Guatemala: Selected Issues and Analytical Notes

Abstract

Guatemala: Selected Issues and Analytical Notes

Selected Real Sector Issues

A. Potential Growth and the Output Gap1

This section estimates potential output growth and the output gap for Guatemala. Potential output growth averaged at 4.4 percent just before the global financial crisis but has declined since to 3¾ percent due to the lower capital accumulation and TFP growth. It is estimated at 3.8 percent in 2016, and the output gap is virtually closed. Looking forward, potential growth is expected to reach 4 percent in the medium-term due to the expected improvements in TFP growth.

1. A number of methodologies were employed to estimate potential output for Guatemala. They included univariate and multivariate filters, as well as the production function approach (see Analytical Note (AN) on Assessing Potential Output, 2014 and Appendix for details). Averaging the results from different methodologies we estimate potential output growth in 2016 at 3.8 percent. At 0.2 percent of potential output the output gap is essentially closed. In the following paragraphs, we elaborate on some specific findings from the multivariate filter analysis, which adds economic structure to the estimates by conditioning them on some basic theoretical relationships, such as the Phillip’s curve relating inflation to the output gap, and the Okun’s law relating cyclical unemployment to the output gap,2 as well as the production function approach.

Table 1.

Potential Output Growth and Output Gap Estimates

article image
Source: Fund staff estimates.

2. Potential growth increased from 3.3 percent to 4.4 percent from early to the mid-2000s. The increase was driven by higher employment growth and less negative TFP growth. Compared to other Central American economies such as Costa Rica, the Dominican Republic, and Panama where an acceleration in TFP growth lifted potential growth by at least 2 percentage points and as high as 5 percentage points in the case of Panama, the increase in Guatemala was small.

3. Potential growth declined from 4.4 percent in 2006-07 to 3.7 percent after the crisis in 2013-14. Lower capital accumulation and productivity growth explain the decline in Guatemala’s potential growth during this period. Most other Central American economies also experienced declines in their potential growth during that period on the account of lower capital accumulation, employment, and TFP growth: by about 2 percentage points in Costa Rica, the Dominican Republic, and Honduras and by about 1 percentage point in El Salvador and Panama. Potential growth increased slightly only in Nicaragua.

4. TFP growth has not been contributing materially to Guatemala’s potential growth. In fact, in the early 2000s, TFP growth was negative. This was also the case in Honduras, El Salvador, Nicaragua, and Costa Rica. TFP growth in Guatemala turned positive in late 2006-07, but remained low compared to other Central American countries (with the exception of Honduras and El Salvador). Weak productivity growth in Guatemala may reflect, among other factors, low investment in education and R&D, as well as more generally the lack of opportunities in terms of access to basic services that are found necessary to succeed in life (running water and electricity). Migration of high-skilled workers to the United States as well as a large size of the informal economy, which diverts resources to less productive activities, might have also contributed to lower TFP growth. In addition, productivity gains may be hindered by the lack of competition, weak business environment, high red tape and corruption, lack of legal/judicial stability, weak protection of investors and enforcement of contracts, poor security, high costs and poor quality of infrastructure. After the crisis, TFP growth, while recovering to small positive rates, remained very low in Guatemala (at 0.2 percent in 2013-14); it declined in most other Central American economies. However, TFP growth has recovered to the pre-crisis rates in the Dominican Republic and Nicaragua (where it reached 1 percent in 2013-14), and its contribution to the potential growth remained over 2.5 percent in the Dominican Republic and Panama, which have the highest TFP growth rates in the region. On the other hand, TFP growth continues to be low in Costa Rica (0.2 percent), and negative in Honduras (negative 0.1 percent) as well as El Salvador (negative 0.7 percent).

A01ufig1

Latin America: Human Opportunity Index, 2010

Citation: IMF Staff Country Reports 2016, 282; 10.5089/9781475531534.002.A001

Source: World Bank.Note: The HOI calculates how personal circumstances (like birthplace, wealth, race or gender) impact a child’s probability of accessing the services that are necessary to succeed in life, like timely education, running water or connection to electricity.

5. Capital accumulation was an important contributor to Guatemala’s potential growth before the crisis, but its contribution after the crisis declined sharply. Capital growth increased from 4.5 percent in the mid-2000s to 5.5 percent in 2007. A large decline in capital growth accounted for most of the decline in potential growth in Guatemala after the crisis. Capital growth dropped over 2.5 percentage points from 2006-07 to 2013-14, one of the largest declines in Central America.

