77320 THE WORLD BANK ECONOMIC REVIEW, VOL. 14, NO. 2: 2S1-S5 Macro'economic Fluctuations in Developing Countries: Some Stylized Facts Pierre-Richard Agenor, C. John McDermott, and Eswar S. Prasad This article documents the main stylized features of macroeconomic fluctuations for 12 developing countries. It presents cross-correlations between domestic industrial output and a large group of macroeconomic variables, including fiscal variables, wages, infla- tion, money, credit, trade, and exchange rates. Also analyzed are the effects of economic conditions in industrial countries on output fluctuations in the sample developing coun- tries. The results point to many similarities between macroeconomic fluctuations in de- veloping and industrial countries (procyclical real wages, countercyclical variation in government expenditures) and some important differences (countercyclical variation m the velocity of monetary aggregates). Their robustness is examined using different detrending procedures. Understanding and distinguishing among the factors that affect the short- and long-run behavior of macroeconomic time series have been among the main ar- eas of recent research in quantitative macroeconomic analysis. Using a variety of econometric techniques, a substantial body of literature has documented a wide range of empirical regularities in macroeconomic fluctuations and business cycles across countries. These stylized facts have often been used as an empirical basis for formulating theoretical models of the business cycle and as a way to discrimi- nate among alternative classes of models. Most of the new research in this area has focused on industrial countries, paying less attention to developing countries.1 At least two factors may account for this. First, limitations O n the quality and frequency of data may be constrain- ing factors. For instance, quarterly data on national accounts are available for only a handful of developing countries, and even where they are available, they are considered to be of significantly lower quality than annual estimates. Second, developing countries tend to be prone to sudden crises and marked gyrations in 1. For an overview of the literature on industrial countries, see, for example, Backus and Kehoe (1992), Horito and Kollintzas (1994), and van Els (1995). Pierre-Richard Agenor is lead economist and director of the Macroeconomic and Financial Management Program at the World Bank, C. John McDermott is an advisor at the Reserve Bank of New Zealand, and Eswar Prasad is a senior economist at the International Monetary Fund. Their e-mail addresses are pagenor@woridbank.org, mcdermottf@rbnt.gpvt.nx, and eprasad@imf.org. The authors would like to thank Nadeem Haque, Alexander Hoffmaister, Philip Lane, James Nason, and Julio Santaella for helpful discussions and comments; Marianne Baxter for the computer code for the band-pass filter, Brooks Calvo for excellent research assistance; and three anonymous referees for their comments. C 2000 The International Bank for Reconstruction and Development/THE WORLD BANK 251 2S2 THE WORLD BANX ECONOMIC REVIEW, VOL. 14, NO. 1 macroeconomic variables, often making it difficult to discern any type of cycle or economic regularity. At the same time documenting the stylized facts on macroeconomic fluctua- tions in developing countries could be useful for a number of reasons. Such an exercise could be valuable for analyzing whether similar empirical regularities are observed across countries with different income levels. Differences in the types of reduced-form relationships observed in industrial countries could provide an empirical basis for constructing analytical models of short-run fluctuations that incorporate features particularly important to developing countries. In addition, as argued, for instance, by Ag6nor and Montiel (1996), these findings may have important policy implications. They may, for example, be crucial for designing stabilization and adjustment programs. A burgeoning literature has begun to document these stylized facts for devel- oping countries. Some of the studies focus on specific stylized facts and construct theoretical models that can replicate those facts. Mendoza (1995), for instance, documents a strong positive correlation between terms-of-trade and output fluc- tuations in developing countries. Other studies in this genre include Kouparitsas (1997) and Kose and Riezman (1998), although these articles focus on one or two specific sets of bivariate correlations. Another set of articles documents a broader set of cross-correlations, but typically only for one country. Further, most use only one detrending procedure. Representative papers include Kydland and Zarazaga's (1997) work on Argentina and Rodrfguez-Mata's (1997) analy- sis of fluctuations in Costa Rica. This article builds on the existing literature by systematically documenting a wide range of regularities in macroeconomic fluc- tuations for a large group of developing countries. We chose the countries in our sample on the basis of several considerations. The first was the desire to select a group of countries for which we could as- semble data of reasonable quality, thereby addressing the criticism that such ex- ercises have limited validity because of data inaccuracies. The second consider- ation was the need to include different geographic areas and a wide range of macroeconomic experiences, at the same time selecting countries that did not suffer substantial economic turmoil (in the form of, say, sustained episodes of hyperinflation) over the relevant sample period. With this criterion we avoid crisis-prone countries and the difficulties associated with data interpretation in such cases. Moreover, by