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Indonesia: Selected Issues

Author(s):
International Monetary Fund
Published Date:
September 2012
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II. What Determines Investment in Indonesia?1

Rising investment has become a key driver of Indonesia’s recent robust growth and its continuation is important to sustain growth going forward. This raises the following question: What macroeconomic policy-related variables are important for determining investment? While attention generally and rightly focuses on the levels of the macroeconomic variables, regression results suggest that reducing the volatility of interest and real exchange rates, as well as capital markets deepening, may also be important factors for policy makers to take into account.

A. Introduction

1. Promoting investment is key to achieving the growth target in Indonesia’s Master Plan. The economic Master Plan, unveiled in 2011, targets a growth rate of 7–8 percent after 2013 and aims to transform Indonesia into one of the world’s largest economies by 2025. Achieving this growth rate, however, would require substantial enhancements in capital and efficiency.2

2. Recent investment performance is strong but there exists room for improvement. After collapsing in the late 1990s, the investment-to-GDP ratio recovered very sluggishly, and has only recently regained earlier levels. Strong recent investment has focused on the booming commodities sector and is driven by favorable terms of trade. Infrastructure needs, however, remain pressing with the overall public investment ratio among the lowest in the region. To achieve Indonesia’s long-term growth objectives outlined in the economic Master Plan, high investment needs to be sustained. Furthermore, putting in place the necessary infrastructure, which is cited as one of the main growth constraints for Indonesia in numerous business surveys, calls for substantially boosting public investment from the current low levels.

3. This chapter examines the main determents of Indonesia’s investment. Empirical results from aggregate and firm level data show that not only the levels of various macroeconomic variables matter, but also that their volatility affects investment. In other words, it is not only the means that matter but also the standard deviations. Our analysis also suggests that enhancing monetary policy to reduce uncertainty, improving the business environment, enhancing financial access, and developing infrastructure could all support investment.

B. Some Stylized Facts

4. Indonesia has experienced a sizable increase in investment in recent years. Investment collapsed during the late 1990s and only started recovering very recently, with aggregate investment reaching the pre-crisis 30 percent of GDP in 2008. However, with public investment remaining very low, this increase reflects a sharp increase in private investment.

Indonesia: Investment

Sources: CEIC Data Company Ltd; and IMF staff calculations.

5. Lower cost of capital, thanks to the prudent and stable macroeconomic environment, has helped support the rapid investment growth. Indonesia’s cost of capital is on a structural decline, with the prudent and stable macroeconomic environment leading to lower inflation and an improved credit rating.3 As a result, the credit default swap (CDS) and long-term government bond yield spreads have been falling, and Indonesia’s real lending rate has also declined remarkably—dropping by over 4 percentage points between 2000–04 and 2005–11. At the same time, the improved public finances have allowed the government to increase the absolute magnitude of investment, even if as a share of GDP public investment remains very low.

Indonesia: CDS Spreads and Government Bond Yields

Source: Bloomberg LP.

6. Strong regional demand for commodities has driven up commodity prices and spurred investment in these sectors. 4 Despite some correction during the global crisis, Indonesia enjoyed a sizable gain in its terms of trade (TOT) in the past decade. Moreover, Indonesia’s export destinations have shifted from slow growing advanced economies to fastgrowing emerging market economies like China and other emerging Asian countries. Strong demand for commodities, especially from China, has driven up global commodity prices and spurred investment in Indonesia in these sectors. For example, the mining sector in Indonesia accounted for over 13 percent of the total investment in 2010–11, while the share was only 1 percent a decade ago. Similarly, investment in food crops and plantations (sources of rubber and palm oil), also more than doubled during the same period. In fact, mining and plantation together have contributed on average around 47 percent of the total investment growth during 2010–11.

7. Infrastructure investment, however, remains relatively subdued, leading to inadequate infrastructure indicators compared to regional peers. Despite notable improvements, Indonesia’s roads and railroads remain in poor condition, and the capacity of seaports remains limited.5 In the latest World Economic Forum global competitiveness index (GCI) (2010−11), Indonesia ranks 82 out of 139 economies in infrastructure.6 An index of basic infrastructure quantity—capturing information in three key sectors: communication, power, and the road network—shows that Indonesia continues to lag regionally in infrastructure.7 Looked at another way, when one considers construction, electricity, water and gas (EWG), and transportation and communication, only the share of the last in total investment has increased during the past decade. Investment share in EWG has remained steady between the two periods of 2000–01 and 2010–11, while investment share in construction actually decreased, from the original low level. As a result, the total share of investment in the three sectors (construction, EWG, and transportation and communication) has declined from around one-third in 2000–01 to only 10 percent in 2010–11.

