance Decomposition by Absorptive Capacity Index and Resource Dependency Contribution of Commodity Terms of Trade Note: Average of 24 quarters variance decomposition is plotted. Source: Authors’ estimates. IV. CASE STUDIES: PAPUA NEW GUINEA AND TIMOR-LESTE This section considers the cases of Papua New Guinea (PNG) and Timor-Leste, where the absorptive capacity played a major role in amplifying impacts of CTOT shocks on the real economy—mainly through fiscal policy. The role of absorptive capacity is evident in both countries, which have been among the Pacific region’s fastest growing economies over the past decade—propelled by natural resource exploitation and associated foreign direct investment. Between 2002 and 2013, PNG recorded 12 consecutive years of economic growth averaging around 6% per annum, while non-oil GDP in Timor-Leste grew at an average rate of 11.0% per annum between 2007 and 2013. The rapidly expanding fiscal space generated by resource extraction activity has raised expectations in both countries that government can play a more active role in overcoming persistent development constraints through expanded public service delivery to their populations, but it also raised the issue of institutional capacity of these governments. However, the fall in global commodity prices began in 2014, growth in both economies has slowed considerably—with 20142015 growth in PNG averaging 8.7% and 5.6% in Timor-Leste—and both countries have faced increasingly challenging fiscal conditions. 0 2 4 6 8 10 % 12 14 16 18 High absorptive capacity (A) Low absorptive capacity (B) High absorptive capacity resource dependent (C ) Low absorptive capacity resource dependent ( D)
10 | ADB Economics Working Paper Series No. 487 Between 2009 and 2013, total government expenditure in PNG and Timor-Leste grew at average annual rates of 12% and 19%, respectively. Both countries maintain ambitious national transport and energy infrastructure plans and are making large investments in health, education, and social services. Yet, while expanded public investment has been broadly aligned with those sectors commonly assumed to promote a more inclusive growth path, the success of this strategy has been hampered in both countries by implementation challenges. Government agencies have struggled to accomplish rapidly rising workloads and effectively translate growing budgetary resources into improved service delivery. Figure 7: Gross Domestic Product Growth and Government Expenditure in Papua New Guinea and Timor-Leste GDP = gross domestic product, lhs = left-hand side, PNG = Papua New Guinea, rhs = right-hand side, TIM = Timor-Leste. Sources: ADB estimates using data from Papua New Guinea Department of Treasury; Democratic Republic of Timor-Leste, Ministry of Finance. https://www.mof.gov.tl (accessed 15 May 2016). Weak absorptive capacity has been hindering effective implementation of the public investment in these economies. A number of factors are contributing to implementation difficulties. First, the availability of well qualified personnel to administer government infrastructure projects has been limited relative to the rapid rise in capital project implementation needs. More broadly, government agencies have also struggled to compete with salaries offered by growing private sector employers, which has hollowed out their pool of skilled technical staff with project implementation experience. Sometimes, unpredictable budgetary processes have delayed approval of plans and the availability of financial resources, leaving public agencies poorly prepared to execute projects at the beginning of each fiscal year. In PNG, this has been particularly true of the political tendency to fund infrastructure projects before detailed feasibility and preparatory design studies have been completed. Poor execution of budget has been particularly severe in agencies tasked with delivering major infrastructure projects, which require detailed project preparation and supervision. For instance, the PNG Department of Works and Implementation, which is responsible for approximately 85% of the –2,500 0 2,500 5,000 7,500 –6 0 6 12 18 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 $ million % PNG: Govt expenditure, rhs TIM: Govt expenditure, rhs PNG: GDP growth, lhs TIM: GDP growth, lhs
