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Behind the Investment Climate: Back to Basics—Determinants of Corruption Veronica Alaimo, Pablo Fajnzylber, J. Luis Guasch, J. Humberto Lopez, and Ana Maria Oviedo
Previous chapters discussed the role of the investment climate as a determi-nant of growth in Latin America and suggested priority areas of reform for each country. Yet to effectively tackle reform, one needs to understand in more depth the mechanisms behind the investment climate determinants. For example, what are the mechanisms behind corruption activities? What incentives sustain corruption activities, which firms are more likely to pay bribes, and why? What is the role played by regulation, oversight, and the courts? What are the policy implications of these aspects and instruments for reform?
One of the main messages emerging from chapter 2 is that bribe pay-ments—and more generally corruption—have a strong negative impact on firm labor productivity. Moreover, in chapter 3, the assessment of the relative importance of the different investment climate attributes revealed that in 7 of the 16 Latin American countries under analysis, corruption appears among the top three areas where progress can be expected to contribute the most to firm performance improvements. The finding that corruption is a barrier to firm performance is consistent with a large num-ber of cross-country econometric studies that have found that poor gover-nance—fueled, among other factors, by rampant corruption—negatively
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UNKNOWN IMF Regional Economic Outlook: Western Hemisphere - Shifting Winds, New Policy Challenges, October 2011 REGIONAL ECONOMIC OUTLOOK: WESTERN HEMISPHERE 28 loss of confidence. The recent moderation in world commodity prices provides some relief to an otherwise difficult global and domestic environment. In this context, greater resolve is required in reducing public debt (which is up over 9 percent of GDP since the crisis) and resisting fatigue in some countries, where pressures to increase wages and subsidies have intensified. Fiscal consolidation efforts should, to the extent possible, preserve growth and competitiveness by avoiding steep cuts in infrastructure spending. Section 2.5 discusses in detail the challenges for fiscal consolidation in high- debt tourism-intensive Caribbean economies. Financial sector fragilities in the region have become more troubling. In the Eastern Caribbean Currency Union (ECCU), financial sector health indicators have continued to deteriorate, highlighting the importance of steps to further strengthen the sector.11 In this context, the authorities need to diagnose the health of the financial system quickly and develop options for strengthening balance sheets, and avoid further compromising public finances. Moreover, financial regulation and supervision frameworks require significant strengthening, including ensuring that the resolution of failed institutions is carried out transparently. 2.3. How Tight Are Labor Markets in Inflation-Targeting Countries? As one gauge of the extent of overheating, we examine estimates of the nonaccelerating inflation rate of unemployment (NAIRU) for selected countries. Our findings suggest that labor markets have been showing signs of overheating in several economies. Unemployment rates in the faster-growing Latin American commodity exporters are currently near or _______ 11In July 2011, the largest indigenous bank in Antigua and Barbuda was intervened. Meanwhile, the resolution of the failed insurance companies, British American Insurance Company (BAICO) and Colonial Life Insurance Company (CLICO), of the Trinidad and Tobago-based CL Financial Group, remains pending. Figure 2.11. Unemployment rates in key Latin American countries are below their historic levels and approaching their natural rates, signaling overheated labor markets. -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1990 1994 1998 2002 2006 2010 Selected Latin America: Correlation of Unemployment and Output ¹ (Percent) Sources: Ball, De Roux, and Hofstetter (2011); and IMF staff calculations. ¹ Ten-year rolling correlation of the unemployment gap and output gap, measured as the series over its Hodrick- Prescott-filtered trend. Simple average of Brazil, Chile, Colombia, Mexico, Peru, and Uruguay. 4 6 8 10 12 14 16 4 6 8 10 12 14 16 1980 1985 1990 1995 2000 2005 2010 One-standard-deviation range Unemployment rate Average unemployment (1980—2010) Sources: Ball, De Roux, and Hofstetter (2011); and IMF staff calculations. ¹ Simple average of Brazil, Chile, Colombia, Mexico, Peru, and Uruguay. Selected Latin America: Unemployment Rates, 1980/2010¹ (Percent) Sources: Haver Analytics; national authorities; and IMF staff calculations. ¹Seasonally adjusted quarterly data; historical average for entire available series. Constant and time-varying NAIRU estimated from the Phillips curve. In the case of Chile, estimates may be affected by methodological changes to the measurement of unemployment that were introduced in 2010. Structural changes and supply shocks tend to distort NAIRU estimates for Uruguay, which are therefore not reported. ² Unemployment at near-zero output gap represents the observed unemployment rate in Q4 of the year in which the output gap was closest to zero in the series. Data unavailable for Colombia and Uruguay. 