looking for a consistent set of relationships among macroeconomic variables in a relatively large group of countries that have had diverse experiences with structural change, we provide a set of stylized macro- economic facts that are unlikely to reflect country-specific episodes. Our study of business cycle regularities is based on quarterly data for a group of 12 middle-income countries: Chile, Colombia, India, the Republic of Korea, Malaysia, Mexico, Morocco, Nigeria, the Philippines, Tunisia, Turkey, and Uru- guay. On the one hand, the decision to use quarterly, rather than annual, data imposes an additional constraint on the size of our sample, because relatively few developing countries produce quarterly output indicators. On the other hand, Aginor, McDermott, and Prasad 253 quarterly data provide us with sufficiently long time series for reliable statistical inference.2 The data cover a wide range of macroeconomic variables and include indus- trial output, prices, wages, monetary aggregates, domestic private sector credit, fiscal variables, exchange rates, and trade variables. (See the appendix for a de- scription of the data and sources.) Thus we are able to examine macroeconomic fluctuations in various dimensions, in contrast to earlier studies. In addition, we examine the relationship between economic fluctuations in these countries and two key indicators that proxy for economic activity in industrial countries—an index of industrial-country output and a measure of the world real interest rate. Two methodological aspects of this article are worth highlighting at the out- set. First, in line with the recent literature on business cycles for industrial coun- tries, many of the results discussed in the article are based on unconditional correlations between different variables. We naturally recognize that such cor- relations do not imply causal relationships and, in some cases, attempt to comple- ment our correlation results by examining bivariate exogeneity tests. We also recognize that reduced-form relationships between certain variables depend crucially on the sources of macroeconomic shocks. Nevertheless, our results are useful in that they indicate the types of shocks that could be important for different countries and set the stage for more formal structural models of busi- ness cycle fluctuations. Second, many of the macroeconomic series used in this article have distinct trends over time and, hence, need to be rendered stationary prior to empirical analysis. Empirical results could, of course, be sensitive to the choice of econo- metric procedure used to remove long-term trends from the data and derive cycli- cal components. This article makes an additional methodological contribution by examining the sensitivity of correlations and other stylized facts to the detrending procedure used. We use two detrending techniques: a modified ver- sion of the Hodrick-Prescott (1997; HP) filter developed by McDermott (1997) and the band-pass (BP) filter proposed by Baxter and King (1995).3 Thus this article's main contribution is to document a comprehensive set of stylized facts that are comparable across countries and to examine their sensitiv- ity to different detrending techniques. Consistent with the work of other authors (such as Mendoza 1995), we find that output volatility is greater in developing countries than in industrial countries, terms-of-trade and output fluctuations are strongly positively correlated, and there is no consistent relationship between the 2. There are two additional considerations in choosing quarterly rather than annual data. First, some of the series we use have been readily available (and comparable across countries) for only a limited time. For instance, our data on effective exchange rates have been published by the International Monetary Fund only since 1978. Second, establishing large enough samples on an annual basis would imply going back to the early 1960s. It is likely that the quality of the data, where available, was substantially lower in those earlier yean. 3. In Agenor, McDermott, and Prasad (1998) we provide robustness checks for our results using two other detrending techniques—first differences and a nonparametric technique. 254 THE WORLD BANK ECONOMIC REVIEW, VOL. 14, NO. 2 trade balance and fluctuations in domestic output. As in Kouparitsas (1996), we find some evidence that output fluctuations in developing countries are posi- tively correlated with business cycles in industrial countries and negatively corre- lated with real interest rates in industrial countries. We also find evidence of procyclical variation in monetary aggregates and real wages and countercyclical variation in government expenditures. The remainder of the article is organized as follows. Section I briefly describes the detrending procedures used. Section II describes a number of economic fea- tures of the countries included in the data set and presents summary statistics for the behavior of output. Section HI provides a more rigorous characterization of macroeconomic fluctuations in these countries and contrasts the results with avail- able stylized facts of business cycles in industrial and developing countries. Sec- tion IV summarizes the main results of the article. Section V offers some final remarks and suggestions for further empirical and theoretical analysis. I. UNIVARIATE DETRENDING TECHNIQUES As indicated earlier, the objective of our article is to examine economic fluc- tuations at business cycle frequencies rather than to study longer-term growth. 