# WHERE ARE WE HEADED? PERSPECTIVES ON POTENTIAL OUTPUT

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4 WORLD ECONOMIC OUTLOOK: UNEVEN GROWTH SHORT- AND LONG-TERM FACTORS (Phillips curve) on one hand and between cyclical unemployment and the output gap (Okun s law) on the other. These relationships are given by the following equations: p t = p e t + du t + e t p, (3.1) u t = ty t + e tu, (3.) in which p t is inflation, y t is the output gap, u t is cyclical unemployment, p e t is inflation expectations, and e t p and e t u are shock, or disturbance, terms. The parameters in these equations (d, t) or equivalently the strength of the aforementioned economic relationships are estimated separately for each country, and together with data on actual output growth, inflation, and unemployment they provide an economic basis for identifying potential output and the NAIRU, which are unobserved. 4 In addition, the analysis uses Consensus Economics forecasts for both growth and inflation to help pin down the model s expectations for these variables: for example, if consensus expectations are for higher growth, the model-consistent expectation for growth would also tend to be higher, all else equal (see Annex 3. for complete details on the multivariate filtering framework). Two situations help illustrate how the multivariate filtering framework uses the information from economic data to estimate potential. First, if at a point in time, actual inflation is below inflation expectations and unemployment is above the estimated equilibrium rate, the framework will identify a situation of excess supply (a negative output gap), all else equal. Second, consider a more complicated situation in which inflation rises sharply in one year but with no corresponding decrease in unemployment: these conflicting signals suggest a shock to inflation rather than excess demand (a positive output gap). In the second case, the multivariate filtering framework will assign a lower positive output gap than would otherwise be the case, especially if the rise in inflation in a given year unwinds in the following year which is not uncommon following a sharp change in commodity prices or an increase in the value-added-tax rate. In sum, the multivariate filtering framework specified in this chapter strikes a balance between statistical filters, which are easily applicable to a wide range of countries but are atheoretical, and structural models of potential output, which offer greater theoretical rigor but are difficult to construct and apply broadly. As a caveat, it should be noted that potential output is not directly observable. Therefore, the estimates are subject to statistical and model uncertainty. The latter implies that the estimates tend to vary depending on the underlying methodology. In practice, however, the different methodologies deliver qualitatively similar results regarding the trajectory of potential output in advanced and emerging market economies, which is the focus of this chapter (see Annex 3.). With the estimates of potential output and the NAIRU in hand, the analysis proceeds to investigate the drivers of potential growth using a growth accounting framework. This framework describes how the economy s potential output is determined by the basic factor inputs (capital, labor) and productivity (total factor productivity). Specifically, the growth accounting framework is based on a standard Cobb-Douglas production function: Y t = A t K t a L t 1 a, (3.3) in which Y t is potential output, K t is the stock of productive capital, L t is potential employment, A t is potential total factor productivity which includes human capital and is measured as a residual, and a is the share of capital in potential output. 5 Potential employment is then decomposed into the NAIRU, the working-age population, and the trend labor force participation rate: L t = (1 U t ) W t LF PR t, (3.4) in which U t is the NAIRU as estimated in the multivariate filter, W t is the working-age population, and LF PR t is the trend labor force participation rate. The decomposition of potential employment also shows how demographic factors affect potential growth. Two variables play a key role in this regard: working-age population and trend labor force participation rates. The former is a function of the same variables as population growth more broadly. For example, declines in fertility rates slow future working-age population 4 Although the estimated parameters are not time varying, recent evidence suggests that a great deal of the flattening of the Phillips curve relationship, which links inflation to cyclical unemployment (the parameter d in equation 3.1), likely occurred before 1995, suggesting that the estimated parameters in this analysis should be broadly stable over the estimation period (Chapter 3 in the April 13 World Economic Outlook). 5 The measure of productive capital is consistent with the approach of estimating capital services (that is, excluding housing). See Beffy and others 6 for a detailed discussion. The residual is likely also to include utilization of the inputs of production (labor and capital) such as hours worked and capacity utilization, labor quality (that is, human capital accumulation), and possible measurement errors in the inputs of production. 7 International Monetary Fund April 15

