Albania: Selected Issues

International Monetary Fund. European Dept.

doi:10.5089/9781484379097.002.A001

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Albania: Selected Issues

Author: International Monetary Fund. European Dept.

Publication Date:: 09 Jun 2016

eISBN:: 9781484379097

Language:: English

Keywords:: ISCR; CR; Albania; tax; HP filter; construction boom; production function approach; output gap law of motion; TFP gain; Output gap; Total factor productivity; Production growth; Human capital; Global

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Albania: Selected Issues

Abstract

Albania: Selected Issues

Potential Growth and Output in Albania¹

Growth in Albania has slowed since the global financial crisis. This note aims to determine how much of the slowdown is due to cyclical conditions and how much to a reduction in potential growth. The analysis below shows that average growth in 2009–14 dropped by 3.2 percentage points relative to 1997–2008, of which 2.8 percentage points are due to lower potential growth. The first section of this note focuses on growth accounting to understand the drivers of growth. The second section looks at the medium term outlook. Finally, the third section estimates and discusses potential output.

A. Background and Growth Accounting

1. Albania’s real GDP growth² has weakened since the global financial crisis. The average growth rate fell from 5.5 percent in 1997–2008 to 2.4 percent in 2009–14. Whereas Albania’s pre-crisis growth was among the highest in the Western Balkan region, post-crisis growth has decelerated to around the regional average (Figure 1). During the 2000s, the Albanian economy underwent a transformation which included large-scale privatization and massive reallocation of resources across sectors, mainly from agriculture and large SOEs to construction, retail trade, and the financial sector. Non-tradable sectors, in particular construction, expanded considerably thanks in part to rapid credit growth (see section C). Since 2008, the contributions of construction and services have declined significantly (Figure 2).

2. A growth accounting exercise shows declining contributions of total factor productivity (TFP) and capital (both physical and human). During 2009–14, their contributions halved compared to 1997–2008 (see Table 1 and Box 1 for details), in line with regional trends (Figures 3 and 4). Lower contribution from human capital also explains the relatively low growth during 2010–15 compared to other countries in the region.

The slowdown in TFP reflects trends observed in other transition economies, as well as delays in key structural reforms. During the 2000s, Albania’s TFP increased due to four factors: fast convergence to the technological frontier, as in other emerging economies (WEO April 2015); the domestic reallocation of resources from low productivity to high productivity sectors (Kota 2009); large-scale privatization; and the expansion of financial intermediation. The slowdown in TFP since 2009 is explained by decelerating technological convergence after the fast catch-up, the end of the privatization program, and decreasing returns from resource reallocation. The sluggish growth in TFP is further attributed to a slower reform implementation relative to new EU member states, particularly in the areas of property rights, rule of law, and governance (Murgasova and others, 2015).
The deceleration in physical capital accumulation is attributed to the global financial crisis and the end of the construction boom. During the 2000s, easy credit conditions fed a construction boom which accelerated capital accumulation. By 2009–10, a housing glut in Albania and increased risk aversion as a result of the global financial crisis halted credit growth and the construction boom. A drop in remittances contributed to the decline in construction. The crisis also increased uncertainty which reduced the firms’ incentives to invest.
In the 2000s, human capital accumulation—approximated by average years of schooling—decelerated relative to the 1990s as well as regional peers (Figure 5). Average years of education increased from 9.5 in 2000 to 9.8 in 2005 and reached to 9.9 in 2010.
Labor contribution remains negative mainly reflecting Albania’s demographic trends. Population fell by more than 10 percent since the end of the communist regime in the early 1990s, mainly due to mass emigration (Figure 6). Emigration continues but its pace has declined significantly. In 1995–99, net emigration accounted for 2 percent of the population per year, while in 2010–14 it shrank to ¼ percent per year. Labor force participation decreased during the last three decades (Figure 7), in part due to the steady inflow of remittances. Employment rates fell gradually until 2013, when construction activity collapsed in Albania. Labor force participation rate has improved in the postcrisis period, boosted by the opening of government employment offices in villages, to facilitate job searches.