Infrastructure Quantity Index 1/

Source: Forthcoming working paper, ″Infrastructure and Income Distribution in ASEAN5.″

1/ A higher index indicates better infrastructure quantity.

Indonesia: Investment Realization by Sectors

(In percent of total investment realization)

Sources: CEIC Data Co. Ltd.; and IMF staff estimates.

Indonesia: Contribution to Investment Realization Growth by Sectors

(In percent)

Sources: CEIC Data Co. Ltd.; and IMF staff estimates.

Public Investment 1/

(In percent of GDP, current prices)

Source: IMF, World Economic Outlook database; and IMF staff calculations.

1/ Average pulic gross fix capital formation over 2008–2011.

8. Furthermore, public investment is low and progress with public private partnership (PPP) to promote infrastructure investment has been slow. Public capital spending, which collapsed during 1998–99, has only recovered partially and is currently at about 3 percent of GDP—among the lowest in the region. While increasing in absolute terms, only 80 percent of the budgeted amount was executed in 2011, with about half disbursed only in the last two months of the year. The government has taken several recent steps to improve the implementation of infrastructure projects. A new procurement regulation has been adopted and budget preparation and payment processes streamlined. However, the PPP program is being held back by weaknesses in project selection and preparation, especially at the local government level. Recent success with a specific project that can serve as a model for others, however, augurs well going forward.

C. Determinants of Investment

Aggregate trend

9. The accelerator model from the economic literature combined with the neoclassical theory of investment can be used to estimate long run investment trends. The accelerator model assumes that investment depends on real GDP and its growth. Neoclassical theory shows that, in the long-run equilibrium, there is a stable relationship between an economy’s capital stock, the level of real output and the real user cost of capital. Investment is therefore modeled8 as a function of: (i) its own lagged value to capture persistence; (ii) lagged GDP value for accelerator effect; (iii) GDP growth as a proxy for the aggregate return on investment; and (iv) the real lending rate as the cost of capital.

Long-Run Aggregate Investment Equation 1/(Dependant variable of natural logarithm of real investment growth)
CoefficientStandard Error
Log real Investment (lag 1)0.685***0.055
Log real GDP (lag 1)0.464***0.079
Δ(Log real GDP)1.133***0.221
Real lending rate (lag 1)0.002***0.000
Constant-2.435***0.430
Adjusted R-squared0.99
Durbin-Watson stat2.03
Source: IMF staff estimates.

An * indicates significance at 10 percent, ** significant at 5 percent, and *** at 1 percent. All explanatory variables are in natural logarithm except for the base lending rate. The equation is estimated by OLS.

Source: IMF staff estimates.

An * indicates significance at 10 percent, ** significant at 5 percent, and *** at 1 percent. All explanatory variables are in natural logarithm except for the base lending rate. The equation is estimated by OLS.

10. Long-run results. Using quarterly data over the period 1993–2011, our analysis identifies the following key relationships for Indonesia:

  • The investment growth is positively associated with real GDP growth. During 2000–11, average real GDP growth in Indonesia accelerated, driving up investment growth by 8 percentage points on average.

  • The real lending rate is positively correlated with investment. This appears counterintuitive, but is not surprising for developing countries with shallow financial markets. In the absence of sufficient financial deepening, high interest rates could drive up savings necessary to finance higher investment. The other reason may be that strong investment demand surpasses investment supply and therefore drives up the equilibrium interest rate.

11. We use an Error Correction Model with quarterly data from 2001−11 to capture short term investment dynamics. 9 After identifying the long run relationship between investment and growth; short run investment growth is then regressed on past investment growth, past GDP growth, the error correction term (deviation from the long-run equilibrium), interest rate volatility, real exchange rate volatility, terms of trade growth, real lending rate, real exchange rate growth and a crisis dummy to exclude the impact on investment from crises.