6 WORLD ECONOMIC OUTLOOK: UNEVEN GROWTH SHORT- AND LONG-TERM FACTORS Figure 3.4. Variation in Potential Output Growth across Countries (Percent) The patterns of potential output growth held for most countries within each group. 1. Advanced Economies Emerging Market Economies Emerging Market Economies Excluding China Figure 3.5. Determinants of Potential Output Growth in Advanced Economies (Percent) In advanced economies, potential growth declined in 1 7 because of lower total factor productivity growth, resulting in part from a decline in human capital growth. Potential employment growth fell only slightly as a result of demographic factors. Growth in the capital stock remained stable. 1. Contributions of Components 3.5 of Potential Output Growth 3. Pot. output gr. Cap. gr..5 Pot. emp. gr. TFP gr Components of Potential.4 Employment Growth Pot. emp. gr. NAIRU 1.8 WAP LFPR due to aging 1. LFPR excluding aging Human Capital Growth Components of Capital Growth Investment-to-capital ratio Depreciation rate effect Net effect (capital growth) Sources: Barro and Lee 1; and IMF staff estimates. Note: Human capital is measured as the percentage of people in the population over 15 years old who have secondary education or higher. Advanced economies are defined in Annex 3.1. Cap. gr. = capital growth; LFPR = labor force participation rate; NAIRU = nonaccelerating inflation rate of unemployment; pot. emp. gr. = potential employment growth; pot. output gr. = potential output growth; TFP gr. = total factor productivity growth (including human capital growth); WAP = workingage population Source: IMF staff estimates. Note: The upper and lower ends of each line show the top and bottom quartiles; the marker within the line shows the median within the group over the corresponding period. Economy groups are defined in Annex 3.1. tial employment growth fell only slightly, while capital growth remained broadly stable. Total Factor Productivity Growth Several developments may explain the decline in total factor productivity growth. First, in the United States, whose technological development is commonly regarded as representing the world frontier, the growth in total factor productivity started to decline in 3. This decline seems to reflect the waning of the exceptional growth effects of information and communications technology as a general purpose technology observed in the late 199s to early s (Fernald 14a, 14b). 8 In particular, industry-level data suggest that the slowdown in U.S. total factor 8 The reduced dynamism of the U.S. economy as measured by rates of firm entry and job creation and destruction may have also contributed to the observed decline (Decker and others 13). 74 International Monetary Fund April 15

12 WORLD ECONOMIC OUTLOOK: UNEVEN GROWTH SHORT- AND LONG-TERM FACTORS Figure 3.8. Components of Potential Output Growth during the Global Financial Crisis in Emerging Market Economies (Percent) In emerging market economies, the decline of potential growth during the global financial crisis is mainly explained by a reduction in total factor productivity growth. Potential employment and capital growth were not affected by the crisis. 1. Emerging Market Economies China Potential output growth Potential employment growth Capital growth Total factor productivity growth from about 5½ percent to 3½ percent (Figure 3.8, panel 3). For emerging market economies as a group, the decline in total factor productivity growth from about 4¼ percent to about ¼ percent during this period accounted for the entire decline in potential growth (Figure 3.8, panel 1). In contrast, potential employment growth remained broadly stable, and capital growth was not affected by the crisis and actually increased temporarily likely because of some countries efforts to counter the effects of the crisis by adopting investment stimulus measures. The fact that almost all of the decline in postcrisis potential output growth in emerging market economies results from a decline in total factor productivity growth measured as a residual in the growth- accounting framework does not fit easily with theoretical predictions. Although this decline may partly reflect the higher volatility in measured total factor productivity in emerging market economies which in turn might reflect greater measurement errors (Cubeddu and others 14) other factors could be at work. These factors could include a gradual slowdown in convergence to the technological frontier after rapid catchup in the decade before the crisis, reduced growth in input utilization, and lower human capital growth Emerging Market Economies Excluding China Source: IMF staff estimates. Note: Economy groups are defined in Annex Where Are We Headed? What is the likely trajectory of potential output in the medium term? To answer this question, this section considers prospects for the components of potential growth labor, capital, and total factor productivity in the medium term, which is defined here as the sixyear period from 15 to. The scenario presented in the section builds on the previous analysis of the evolution of potential growth until now and extends it, based on projected demographic patterns and the experience from past financial crises. This scenario should be considered as illustrative, given the considerable uncertainty surrounding many elements of the analysis, including possible errors in demographic projections, alongside the wide variations in the experience with previous crises. 1 In emerging market economies, human capital growth declined by about 1 percentage point during the crisis (see Annex Figure 3.5.1). Demographic projections are based on estimates of fertility and mortality rates, and net migration flows. See the UN World Population Prospects: The 1 Revision (http://esa.un.org/wpp/) for details. 8 International Monetary Fund April 15