Table 1.

Albania: Growth Accounting

Source: IMF staff estimates.

Table 1.

Albania: Growth Accounting

		1993-1996 (Early transition)	1997-2008 (Boom)	2009-2014 (Post-crisis)	2015	2016
Real GDP growth		10.1	5.5	2.4	2.6	3.4
	Capital contribution	−0.4	2.3	1.5	1.1	1.7
	Labor contribution	2.4	−0.4	−0.5	0.3	0.2
	Human capital contribution	0.3	0.9	0.1	0.1	0.1
	TFP contribution	7.7	2.8	1.2	1.1	1.4

Source: IMF staff estimates.

Table 1.

Albania: Growth Accounting

		1993-1996 (Early transition)	1997-2008 (Boom)	2009-2014 (Post-crisis)	2015	2016
Real GDP growth		10.1	5.5	2.4	2.6	3.4
	Capital contribution	−0.4	2.3	1.5	1.1	1.7
	Labor contribution	2.4	−0.4	−0.5	0.3	0.2
	Human capital contribution	0.3	0.9	0.1	0.1	0.1
	TFP contribution	7.7	2.8	1.2	1.1	1.4

Source: IMF staff estimates.

B. Medium-Term Growth Outlook

3. Albania’s medium-term growth is expected to recover to around 4 percent of GDP, broadly in line with regional peers with similar per-capita income levels. The medium-term growth projections for the Western Balkans are generally higher than those for other Central and Southeastern European peers reflecting convergence dynamics (Figure 8). The key assumption behind these projections is that technologies and institutions converge and that international capital flows fuel this catch-up process. However, the weak growth observed during 2010–15 indicates that the speed of convergence may have slowed down compared to the pre-crisis period.

4. Growth is expected to accelerate in the medium-term supported by ongoing FDI and continued reforms towards EU accession. FDI projects will boost the capital stock, but reforms to increase TFP as well as labor utilization will be needed as well (Table 2). TFP gains will be driven by increased financial development, the clarification of property rights over land, improvements in the rule of law, and judiciary reform.³ This scenario also assumes that labor’s contribution increases as a result of three factors: a more stable population as migration decelerates, a small improvement in the labor force participation rate, and a gradual reduction in unemployment.

Table 2.

Albania: Medium Term Growth

Source: IMF staff estimates.

^{^1/}Reform scenario is consistent with the IMF program.

^{^2/}Assumes labor and TFP contributions consistent with historical averages for 2009-14, and the capital stock growing at a rate 85 percent lower than the reform scenario.

Table 2.

Albania: Medium Term Growth

		2016-19 Reform ^1/	2016-19 No reform ^2/
Real GDP growth		3.9	2.4
	Capital contribution	1.8	1.5
	Labor contribution	0.2	−0.5
	Human capital contribution	0.1	0.1
	TFP contribution	1.7	1.2

Source: IMF staff estimates.

^{^1/}Reform scenario is consistent with the IMF program.

^{^2/}Assumes labor and TFP contributions consistent with historical averages for 2009-14, and the capital stock growing at a rate 85 percent lower than the reform scenario.

Table 2.

Albania: Medium Term Growth

		2016-19 Reform ^1/	2016-19 No reform ^2/
Real GDP growth		3.9	2.4
	Capital contribution	1.8	1.5
	Labor contribution	0.2	−0.5
	Human capital contribution	0.1	0.1
	TFP contribution	1.7	1.2

Source: IMF staff estimates.

^{^1/}Reform scenario is consistent with the IMF program.

^{^2/}Assumes labor and TFP contributions consistent with historical averages for 2009-14, and the capital stock growing at a rate 85 percent lower than the reform scenario.