12. Short run results. The main results for different specifications are summarized below.

Short-Run Aggregate Investment Equation 1/(Dependant variable of natural logarithm of real investment growth)
Model 1Model 1 (2005-11)Model 2Model 3Model 4Model 5Model 6
Real Investment growth (lag 1)0.749***-0.1210.775***0.690**0.738***0.717**0.762***
(0.26)(0.341)(0.261)(0.299)(0.258)(0.296)(0.264)
Error correction term-0.845***0.184-0.877***-0.804***-0.875***-0.823***-0.838***
(0.272)(0.345)(0.274)(0.293)(0.27)(0.292)(0.275)
Interest rate volatility-0.055**-0.096***-0.05**-0.054**-0.049**-0.055**-0.054**
(0.02)(0.022)(0.021)(0.021)(0.021)(0.021)(0.021)
Real lending rate (lag 1)-0.002**0.000-0.002**-0.002*-0.002*-0.002*-0.003**
(0.002)(0.002)(0.002)(0.002)(0.002)(0.002)(0.002)
REER volatility-0.004**-0.001-0.003*-0.004**-0.003-0.004***-0.004*
(0.002)(0.002)(0.002)(0.002)(0.002)(0.002)(0.002)
Terms of trade growth0.004*0.0010.005*0.0040.0030.0040.004*
(0.002)(0.002)(0.002)(0.001)(0.003)(0.001)(0.002)
VIX-0.000
(0.000)
Base lending rate volatility-0.003
(0.008)
REER appreciation-0.093
(0.070)
Credit growth0.023
(0.096)
Market capitalization growth0.015
(0.026)
Constant0.032***0.029***0.038***0.033***0.031***0.032***0.031***
(0.009)(0.008)(0.011)(0.010)(0.009)(0.010)(0.010)
Adjusted R-squared0.440.650.440.420.450.420.43
Durbin-Watson stat1.731.971.711.681.631.721.69
Source: IMF staff estimates.

Standard errors are in parentheses. An * indicates significance at 10 percent, ** significant at 5 percent, and *** at 1 percent. The equations are estimated as error correction models.

Source: IMF staff estimates.

Standard errors are in parentheses. An * indicates significance at 10 percent, ** significant at 5 percent, and *** at 1 percent. The equations are estimated as error correction models.

  • Despite its long run one-to-one impact on investment, output growth is not related robustly to the short run investment growth. This is as expected since short-term investment is normally more volatile than output because of the importance of expectations and confidence. The negative adjustment coefficient on the error correction term in most model specifications, however, suggests that investment tends to return to its long run relationship to output growth.

  • The real lending rate has a statistically significant impact on investment growth in the short run, with 1 percent lower real interest rate leading to 0.2 percent decline of investment growth. The recent prudent macro environment, which has helped to reduce the cost of capital, has contributed to around 39 percent of investment growth.

  • Interest rate volatility has the single biggest short-run impact on investment growth. It is statistically significant and negative in all specifications. The measured coefficient, which almost doubles after 2005, implies that the detrimental effects of uncertainty appear more pronounced in the more recent period.

  • REER volatility, which captures direct investment risk as well as the overall role of macroeconomic stability, is negative and statistically significant as a determinant of investment growth.

  • Terms of trade gains are the biggest driving factor behind the recent robust investment growth, contributing about one-third of the total recent investment growth. Despite some correction during the global crisis, Indonesia enjoyed a sizable gain in its TOT (of an average about 3 percent over 2007−11). The favorable TOT developments have clearly driven up investment, especially in the commodity sector, which is also apparent in the BOP data (see Chapter III).

Economic Contribution from the Variation in Independent Variables to Investment Growth 1/(In percent)
2000-112000-042005-11
Interest rate volatility-34.47-25.02-49.77
Real lending rate-37.19-27.86-38.96
REER volatility-24.95-22.34-29.29
Terms of trade growth20.6513.0031.19
Memorandum item:
Mean investment growth1.881.861.89
Source: IMF staff estimates.

Estimated based on the standard deviations of independent variables and mean investment growth rate over the period.

Source: IMF staff estimates.

Estimated based on the standard deviations of independent variables and mean investment growth rate over the period.