6. Employment growth, higher than elsewhere in the region, has been the main driver of potential growth in Guatemala over the past decade. It increased from 3.3 percent to 3.5 percent during the 2001-07, mainly attributable to higher working-age population growth. Fertility rates and population growth in Guatemala are one of the highest in Central America and life expectancy has been steadily increasing, which can explain in part the high growth in working-age population. Potential employment growth continued increasing in Guatemala after the crisis. It increased by about 0.3 percentage points (from 3.5 percent in 2007 to 3.8 percent in 2014), while other Central American economies went through important declines in potential employment growth.

A01ufig2

Determinants of Potential Output Growth

(% and contributions to potential output growth, average for the period)

Citation: IMF Staff Country Reports 2016, 282; 10.5089/9781475531534.002.A001

Source: IMF staff estimates.

7. From a cyclical perspective, the Guatemalan economy is assessed to be operating at potential in 2015/16. The negative output gap in Guatemala in 2009 (negative 1.2 percent of potential output) has significantly shrunk and the slack in the economy has been reduced since then. Both the output gap and the unemployment gap (see Appendix for details of the calculation) are now closed. The closed unemployment gap reflects improved labor conditions, where the unemployment rate is at its equilibrium value having steadily fallen since 2009.

A01ufig3

Output gap

(% of potential output)

Citation: IMF Staff Country Reports 2016, 282; 10.5089/9781475531534.002.A001

Source: IMF staff estimates.

8. Potential growth in Guatemala is likely to increase to 4 percent in the medium term. The scenario analysis builds on the analysis of potential growth until 2014 and extends it 2015-2020, based on the projected demographic patterns, prospects for capital growth and improvements in TFP growth. These scenarios are subject to significant uncertainty, as a number of country-specific factors could influence potential growth, and the evolution of TFP growth in the medium term. The working-age population growth and labor force participation growth are likely to continue at similar rates. Investment-to-capital ratios have not changed much since 2011 and are likely to remain below pre-crisis rates, while improving only slightly over the medium term given improvements in the efficiency of public investment. This is because of less favorable external financing conditions, and weaknesses in the institutional, regulatory, and legal environment. TFP growth is expected to slightly increase towards the end of the horizon driven by improvements in institutions providing legal and judicial certainty, and a reduction in corruption.

A01ufig4

GTM: Components of Potential Output Growth (%)

Citation: IMF Staff Country Reports 2016, 282; 10.5089/9781475531534.002.A001

Source: IMF staff estimates.

9. The main challenge in the longer term in Guatemala is to foster TFP growth. Relative to the region and emerging markets, Guatemala performs poorly in various facets of innovation such as spending on R&D, tertiary enrollment rates, number of patent applications, FDI inflows, ease of protecting investors, knowledge-intensive employment, and creative services exports. Enhancing R&D/technological diffusion will require strengthening institutions, human capital and research, and achieving higher business and market sophistication, and competition in product and labor markets. Adopting the Competition Law currently under consideration and sanctioning anti-competitive behavior would support entry of new innovative firms and punish practices protecting incumbents. Important improvements in the quality of schooling will also be needed to enhance human capital.

Figure 1.
Figure 1.
Figure 1.

Investment, Innovation, and Capital

Citation: IMF Staff Country Reports 2016, 282; 10.5089/9781475531534.002.A001

10. Policies should also prioritize mobilizing domestic savings to invest and build a higher capital stock. Savings are much lower than in other LAC countries and emerging markets. Investment-to-capital ratios are among the lowest in Central America, and much lower than those in other LAC countries. Raising savings would require a deeper, more diversified and more inclusive financial system (see AN on Financial Development and Inclusion). Attracting private domestic and foreign investment will require strengthening institutions to secure property rights and reducing red tape and corruption, ensuring legal and judicial stability, and improving security. Higher and more efficient public investment is critical to address infrastructure deficiencies. The latter will require an increase in government revenue and improvements in public investment management framework.

B. Food Inflation in Guatemala3

Food prices have been the main driver of headline inflation during the last few years. While there have been some weather-related supply shocks, demand pressures, especially from strong remittances inflows, have played an important role in driving food inflation, in particular, in 2015. Transport infrastructure deficiencies and other structural factors have likely contributed to a sustained high food inflation in Guatemala, compared to the regional peers, particularly in rural areas. Hence, structural policies to increase elasticity of food supply would be beneficial.