5. A reduction of migration outflows can help output growth. The reform scenario above assumes that for the next 10 years population continues decreasing at 0.2 percent per year (INSTAT’s estimate for 2015). This is a conservative assumption considering that the UN Population Prospects Report projects population growing for Albania—even assuming emigration—over the next 10 years. If net migration is reduced to zero, real GDP growth will accelerate by an additional 0.2 percentage points. The effect on real GDP per capita growth will depend on the level of human capital of the emigrants and the effect of that additional human capital on productivity. A higher human capital stock will increase productivity (through learning effects) and accelerate real GDP per capita growth (Lucas, 1988).

C. Potential Output and Output Gap Estimations

Challenges of Potential Output Estimation

6. Measuring potential output is a complex task in developing economies. Potential output is defined in Okun (1962) as the maximum production level that avoids inflationary pressures. Potential output is unobservable and therefore it needs to be estimated. Each of the three standard approaches—univariate filters, production function, and multivariate filters—has advantages and disadvantages. No approach is free from controversy. The task is more complex in emerging economies where structural breaks and supply shocks are larger and more frequent. Short time series and limited data availability also constrain the estimations.

7. Univariate filters are simple but lack economic structure. These statistical filters, such as the Hodrick-Prescott filter, require a single input (only GDP series) and are easy to implement. Potential output is computed as a smoothed sequence over the actual output data. This implies that the average output gap is zero, by definition. However, these filters are sensitive to the smoothing parameters used and subject to the endpoint problem (the substantial revision to the end values of the series as the sample is expanded or forecast uncertainty is reduced). Another limitation of these filters is the lack of economic structure which hampers their ability to capture structural changes in the economy (Kuttner, 1994). While these filters can produce sensible results for large and advanced economies where aggregate supply shocks are smaller, they are less appropriate for developing economies where structural changes are important.

8. The production function approach identifies the drivers of growth, but is vulnerable to parameter mis-specification. A production function is assumed and potential output derives from combining the actual stock of capital with filtered series of employment and TFP. The main issue here is that it requires additional information such as employment, estimates of the capital stock, and an assumption regarding capital’s income share. The estimation is sensitive to the parameters assumed—in particular, the depreciation rate used to construct the capital stock series and the filtering method applied to employment and TFP.

9. Multivariate filters add economic structure, including indicators such as the unemployment rate or the capacity utilization rate; however, their estimation is complex. The method combines a univariate filter with a Phillips Curve and Okun’s Law to incorporate information from inflation and unemployment data to estimate potential output. These filters produce real-time estimates that are less sensitive to the endpoint problem when they are complemented with expectations of growth and inflation, but they are still subject to uncertainty from model or parameter mis-specification.

Measuring Potential Growth and the Output Gap

10. We estimate potential output using five models: two Hodrick-Prescott filters, two versions of the production function, and a multivariate filter. The estimations have been computed using annual data because the quarterly output series are short—starting only in 2005—and problematic, due to the high shares and volatility of agriculture and hydropower generation.

Hodrick-Prescott (HP) filter: Two cases are considered for which the smoothing parameter is set at 100 and 6.25, respectively. These values reflect discussions in the literature—see Ravn and Uhlig (2002), for example. The real GDP series have been forecasted until 2020 to mitigate the endpoint problem. Only the HP filter is considered because at annual frequencies other filters deliver similar results.
Production function: This method breaks down output growth into contributions from TFP, capital, and labor. The actual capital stock is combined with the filtered labor and TFP series to obtain potential output. The parameters of the production function are detailed in Box 1 and the filtering technique is HP with smoothing parameters 6.25 and 100.
Simple multivariable filter (IMF, 2015): This method considers additional variables such as unemployment, expectations of output growth and inflation, and relationships among variables such as the Phillips Curve and Okun’s Law. Details of the filter are provided in Box 2. The method is a general case of an extended Kalman filter model.

11. These estimations show similar patterns for potential output growth but higher dispersion in terms of the output gap (Figure 9–11). In 2016, all the methods show potential growth around 3 percent. Estimates of output gap, however, range between -0.2 to -2.6 percent of GDP (Table 3). To mitigate the specification errors of the different approaches, the results are aggregated using the mean across estimation techniques—as in Medina (2010).

