Nicaragua: Selected Issues

International Monetary Fund

doi:10.5089/9781475506631.002.A001

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Nicaragua

Selected Issues

Author: International Monetary Fund

Publication Date:: 05 Sep 2012

eISBN:: 9781475506631

Language:: English

Keywords:: ISCR; CR; tariff; minimum wage; production function approach; equilibrium unemployment; Cobb-Douglas production function; TFP series; decrease poverty; Potential output; Productivity; trend productivity gain

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The paper is an elaborated report on Nicaragua’s potential economic growth. The challenges and idiosyncratic shocks were immense but the policies of better education, labor contracts, and accomplishments in public investments paved the way for movement of the economy. The external competitiveness and exchange rate assessment also have an important hand. The achievements in the electricity sector and the improvement in reforming the pension system are the prominent aspects. On the whole, the Board considers this growth as a positive trial of development in the global panorama.

Abstract

I. Raising Potential Output: The Challenge of Inclusive Growth¹

Alternative techniques produce estimates consistent with a 4-percent potential growth rate for Nicaragua. With population growing about 2 percent annually, the current increase in GDP per capita is not enough to reduce poverty decisively. To raise potential growth, appropriate structural reforms to enhance productivity should be implemented.

A. Introduction

1. Albeit unobservable, potential growth is a crucial variable for economic policy discussions. In the case of monetary policy, potential output and output gap are useful concepts to evaluate the existence of inflationary pressures, while a good estimate of potential GDP allows measuring the structural fiscal stance (as it allows the identification of cyclical factors), thus allowing planning a sustainable fiscal path.² Moreover, by assessing Nicaragua’s growth potential, staff can assess the speed of future reductions in poverty.

2. Several estimations of potential output and output gaps are discussed in this chapter. Results are consistent across different methodologies, comprising a production function approach, and switching and state-space models. They are consistent with a 4-percent potential growth rate and a zero output gap in 2011. Given projected population growth levels, sustained efforts should be made over a prolonged period of time to increase GDP per capita and hence decrease poverty faster.

3. Policies should aim at raising total factor productivity (TFP) growth. According to the estimates in this chapter, productivity has been the Achilles’ heel of the Nicaraguan economy. Low productivity is associated with lack of human capital, and inadequate investment and production organization. Structural policies should aim at raising productivity by increasing the quality of education and, more broadly, raising incentives to human capital accumulation, including by lowering labor market informality. Better protecting property rights, and improving the business environment and institutions would raise incentives to invest and innovate in the country, thus boosting productivity and capital levels.

B. Potential Output and Output Gap: The Production Function Approach

4. The growth accounting exercise assumes standard production function parameters and equilibrium labor utilization. The Nicaraguan economy is assumed to be characterized by a Cobb-Douglas production function with constant returns to scale (CRS) technology $Y_{t} = A_{t} K_{t}^{α} L_{t}^{1 - α}$ where Y_t is output, K_t and L_t are capital and labor services, while A_t is the contribution of technology or total factor productivity (TFP); and where the output elasticities (α denotes the capital-output elasticity) sum up to one reflecting CRS (Box 1). The natural rate of unemployment is assumed to be either 7.8% (the average between 1997 and 2010) or the result of applying a Hodrick-Prescott (HP) filter to the unemployment series. The estimation used annual data from 1994 to 2011.

5. This method points to potential growth between 3.3 percent and 3.7 percent, depending on the assumed natural rate of unemployment. Average GDP growth for 1995-2011 is 3.8 percent with a volatility of 2 percent due to episodes of slow growth in 2002, the recession in 2009, and the economic boom in the second half of the 90s. The model based on a fixed rate of unemployment produces an output gap series with a large standard deviation: 2.5 percent or almost 1 percentage point above the alternative model with filtered unemployment rate. The average output gap for the whole sample period is -1 percent if the equilibrium unemployment rate is assumed constant, which suggests that it is a biased measure of cyclical variations, while the model assuming a variable equilibrium unemployment rate produces an average output gap for the same period near zero. If the recent macroeconomic stability is maintained, it is possible that potential growth going forward would be a bit better than observed historically, and a potential growth rate around 4 percent looks reasonable.