Recent Developments

11. Food prices have been rising strongly during the last few years. Food price increases have been the main driver of headline inflation, more than offsetting the negative contributions from other components of the consumer price index (CPI) directly related to oil prices—transport and utilities. On average, food price inflation has been double that of the general price index, resulting in an average contribution of about two thirds to total inflation since 2012, despite having a weight of less than one third in the CPI. While rapidly rising international prices of the main imported food products—corn and wheat—contributed to high food inflation in 2010-11, this has not been the case since 2012 when international food prices have been mostly falling. Moreover, lower food inflation across countries in Central America suggests that country-specific factors have been driving the high food inflation in Guatemala.

Figure 2.
Figure 2.

Guatemala and CAPDR: Inflation and Contributions to Inflation

Citation: IMF Staff Country Reports 2016, 282; 10.5089/9781475531534.002.A001

12. Food inflation has been concentrated in a few product groups and rural regions of the country. The acceleration in food inflation has been largely driven by a few product categories, in particular, vegetables, breads/grains, and meat despite having a combined weight in the CPI of less than 20 percent. There has also been a clear differentiation of food inflation by region, with consistently higher food inflation in rural areas, compared to urban areas.4 Rural areas have contributed almost half of overall food inflation at the national level during the last few years, despite representing about one fourth of the national CPI.

Figure 3.
Figure 3.

Food Prices

Citation: IMF Staff Country Reports 2016, 282; 10.5089/9781475531534.002.A001

Drivers of Food Inflation

13. Demand pressures, especially from remittances flows, have played an important role in driving food inflation. While there have been some weather related shocks temporarily pushing up prices of some vegetable products—especially in late 20155—demand factors, including strong remittances growth, appear to have been the main driver of high food inflation in the last few years, judging from high correlations of food inflation with the output gap and remittances growth. Prices of food products that have been experiencing important increases—especially vegetables and meat that tend to be added to the basic diet as incomes of poor households increase—are particularly highly correlated with remittances inflows. The strong growth in remittances, significantly above other countries in the region in 2015, likely contributed to ease budget constraints of poorer families with high levels of malnutrition.

A01ufig5

Guatemala: Contributions to Food Inflation

(In percent)

Citation: IMF Staff Country Reports 2016, 282; 10.5089/9781475531534.002.A001

Figure 4.
Figure 4.

Drivers of Food Inflation

Citation: IMF Staff Country Reports 2016, 282; 10.5089/9781475531534.002.A001

14. The greater incidence of food inflation in rural areas also points to the importance of remittances. While a similar share of remittances goes to rural and urban areas (with about half of households living in each of those areas), remittances represent a greater share of total income in rural areas that tend to be significantly poorer. Also, compared to urban areas, a much larger share of remittances goes to the mid- and lower-end of the income distribution. Lower income households spend a larger share of their income on food—almost 60 percent of total household consumption in the lowest quintile, compared to about 35 percent in the highest quintile.

Figure 5.
Figure 5.

Remittances and Share of Food by Household Quintiles

Citation: IMF Staff Country Reports 2016, 282; 10.5089/9781475531534.002.A001

1/ Based on distribution of per capita annual consumption.

15. Other structural factors may also be contributing to high food inflation, especially in rural areas. Guatemala has the lowest level of urbanization in Central America. Low public investment (Section a) resulting in transport infrastructure deficiencies that are particularly acute in rural areas could be an important driver of the higher trade and transportation margins. However, other factors may also be contributing to the differences with other Central America countries given that countries like Honduras or Nicaragua, that also have low urbanization levels, have much lower trade margins. Literature finds that the degree of competition affects both the price level and inflation. For example, Przybyla and Roma (2005) demonstrate that the extent of product market competition, in particular, measured by the level of mark-up, is an important driver of inflation and that higher product market competition reduces average inflation rates for a prolonged period of time. While there is no sufficient information on the severity of competition problems in the transport and food distribution sectors in Guatemala given the absence of a competition law and a competition authorityi, the experience in the neighbouring countries that have adopted competition laws suggests that anti-competitive business practices cannot be completely ruled out.6 Moreover, non-tariff barriers could also be limitting regional trade of food products despite the progress already made to harmonize trade policies and develop a common market in Central America.7

Figure 6.
Figure 6.