Table 3:

Potential Growth and Output Gap 2016

Source: IMF, staff estimates.

Table 3:

Potential Growth and Output Gap 2016

	Potential Growth	Output gap
HP (lambda=6.25)	2.9	−2.3
HP (lambda=100)	3.0	−0.4
Production function (lambda=100)	3.1	−1.4
Production function (lambda=6.25)	3.3	−0.2
Simple multivariate filter (IMF WP/15/79)	3.0	−2.6

Source: IMF, staff estimates.

Table 3:

Potential Growth and Output Gap 2016

	Potential Growth	Output gap
HP (lambda=6.25)	2.9	−2.3
HP (lambda=100)	3.0	−0.4
Production function (lambda=100)	3.1	−1.4
Production function (lambda=6.25)	3.3	−0.2
Simple multivariate filter (IMF WP/15/79)	3.0	−2.6

Source: IMF, staff estimates.

12. Estimation results suggest that potential growth has declined since 2007–08. Average potential growth fell from around 6 percent during 1997–2008 to around 3 percent during 2009–14 (Table 4). The estimates also point to an increase in potential growth during 2015 and 2016. In addition, the range of estimates of potential growth across the different methodologies has narrowed compared to 1994–97, likely reflecting the stabilization of the economy relative to that period. Below, we present actual real GDP growth (Δy_t) as function of potential growth $(Δ y_{t}^{*})$ and the change in the output gap:

Table 4.

Real GDP Growth

Source: IMF staff estimates.

Table 4.

Real GDP Growth

	1997-2008 (Boom)	2009-14 (Post-crisis)	2010	2011	2012	2013	2014	2015	2016
a) Actual GDP growth	5.5	2.4	3.7	2.5	1.4	1.1	2.0	2.6	3.4
b) Potential GDP growth (average)	5.9	3.1	3.6	3.2	2.6	2.4	2.3	2.6	3.1
c) Change in output gap (a - b)	−0.4	−0.7	0.1	−0.6	−1.2	−1.3	−0.3	0.0	0.4

Source: IMF staff estimates.

Table 4.

Real GDP Growth

	1997-2008 (Boom)	2009-14 (Post-crisis)	2010	2011	2012	2013	2014	2015	2016
a) Actual GDP growth	5.5	2.4	3.7	2.5	1.4	1.1	2.0	2.6	3.4
b) Potential GDP growth (average)	5.9	3.1	3.6	3.2	2.6	2.4	2.3	2.6	3.1
c) Change in output gap (a - b)	−0.4	−0.7	0.1	−0.6	−1.2	−1.3	−0.3	0.0	0.4

Source: IMF staff estimates.

Δ y_{t} = Δ y_{t}^{*} + Δ o u t p u t g a p_{t}

13. Despite the wide dispersion in output gap estimates, they all indicate that since 2013 the economy has been below its potential. The different estimates all point to the conclusion that the output gap is now gradually closing.

The Impact of Credit Cycles

14. Estimates of potential GDP growth are likely impacted by the credit boom in the 2000s. The credit expansion caused real estate prices to grow much faster than the general price level. Between 2002 and 2010 property prices increased by more than 70 percent (in real terms) and credit to the private sector expanded from 6 to 36 percent of GDP (Figure 12). Such large credit booms can lift estimates of potential output temporarily, and vice versa (Berger and others, 2015).

15. The estimate of potential output is therefore adjusted to take into account credit cycles. The impact of the credit cycle on potential output is estimated by considering the HP filter as the baseline potential output and using a simplified version of Borio and others (2013). See Box 3 for details. The HP filter is augmented to consider the effect of private sector credit and property prices. The estimation results show that output gaps have been larger than those estimated by the simple HP filter during the credit boom (Figure 13). The results also imply that the average increase in potential growth due to the credit cycle was around 0.5 percentage point during 2002–08 (Figure 14).