4 To do so, it is necessary to decompose all of our macroeconomic series into nonstationary (trend) and stationary (cyclical) components, because certain em- pirical characterizations of the data, including cross-correlations, are valid only if the data are stationary. For a given series, in finite samples, stationary components obtained using different filters can often display very different time-series properties (see Canova 1998). In this article we take an agnostic approach and report results obtained using the two filters mentioned above. The variant of the HP filter we use here chooses the smoothing parameter optimally for each series rather than imposing the same exogenous smoothing parameter for all series (see McDermott 1997).5 n. KEY CHARACTERISTICS OF SAMPLE COUNTRIES In this section we describe a number of important economic features of the developing countries in our sample that are relevant for our analysis. In addition, we present summary statistics for output and inflation and provide a preliminary characterization of business cycle fluctuations in our group of countries. We also compare the properties of business cycles in these countries with those observed in industrial countries. The sample period for most of the data series used in this study runs from the first quarter of 1978 to the fourth quarter of 1995. The data sources are described in detail in the appendix. 4. The real business cycle literature makes no clear distinction between trend and cycles since both short- and long-term fluctuations are regarded as being driven by the same stochastic process. 5. A detailed discussion of the detrending techniques and the algorithms for these filter*, along with a discussion of their properties, can be found in Agenor, McDermott, and Prasad (1988). Aginor, McDermott, and Prasad 2SS Most of the countries in our sample could be reasonably characterized as middle- income countries.] Although India and Nigeria have relatively low per capita in- comes, we include them in the sample because they are among the largest market economies in Asia and Africa (figure la). The urbanization rate and the propor- tion of agricultural output as a share of gross domestic product (GDP) indicate that agriculture is an important, but not dominant, sector in most of the sample (figures l b and lc). Because we were unable to obtain reliable quarterly GDP data for all of the countries in our sample, we use indexes of industrial output to construct mea- sures of the aggregate business cycle. The manufacturing sector accounts for a significant fraction of total GDP (figure Id). Except for Nigeria, this share is more than 15 percent for all countries in our sample, compared with an average share of 25 to 30 percent for most industrial countries. In addition, because output in the industrial sector roughly corresponds to output in the traded goods sector (excluding primary commodities) and is most closely related to what are tra- ditionally thought of as business cycle shocks, either exogenous or policy- determined, we argue that this variable is a reasonable proxy for measuring the aggregate cycle.6 For all countries except Nigeria, export growth is an important contributor to overall GDP growth (figure lh). Standard measures of openness to international trade—as indicated by the average openness ratio (the ratio of the sum of im- ports and exports to GDP)—illustrate the importance of foreign trade transac- tions in our sample (figure li). Hence an important part of our analysis focuses on the relationship between the domestic business cycle and the prices and quan- tities related to international trade. An important consideration in choosing our sample was to exclude countries that had suffered sustained episodes of hyperinflation during the period under study. Although some of the countries in the sample (such as Mexico, Turkey, and Uruguay) had high levels of inflation over the past two decades, none suf- fered sustained episodes of hyperinflation (figure Ik). This is also apparent from the average annual rates of consumer price inflation and the volatility of infla- tion, as measured by the standard deviation of annual inflation rates (last two columns of table 1). A key issue concerning business cycle fluctuations in developing countries is whether aggregate fluctuations are characterized by basic time-series properties, such as volatility and persistence, that are similar to those observed in industrial countries. A simple way of approaching this issue is by examining summary sta- tistics for the stationary components of industrial output (table 1). The first two columns of table 1 report means and standard deviations of output growth rates as well as standard deviations of the cyclical components of output derived using 6. In genual, the use of GDP data for measuring business cycle activity in a developing country can be problematic. Agriculture, which still accounts for a Urge share of aggregate output in many developing countries (including several in our sample) is inP^Tm^T^ more by weather conditions than by cyclical factors. Poor measurement of services and informal sector activities may also impart signifirant biases. 256 THE 'WORLD BANK ECONOMIC REVIEW, VOL. 14, NO. 2 Figure 1. Economic Indicators in Selected Developing Countries (data are for 1993, unless otherwise indicated) Hgure la. Per capita income (US. defers) Figure lb. Urbanization rate (percent) 2,000 4,000 6,000 8,000 80 100 Figure l c Agdcukure/GDP (percent) Hgure Id Maruf)cturingA3OP (percent) Figure I t Total government expendkureVGDP (percent) Hgure If. Total government rrwenuc/GDP (percent) Aginor, McDermott, and Prasad 2S7 Figure 1. (continued) Figure lg. Import grown (percenxr Figure lh. Export growth (percenO1 Chile Chile Colombia Colombia India India Eorea, Rep. of Korea, Rep. of Malaysia Malayiia Mexico Mexico Morocco • ^ Morocco Nigeria Nigeria Philippine! Philippines Tunisia Tunisia Turkey Turkey Uruguay Uruguay •. . . . . 