18 WORLD ECONOMIC OUTLOOK: UNEVEN GROWTH SHORT- AND LONG-TERM FACTORS Annex Table Data Sources Indicator Source Potential Output Growth and Its Components Potential output growth IMF staff estimates using multivariate filter Capital OECD, Economic Outlook: Statistics and Projections database Working-age population UN, World Population Prospects: The 1 Revision Labor force participation OECD, Labour Force Statistics database; and International Labour Organization, Key Indicators of the Labour Market database Nonaccelerating inflation rate of unemployment IMF staff estimates using multivariate filter Indicators Used in the Potential Output Growth and Cohort Model Estimations Inflation expectations Consensus Economics Gross domestic product growth expectations (constant prices) Consensus Economics Life expectancy UN, World Population Prospects: The 1 Revision Fertility UN, World Population Prospects: The 1 Revision Years of schooling Barro and Lee 1 Investment OECD, Economic Outlook: Statistics and Projections database Depreciation rate OECD, Economic Outlook: Statistics and Projections database Others Gross domestic product (constant prices) IMF, World Economic Outlook database Inflation IMF, World Economic Outlook database Unemployment IMF, World Economic Outlook database Human capital accumulation Barro and Lee 1 Financial crises Laeven and Valencia 14 Note: OECD = Organisation for Economic Co-operation and Development; UN = United Nations. The level of potential output (Y t ) evolves according to potential growth (G t ) and a level-shock term (ey t ), which can be interpreted as supply-side shocks. Potential growth is also subject to shocks (e tg ), with their impact fading gradually according to the parameter q (with lower values entailing a slower reversion to the steady-state growth rate following a shock). Finally, the output gap is also subject to shocks (e ty ), which are effectively demand shocks. To help identify the three aforementioned output shock terms (ey t, e tg, and e ty ), a Phillips curve equation for inflation is added, which links the evolution of the output gap (an unobservable variable) to observable data on inflation. In this way, the filter s estimates of the output gap are, in part, determined by inflation outcomes: 31 p t = lp t+1 + (1 l)p t 1 + by t + e tp. (A3..5) In addition, equations describing the evolution of unemployment are included to provide further identi- 31 The degree to which inflation outcomes influence estimates of the output gap in a given country depends on the estimated strength of the relationship between the two (b) and the persistence of any deviation of inflation from target (since a short-lived deviation of inflation from target tends, all else equal, to be interpreted by the filter as an inflation shock rather than being associated with an output gap). Recent evidence (see Chapter 3 in the April 13 WEO) suggests that there has been considerable flattening in the Phillips curve during the past several decades, but that much of this flattening took place before the start of the sample period, which begins in fying information for the estimation of the aforementioned output shocks and output gap: U t = t 4 U SS + (1 t 4 )U t 1 + gu t + e t U, gu t = (1 t 3 )gu t 1 + e t gu, u t = t u t 1 + t 1 y t + e tu, u t = U t U t. (A3..6) (A3..7) (A3..8) (A3..9) In these equations, U t is the equilibrium value of the nonaccelerating inflation rate of unemployment (NAIRU), which is time varying and subject to shocks (eu t ) and variation in the trend (gu t ), which is itself also subject to shocks (egu t ) this specification allows for persistent deviations of the NAIRU from its steady-state value. Most important, equation (A3..8) is an Okun s (197) law relationship, in which the gap between actual unemployment (U t ) and its equilibrium process (U t ) is a function of the amount of slack in the economy ( y t ). As such, this equation behaves in much the same way as equation (A3..5): it dictates that estimates of the output gap are, in part, determined by deviations of the unemployment rate from the NAIRU. The empirical implementation of the filter requires data on just three observable variables: real GDP growth, consumer price index inflation, and the unemployment rate. Annual data are used for these variables for the 16 countries considered. Parameter values and 86 International Monetary Fund April 15

19 CHAPTER 3 WHERE ARE WE HEADED? PERSPECTIVES ON POTENTIAL OUTPUT the standard errors for the variances of shock terms for these equations are estimated using Bayesian estimation techniques. 3 Data on growth and inflation expectations are added to the model s core structure, in part to help identify shocks during the sample period, but mainly to improve the accuracy of estimates at the end of the sample period: p C t+j = p t+j + ep C t+j, j =, 1, (A3..1) GROWTH C t+j = GROWTH t+j + e GROWTH C t+j, j =,..., 5, (A3..11) in which p C t+j and GROWTHC t+j are Consensus Economics forecasts of inflation and GDP growth, respectively. The addition of these equations imparts some additional stability to the filter s model-consistent growth and inflation expectations estimates. In particular, the inclusion of the e pc t+j and e GROWTH C t+j terms allows Consensus Economics forecasts to influence, but not override, the model s own expectations process (which is dictated by the model s estimates of slack in the economy) when potential output is being estimated. Annex Figure Potential Output Growth (Percent) 1. Advanced Economies HP MVF. Emerging Market Economies HP MVF MVF excluding labor market variables Alternative Approaches Estimates of potential output are inherently uncertain because this variable is not observable and may vary across different estimation methodologies. To illustrate the possible sensitivity of estimates of potential output to different statistical techniques, this section compares the baseline results with those obtained using (1) the Hodrick-Prescott statistical filter, and () for emerging market economies, a modified version of the multivariate filter excluding the Okun s (197) law relationship (that is, equations A3..6 A3..9). This second alternative approach seeks to reduce possible measurement errors stemming from limited unemployment data quality. The results in Annex Figure 3..1 suggest that these alternative methodologies produce qualitatively similar findings to those presented in the chapter text. In particular, in advanced economies, the decline in potential growth started in the early s and was worsened by Source: IMF staff estimates. Note: Economy groups are defined in Annex 3.1. HP = Hodrick-Prescott filter with smoothing parameter equal to 6.5; MVF = multivariate filter. the global financial crisis. In emerging market economies, in contrast, it began only after the crisis. Annex 3.3. Estimating Trend Labor Force Participation Rates This annex describes the methodology used to estimate trend labor force participation rates for the 16 advanced and emerging market economies considered in the chapter (see Annex 3.1) from 198 to 13. The methodology relies on a cohort-based model as, for example, in Aaronson and others 14 and Balleer, Gomez-Salvador, and Turunen 14 to decompose the aggregate participation rate into the participation rates of disaggregated age-gender groups and estimate their determinants. 3 See Hamilton 1994 for a general discussion of the Kalman filter, which is used to obtain estimates of the unobservable variables as part of the estimation process. Estimates for each country are available in Blagrave and others 15. Model For each age group a, gender g, in year t, the time series of group-wise labor force participation rates International Monetary Fund April 15 87