D. Conclusions

16. We conclude that average growth in 2009–14 dropped around 3.2 percentage points relative to 1997–2008, of which 2.8 percentage points are due to lower potential growth. The main policy implication is that countercyclical policy should be centered on a potential growth of 2.9–3.2 percent and not the historical values of 5–6 percent. The second policy implication is that enhancing potential growth through structural reforms should be a top priority. Key growth-enhancing reforms should cover land property rights, the rule of law, fighting corruption, and the judiciary system. Improvements in land property rights will facilitate the reallocation of resources towards more productive sectors (such as tourism and agriculture). The rule of law and judicial reform will enhance growth across sectors by improving the return on investment.

Box 1.

Growth Accounting Assumptions

The calculations assume a Cobb-Douglas production function. Output (Y) depends on physical capital (K), labor (L), human capital (H), and total factor productivity (A). Capital’s income share (α) is assumed to be 0.35, as in D’Auria and others (2010).

Y_{t} = A_{t} K_{t}^{\propto} {(L_{t} H_{t})}^{1 - \propto}

The stock of physical capital (K) is computed using the permanent inventory method, using the series for real GDP and real gross fixed investment (I) since 1980. The depreciation rate (δ) is set at 8 percent, consistent with Kota (2007), and the exogenous trend growth (g) is 2.6 percent, consistent with the historical data for the 1980–2016. The initial capital stock and its dynamics are described by:¹

\begin{matrix} K_{1980} = \frac{I_{1980}}{δ + g} \\ K_{t + 1} = (1 - δ) K_{t} + I_{t} \end{matrix}

Labor (L) is defined as the employed population:

L = (P o p_{15 - 64}) (P a r t . r a t e) (1 - U R)

The series for population between ages of 15 and 64 was constructed based on INSTAT, World Bank, and United Nations statistics. The labor force participation and unemployment rates (UR) were built by splicing series from INSTAT and ILO.

H_{t} = (a v g . y e a r s o f s c h o o l i n g) * r e t u r n o n e d u c a t i o n

H_t is the stock of human capital measured by the average years of schooling, which are computed by interpolating the Barro-Lee (2013) dataset. Finally, the return on education is assumed to be 0.11 per year consistent with the estimates of Psacharopoulos (1994).

¹ The lack of a long time series for capacity utilization constrained the analysis. Estimations using weak data on capacity utilization point to similar results for the period 2000–16.

Box 2.

Simple Multivariate Filter

Potential output is estimated following Blagrave, Garcia-Saltos, Laxton, and Zhang (2015). The filter assumes that the dynamics for potential output and the non-accelerating inflation rate of unemployment (NAIRU) are subject to shocks. It also includes empirical relations, such as the Phillips Curve and Okun’s Law, as well as expectations of inflation and output growth. The central parts of the model are the output and employment gaps that are inferred by the rest of the equations. The full model is detailed below.

Potential output and output gap dynamics

Phillips Curve

5) $π_{t} = λ π_{t + 1} + (1 - λ) π_{t - 1} + β y_{t} + ɛ_{t}^{π}$

Unemployment and NAIRU Dynamics

Expectations

where Y_t is the log of real GDP, ${\bar{Y}}_{t}$ is the log of potential output. G_t is the unobservable long term potential growth, with a steady state at G^ss. y_t is the output gap and π_t is the inflation rate. U_t is the unemployment rate, and ${\bar{U}}_{t}$ is the NAIRU with a steady state at ${\bar{U}}^{s s}$ . ${\bar{g U}}_{t}$ is the unobservable long-term change in the NAIRU and u_t is the unemployment gap. Finally ${G r o w t h}_{t + j}^{E}$ and $π_{t + j}^{E}$ denote GDP growth and inflation forecasts from WEO (and measure expectations). Finally the ε’s are shocks to the different variables. The filter is applied to data for the period 1994-2020.