		Mean	Std. Dev.	Min	Max
Actual GDP Growth		3.83	2.03	−1.47	7.04
	Potential Growth (u=HP)	3.69	0.81	2.62	5.72
	Potential Growth (u=7.8)	3.33	0.55	2.65	4.97

	Mean	Std. Dev.	Min	Max
Output Gap (u=HP)	−0.15	1.47	−3.11	1.59
Output Gap (u=7.8)	−0.97	2.46	−7.21	2.43

		Mean	Std. Dev.	Min	Max
Actual GDP Growth		3.83	2.03	−1.47	7.04
	Potential Growth (u=HP)	3.69	0.81	2.62	5.72
	Potential Growth (u=7.8)	3.33	0.55	2.65	4.97

	Mean	Std. Dev.	Min	Max
Output Gap (u=HP)	−0.15	1.47	−3.11	1.59
Output Gap (u=7.8)	−0.97	2.46	−7.21	2.43

6. Since the end of the 1990s, the output gap has been fluctuating between ±2 percent. (Figure 1) Our results suggest that the typical business cycles duration is around eight years. We can identify 2002-2003 as the beginning of the last cycle which ends in 2009 after a one-year recession associated with the international crisis. Unambiguously, the two models suggest that the output gap is practically closed during 2011 after two years of GDP growing over 4 percent (4.5 and 4.7 percent in 2010-2011 respectively). This would imply no inflationary pressures from aggregate demand; however estimations of traditional Philips curves using low frequency data and optimal number of lags suggest that the feedback from output gap to inflationary pressures is statistically insignificant.

7. Productivity growth, as measured by this approach, is low independently of specific assumptions and the sample period used. From the mid 1990s until 2011, capital growth explained 55 percent of the growth in GDP while labor services explained 42 percent, leaving only a marginal role for total factor productivity. Breaking the sample period in two, the Nicaraguan economy grew 5.4 percent in the 1990s, with total factor productivity either explaining only about 7 percent of this performance (assuming a fixed equilibrium unemployment rate) or actually declining. TFP is estimated to have either remained unchanged or declined in the following decade with some cyclicality during the recent recession and recovery period. This performance is much worse than observed in Latin America, where productivity explained on average about 50 percent of GDP growth (6.3 percent) in the last 30 years, with labor and capital formation sharing equalts parts of the remaining contribution (Harberger, 2007).³

8. Low productivity is an obstacle to decisive reductions in poverty. If output grows at about 3.7 percent, as suggested by the production function approach, and considering a 2-percent population growth, the potential output growth per capita GDP is about 1.7 percent. Using an elasticity poverty reduction-growth of -0.5 percent (FUNIDES, 2012), that implies a reduction of about 8 percentage points in the poverty rate by the end of the decade. With the right structural measures and policies, the economy could grow faster, say about 5 percent, which would imply poverty rates of about 27 percent of the population. An even higher potential growth, say 6 percent, would reduce poverty to almost 20 percent of the population by 2020.

C. Potential Growth: A Switching Model Approach

9. An alternative way to measure potential growth would be to consider explicitly that the economy could be in any of three basic states: overheating, sustainable growth, and recession. (Box 2) Using this approach we can derive the distribution functions for each predefined scenario. The left distribution represents the recessionary state scenario, with growth rate of about -0.3 percent and a standard deviation of about 1.2 percent. The distribution at the center represents the sustainable growth, with a mean value of about 3.9 percent and a standard deviation of 0.8 percent, and finally, the distribution on the right represents an overheating economy, with a mean growth rate of about 6.4 percent and a standard deviation of 0.5 percent.

Convergence Results for Each Scenario

Source: Fund staff calculations.

^{^1/}Unconditional.

Convergence Results for Each Scenario

Statistic	Overheating	Sustainable Growth	Moderate Growth or Recession
Growth	6.4	3.9	−0.3
Std. Deviation	0.5	0.8	1.2
Probability^1/	17.8	70.2	12.0

Source: Fund staff calculations.

^{^1/}Unconditional.

Convergence Results for Each Scenario

Statistic	Overheating	Sustainable Growth	Moderate Growth or Recession
Growth	6.4	3.9	−0.3
Std. Deviation	0.5	0.8	1.2
Probability^1/	17.8	70.2	12.0

Source: Fund staff calculations.

^{^1/}Unconditional.