1 -15 -10 10 15 -5 0 5 10 15 Hgure lj. Extemal debt sovmx/exporo of goods Hgure 11 Otjemtos and services (percent) 0 50 100 150 Hgure Ik. Consumer price Inflation (peroent/ Figure 1L Broad money growth (percent/ 10 20 30 40 a. Average annual growth rate, 1960-93. b. Avenge annual tarJoof sum of expanand Imports to GDP, in percent, 19B0-93- Sounx maraoiond Monaary Rmd and World Bank. 258 THE 'WORLD BANK ECONOMIC REVIEW, VOL 14, NO. 2 Table 1. Summary Statistics for Industrial Output and Inflation 1 Output Annual inflation Country and Mean Standard Autocorrelations Mean Standard filter (percent)deviation Lagl Lag 2 Lag 3 Lag 4 1 deviation (percent, Chile Growth 3.81 7.03 0.75 0.53 0.25 -0.07 19.69 8.69 HP 4.53 0.68 0.51 0.27 0.00 BP 1.45. 0.56 0.54 0.42 0.25 Colombia Growth 2.57 4.61 0.70 0.52 0.39 0.15 24.19 4.02 HP 2.33 0.51 0.27 0.17 0.02 BP 1.40 0.63 0.65 0.59 0.49 India Growth 6.02 4.31 0.67 0.54 0.27 0.05 9.34 2.78 HP 2.45 0.48 0.35 0.10 0.02 BP 1.13 0.24 0.49 0.28 0.27 Korea, Rep. of Growth 9.22 6.06 0.75 0.49 0.22 -0.11 8.23 7.34 HP 3.47 0.71 0.44 0.20 -0.14 BP 1.48 0.67 0.57 0.61 0.37 Malaysia Growth 9.22 6.79 0.71 0.29 -0.04 -0.29 3.79 2.55 HP 4.06 0.69 0.30 0.07 -0.16 BP 1.41 0.46 0.23 0.41 0.15 Mexico Growth 2.39 6.21 0.79 0.51 0.24 0.00 48.55 40.36 HP 3.31 0.72 0.40 0.14 -0.13 BP 1.42 0.76 0.64 0.53 0.30 Morocco Growth 2.57 4.44 0.12 0.27 0.06 -0.32 7.03 3.38 HP 2.77 0.06 0.25 0.08 -0.18 BP 1.14 0.01 0.43 0.33 0.13 Nigeria Growth 3.05 12.34 0.62 0.33 0.17 0.00 29.54 23.56 HP 6.69 0.45 0.09 -0.06 -0.12 BP 3.29 0.50 0.43 0.44 0.40 Philippines Growth 13.85 11.69 0.63 0.37 0.03 -0.29 13.69 11.48 HP 7.45 0.63 0.42 0.10 -0.15 BP 2.62 0.18 0.41 0.19 0.04 Tunisia Growth 2.34 4.79 0.77 0.57 0.30 0.13 7.50 2.35 HP 2.72 0.63 0.42 0.13 0.06 BP 1.25 0.61 0.70 0.44 0.46 Turkey Growth 6.19 6.14 0.48 0.27 0.11 -0.23 61.78 25.45 HP 3.67 0.38 0.14 0.06 -0.12 BP 1.42 -0.08 0.20 0.20 0.07 Uruguay Growth -0.94 8.55 0.72 0.55 0.34 0.04 62.04 23.93 HP 4.94 0.63 0.50 0.27 -0.01 BP 2.37 0.62 0.75 0.63 0.53 Note-. Growth refers to the four-quarter differences of the log levels of relevant variables (as in text). HP and BP refer to the stationary components of output derived using the modified Hodrick-Prescott and band-pass filters, respectively. Source: Authors' calculations based on IMF data. Agirtor, McDermott, and Prasad 259 the HP and BP filters.7 Growth rates are measured here as four-quarter differences of the log leyels of the relevant variables. Mean annual growth rates of industrial output over the past two decades var- ied substantially across the countries in our sample, ranging from almost 14 per- cent for the Philippines to about 2.5 percent for Colombia, Mexico, Morocco, and Tunisia. Uruguay, in fact, recorded a negative mean growth rate over this period. The volatility of growth rates also varies markedly across countries. On average, volatility in our sample is much higher than the level typically observed in industrial countries. A similar picture emerges from the standard deviations of the filtered cyclical components of industrial output.8 Because the filters used here tend to eliminate more of the low-frequency variation than, say, a first-difference filter, these stan- dard deviations are generally lower. However, the ordering of countries by their cyclical volatility is similar, and their volatility is generally higher than that ob- served for industrial countries. The volatility of the cyclical components obtained using the BP filter is generally much lower than that using the HP filter; the BP filter eliminates some of the high-frequency variation in the data, whereas the HP filter eliminates only low-frequency variation. To examine the persistence of business cycle fluctuations, we also measure the first four autocorrelations of the filtered series (table 1). The autocorrelations are generally strongly positive, indicating considerable persistence in the cyclical com- ponents. These results suggest that it is appropriate to view the developing coun- tries in our sample as having short-term fluctuations that could reasonably be characterized as business cycles. HI. MAIN FEATURES OF MACROECONOMIC FLUCTUATIONS We measure the degree of comovement of a series y, with industrial output x, by the magnitude of the correlation coefficient A(j), j e{0, ±1, ±2, . . .}. These correlations are between the stationary components of yt and xo with compo- nents in both derived using the same filter. In the discussion that follows, we consider the series yt to be procyclical, acydical, or countercyclical if the contem- poraneous correlation coefficient A(0) is positive, zero, or negative, respectively. In addition, we deem the series yt to be strongly contemporaneously correlated if 0.26 <, IA(O)I < 1, weakly contemporaneously correlated if 0.13 £ IA(O)I < 0.26, and contemporaneously uncorrelated with the cycle if 0 <, IA(O)I < 0.13. 9 7. These filters, by construction, deliver stationary components that have zero means. The output series as well as all the other tune series used in this article were deseasonalized using the X - l l procedure. 8. We can interpret these standard deviations at quarterly percentage standard deviations. For purposes of comparison, the standard deviation of HP-filtered postwar quarterly industrial production for the United Stares is about 2 percent. 9. The approximate standard error of these correlation coefficients, computed under the null hypothesis that the true correlation coefficient if zero and given the average number of observations per country in our sample, is about 0.13. 260 THE WORLD BANX ECONOMIC REVIEW, VOL. 14, NO. 2 The cross-correlation coefficients A(/),; e {0, ±1, ±2, ...