The methodology requires some assumptions. The NAIRU steady state is assumed at 13 percent, close to the minimum unemployment observed during 2003-2014, and the steady state of potential growth is assumed at 3.5, close to the values observed in other Balkan countries. The model was estimated using Bayesian estimation techniques, with the priors for the parameters displayed below.

Priors used in the estimation

The main results are summarized in the equations below:

\begin{matrix} y_{t} = 0.70 y_{t - 1} + ɛ_{t}^{y} \\ π_{t} = 0.5 π_{t + 1} + 0.5 π_{t - 1} + 0.05 y_{t} + ɛ_{t}^{π} \\ u_{t} = 0.24 y_{t} + 0.44 u_{t - 1} + ɛ_{t}^{u} \\ G_{t} = 0.05 G^{s s} + 0.95 G_{t - 1} + ɛ_{t}^{G} \end{matrix}

Box 3.

Credit Cycles and “Sustainable” Output

“Sustainable” output is the output that an economy can produce in the absence of imbalances. This concept differs from potential output, which refers to the capacity to produce without accelerating inflation. During a credit boom, an economy may be producing at the potential level, but that level may not be sustainable as the financial cycle lifts potential output temporarily.

To estimate sustainable output, we start out by expressing the HP filter as a state space model and then expand the model to include financial cycle variables. The idea is to compute sustainable output which is potential output adjusted for the effect of the credit cycle. The left column below presents the HP model, while the right column presents the expanded model and details how the filter removes the financial cycle effects from potential output to arrive at sustainable output.

where y is the log of output and y* is the log of potential output. The expanded model includes Credit which is the natural log of real credit to the private sector and HousePrice which is the natural log of real housing prices. Both variables have been deflated with the CPI. $ɛ_{t}^{c}$ and $ɛ_{t}^{y *}$ represent cycle and trend shocks respectively.

The smoothing parameters of the HP filter and the expanded model are set at 100.¹

^1/ Given the limited space, we focus on a smoothing parameter of 100, but results using 6.25 instead do not change the main results.

Annex I. The Determinants of TFP

1. TFP is difficult to predict as it is a mix of structural features, such as technology and institutional quality, but also includes measurement errors from labor and capital. This appendix aims to estimate TFP as a function of the current level of institutions. This would allow us to estimate the path of TFP in the near future. Using a panel of 17 CESEE¹ economies, a model is estimated for the period 2000–14. The explanatory variables include World Bank Governance indicators, political risk from ICRG, EU-3 potential growth, a time trend, and a dummy to account for the 2009 global financial crisis.

2. The estimations imply that TFP’s contribution to growth will be 1.2–1.7 percentage points on average for the period 2016–20, depending on the institutional improvements. At the current institutional level, reflecting the current indicators of regulatory quality, rule of law and political risk, the TFP will contribute 1.2 percentage points, and assuming a 25 percent improvement in the institutional level, the TPF will contribute around 1.7. Although the results show statistically significant coefficients, they should be interpreted with caution as they are sensitive to model specification.

*** p<0.01, ** p<0.05, * p<0.1

^{^1/}Albania, Armenia, Belarus, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Moldova, Poland, Romania, Serbia, Slovak Republic, Slovenia, and Ukraine.

	Dependent variable: Coefficient	TFP contribution¹ Standard errors
Lag TFP contribution	0.107*	(0.058)
Change in regulatory quality	3.612	(2.537)
Change in rule of law	2.197	(3.206)
Change in political risk	−0.226**	(0.112)
EU-3 potential growth	3.578**	(1.801)
2009 crisis dummy	−5.854***	(1.334)
Trend	−3.469	(2.142)
Constant	8.462	(7.842)
Observations	200
Countries	17
R-squared	0.536	R-squared

*** p<0.01, ** p<0.05, * p<0.1

^{^1/}Albania, Armenia, Belarus, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Moldova, Poland, Romania, Serbia, Slovak Republic, Slovenia, and Ukraine.