10. Under a sustainable growth scenario, potential output grows 3.9 percent while in the recessionary and overheating economy growth is -0.3 and 6.4 percent, respectively. One interesting result is the homogeneity in the standard deviations. Independently of the state, the level of uncertainty is not much different: 0.5 for the overheating state, 0.8 for the sustainable growth, and 1.2 percent for the moderate growth (recessionary) scenario. This methodology yields a potential growth very close to the preferred specification for the production function approach (3.7 percent). With an economy growing at 4 percent the poverty reduction is not substantive, as the poverty headcount declines to only 32 percent by the end of the decade.

D. Potential Output: A State-Space Representation

11. A state-space approach produces a 4-percent potential growth for Nicaragua. This method looks for joint identification of the output gap and potential output growth—both treated as latent or unobserved variables. The approach considers two specifications (constant and cyclical drifts), with potential growth measured by the parameter µ (Box 3). In the case of constant drift, growth is calculated at 3.92 percent while in the cyclical drift model, potential growth is 3.96 percent, with output gap uncertainty between 1.8 and 2.2 percent—figures similar to estimates obtained by the previous approaches.

Source: Fund staff calculations.

E. Idiosyncratic Shocks and Potential Output: Spillovers from the Electricity Sector

12. A vector error correction (VEC) analysis is implemented to assess the impact of energy sector developments on the industrial sector. Since the middle of the last decade, Nicaragua has suffered from electricity shortages, which has limited growth. Using monthly data since 2005, a baseline vector autoregression (VAR) model estimates the relationship between industrial activity and electricity usage as a proxy of energy production. Standard causality tests indicate that the industrial sector is vulnerable to shocks from energy supply and unit root tests also indicate non stationarity in both variables. With this information at hand, a vector error-correction model (VEC) can be estimated and the impulse-response functions for industry activity following a standard shock in the electricity sector can be computed.⁴

13. An improvement in electric power supply drives industrial production to a significantly higher level. For instance, an expansion in electricity generation of 5 percent causes a statistically significant increase in industrial production of about 3 percent in the medium term.

F. Growth and Public Investment: how to raise growth with productive investment

14. A vector autoregression (VAR) analysis is implemented to assess the impact of public investment on growth. An increase in the public investment-to-GDP ratio of 1.4 percentage points will increase growth by about 1 percentage point in the first year, with the positive effect declining rapidly after three years. Or, a percentage point increase in public investment/GDP will raise growth by about 0.7 percentage point in the short run, with a declining impact in the following years. An important caveat is that this analysis uses historical data (1994-2011), so the expected impact on growth will depend on the quality of past investment. If the quality of future investment is larger, the final growth effect would also be larger and could even be more permanent if public investment unleashes trend productivity gains. The ultimate impact of public investment on growth could also depend on its effect on private investment. If public investment complements private investment, public investment would crowd-in private investment. If public investment is a substitute to private investment, an increase in public investment would reduce private investment, and growth effects of raising public investment would be smaller. Exploratory analysis including private investment rates in the baseline VAR shows neither crowding-out nor crowding-in effects from increases in public investment.

G. Policies to Boost Growth

15. Nicaragua’s moderate output growth is explained by low productivity growth. If total factor productivity growth is raised to, say, 1.5 percent a year, potential growth would exceed 5 percent and the poverty rate would be near 20 percent by the end of this decade.

16. A recipe for higher productivity growth would include better incentives for formal labor contracts, improved education, pro-productivity public investment and better business environment. Indeed, high informality in Nicaragua lowers incentives to innovation and growth; low education levels limit the types of products and production methods that can be used in the country affecting negatively productivity and growth; and available indicators of business and institutional conditions consistently place Nicaragua in the bottom-third of nations (Chapter 4). To change these structural problems, enforcement of formal labor relations needs to improve. Benefits associated to having a formal job also need to be raised, including by designing flexible social security plans that look more attractive to specific, high-informality groups (e.g. rural workers). Education needs to be geared at forming efficient workers, which may demand a focus on technical education that can be directly applied in the agro industry, manufacturing and key service sectors. Institutional reforms would also help, including strengthening property rights and improving business conditions (for instance, decreasing the number of days to start a business and to obtain construction permits). More broadly, larger investments in electricity production and distribution, and in infrastructure; modernization of the agro industry; broad access to credit and capital markets;⁵ and funding for business plans and marketing studies, would help raise productivity (FUNIDES, 2012). Reforms across a broad range of sectors are better-suited to raise growth than piecemeal reforms (Swiston and Barrot, 2011).