Share of Rural Population and Trade Margins

Citation: IMF Staff Country Reports 2016, 282; 10.5089/9781475531534.002.A001

Policy Recommendations

16. Monetary policy should react if there are second round effects from high food inflation. If remittances remain strong while downward pressures from oil price declines continue to dissipate, sustained high food price inflation—particularly in rural areas where remittances play a disproportionately greater role in final demand and where food supply is more inelastic—could drive overall inflation above target. If this is seen as a temporary development, there is no need for monetary policy to react, but if it has second round effects on inflation expectations or core inflation, monetary tightening would be warranted (see AN on Monetary Policy Management).

17. Structural policies to increase supply of food, particularly in rural areas, could also help.

  • Investments in transport infrastructure should be prioritized. This will facilitate access to national food and other markets for those living in rural areas.

  • The new competition law should be adopted and a competition authority should be established promptly. The new competition authority should prioritize analysis of food and transport industries to determine whether issues within its mandate are contributing to the relatively higher trade and transport margins in food prices compared to other countries in the region.

  • Improvements in customs procedures would not only be beneficial for competitiveness of exporters, but could also remove one of the constraints on greater imports of perishable products.

  • Additional advances in regional integration—beyond tariff reductions already implemented—to reduce other non-tariff barriers, including SPS regulations would be helpful.8

  • Programs for rural development and poverty reduction should be comprehensive, supporting both capacity to increase food consumption—through transfers or other means—and to ensure adequate supply of food, including through programs to improve access to irrigation, financing, and technical assistance in the agricultural sector.

  • Finally, measures to improve the business climate would also help provide a more enabling environment for remittances to be directed to investment, rather than consumption as is currently the case, especially in rural areas.

C. Female Labor Force Participation in Guatemala9

In contrast to male labor force participation (LFP), which is high in Guatemala by regional standards, female labor force participation is lower than that in other Latin American countries, though at par with that in other CAPDR countries. Using evidence from household surveys and cross-country data, this note examines the determinants of female labor force participation and the factors behind a relatively low female LFP rate in Guatemala. Income, education and fertility levels, are important determinants of female LFP. Increasing access to education, investment in infrastructure and information technology as well as taking measures to support working mothers with children could help raise female LFP in Guatemala.

Guatemala’s Labor Market

18. Guatemala’s labor market is characterized by low unemployment and high informality and inequality, contributing to sub-par social outcomes. Unemployment is low and stable in Guatemala, hovering around 3 percent during the last decade, with slightly higher rate for women and slightly lower for men. High degree of informality and poor social protection schemes have likely contributed to low unemployment rates in Guatemala. Inequality of labor conditions across groups within the society is high, with the indigenous and rural populations having the highest rates of informality and lowest average incomes, contributing to their higher poverty rates. Informality is similar for women and men, although the average income is somewhat lower for women. The higher share of vulnerable employment among women does not seem to be associated with worse social outcomes, as they have similar poverty rates and significantly lower extreme poverty rates than men.10

Figure 7.
Figure 7.

CAPDR: Employment Indicators

Citation: IMF Staff Country Reports 2016, 282; 10.5089/9781475531534.002.A001

Cross-Country Evidence

19. Guatemala’s relatively low female LFP is consistent with its level of development. In contrast to male LFP, which is high by regional standards, female LFP is lower than that in other Latin American countries, though at par with that in other CAPDR countries. One potential explanation of the relatively low female LFP in Guatemala, as well as in other countries in Central America, is the country’s relatively low income status (lower middle income). The literature finds a U-shaped relationship between the level of economic development (e.g. GDP per capita) and female LFP rates.11 Women tend to work out of necessity in poor countries, mainly in subsistence agriculture or home-based production. With income growth, activity tends to shift from agriculture to industry, with jobs which are away from home, making it more difficult for women to juggle children with a market job—especially with limited public childcare services still provided at intermediate levels of development. At the household level, as the husband’s wage rises, there is a negative income effect on the supply of women’s labor. Once wages for women start to rise, however, the substitution effect increases incentives for women to increase their labor supply, until this effect dominates the negative income effect.12

Figure 8.
Figure 8.