} indicate the phase- shift pf yt relative to the cycle in industrial output. We say that y, leads the cycle by; periods if IA(/)I is a maximum for a positive/, is synchronous with the cycle if IA(/)I is a maximum for ; = 0, and lags the cycle if IA(/)I is a maximum for a negative/. To conserve space, we report only contemporaneous correlations and correlations at the fourth and eighth lags and leads. Results for a larger set of lags and leads can be found in Agenor, McDermott, and Prasad (1998). Correlations with Industrial-Country Business Cycles Here we examine the relationship between fluctuations in domestic industrial output in our sample countries and variables that represent economic activity in the main industrial countries—a relationship that could be particularly impor- tant for developing countries that have substantial trade links with industrial countries.10 As discussed earlier, the magnitude of the links between macroeco- nomic fluctuations in industrial and developing countries and the channels through which shocks propagate between these two sets of countries are of considerable interest from a number of different perspectives. The contemporaneous correlations are positive for a majority of the sample countries, indicating that business cycle fluctuations in developing countries tend to be correlated with business cycle fluctuations in industrial countries (table 2). For many of the countries that have positive contemporaneous correlations, the correlations generally peak at or near a zero lag, suggesting that output fluctua- tions in industrial economies are transmitted fairly quickly.11 These results are generally robust across filters, barring a couple of excep- tions. For instance, in the case of Mexico the BP filter yields a strong negative contemporaneous correlation, whereas the HP filter yields a positive correlation. The correlations at the four-quarter lag are, however, all strongly positive, indi- cating that industrial-country output has a lagged effect on Mexican output. The contemporaneous correlations are close to zero for Morocco and Nigeria and marginally negative for Turkey. For these countries there is some evidence that industrial-country output has a positive effect on domestic industrial output with a lag of about four to eight quarters. Business cycle conditions in industrial economies also could influence fluctua- tions in developing economies through the world real interest rate. The world real interest rate is likely to have an important effect on economic activity in the developing world, not only because it affects domestic interest rates, but also because it reflects credit conditions in international capital markets. These capi- tal markets may be especially important for developing countries (even those in the middle-income range) that do not have well-developed domestic capital mar- kets. To examine this issue, we measure correlations of industrial output in our 10. The industrial-country variables used in this section are described in the appendix. 11. Business cycles in the industrial economies are, of course, not perfectly synchronized. But Lumsdaine and Prasad (1997), among others, argue that there is a substantial common component in business cycle fluctuations across the main industrial economies. Agenor, McDermott, and Prasad 261 Table 2. Cross Correlations between Domestic Output and Industrial- Country Output Country Eight-quarter Four-quarter Zero Four-quarter Eight-quarter and filter lag lag lag lead lead Chile HP -0.04 0.09 0.52 -0.11 -0.48 BP 0.24 0.38 0.32 -0.03 -0.28 Colombia HP -0.44 -0.07 0.43 0.21 -0.12 BP -0.37 0.02 0.49 0.55 0.35 India HP 0.13 0.15 0.24 -0.20 -0.11 BP 0.32 0.56 0.46 0.07 -0.15 Korea, Rep. of HP 0.00 -0.49 0.36 0.22 0.22 BP 0.28 0.07 0.36 0.66 0.49 Malaysia HP -0.49 0.14 0.59 0.08 -0.40 BP -0.29 0.30 0.57 0.21 -0.35 Mexico HP 0.08 0.38 0.19 -0.62 -0.37 BP 0.36 0.35 -0.29 -0.83 -0.70 Morocco HP 0.10 -0.07 -0.06 0.02 -0.03 BP 0.20 0.06 -0.05 -0.06 -0.17 Nigeria HP 0.22 0.26 0.03 -0.22 -0.05 BP 0.59 031 -0.15 -0.41 -0.38 Philippines HP -0.63 -0.05 0.53 0.10 -0.48 BP -0.32 -0.11 0.38 0.26 -0.36 Tunisia HP -0.43 -0.24 0.45 0.13 -0.36 BP 0.80 -0.48 0.04 0.07 -0.27 Turkey HP 0.23 0.04 -0.14 -0.02 0.12 BP 0.24 -0.32 -0.36 -0.16 0.43 Uruguay HP 0.33 0.08 0.21 -0.30 -0.29 BP 0.66 0.49 0.18 0.01 0.17 Note: H P and B P refer to the stationary components derived using the modifed Hodrkk-Prescott and band-pass filters, respectively. The correlations reported are between the contemporaneous values of domestic output and the /th lag or lead of industrial-country output, with both variables detrended using the same filter. The data series and sources are described in the appendix. Source: Authors' calculations based on I M F data. sample countries with a weighted index of real interest rates in the major indus- trial countries (table 3). For most of the countries in our sample the contemporaneous correlations between HP-filtered output and the world real interest rate are positive. This could reflect the facts that the real interest rate in industrial economies tends to be procyclical and that changes in industrial-country output, through trade links, 262 THE WOR1D BANX ECONOMIC REVIEW, VOL. 14, NO. 2 Table 3. Cross Correlations between Domestic Output and the World Real Interest Rate Country Eight-quarter Four-quarter Zero Four-quarter Eight-quarter and filter lag lag lag lead lead Chile HP 0.06 -0.43 0.17 0.10 0.06 BP -0.18 -0.42 -0.25 0.06 0.16 Colombia HP -0.50 -0.33 0.22 0.32 0.33 BP -0.55 -0.13 0.24 0.58 0.39 India HP 0.00 0.14 0.29 -0.28 -0.28 BP -0.08 0.14 0.09 -0.36 -0.25 Korea, Rep. of HP 0.12 -0.28 0.34 -0.08 0.08 BP -0.21 -0.08 