Box 1.1.

Production Function Approach

The growth accounting exercise assumes a Cobb-Douglas production function with constant returns to scale (CRS) technology $Y_{t} = A_{t} K_{t}^{α} L_{t}^{1 - α}$ where Y_t is output, K_t and L_t are capital and labor services, while A_t is the contribution of technology or total factor productivity (TFP). The labor input is defined as the number of employees in the economy and can be derived using the labor force (LF_t) and the rate of unemployment (u_t) by L_t = LF_t · (1 - u_t). Because capital input is not available, it is generated using a procedure standard in the literature (Estevão and Tsounta, 2010, Epstein and Macchiarelli, 2010, Teixeira de Silva, 2001). Assuming the following law of motion for the capital stock: K_t = (1 - δ)K_t-1 + I_t where δ is the depreciation rate and consistent with previous studies is assigned the value of 0.05, while the initial capital stock is computed as K₀ = I^*/(g + δ).I^* is the benchmark investment (calculated as the average proportion of investment in the total GDP which for Nicaragua: 0.22) while g is the average growth of the economy during the sample period 1994-2011, equivalent to 3.8 percent. Hence, based on these parameters, the initial capital stock is derived by: K₀ = 0.22 · Y₁₉₉₄/(g + δ). Since TFP is not observable, the usual procedure applies and is computed inverting the technological process from the production function as follows:

A_{t} = \frac{Y_{t}}{K_{t}^{α} L_{t}^{1 - α}} = \frac{Y_{t}}{K_{t}^{α} {({LF}_{t} \cdot (1 - u_{t}))}^{1 - α}}

Now with the TFP series and using the other inputs it is possible to decompose GDP growth in the sample period. In the production function approach, the output gap is computed using the TFP generated from the previous equation, but evaluating the production function using trends for all the variables. The usual procedure to generate trends is applied here (HP filter assuming a smoothness parameter lambda of 100). It is also assumed that the elasticity of labor to output (1 - α) is 0.5 following Harberger (2007).

Box 1.2.

Regime-Switching Model

Regime-switching models are created in order to provide a numerical interpretation of the idea that the data generating process for a time series can come from a set of stationary processes with different probability density functions. The actual data is represented by a continuum jumping from one probability density function to another, where each probability density function represents a specific scenario.

Regarding a variable y_t that comes from N alternative and possible states (s_t=1,…, N), and each one represented by its own probability density function $y \to N (θ_{s_{t}}, σ_{s_{t}}^{2})$ , it is straightforward to define the maximum likelihood function as:

f (y_{t} | s_{t} = j, Ψ_{t - 1}; Γ) = \frac{1}{\sqrt{2 π σ_{s_{t}}^{2}}} . e^{- \frac{1}{2} [\frac{y_{t} - θ_{s_{t}}}{σ_{s_{t}}}]^{2}}, \forall j = 1, 2, ....., N

where $Γ = {[θ_{1}, θ_{2}, ...., θ_{N}, σ_{1}^{2}, σ_{2}^{2}, ...., σ_{N}^{2}]}^{'}$ . The traditional optimization procedure consists in estimating the linear transformation of this expression maximizing its logarithmic function through traditional gradient methods. Assuming iid observations for all t=1, 2, 3...., T, the target equation can be represented by the transformation of the maximum likelihood equation using the natural logarithm of the function. With this methodology we can get the probability of being in each of the N alternatives states. In our exercise N is equal to three representing overheating, sustainable growth and recession.

Box 1.3.

State-Space Models

The general structure of the model is represented by two blocks of equations which represent the state space system: the measurement and the state equations.

\begin{matrix} y_{t} = H B_{t} + {Ax}_{t} + ε_{t}^{y} & (1) \end{matrix}

\begin{matrix} B_{t} = Γ_{0} + Γ_{1} B_{t - 1} + ε_{t} & (2) \end{matrix}

Equation (1) represents the dynamic of the measurement variables defined by y_t (log of GDP) explained by a vector of observed exogenous variables x_t, a vector of unobserved state variables B_t and an iid error term $ε_{t}^{y} \overset{iid}{\to} N (0, Θ)$ . For one measured variable the variance covariance matrix is defined by the scalar $Θ = σ_{y}^{2} < \infty$ and should be estimated by maximum likelihood procedures (Benes, J. and P N’Diaye, 2004, and Johnson, 2012).