	Dependent variable: Coefficient	TFP contribution¹ Standard errors
Lag TFP contribution	0.107*	(0.058)
Change in regulatory quality	3.612	(2.537)
Change in rule of law	2.197	(3.206)
Change in political risk	−0.226**	(0.112)
EU-3 potential growth	3.578**	(1.801)
2009 crisis dummy	−5.854***	(1.334)
Trend	−3.469	(2.142)
Constant	8.462	(7.842)
Observations	200
Countries	17
R-squared	0.536	R-squared

*** p<0.01, ** p<0.05, * p<0.1

^{^1/}Albania, Armenia, Belarus, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Moldova, Poland, Romania, Serbia, Slovak Republic, Slovenia, and Ukraine.

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Prepared by Ezequiel Cabezon.

In this paper, “real GDP” and “output” are used interchangeably.

Cross country estimations (see Annex I) show that TFP’s contribution should be around 1.2–1.7 percentage points on average during 2016–20.

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Albania: Selected Issues

Author: International Monetary Fund. European Dept.

Volume/Issue:: Volume 2016: Issue 143

Publisher:: International Monetary Fund

ISBN:: 9781484379097

ISSN:: 1934-7685

Pages:: 41

DOI:: https://doi.org/10.5089/9781484379097.002

Keywords
Potential Growth and Output in Albania1
References

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Western Balkans: Real GDP Growth

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Real GDP Growth

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Growth Accounting

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Western Balkans: Real GDP Growth Contributions

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Average Years of Schooling

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Albania: Population

(Millions of people)

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Labor Force Participation and Employment Rate

(Percent)

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Selected CESEE Countries: Expected Convergence

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Potential Real GDP Growth

(Percent)

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Output Gap

(Percent)

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Average Output Gap

(Percent)

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Albania: Housing Prices and Credit to Private Sector

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Output Gap and Credit Cycles

(Percent of potential output)

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Real Potential GDP Growth and Credit Cycles

(Percent)

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Albania: TFP Contribution to Real GDP Growth

(In percent)

	Dependent variable: Coefficient	TFP contribution¹ Standard errors
Lag TFP contribution	0.107*	(0.058)
Change in regulatory quality	3.612	(2.537)
Change in rule of law	2.197	(3.206)
Change in political risk	−0.226**	(0.112)
EU-3 potential growth	3.578**	(1.801)
2009 crisis dummy	−5.854***	(1.334)
Trend	−3.469	(2.142)
Constant	8.462	(7.842)
Observations	200
Countries	17
R-squared	0.536	R-squared

*** p<0.01, ** p<0.05, * p<0.1

^{^1/}Albania, Armenia, Belarus, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Moldova, Poland, Romania, Serbia, Slovak Republic, Slovenia, and Ukraine.

1)	$y_{t} = Y_{t} - {\bar{Y}}_{t}$	(Output gap)
2)	$y_{t} = ϕ y_{t - 1} + ɛ_{t}^{y}$	(Output gap law of motion)
3)	${\bar{Y}}_{t} = {\bar{Y}}_{t - 1} + G_{t} + ɛ_{t}^{\bar{Y}}$	(Potential output law of motion)
4)	$G_{t} = θ G^{s s} + (1 - θ) G_{t - 1} + ɛ_{t}^{G}$	(Potential output trend growth)

1)	$y_{t} = Y_{t} - {\bar{Y}}_{t}$	(Output gap)
2)	$y_{t} = ϕ y_{t - 1} + ɛ_{t}^{y}$	(Output gap law of motion)
3)	${\bar{Y}}_{t} = {\bar{Y}}_{t - 1} + G_{t} + ɛ_{t}^{\bar{Y}}$	(Potential output law of motion)
4)	$G_{t} = θ G^{s s} + (1 - θ) G_{t - 1} + ɛ_{t}^{G}$	(Potential output trend growth)