Labor Force Participation Rates

Citation: IMF Staff Country Reports 2016, 282; 10.5089/9781475531534.002.A001

20. Other factors beyond income level also help explain Guatemala’s relatively low female LFP. A cross-country panel that analyzes the main factors explaining female LFP across countries is used to analyze the contribution of various factors to explaining the level of female LFP in Guatemala. The gap with LA5 is then computed as the difference between the contribution of various components to explaining LFP in Guatemala and that to explaining that in LA5 on average. The explanatory variables used in the analysis include income per capita, income per capita squared, fertility rate, number of internet users per 100 people, male and female secondary and tertiary education participation rates, percentage of urban residents, an indicator of labor market efficiency, and investment in transportation and telecommunications. In the case of Guatemala, low female participation in education, high fertility rates, and low investment in infrastructure that facilitates access to the labor market are the largest drivers of the female LFP gap with LA5.

Evidence from Microdata

21. To supplement cross-country analysis, we estimate a model linking female LFP with the factors found important in the literature using microdata.13 The following regression is run using household survey data from the 2014 household survey data (Encuesta Nacional de Condiciones de Vida, ENCOVI):

FLFPirt=α+β1primsecondedui+β2secondtertiaryedui+β3morethantertiaryedui+β4urbani+β5marriedi+β6agei+β7(age)i2+β8cellphonei+β9computeri+β10kid0to6i+β11kid6to12i+β12oldmorethan70i+β13log(headincome)i+γr+ɛir

where prim_second_edui, second_tertiary_edui, and more_than_tertiary_edui are dummy variables for the woman i’s final educational attainment level, and urbani, marriedi, cellphonei, and computeri are dummy variables for the location of the household in urban area, household being a married couple, and household having a cell-phone. kid_0to6, kid_6to12i, and old_morethan_70i are equal to one if a household has a member in these categories, respectively. log(headincome)i is the log of income of a household head? Regional fixed effects are also included.

22. Evidence from micro-data confirms that education, marital status, and urbanization are important factors for female LFP. The regression results are reported in Table 2. The results show the usual “hump-shaped” relationship between female LFP rates across the life-cycle with the age terms being significant and with the expected signs—the difference in participation rates is particularly large for women aged between 20 and 40, which is also a prime age for accumulation of experience. Second, a higher educational attainment is related to a higher participation rate. Third, ownership of cell-phones and computers, as well as living in an urban area are positively and significantly associated with higher female LFP rate-these results point towards the importance of information and physical ability to reach jobs. Fourth, being married has a negative and significant association with female LFP—the differences reach nearly 15 percentage points during ages 20 to 40. Fifth, the presence of young children and the elderly in the household are also related to lower participation, albeit insignificantly for the latter. Lastly, attesting to the wealth effect in household labor supply, a higher income of the household head is associated with the lower female LFP.

Figure 9.
Figure 9.

Female Labor Force Participation Rates

Citation: IMF Staff Country Reports 2016, 282; 10.5089/9781475531534.002.A001

Policy Recommendations

23. Public policies can help raise female FLP. While increasing female LFP is likely to be a long-term process that will take place naturally as the country develops further and fertility rates fall, policies aimed at improving education, increasing investment in infrastructure and information technology, as well as taking measures to support working mothers with children through increased provision of childcare services would help support this process. While women’s participation rates in education are only slightly lower than for men, overall participation is much lower than in LA5 countries, highlighting the need for public policies aimed at substantially raising education levels in the country. In this sense, increasing access to education, raising investment in infrastructure and information technology could help raise female labor participation and labor force participation more generally. According to the latest household survey, 65% of the indigenous women above 30 years old have not completed any education level, whereas among non-indigenous, only 26% fall into this category. Guatemala should aim at improving provision of childcare and expanding conditional cash transfer and dedicated programs targeting indigenous women, including to provide training, food assistance and nutrition for their children, and health care for pregnancy could help narrow the gender gap in the labor market.

Figure 10.
Figure 10.

Gender Gap: Labor Force Participation and Education

Citation: IMF Staff Country Reports 2016, 282; 10.5089/9781475531534.002.A001

Sources: WDI, ENCOVI 2014 and Fund staff estimates.1/ Gender gap refers to the difference between men and women for each indicator. An arrow indicates the direction of increase in less favorable outcomes for women.
Table 2.

Regression Results Using Microdata

article image
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

References

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  • Bloom, David E, David Canning, Gunther Fink, and Jocelyn E. Finlay. (2007) “Fertility, Female Labor Force Participation, and the Demographic Dividend.NBER Working Paper #13583.