0.05 0.16 0.06 Malaysia HP -0.03 -0.18 0.18 -0.09 -0.04 BP 0.56 -0.02 -0.12 -0.24 -0.29 Mexico HP -0.32 0.00 0.22 -0.03 -0.14 BP 0.11 0.11 0.09 -0.02 -0.06 Morocco HP 0.24 0.18 -0.16 0.00 -0.04 BP 0.32 0.23 -0.06 -0.25 -0.22 Nigeria HP -0.23 0.32 -0.01 -0.05 0.22 BP -0.04 0.01 0.08 0.05 -0.17 Philippines HP -0.15 -0.08 0.26 0.06 -0.34 BP -0.04 0.07 0.21 0.17 -0.41 Tunisia • HP 0.26 -0.25 0.04 0.07 -0.14 BP 0.27 0.13 0.10 -0.01 -0.32 Turkey HP 0.13 0.02 -0.22 -0.05 0.22 BP 0.05 -0.25 -0.46 -O.04 0.44 Uruguay HP -0.10 -0.32 0.19 0.13 -0.02 BP -0.20 -0.35 -0.19 0.04 0.22 Note: The world real interest rate is proxied by a weighted index of real interest rates in the major industrial countries. HP and B P refer to the stationary components derived using the modifed Hodrick- Prescott and band-passfilters,respectively. The correlatiom reported are between the contemporaneous values of domestic output and the /th lag or lead of the world real interest rate, with both variables detrended using the same filter. The data series and sources are described in the appendix. Source: Authors' calculations based on I M F data. have positive spillover effects on output in these middle-income countries.12 Morocco and Turkey are the only sample countries for which this correlation is negative using either filter. For a few countries the lagged correlations are nega- tive. Mexico is an interesting case: the contemporaneous correlation is positive, 12. The correlation between the cyclical components of the output and real interest rate indexes for industrial countries is strongly positive for 1975-95, irrespective of the detrending procedure used. Aginor, McDermott, and Prasad 263 but most of the correlations at short leads and lags are close to zero, indicating that the effects of changes in the world interest rate are transmitted quite rapidly to Mexican industrial output. This is not surprising given Mexico's physical prox- imity to and close trade links with the United States, which is the dominant in- dustrial economy and therefore has a high weight in the composite index of industrial-country output and our proxy for the world real interest rate. Overall, these results suggest that the level of economic activity in industrial countries has a significant, positive relationship with industrial output in the middle-income countries in our sample.13 Because real interest rates are procydical in industrial countries, their relationship to industrial output in developing coun- tries may be muted by the opposite, indirect effect of aggregate economic activity in industrial countries. Further research is needed to separate out the quantita- tive importance of these different influences on business cycle propagation. An important issue in this context (which we return to later) is the inability to mea- sure interest rates that individual countries face on world capital markets with- out adequate data on country-specific premiums. Cyclical Behavior of Public Sector Variables The relationship between fluctuations in aggregate output and the compo- nents of aggregate demand has been well documented for industrial countries. Unfortunately, we were unable to obtain consistent and sufficiently long series of quarterly data on consumption and investment for all countries in our sample. We were, however, able to obtain data on the public sector, although only for a limited set of countries. Examining the relationship between aggregate economic activity and public sector expenditures and revenue has analytical value from the perspective of business cycle modeling and is important from a policy perspec- tive, including in the design of macroeconomic stabilization programs. There is a robust negative relationship between government expenditures and the domestic business cycle in all four countries for which we have data—Chile, Korea, Mexico, and the Philippines (top panel of table 4). Thus there is fairly clear evidence of a countercyclical role for government expenditures. These re- sults contrast with those obtained for industrial countries. Fiorito and Kollintzas (1994), for instance, find no dear pattern. The negative contemporaneous corre- lation between government consumption expenditures and industrial output is consistent with the prediction of a variety of models, such as the class of intertemporal optimizing models with imperfect capital mobility and flexible prices (see Agenor 1997). In these models an increase in public spending leads to a net increase in domestic absorption (if the degree of intertemporal substitution in consumption is not too large), a real exchange rate appreciation, and a fall in output of tradable goods on impact. Government revenues are significantly countercyclical in Korea, the Philip- pines, and Uruguay (second panel of table 4). 14 This negative correlation may 13. This finding is consistent with the results of Kouparitsas (1996). 