6)	${\bar{U}}_{t} = τ_{4} {\bar{U}}^{s s} + {(1 - τ_{4}) {\bar{U}}_{t - 1} + \bar{g U}}_{t} + ɛ_{t}^{\bar{U}}$	(NAIRU law of motion)
7)	${\bar{g U}}_{t} = (1 - τ_{3}) {\bar{g U}}_{t - 1} + ɛ_{t}^{\bar{g U}}$	(NAIRU trend growth)
8)	$u_{t} = τ_{1} y_{t} + τ_{2} u_{t - 1} + ɛ_{t}^{u}$	(Okun’s Law)
9)	$u_{t} = {\bar{U}}_{t} - U_{t}$	(Unemployment gap)

6)	${\bar{U}}_{t} = τ_{4} {\bar{U}}^{s s} + {(1 - τ_{4}) {\bar{U}}_{t - 1} + \bar{g U}}_{t} + ɛ_{t}^{\bar{U}}$	(NAIRU law of motion)
7)	${\bar{g U}}_{t} = (1 - τ_{3}) {\bar{g U}}_{t - 1} + ɛ_{t}^{\bar{g U}}$	(NAIRU trend growth)
8)	$u_{t} = τ_{1} y_{t} + τ_{2} u_{t - 1} + ɛ_{t}^{u}$	(Okun’s Law)
9)	$u_{t} = {\bar{U}}_{t} - U_{t}$	(Unemployment gap)

10)	$π_{t + j}^{E} = π_{t + j} + ɛ_{t + j}^{π^{E}}$	for j=0,1
11)	${G r o w t h}_{t + j}^{E} = {G r o w t h}_{t + j} + ɛ_{t + j}^{{G r o w t h}^{E}}$	for j=0, …, 5,

Parameter	Prior
β	0.25
λ	0.25
Θ	0.10
Φ	0.60
τ₁	0.30
τ₂	0.30
τ₃	0.10
τ₄	0.10
$std (ɛ_{t}^{\bar{Y}})$	0.50
$std (ɛ_{t}^{G})$	0.50
$std (ɛ_{t}^{y})$	1.00
$std (ɛ_{t}^{π})$	0.25

Parameter	Prior
β	0.25
λ	0.25
Θ	0.10
Φ	0.60
τ₁	0.30
τ₂	0.30
τ₃	0.10
τ₄	0.10
$std (ɛ_{t}^{\bar{Y}})$	0.50
$std (ɛ_{t}^{G})$	0.50
$std (ɛ_{t}^{y})$	1.00
$std (ɛ_{t}^{π})$	0.25

HP Filter	Expanded model
$Δ y_{t}^{} = Δ y_{t - 1}^{} + ɛ_{t}^{y *}$	$Δ y_{t}^{} = Δ y_{t - 1}^{} + ɛ_{t}^{y *}$
$y_{t} = y_{t}^{*} + ɛ_{t}^{c}$	$y_{t} = y_{t}^{*} + γ_{1} Δ C r e d i t + γ_{2} Δ H o u s e P r i c e + ɛ_{t}^{c},$

HP Filter	Expanded model
$Δ y_{t}^{} = Δ y_{t - 1}^{} + ɛ_{t}^{y *}$	$Δ y_{t}^{} = Δ y_{t - 1}^{} + ɛ_{t}^{y *}$
$y_{t} = y_{t}^{*} + ɛ_{t}^{c}$	$y_{t} = y_{t}^{*} + γ_{1} Δ C r e d i t + γ_{2} Δ H o u s e P r i c e + ɛ_{t}^{c},$

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Abstract

Potential Growth and Output in Albania1

A. Background and Growth Accounting

B. Medium-Term Growth Outlook

C. Potential Output and Output Gap Estimations

Challenges of Potential Output Estimation

Measuring Potential Growth and the Output Gap

The Impact of Credit Cycles

D. Conclusions

Annex I. The Determinants of TFP

References

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Inter-American Development Bank

International Labour Organization

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Potential Growth and Output in Albania¹