13. The effects of interest rate and real exchange rate volatility on investment are robust when adding other controls. Model 2-6 suggests that both VIX and lending volatility affect investment negatively, but the impact is not statistically significant. The insignificant impact also applies to real credit and market capitalization growth, despite the fact that both variables are positively associated with investment growth.

Corporate investment

14. We use firm-level panel data on listed companies from the Worldscope database to estimate the standard neoclassical investment model. This relates current investment to expectations of future profitability through Tobin’s Q ratio,10 and is augmented by additional variables including: (i) liquidity, which measures the internal funds available to finance investment projects and is typically used in the literature as a proxy for financing constraints; (ii) leverage as a proxy for the effect of financial structure on investment; and (iii) interest rate volatility to capture the potential negative impact of uncertainty11 on investment (Box 1).

Estimation of Firm-Level Investment Function 1/2/3/4/
Expected ProfitabilityLiquidityLeverageInterest Rate VolatilityLagged Investment Rate
All nonfinancial firms
1990-101.726***0.104**-0.102***-0.001*0.274***
2005-101.939***0.079**-0.085***-0.010**0.280***
Manufacturing firms
1990-101.008**0.062-0.090***-0.001*0.261***
2005-100.786*0.045-0.085***-0.0100.272***
Services firms
1990-103.626*0.192**-0.058-0.0020.452***
2005-104.715**0.099**-0.058*-0.0120.420***
Small firms
1990-101.976**0.135***-0.101***-0.002***0.199***
2005-104.315***0.066-0.101***-0.0100.123**
Large firms
1990-100.410.199***-0.077***-0.002***0.404***
2005-100.3680.148***-0.052*-0.033***0.375***
Source: IMF staff estimates.

Two-step robust Arellano and Bond GMM estimates.

An * indicates significance at 10 percent ** significant at 5 percent, and *** at 1 percent.

Instruments are second and third period lags of Tobin’s Q, liquidity, leverage, and the investment rate.

Year dummies are included in the estimation, but not reported here

Source: IMF staff estimates.

Two-step robust Arellano and Bond GMM estimates.

An * indicates significance at 10 percent ** significant at 5 percent, and *** at 1 percent.

Instruments are second and third period lags of Tobin’s Q, liquidity, leverage, and the investment rate.

Year dummies are included in the estimation, but not reported here

15. Estimation results show that there are strong links between investment and fundamentals, although the significance and magnitude of links vary based on time and firm characteristics. In the corporate sector, balance sheets and profits strengthened considerably, leverage declined and profits are high. All of this has contributed to recent high investment growth. Increased interest rate volatility and financing constraints, on the other hand, have hindered recent investment, particularly for large firms.

  • The results of the main model confirm that the profitability expectations, positive and mostly significant, are crucial in sustaining higher investment rates. Profitability expectations are more important for investment in the services sector and small firms, as evident from the much larger coefficients to investment growth.

  • Liquidity is positively associated with investment and significant in all samples, suggesting that firms may be financially constrained in exercising their investment decisions. The model suggests liquidity is more important for larger firms.

  • Leverage is significant at 1 percent with a negative coefficient, implying that higher debt-to-assets ratio would impede investment. The leverage coefficients of smaller and manufacturing firms are much larger and significant, implying that greater access to debt by those firms could also lead to negative implications if debt accumulation increases thus ultimately offsetting the benefit of lax liquidity constraints.

Indonesia: Corporate Investment Rate and Liquidity

(Median, in percent)

Sources: Thomson Reuters Worldscope; and IMF staff estimates.

Firm-Level Investment and Interest Rate Volatility

Sources: Bloomberg LP.; Thompson Reuters Worldscope; and IMF staff estimates..

1/ Calculated by using SBI 1-month and JIBOR 1-month interest rates.

  • Moreover, high market volatility acts as an impediment to investment as suggested by the negative coefficient of the interest rate volatility measure. This is also evident from the historical volatility and investment trends. Specifically post-2004, volatility has become a more crucial factor for investment. Although interest rate volatility is of equal importance for small and large firms, it is more significant for larger firms.