14. Rodriguez-Mata (1997) establishes a countercyclical pattern of government revenue for Costa Rica. 264 THE WORLD BANK ECONOMIC REVIEW, VOL. 14, NO. 2 Table 4. Cross Correlations between Domestic Output and Government Country Eight-quarter Four-quarter Zero Four-quarter Eight-quarter and filter lag lag lag lead lead Between domestic output and government expenditures Chile HP -0.13 -0.04 -0.16 0.30 0.00 BP -0.27 -0.01 -0.13 0.43 -0.34 Korea, Rep. of HP -0.03 -0.17 -0.39 0.13 0.32 BP -0.14 -0.33 -0.46 -0.02 0.39 Mexico HP -0.10 -0.11 -0.21 0.22 0.36 BP -0.11 -0.35 -0.10 0.21 0.27 Philippines HP 0.59 0.22 -0.72 0.00 0.57 BP 0.69 0.10 -0.54 -0.06 0.25 Between domestic output and government revenue Colombia HP -0.03 0.05 -0.17 0.03 0.23 BP 0.15 0.20 -0.01 -0.03 0.14 Korea, Rep. of HP -0.06 -0.17 -0.28 0.14 0.30 BP -0.18 -0.20 -0.31 0.08 0.41 Mexico HP 0.07 0.21 -0.08 -0.03 0.17 BP -0.11 0.15 0.13 0.15 0.41 Philippines HP 0.49 0.32 -0.69 -0.14 0.45 BP 0.35 0.22 -0.57 -0.22 0.24 Uruguay HP -0.13 -0.09 -0.26 0.28 0.22 BP -0.27 -0.26 -0.13 0.15 0.12 Between domestic output and the fiscal impulse" Korea, Rep. of HP -0.03 -0.09 -0.23 0.00 0.11 BP -0.02 -0.23 -0.27 -0.08 0.15 Mexico HP -0.13 -0.25 -0.16 0.28 0.24 BP -0.07 -0.40 -0.14 0.17 0.10 Philippines HP 0.32 -0.10 -0.30 0.16 0.30 BP 0.46 -0.16 -0.17 0.15 0.10 Note: H P and B P refer to the stationary components derived using the modifed Hodrick-Prescott and band-pass filters, respectively. The correlations reported are between the contemporaneous values of domestic output and the /th lag or lead of government expenditures, government revenue, or the fiscal impulse, with both variable* detrended using the same filter. The data series and sources are described in the appendix. a. The fiscal impulse is defined as the ratio of government expenditures to government revenue. Source: Authors' calculations based on I M F data. Aginor, McDermott, and Prasad 265 result from the negative effects of increases in tax revenues (possibly induced by increases in effective tax rates) on disposable income and aggregate demand.15 In Mexico the relationship appears to be acyclical, although this result is sensitive to the choice of filter. To examine the net effect of government revenue and expenditures on the domestic business cycle, we construct a measure of the fiscal impulse—the ratio of government expenditures to government revenue—for the three countries for which both revenue and expenditure series were available. The fiscal impulse is negatively correlated with the business cycle, either contem- poraneously or at short lags, in Korea, Mexico, and the Philippines (third panel of table 4). Thus the fiscal impulse is countercyclical and plays a role in short-run macroeconomic stabilization. To summarize, the correlations examined in this subsection suggest that the government balance does play a significant role in dampening domestic fluctua- tions in Korea, Mexico, and the Philippines. However, the countercyclical be- havior of government revenue in some countries highlights the need to reexamine revenue sources to ensure that they do not exacerbate domestic fluctuations. An alternative possibility is that tightening government finances could raise future output growth by, for instance, crowding in private investment or by signaling the future stability of domestic macroeconomic policy, thereby stimulating for- eign investment. Based on the negative lagged correlations, there is some evi- dence of this effect in Korea. Correlations with Wages and Prices Establishing stylized facts about the cyclical behavior of wages and prices has important implications for discriminating among different classes of models (based on their predictions concerning such behavior). For instance, Keynesian models imply that real wages are countercyclical, whereas equilibrium models of the business cycle imply that real wages are procyclical (Abraham and Haltiwanger 1995). Similarly, the implications of the cyclical behavior of prices, inflation, and (as discussed later) various monetary aggregates for discriminating among differ- ent classes of business cycle models have been the subject of considerable debate in the business cycle literature recently (Chadha and Prasad 1994). We begin by examining correlations between average nominal wages in the industrial sector and industrial output. Consistent time-series data on wages were available for only 5 of the 12 countries in our sample. The cyclical behavior of nominal wages varies markedly across the five countries (first panel of table 5). In Chile nominal wages appear to be procyclical, whereas there is some evidence that nominal wages are countercyclical in Korea, Colombia, and Mexico. In interpreting these results, it is useful to look at the cyclical behavior of real wages, often the relevant wage variable for analyzing business cycles. We con- struct real wages by deflating nominal wages by the consumer price index (CPl). 15. A highly positive short-run correlation between current income and expenditures in developing countries has been well documented and has been attributed to the existence of liquidity constraints and finite horizons. See Aginor and Montiel (1996: ch. 3). 2 66 THE 'WORLD BANK ECONOMIC REVIEW, VOL. 14, NO. 2 Table 5. Cross Correlations between Domestic Output and Nominal Wages and between Domestic Output and Real Wages Country (^ ill Eight-quarter / m i I "Clrwlm* Hi •• t • • Four-quarter Cruu. • / • m Zero *\ Four-quarter Eight-quarter and filter lag lag lag lead lead Between domestic output and nominal wages Chile HP -0.13 -0.08 0.08 0.03 0.06 BP -0.54 -0.29 0.38 0.53 0.08 Colombia HP -0.05 0.07 -0.13 0.41 0.15 BP -0.03 -0.35 -0.45 -0.47 0.41 Korea, Rep. of HP -0.05 -0.28 -0.08 0.06 0.29 BP -0.58 -0.56 -0.43 -0.12 0.33 Mexico HP 0.16 -0.07 -0.15 -0.17 0.1 BP 0.73 0.20 -0.30 -0.29 -0.18 Turkey HP 0.02 0.32 0.08 -0.20 -0.22 BP 0.47 0.50 0.05 -0.53 -0.36 Between domestic output and reed wages Chile HP 0.06 -0.27 0.31 -0.01 0.04 BP -0.15 -0.04 0.15 0.06 0.00 Colombia HP -0.24 0.02 0.68 -0.44 -0.07 BP 0.27 0.09 0.27 -0.43 -0.46 Korea, Rep. of HP -0.33 -0.21 0.38 0.21 -0.11 BP -0.24 0.01 0.32 0.34 0.34 Mexico HP -0.40 -0.26 0.64 0.15 -0.24 BP 0.15 0.28 0.47 0.14 -0.48 Turkey HP 0.07 0.19 0.43 -0.21 0.64 BP 0.38 0.61 0.15 -0.63 -0.64 Note: HP and B P refer to the stationary components derived using the modifed Hodrick-Prescott and band-pass filters, respectively. The correlations reported are between the contemporaneous values of domestic output and the /th lag or lead of nominal wage* or real wages, with both variables detrended using the samefilter.The data series and tource* are described in the appendix. Source: Authors' calculations based on IMF data. Alternative theories offer different predictions of wage behavior. For instance, traditional Keynesian models of the business cycle posit short-run movement along a stable labor demand schedule and, therefore, predict that real wages are countercyclical. However, real business cycle models, as well as new Keynesian macroeconomic models with imperfect competition and countercyclical mark- ups, predict procyclical wages.16 Finally, efficiency wage models predict no tight contemporaneous relationship between output (employment) and real wages. 