The dynamic of the state variables is represented by the state equation (2). The error term is uncorrelated with the error term of the measurement equation (3), and in general is represented by a data generating process centered in zero, normally distributed, and with a diagonal variance covariance matrix $Q : ε_{t} \overset{iid}{\to} N (0, Q)$ . In our exercise we use two structures: first. the model with constant drift is defined by: $y_{t} = y_{t}^{p} + {ygap}_{t}, y_{t}^{p} = \bar{μ} + y_{t - 1}^{p}, {ygap}_{t} = ρ_{1} {ygap}_{t - 1} + ρ_{2} {ygap}_{t - 2} + ε_{t}^{ygap}$ , and second a model with mean reverting process defined by: $\begin{matrix} y_{t} = y_{t}^{p} + {ygap}_{t}, y_{t}^{p} = μ_{t - 1} + y_{t - 1}^{p} \end{matrix}$ $\begin{matrix} {ygap}_{t} = ρ_{1} {ygap}_{t - 1} + ρ_{2} {ygap}_{t - 2} + ε_{t}^{ygap}, μ_{t} = (1 - β) \bar{μ} + β μ_{t - 1} + ε_{t}^{μ} \end{matrix}$ . In both models the potential growth is defines by the coefficient μ, where output is defined by {y_t} and the two unobserved state variables, potential output and output gap, are represented by ${y_{t}^{p}, {ygap}_{t}}$ respectively.

The maximum likelihood estimation of the state space representation (1)-(2) is implemented by the Kalman filter. This is a recursive procedure based on two stages: prediction and correction. For prediction we use some prior information on estimates of the parameters Γ₀, Γ₁, H and A, and the variance covariance matrices Θ and Q, while for the correction, we use the posteriors on the estimates and the variance covariance matrix. The Kalman factor uses prior information to generate the posteriors, and this learning procedure is iteratively repeated until all the sample data is analyzed (similar to a recursive estimates procedure).

References

Benes, J. and P N’Diaye, 2004, “A multivariate Filter for Measuring Potential Output and the NAIRU: Application to the Czech Republic.” IMF Working Paper 04/45 (Washington D.C.: International Monetary Fund).
- Search Google Scholar
- Export Citation
Estevão, M., and E. Tsounta, 2010, “Canada’s Potential Productivity and Output Gap: A Post-Crisis Assessment.” International Productivity Monitor, No 20: 3–21.
- Search Google Scholar
- Export Citation
Epstein, N. and C. Macchiarelli, 2010, “Estimating Poland’s Potential Output: A Production Function Approach.” IMF Working Paper 10/15 (Washington D.C.: International Monetary Fund).
- Search Google Scholar
- Export Citation
FUNIDES, 2012, “Crecimiento Inclusivo.” Serie Estudios Especiales No 11.
- Search Google Scholar
- Export Citation
Harberger, A., 2007, “The Nicaraguan Economy: Situation and Prospects.” Manuscript presented at a conference sponsored by USAID, Managua, Nicaragua.
- Search Google Scholar
- Export Citation
Johnson, C., 2012, “Potential Output and Output Gap in CAP-DR.” IMF Working paper, unpublished (Washington D.C.: International Monetary Fund).
- Search Google Scholar
- Export Citation
Swiston, A. and L. Barrot, 2011, “The Role of External Reforms in Raising Economic Growth in Central America.” IMF Working Paper 11/248 (Washington D.C.: International Monetary Fund).
- Search Google Scholar
- Export Citation
Teixeira de Silva, Tito, 2001, “Estimating Brazilian Potential Output: A Production Function Approach.” Working Paper, Research Department, Central Bank of Brazil.
- Search Google Scholar
- Export Citation

Prepared by Christian Johnson.

Closing large and persistent fiscal gaps lead to a crowding in of private capital raising potential growth.

Harberger (2007) reports a TFP contribution of 0.2 percent for Nicaragua while our analysis suggests a marginal contribution between -0.2 and 0.1 percent depending on the sample.

VAR estimated with 2 lags and the Johansen Cointegration test indicates one cointegrating equation.

Swiston and Barrot (2011) found that sound bank supervision and well-developed securities markets have the largest impact on growth.

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