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  • Goldin, Claudia. (1994) “The U-shaped Female Labor Force Function in Economic Development and Economic History.NBER Working Paper #2707.

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  • International Monetary Fund. (2016) “Costa Rica Selected Issues and Analytical Notes,Analytical Note IV, IMF Country Report No. 16/132.

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  • Kelleher, Sinéad and Reyes, José-Daniel. (2014) “Technical measures to trade in Central America: incidence, price effects, and consumer welfareWorld Bank Policy Research Working Paper 6857.

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  • Przybyla and Roma, (2005) “Does Product Market Competition Reduce Inflation? Evidence from EU Countries and Sectors,European Central Bank working paper No. 453.

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  • USAID. (2015) “A Report on Barriers to Competition in Food-Related Markets in El Salvador, Guatemala, and Honduras.

Appendix. Multivariate Filter Methodology

The multivariate filter approach specified in this selected issues paper requires data on three observable variables: real GDP growth, CPI inflation, and the unemployment rate. Annual data is used for these variables for the 7 countries considered. In this section, we present the equations which relate these three observable variables to the latent variables in the model. Parameter values and the variances of shock terms for these equations are estimated using Bayesian estimation techniques.

In the model, the output gap is defined as the deviation of real GDP, in log terms (Y), from its potential level (Y¯):

y=YY¯

The stochastic process for output (real GDP) is comprised of three equations, and subject to three types of shocks:

Y¯t=Y¯t1+Gt+ɛtY¯Gt=θGSS+(1θ)Gt1+ɛtGyt=ϕyt1+ɛty

The level of potential output (Y¯t) evolves according to potential growth (Gt) and a level-shock term (ɛtY¯). Potential growth is also subject to shocks (ɛtG), with their impact fading gradually according to the parameter θ (with lower values entailing a slower adjustment back to the steady-state growth rate following a shock). Finally, the output-gap is also subject to shocks (ɛty), which are effectively demand shocks.

All else equal, output would be expected to follow its steady-state path, which is shown above by the solid blue line (which has a slope of Gss). However, shocks to: the level of potential (ɛtY¯); the growth rate of potential (ɛtG); or the output gap (ɛty), can cause output to deviate from this initial steady-state path over time. As shown by the dashed blue line, a shock to the level of potential output in any given period will cause output to be permanently higher (or lower) than its initial steady-state path. Similarly, shocks to the growth rate of potential, illustrated by the dashed red line, can cause the growth rate of output to be higher temporarily, before ultimately slowing back to the steady-state growth rate (note that this would still entail a higher level of output). And, finally, shocks to the output gap would cause only a temporary deviation of output from potential, as shown by the dashed green line. In order to help identify the three aforementioned output shock terms, a Phillips Curve equation for inflation is added, which links the evolution of the output gap (an unobservable variable) to observable data on inflation according to the process:

πt=λπt+1+(1λ)πt1+βyt+ɛtπ

Finally, equations describing the evolution of unemployment are included to provide further identifying information for the estimation of the output gap:

U¯t=(τ4U¯ss+(1τ4)U¯t1)+gU¯t+ɛtU¯gU¯t=(1τ3)gU¯t1+ɛtgU¯ut=τ2ut1+τ1yt+ɛtuut=U¯tUt

Here, U¯t is the equilibrium value of the unemployment rate (the NAIRU), which is time varying, and subject to shocks (ɛtU¯) and also variation in the trend (gU¯t), which is itself also subject to shocks (ɛtgU¯)—this specification allows for persistent deviations of the NAIRU from its steady-state value. Most importantly, we specify an Okun’s law relationship wherein the unemployment gap between actual unemployment (Ut) and its equilibrium process (given by ut) is a function of the amount of slack in the economy (yt).

Equations 1-9 comprise the core of the model for potential output. In addition, data on growth and inflation expectations are added, in part to help identify shocks, but mostly to improve the accuracy of estimates at the end of the sample period:

πt+jC=πt+j+ɛt+jπC,j=0,1GROWTHt+jC=GROWTHt+j+ɛt+jGROWTHC,j=0,,5

For real GDP growth (GROWTH) the model is augmented with forecasts from the WEO for the five years following the end of the sample period. For inflation, expectations data are added for one year following the end of the sample period. These equations relate the model-consistent forward expectation for growth and inflation (πt+j and GROWTHt+j) to observable data on how WEO forecasters expect these variables to evolve over various horizons (one to five years ahead) at any given time (GROWTHt+jC). The ‘strength’ of the relationship between the data on the WEO forecasts and the model’s forward expectation is determined by the standard deviation of the error terms (ɛt+jπCandɛt+jGROWTHC) In practice, the estimated variance of these terms allows WEO data to influence, but not completely override, the model’s expectations, particularly at the end of the sample period. In a way, the incorporation of WEO forecasts can be thought as a heuristic approach to blend forecasts from different sources and methods.