Absorptive Capacity and the Impact of Commodity Terms of Trade Shocks in Resource Export-Dependent Economies | 11 government’s 2014 land transport program, disbursed only 52% of its original development budget appropriation in 2013, and only 38% in 2012. In Timor-Leste, the ratio of planned to actual capital expenditure has averaged just 56.7% since 2010, as government agencies repeatedly underperform on their expenditure targets for major national infrastructure projects. Figure 8: Planned and Actual Capital Expenditure in Papua New Guinea and Timor-Leste lhs = left-hand side, PNG = Papua New Guinea, rhs = right-hand side, TIM = Timor-Leste. Sources: ADB estimates using data from Papua New Guinea Department of Treasury; Democratic Republic of Timor-Leste, Ministry of Finance. https://www.mof.gov.tl (accessed 15 May 2016). The rapid rise in public expenditures in both countries also appears to be raising challenges in obtaining value for money for disbursed funds. As can be seen in Figure 9, both countries continue to undertake a disproportionate amount of their spending in the final quarter of the fiscal year—ranging between 30%60% of the capital budget in PNG and averaging 47.8% (30%73% range across years) for the capital budgets in Timor-Leste. While it is normal for disbursement rates against capital project to increase as projects progress, the extreme distribution of budget disbursements toward the end of the fiscal year signals a tendency in both countries to “push money out the door” prior to the close of fiscal accounts—in case a further appropriation during the next year’s budget cycle is not forthcoming. This practice increases the likelihood of unproductive expenditure and lessens prospects that recommended public financial management accountability mechanisms will be strictly adhered to. The national electricity network development project in Timor-Leste may offer a case on point. There, the rush to use funding within the tight schedule established in national plans has been criticized as leading to both cost overruns and the development of excessive capacity. 0 20 40 60 80 100 120 - 500 1,000 1,500 2,000 2,500 2008 2009 2010 2011 2012 2013 2014 2015 % $ million PNG: Capital expenditures-Planned, lhs PNG: Capital expenditures-Actual, lhs TIM: Capital expenditures-Planned, lhs TIM: Capital expenditures-Actual, lhs PNG: Capital expenditures-Actual/Planned, rhs TIM: Capital expenditures-Actual/Planned, rhs
12 | ADB Economics Working Paper Series No. 487 Figure 9: Spending in Each Quarter as a Share of Total Annual Expenditure in Papua New Guinea and Timor-Leste Notes: 1. Papua New Guinea discontinued reporting on quarterly expenditure numbers after 2013. 2. Quarterly spending as a share of total spending during the year. Sources: ADB estimates using data from Papua New Guinea Department of Treasury; Democratic Republic of Timor-Leste, Ministry of Finance. https://www.mof.gov.tl (accessed 15 May 2016). Adding to poor value for money has also been a tendency to favor investment in new capital projects, rather than investing in the underlying institutions and agencies tasked with planning, delivering, and maintaining new assets and services. A lack of maintenance funding in particular, has contributed to an expensive “build-neglect-rebuild” cycle for many national infrastructure assets. For example, the PNG Department of Works, as a key executing agency of the government’s infrastructure plans, received a 60% funding increase in 2014, on top of a 35% increase in 2013. Yet, more than 97% of this increase in funding is for additional capital projects, with limited allocations for new maintenance and operational activities. In 2014, recurrent (operational) spending will comprise just 11% of the Department’s budget, down from 33% in 2011. Likewise, the ratio of personnel costs to total spending down from almost 20% in 2004 to 3% in 2014. In Timor-Leste, expenditures for staff at the main ministries that handle infrastructure development (i.e., the Ministry of Public Works and the Ministry of Transport and Communication) did not keep pace with the rising capital budgets in the late 2000s, with the ratio of staff expenditures to planned capital expenditures falling from about 5% in 2008 to just 2.5% in 2010. However, since 2010, greater resources have been allocated to staff at the ministries and this ratio has risen to about 15%. Since global commodity prices began to fall in 2014, the PNG and Timor-Leste governments had to adjust to rapid downward adjustments in their fiscal resources, facing particular challenges in cutting multiyear expenditure increases adopted during the boom years. By late 2015, the PNG government’s fiscal position had deteriorated to the point where rising debt and a rising fiscal deficit prompted sharp cuts in total expenditure and the government exploring options to float a $1 billion sovereign bond in 2016 to refinance its borrowing. By 2015, the PNG government faced a fiscal deficit of K2.5 billion (equivalent to 4.9% of GDP) and a supplementary budget passed late in the year 0 10 20 30 40 50 60 70 80 Q1 2010 Q2 2010 Q3 2010 Q4 2010 Q1 2011 Q2 2011 Q3 2011 Q4 2011 Q1 2012 Q2 2012 Q3 2012 Q4 2012 Q1 2013 Q2 2013 Q3 2013 Q4 2013 Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015 Q3 2015 Q4 2015 % Papua New Guinea Timor-Leste