Economic Contribution from the Variation in Independent Variables to Investment Growth, 2005-10 1/(In percent)
Full SampleSmall FirmsLarge FirmsManufacturing FirmsServices Firms
Expected profitability20.6063.523.1353.8330.40
Liquidity14.0918.3118.4913.2214.51
Leverage-15.92-29.21-6.93-22.86-7.60
Interest rate volatility-4.07-5.61-10.59-4.73-3.56
Memorandum item:
Median investment rate, 2005-1013.29.316.811.118.2
Source: IMF staff estimates.

Estimated based on the standard deviations of firm-level data for independent variables and median investment rate.

Source: IMF staff estimates.

Estimated based on the standard deviations of firm-level data for independent variables and median investment rate.

D. Policy Implications

16. The sensitivity of investment to interest rate and exchange rate volatility reinforces the benefits of certainty and predictability of the monetary policy framework. The empirical results suggest that investment decisions can be affected by uncertainty of the environment concerning interest rate and exchange rate policy. To the extent that the Bank Indonesia can effectively communicate its policy objectives, it would help guide market expectations better and hence reduce interest and exchange rate volatility.

17. Deepening financial markets is crucial to strengthen investment. Indonesia’s banking sector is far smaller than in other emerging markets, which may reflect, in part, the continued aversion to debt since the 1998 financial crisis. The insignificance of credit and significance of cash flow for investment imply that most firms, especially in the dynamic resource extraction sector, choose to finance investment through retained earnings. Financial deepening will be key to mobilizing domestic savings to fund both private and public investment.

Money Supply and Credit

(In percent of annualized GDP)

Sources: CEIC Data Co. Ltd.; IMF, Integrated Monetary Database; and IMF staff estimates.

1/ Claims on private sector credit at Financial Corporations Survey level.

2/ Includes Malaysia, Thailand, and the Philippines.

18. Promoting financial sector development, especially encouraging bond market development, would help open up additional channels for funding. The authorities’ growth model relies on private investment for a significant part of infrastructure creation, to which the current bank-centric financial market structure is ill-suited. That large firms—exactly the types to engage in risky long-term investment—appear credit constrained suggests that it is not ample liquidity provision by banks at low interest rate that matters for investment, but rather the financing model itself. Specifically, it calls for the development of a corporate bond market, which is extremely small, thin, and illiquid in Indonesia.

Domestic Debt Securities Outstanding-Corporate Issuers 1/

(In percent of GDP)

Sources: Bank for International Settlements; IMF, World Economic Outlook; and IMF staff estimates.

Local Currency Bonds Outstanding

(In percent of GDP)

Source: AsianBondsOnline.

19. Fiscal reforms to increase capital investment and the stock of infrastructure remain a priority. The PPP program could be improved by strengthening project selection and preparation, especially at the local government level. As regards purely private infrastructure investment, a critical constraint is land. The recently approved land acquisition law could be an important means for unlocking this bottleneck once all the related implementing regulations are finalized.

20. While progress has been made, there exists scope to further improve the business environment. Indonesia does relatively poorly in the ease of doing business12 and the rigidity of employment is high. These inhibit investment and employment creation in the formal sector, especially for new labor force entrants. Further business climate reforms could help boost FDI and domestic investment and raise potential GDP growth. Surveys suggest that a streamlined process for business creation, greater labor market flexibility, and improved legal and regulatory framework for entrepreneurs and bankruptcy would reduce risk perceptions. Persistent labor market rigidities could make it more difficult and expensive for companies to manage the workforce, constraining the manufacturing sector.

Ease of Doing Business 1/

(A higher percentile rank indicates a better business environment)

Sources: World Bank, Doing Business Database 2012; and IMF staff estimates.

1/ Included as inverse percentile rank of of actual Ease of Doing Business rankings.

E. Conclusion

21. The recent rapid investment growth has been driven by the booming commodity sector, but investment in infrastructure remains lagging. Indonesia has benefited from demand for commodities from major emerging markets, which has driven up global commodity prices and spurred investment in those sectors in Indonesia. Infrastructure investment—and public investment more generally—however, remain low, with infrastructure bottlenecks identified as pressing issues in the global competiveness ranking.

22. At the aggregate level, investment in Indonesia is negatively correlated with interest rate volatility, exchange rate volatility, and the real lending rate, while it is positively correlated with improvements in the terms of trade. Prudent macroeconomic policies over the past decade have reduced the cost of capital and the recent high investment growth is being supported by strong regional demand for commodities, which has, in turn, strengthened Indonesia’s TOT. On the other hand, continued volatility of real exchange and interest rates has hurt investment. Importantly, interest rate volatility is becoming an increasingly important factor in affecting investment, as indicated by the doubling of the magnitude of the coefficient in regression estimates for the post-2005 period.