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 Current unemployment Current time-varying NAIRU Constant NAIRU Unemployment at near-zero output gap ² Historical average Brazil Chile Colombia Mexico Peru Uruguay Selected Latin America: Current Unemployment Rate versus NAIRU¹ (Percent) 2. OUTLOOK AND POLICY ISSUES FOR LATIN AMERICA AND THE CARIBBEAN 29 at historic lows. In contrast with past crises, the 2008–09 crisis had a relatively small and short-lived adverse impact on unemployment in the region (Figure 2.11). In fact, the strong economic expansion that Latin America experienced over the past decade has been more labor inclusive than that during the 1990s. However, questions have emerged about the sustainability and nature of recent unemployment trends. On the one hand, employment gains have taken place in sectors (construction and services) traditionally thought to be more vulnerable to a reversal in the economic cycle. On the other hand, it is unclear whether further declines in unemployment will add to wage pressures and stoke inflation. Estimating the Nonaccelerating Inflation Rate of Unemployment How tight are labor markets? Monetary policy is often guided by some metric of economic slack for example, output versus its potential level, or unemployment versus its natural rate or the rate at which inflation is “nonaccelerating.”12 Despite its importance, only a few studies have examined the relationship between inflation and unemployment in the region (Texeira da Silva Filho, 2010; Restrepo, 2008). This is not surprising given data constraints, as well as deep structural changes in recent years (Ball, De Roux, and Hoffstetter, 2011). Furthermore, particular labor market features in the region (e.g., informality and underemployment) can affect the inflation-unemployment relationship.13 To analyze this relationship we estimate a Phillips curve equation, with the goal of finding a time- _______ 12 Implicit in this concept is the idea that shifts in aggregate demand coming from either monetary policy or other sources have short-run impacts on unemployment. However, in the long-run, unemployment tends to return to the NAIRU. Although it is tempting to conclude that the NAIRU is determined by supply-side factors, such as labor market frictions, this is not necessarily the case, in particular if demand shocks have hysteresis effects (see Ball and Mankiw, 2002; Ball, 2009). 13 In some instances, long time series cannot be used because episodes of hyperinflation break down the relationship between inflation and unemployment. varying unemployment rate consistent with stable inflation.14 The estimated NAIRUs are also compared with other rule-of-thumb proxies such as (1) the average historical rate of unemployment, (2) the Hodrick-Prescott-filtered unemployment rate, and (3) the unemployment rate consistent with past episodes when our estimated output gaps were near zero. Our results suggest that unemployment is currently below trend or near NAIRU levels for most inflation-targeting countries in the region (Figures 2.11 and 2.12).15 The evidence is fairly robust for Chile, Colombia, and Peru, yet mixed in the case of Mexico. For Brazil and Uruguay we encountered more difficulties in identifying the NAIRU given the prominence of supply and structural factors (the sharp reductions in inflation during the early 2000s in both countries took place in tandem with important declines in unemployment). Policy Implications From a policy perspective, our NAIRU estimates suggest that labor markets have been fairly tight and that further tightening of monetary policy in some countries could be warranted. Nonetheless, this has _______ 14 To this end, we use quarterly data for six inflation-targeting countries in the region (dating back to at least 2001) and follow the methodology developed by Ball and Mankiw (2002). Formally, , where changes in inflation ( are regressed against the unemployment gap (i.e. how far is unemployment, from the nonaccelerating inflation rate of unemployment, ), and supply shocks ( ). To identify the time varying NAIRU a Hodrick-Prescott filter is applied to , based on the assumption that is a slow-moving process and corresponds to high-frequency fluctuations associated with different shocks. A constant NAIRU is also estimated by regressing inflation against a constant, lagged inflation, and unemployment, where the NAIRU is equivalent to the ratio of the estimated constant term to the sum of the lagged unemployment coefficients (see Staiger, Stock, and Watson, 1997). 15 A challenge in examining unemployment dynamics relates to the varying definitions used by statistical agencies across the region (see Ball, De Roux, and Hoffstetter, 2011). This can be a caveat for cross-country analyses, but it does not affect the estimates reported in this section as they do not use the cross- sectional dimension. REGIONAL ECONOMIC OUTLOOK: WESTERN HEMISPHERE 30 Figure 2.12. Unemployment in Latin America is close to proxies for its natural rate, such as the Hodrick-Prescott-filtered unemployment series, the historic average unemployment rate for the period, and the estimated time-varying NAIRU. Selected Latin America: Unemployment versus Estimated Natural Rate (Percent) 0 4 8 12 16 20 2001:Q4 2003:Q4 2005:Q4 2007:Q4 2009:Q4 Brazil 0 4 8 12 16 20 0 4 8 12 16 20 1986:Q1 1990:Q1 1994:Q1 1998:Q1 2002:Q1 2006:Q1 2010:Q1 Unemployment Unemployment (HP-filtered) NAIRU (Ball and Mankiw, 2002) Unemployment (period average) Chile 0 4 8 12 16 20 2001:Q1 2003:Q1 2005:Q1 2007:Q1 2009:Q1 2011:Q1 Colombia 0 4 8 12 16 20 0 4 8 12 16 20 1997:Q1 2000:Q1 2003:Q1 2006:Q1 2009:Q1 Mexico 0 4 8 12 16 20 2001:Q2 2003:Q2 2005:Q2 2007:Q2 2009:Q2 2011:Q2 Peru 0 4 8 12 16 20 0 4 8 12 16 20 1997:Q3 2000:Q3 2003:Q3 2006:Q3 2009:Q3 Uruguay Sources: Haver Analytics; and IMF staff calculations. ¹ The NAIRU is estimated based on a regression of the Philips curve that includes core inflation, unemployment, and supply shocks (See Ball and Mankiw, 2002). For the cases of Peru and Mexico, core consumer price index (CPI) inflation is in levels, not detrended. Exchange rate supply shocks are defined in terms of annual percentage changes: the de-meaned real effective exchange rate is used for Brazil, Colombia, Chile, and Mexico; the nominal exchange rate is used for Peru. Estimates for Chile and Peru also include an inflation supply shock defined as the de-meaned difference between headline and core annual CPI inflation rates. Estimate for Brazil includes one lag of detrended inflation. In the case of Chile, estimates may be affected by methodological changes to the measurement of unemployment that were introduced in 2010. Structural changes and supply shocks tend to distort