Absorptive Capacity and the Impact of Commodity Terms of Trade Shocks in Resource Export-Dependent Economies | 13 identified expenditure cuts of about K1.4 billion to address the growing fiscal deficit. The 2016 budget continued the adjustment to lower revenue expectations (projecting total expenditures of K14.8 billion). The focus on new infrastructure in PNG—instead of providing sustainable funding for operations and maintenance of existing assets—has amplified budgetary pressures, contributed to higher overall costs, and undermined the quality of service delivery. Restrictions on government spending on purchases of goods and services, and wages and salaries started in the 2013 budget are further amplifying this trend. Similarly, Timor-Leste focused on building new infrastructure, including a national electricity grid with large excess capacity that now requires a net operating subsidy that will be equivalent to approximately 7% of non-oil GDP in 2015. Improved absorptive capacity of government authorities is the key to make fiscal policy function. In order for fiscal policy to truly promote inclusive growth, it requires more than just expanding budget allocations to priority sectors. Time and resources are needed to strengthen the underlying systems and institutions charged with executing budgetary plans and to build the human resources needed to effectively implement government programs and investments. In the cases of PNG and Timor-Leste, translating the wealth associated with natural resource extraction into inclusive fiscal policy requires a broader focus on building the capacity of the civil service, improving the coherence and coordination of the budget process, and ensuring adequate funding is provided not just for new projects but also for maintaining and operating assets once they are built. V. CONCLUSION The results of the analyses suggest, broadly, that commodity price shocks per se do not have a significant impact on real output, regardless of whether a country is natural resource dependent or not. Our analysis suggests that the exchange rate regime, existence of wealth funds, level of foreign reserves and dependency alone, do not aggravate the impact of external shocks on either output or REER. A major finding of this paper is that the levels of absorptive capacity and foreign reserves begin to matter when the sample is restricted to resource-dependent countries. Through examining data series focusing on small, open economies, this paper suggests that the combination of resource dependency and low absorptive capacity amplify impacts of CTOT shocks on natural resource-dependent economies and the vulnerability of economies to commodity price fluctuations. The level of absorptive capacity alone does not make a significant difference as commodity price shocks have similar impacts on real output for high and low absorptive capacity countries, but the level of absorptive capacity clearly matters when the shocks are restricted to resource-dependent countries. The CTOT shocks explain more than 16% of output variation if the country is resource dependent and also has limited absorptive capacity—significantly more than the high absorptive capacity group. In other words, those that are less able to use windfall proceeds productively will be less able to shield real output from global commodity price shocks. The case study from PNG and Timor-Leste supports the claim that countries with limited institutional capacity can suffer from swings in commodity prices. Results suggest that countries with limited absorptive capacity are tested when they face uncertainties surrounding commodity price swings and depletion of natural resources, which complicate resource and macroeconomic management. Additional costs can arise whenever the speed of investment adversely impact project selection, management, and implementation as a result
14 | ADB Economics Working Paper Series No. 487 of the poor ability of a country to utilize capital productively—leading to reduced contributions to growth even with higher public investments. This is consistent with the fact that the fiscal multiplier tends to be smaller in low-income countries, which tend to be limited in their absorptive capacity. In summary, the analysis suggests that natural resource-dependent economies need to enhance their absorptive capacity, so windfalls from natural resources can effectively contribute to growth in a sustainable manner.
APPENDIXES Based on Feeny and de Silva (2012), the following components were considered as factors hampering the effectiveness of foreign aid. Appendix 1: Absorptive Capacity of Developing Countries Component Measurement Capital Human capital Number of doctors per thousand people Number of nurses per thousand people Number of primary schools teachers per thousand people Number of secondary schools teachers per thousand people Adult illiteracy Infrastructure Paved roads (per cent of total) Governance Policy / Institutional Voice and accountability Political instability Government effectiveness Regulatory quality Rule of law Control of corruption Donor Donor practices Ratio of the number of DAC donors to the log of government expenditures Ratio of fragmentation to the log of government expenditures DAC = Development Assistance Committee. Source: Feeny and de Silva 2012.