23. Firm level estimation results confirm the important role interest rate volatility plays in firms’ investment decisions and also point to the importance of continued financial market deepening and development. Our analysis suggests that overall investment is negatively affected by interest rate volatility (reinforcing the benefits of focusing on monetary stability) and insufficient financial market deepening (which is again affected by the monetary policy framework). Regression equations with firm-level data show that interest rate volatility negatively affects investment, and similar to the macroeconomic level data results, the responsiveness of investment decisions to interest rate volatility has increased significantly in the more recent sample period. Finally, firms’ investment decisions are highly affected by their internal cash positions, especially for large firms.

Box 1.Estimation Model of the Corporate Investment in Indonesia

Determinants of corporate investment in Indonesia are estimated using the standard neo-classical investment model. Preliminary data are obtained from Thompson Reuters Worldscope database, which covers over 400 listed firms in Indonesia for the years 1990–2010. We have further refined the sample by excluding firms in the financial services sector based on the GICS industry classification system. The majority of listed nonfinancial firms in Indonesia are in the consumer discretionary, industrial, materials, and consumer staples sectors. Firm-level sub-industry models are estimated by further collapsing GICS sectors into three broad sectors: manufacturing, services, and IT. Finally, separate models are estimated based on a more recent period (2005–10) sample. The main sample is also divided into two subsamples, small and large, based on sales size compared to the median.

The model is estimated by modeling the relationship between the current corporate investment rate and expected profitability along with a vector of other related variables. The model can be expressed as follows:

(IK)it=ct+bQit+γZit+ɛit

In the above equation, I/K denotes the investment rate, calculated as capital expenditure over plant, property, and equipment. Furthermore, Q stands for Tobin’s Q, which is introduced as a proxy for expected profitability. Tobin’s Q is calculated as the market value of equity plus the book value of debt over book value of total assets. The vector of additional factors includes leverage, liquidity, and interest rate volatility variables. Leverage is estimated using the debt-to-total assets ratio, while liquidity is estimated by the ratio of liquid assets to capital. Interest rate volatility is estimated as the standard deviation of the SBI rate, gap-filled with JIBOR to obtain a longer series.

The models are estimated as first-differenced Arellano-Bond dynamic panel data models with GMM instruments.1 This method (xtabond2) is widely used to estimate neo-classical investment models using micro-level data. This approach further allows the capture of endogeneity and to estimate a more efficient long run model by eliminating individual effects through first differencing and introducing more robust instruments for the IV estimation. Second and third period lags of the dependant variable, profitability, leverage, and liquidity are included as instruments; the validity of these instruments is confirmed in all models using the Hansen J test. Furthermore, first-differenced lagged dependant variables included in the models contain no second-order serial correlation.

1 The dynamic panel data models with GMM instruments were chosen over several other techniques including fixed effects, OLS, and first-differenced models. Random effects model was rejected by the Hausman test at 95 percent confidence interval.
References

Prepared by Yong Sarah Zhou and Dulani Seneviratne.

Both Fitch and Standard & Poor’s are now rating Indonesia as investment grade. And Moody’s is rating Indonesia one notch below investment grade but with positive outlook.

See Chapter III of this selected issues paper (SIP) and Zhou (2011) on Indonesia’s potential growth.

It is well behind more advanced ASEAN members Singapore (5), Malaysia (30), and Thailand (35), and also behind China (50) and India (62). (World Bank, 2011).

Sun and Seneviratne (forthcoming).

Guimaraes and Unteroberdoerster (2006) have used a similar method to estimate the determinants of long run private investment for Malaysia.

Descriptions of the model and the detailed path of variables will be included in a forthcoming working paper and are available from the author by request.

Defined as the ratio of the stock market value of the firm to the replacement cost of its capital stock.

Recent microeconomic theory emphasizes the role of uncertainty on investment (Dixit and Pindyck, 1994).

There was a slight drop in Indonesia’s ranking in the 2011 Doing Business Report from 115 in 2010 to 121 in 2011.

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