The methodology requires taking a stance on prior beliefs regarding a number of variables. A key assumption fed into the model’s estimation is that supply shocks are the primary source of real GDP fluctuations in Central America. The prior belief that supply is more volatile than demand leads the model to assign much of the observed volatility of real GDP to potential GDP fluctuations. In addition to the prior distributions of parameters, initial values for the steady-state (long-run) unemployment rate and potential GDP growth rates are provided.

After obtaining estimates of potential output and NAIRU from the multivariate Kalman filter, potential TFP is calculated as a residual in the Cobb-Douglas function:

At=Yt/KtαLt1α

where Yt is potential output, Kt and Lt are capital and labor inputs, while At is the contribution of technology or TFP. Output elasticities (α is the capital share in the production function and is set at 0.35) sum up to one. Data on the working age population and the labor force participation rate is obtained from the UN Economic Commission for Latin American and the Caribbean (CEPAL). Potential employment is constructed as a product of working age population, the labor force participation rate, and the employment rate (1-NAIRU).

The capital stock series is constructed using a perpetual inventory method where the level of initial capital stock for a given year, 1990 in our case, is calculated assuming a constant level of depreciation rate of 5 percent per annum and a constant investment share of GDP.

1

Prepared by Iulia Ruxandra Teodoru.

2

Univariate filters, on the other hand, do not incorporate any economic structure (e.g. the assumption is that an economy is, on average, in a state of full capacity, without incorporating information from variables such as inflation or unemployment), and thus are not consistent with an economic concept of potential (e.g. Okun’s definition: the level of output that can be achieved without giving rise to inflation). Relative to previous literature, and particularly to Blagrave and others (2015), the contribution of this section is twofold: first, it uses WEO forecasts to account for inflation and growth expectations, to guide potential output estimates, which could be particularly important for countries where data on inflation and growth expectations do not exist; and, second, it applies this framework to Central American countries, where multivariate filters were not used before.

3

Prepared by Jaume Puig-Forné and José Pablo Valdés.

4

The National Statistics Institute (INE) produces price indices for the eight administrative regions in the country. The regions can be classified into predominantly urban or rural depending on the distribution of large urban centers across regions.

5

Weather related shocks explain the sharp increase in prices of tomatoes and onions in late 2015. The shocks also affected neighboring countries, resulting also in increased external demand for these products.

6

In a report on food security in the Northern Triangle, USAID notes that much progress has been recorded in reducing barriers to competition in food-related markets in El Salvador and Honduras over the past decade as a result of the work of competition agencies. USAID considers that these conducts are likely to still be prevalent in Guatemala, which lacks a competition law or enforcement agency (USAID, 2015)—a competition law has already been submitted to Congress as required under the trade pillar of the Association Agreement between Central America and the EU.

7

USAID finds that delays related to border-crossing procedures in the region—with numerous government agencies present at border crossings, often with separate staff and procedures—increase the price of traded goods, including food products, while also limiting access to perishable products which often spoil at the border (USAID, 2015). Border-crossing procedures also include the application of sanitary and phyto-sanitary (SPS) measures, which differ across countries limiting the ability of imported products to compete; the World Bank finds that in Guatemala, technical measures, including SPS barriers, can increase the average import prices of beef, bread and pastry, chicken meat, and dairy products by an amount equivalent to ad-valorem tariffs of 68, 51, 22, and 5 percent, respectively (World Bank, 2014).

8

For example, mutual recognition of food product registration approval by national food and health authorities would help reduce time and investment for introduction of national food products in new regional export markets.

9

Prepared by Jaume Puig-Forne and Victoria Valente.

10

Vulnerable employment is defined in the World Bank Development Indicators as unpaid family workers and own-account workers.

13

We follow the same approach as in IMF (2016).

Guatemala: Selected Issues and Analytical Notes
Author: International Monetary Fund. Western Hemisphere Dept.