16 | Appendixes Appendix 2: Data Estimation Groups Category Economies Degree of Resource Dependency Resource independent Argentina; Armenia; Australia; Austria; Cambodia; Canada; Denmark; Fiji; Georgia; Germany; Greece; Hong Kong, China; India; Indonesia; Japan; Latvia; Maldives; Mexico; New Zealand; Pakistan; Peru; Philippines; Poland; Samoa; Singapore; Sri Lanka; Switzerland; Sweden; Tonga; Thailand; Turkey; United Kingdom; Vanuatu; and Viet Nam Resource dependent Azerbaijan, Bhutan, Chile, Croatia, Hungary Kazakhstan, Kyrgyz Republic, Lithuania, Malaysia, Norway, and Solomon Islands Absorptive Capacity High absorptive capacity Argentina, Armenia, Azerbaijan, Chile, Croatia, Fiji, Kazakhstan, Kyrgyz Republic, Malaysia, Mexico, Samoa, Tonga, and Turkey Low absorptive capacity Bhutan, Cambodia, Georgia, India, Indonesia, Maldives, Pakistan, Peru, Philippines, Solomon Islands, Sri Lanka, Thailand, Vanuatu, and Viet Nam Exchange Rate Regime Fixed Austria, Azerbaijan, Bhutan, Hong Kong, China, Denmark, Fiji, Germany, Greece, Kazakhstan, Kyrgyz Republic, Lithuania, New Zealand, Pakistan, Sri Lanka, Sweden, Solomon Islands, Tonga, United Kingdom, and Viet Nam Managed Cambodia, Croatia, Georgia, India, Indonesia, Japan, Latvia, Malaysia, Maldives, Mexico, Norway, Peru, Philippines, Samoa, Thailand, Turkey, and Vanuatu Floating Argentina, Armenia, Australia, Canada, Chile, Hungary, Poland, Singapore, and Switzerland Sovereign Wealth Funds With sovereign wealth funds Australia; Azerbaijan; Canada; Chile; Hong Kong, China; Indonesia; Kazakhstan; Malaysia; Mexico; New Zealand; Norway; Peru; Singapore; and Viet Nam Without sovereign wealth funds Argentina, Armenia, Austria, Bhutan, Cambodia, Croatia, Denmark, Fiji, Georgia, Germany, Greece, Hungary, India, Japan, Kyrgyz Republic, Latvia, Lithuania, Maldives, Pakistan, Philippines, Poland, Samoa, Solomon Islands, Sri Lanka, Sweden, Switzerland, Thailand, Tonga, Turkey, United Kingdom, and Vanuatu Foreign Reserves High foreign reserves Bhutan; Cambodia; Hong Kong, China; Croatia; Denmark; Fiji; Hungary; Japan; Kyrgyz Republic; Latvia; Lithuania; Malaysia; Peru; Philippines; Poland; Samoa; Singapore; Solomon Islands; Switzerland; Thailand; Tonga; and Vanuatu Low foreign reserves Argentina, Armenia, Australia, Austria, Azerbaijan, Canada, Chile, Georgia, Germany, Greece, India, Indonesia, Kazakhstan, Maldives, Mexico, New Zealand, Norway, Pakistan, Sri Lanka, Sweden, Turkey, United Kingdom, and Viet Nam Source: Authors’ estimates.
Appendixes | 17 Appendix 3: Pedroni Residual Cointegration Test Series: GDP_STRD REER_STRD INT_STRD CTOT_STRD Null Hypothesis: No cointegration Alternative hypothesis: common AR coefficients (within-dimension) Weighted Statistic Prob. Statistic Prob. Panel v-Statistic 3.947337 0.0000 1.920674 0.0274 Panel rho-Statistic -3.843129 0.0001 -3.763945 0.0001 Panel PP-Statistic -11.67691 0.0000 -12.16601 0.0000 Panel ADF-Statistic -13.30467 0.0000 -12.86999 0.0000 Alternative hypothesis: individual AR coe f ficients (between-dimension) Statistic Prob. Group rho-Statistic -0.988677 0.1614 Group PP-Statistic -13.08829 0.0000 Group ADF-Statistic -13.97425 0.0000 Source: Authors’ estimates. Appendix 4: VAR Lag Order Selection Criteria Lag LogL LR FPE AIC SC HQ 0 –4873.413 NA 0.598809 10.83869 10.86004 10.84685 1 –3141.448 3444.686 0.013219 7.025439 7.132159 7.066207 2 –3019.583 241.2909 0.010448 6.790185 6.982281 6.863567 3 –2928.033 180.4556 0.008833 6.622296 6.899768 6.728292 4 –2853.900 145.4662* 0.007763* 6.493111* 6.855958* 6.631721* AIC = Akaike information criterion, FPE = Final prediction error, HQ = HannanQuinn information criterion, LR = likelihood ratio; sequential modified LR test statistic (each test at 5% level), SC = Schwarz information criterion, VAR = vector autoregressive. Note: * indicates lag order selected by the criterion. Source: Authors’ estimates.
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