20 WORLD ECONOMIC OUTLOOK: UNEVEN GROWTH SHORT- AND LONG-TERM FACTORS (in logs) is estimated according to the following specification: log LFP a,g,t = a a,g + b b,g I a,t (t a = b) n a b=19 + g l a,g cycle t l + l a,g X a,g,t + e a,g,t. (A3.3.1) l= This specification is estimated separately for each country. Group-specific labor force participation rates have four main categories of determinants: An age-gender-specific intercept captures the average labor force participation rate for each age group to reflect the life cycle (bell-shaped) pattern of labor supply: low for youth, increasing and flattening during prime age, and decreasing as retirement age approaches. This life cycle pattern can differ for men and women. Slowly evolving cultural and behavioral changes can shift the whole life cycle participation profile up or down, depending on the birth year of an entire cohort. Such unobservable cohort effects have been widely documented for women born during the baby boom years in the United States (for example, Aaronson and others 14), and similar evolutions are taking place in many European and Asian countries. These cohort effects are captured by a fixed effect (I) for each birth year b (depending on data availability for a particular country; the analysis accounts for cohorts born between 19 and 1988). To obtain the average cohort effect for a given age group, the cohort coefficient is divided by the number of cohorts included in an age group n a. The business cycle can have a different effect on the participation decisions of different age-gender groups. For example, the labor supply of young people is often more sensitive to cyclical conditions than is that of mature prime-age workers. The coefficient g captures the cyclical sensitivity of each group s labor force participation rate while allowing for a partially delayed response of participation rates to cyclical conditions, consistent with existing evidence (see, for example, Balakrishnan and others 15). The cyclical position is proxied by the employment gap (that is, the deviation of current employment from its trend). 33 The model is estimated in logs to ensure that the level labor force participation rate is bounded between zero and one. The model includes structural factors that can have an impact on the trend labor force participation rate of particular age groups (vector X). For young people, the participation decision depends on education enrollment status. For women, the participation decision is positively related to educational attainment and, during early prime working age, negatively correlated with fertility and marriage status. For workers close to statutory retirement age, increasing life expectancy is expected to lead to higher participation rates. Data and Estimation For advanced economies, the sample consists of 11 age groups (with four-year intervals), separated by gender, from 198 to 13; hence there are 11 equations that are jointly estimated for each gender using cross-equation equality restrictions on the cohort coefficients. For emerging market economies, data availability is reduced by both age group granularity (only five age groups for each gender) and period coverage (199 13). Not all cohorts are observed for the same number of years, and in fact, no cohort is observed for the whole life cycle. In particular, cohorts born after 199 entered the labor force only during or after the global financial crisis, making it hard to distinguish the negative effect of the crisis (beyond the average cyclical impact) from any potential cohort-specific trends. To mitigate this end-point problem (and a similar starting-point problem for the oldest cohorts), no cohort effect is estimated beyond 1988 or before 19. An alternative version of the model is also estimated that allows the cohort effect of those born after 1988 and before 19 to equal the average of that for the adjacent five cohorts. The results are robust to this alternative specification. The effects of the other structural determinants for women, young people, and workers older than 54 are explicitly estimated for advanced economies for which such data are available. It is well documented that the labor force participation rate for prime-age men in advanced economies has been trending down for the past several decades (see, for example, Aaronson and others 14 and Balleer, Gomez-Salvador, and Turunen 14), but there is no clear explanation regarding the factors behind this decline. This trend is captured by allowing for linear and quadratic deterministic trends in the labor force participation rate 88 International Monetary Fund April 15

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