16. See Rotemberg and Woodford (1991) for a discussion of macroeconomic models with imperfect competition and countercyclical markups. Aginor, McDermott, and Prasad 267 More generally, as Abraham and Haltiwanger (1995:1230) note, different types of shocks can have very different implications for the cyclicality of the real wage. Technology shocks tend to produce procyclical movements of the real wage, whereas nominal shocks (such as money supply shocks) generate countercyclical movements. The correlations between industrial output and real wages are striking (lower panel of table 5). In all five countries for which data are available, and with both filters, we find strong evidence of procyclical real wage variation. This result is consistent with the implications of real business cycle models that ascribe a domi- nant role to technology shocks that shift the labor demand schedule in the short run. It is also in line with the evidence on real wage rate variation in the United States (see Kydland and Prescott 1994).17 Next we turn to the correlations between prices and output. A substantial literature documents the countercyclical behavior of prices in industrial econo- mies (see, for instance, Backus and Kehoe 1992, Fiorito and Kollintzas 1994, Kydland and Prescott 1994, and Cooley and Ohanian 1991). Many of these studies argue that the countercyclical behavior of price levels provides support for supply-driven models of the business cycle, including real business cycle mod- els that depict technology shocks as predominant in driving business cycle fluc- tuations. However, Chadha and Prasad (1994) argue that the correlation be- tween inflation and cyclical output is the appropriate correlation for discriminating between demand- and supply-driven models of the business cycle.18 They show that inflation in the G-7 countries has in fact been procydical during the postwar period. We therefore examine the cyclical behavior of both the price level and the inflation rate. The contemporaneous correlations between industrial output and the aggre- gate consumer price index are generally negative for Colombia, India, Korea, Malaysia, Morocco, Nigeria, and Turkey, indicating countercyclical variation of the price level (table 6). For a few countries, including Chile and Uruguay, how- ever, the correlations are significantly positive. Thus, unlike industrial countries, our sample countries do not show a consistent negative relationship between the stationary components of output and price levels. The contemporaneous correlations between the level of inflation and the cy- clical component of output indicate that there is little strong evidence of procyclical inflation for most countries in our sample, although the lagged correlations are positive for Chile and Uruguay (table 7).19 The correlations at the leads do not 17. Our analysis of real wage cyclicality only considers the consumption wage in the manufacturing sector, not the producer wage. The two measures could display very different behavior over time, as Abraham and Haltiwanger (1995) illustrate for the United States. 18. Also see Judd and Trehan (1995). 19. Although we yvrliKV from the sample countries with sustained hyperinflationary episodes, unit root tests for inflation indicate that, for about half of the countries in the sample, we cannot reject the null hypothesis of noostationarity. Hence, for all countries we detrend inflation using the same filters that we use for output We also nmmmf the correlations using the raw series for inflation and filtered output. For the countries with signifirant correlations (reported in table 7), the choice of filtered or unfihered inflation does not matter. 268 THE WORID BANK ECONOMIC REVIEW, VOL. 14, NO. 2 Table 6. Cross Correlations between Domestic Output and the Price Level (Consumer Price Index) Country 1 Eight-quarter Four-quarter Zero Four-quarter Eight-quarter and filter lag lag lag lead lead Chile HP -0.41 027 0.42 0.03 -0.14 BP -0.03 0.28 0.51 0.15 -0.23 Colombia HP 0.10 -0.06 -0.43 0.14 -O.05 BP -0.37 -0.60 -0.67 -0.46 0.11 India HP -0.27 -0.21 -0.06 0.03 0.21 BP -0.11 -0.56 -0.47 -0.12 0.09 Korea, Rep. of HP 0.00 0.02 -0.26 -0.22 0.27 BP -0.37 -0.45 -0.58 -0.53 -0.18 Malaysia HP 0.13 -0.17 -0.05 -0.15 -0.09 BP 0.10 -0.04 -0.19 -0.34 -0.37 Mexico HP 0.29 0.17 0.46 -0.26 0.23 BP 0.72 0.08 -0.55 -0.50 -0.18 Morocco HP -0.06 -0.05 -0.28 0.01 0.22 BP -0.38 -0.39 -0.39 -0.10 0.21 Nigeria HP 0.01 0.14 -0.23 -0.05 0.16 BP -&.01 -0.14 -0.21 -O.08 0.10 Philippines HP -0.43 -0.62 0.44 0.38 -0.22 BP -0.36 -0.56 0.00 0.47 0.50 Tunisia HP 0.19 0.28 -0.15 0.03 0.03 BP 0.10 0.50 0.37 029 -0.08 Turkey HP 0.26 0.24 -0.31 -0.15 0.13 BP 0.55 0.15 -0.47 -0.43 0.17 Uruguay HP -0.18 0.47 0.40 -0.27 -0.48 BP 0.22 0.44 0.40 0.15 0.02 Note: H P and BP refer to the stationary components derived using the modifed Hodrick-Prescott and band-pats filters, respectively. The correlations reported are between the contemporaneous values of domestic output and the /th lag or lead of the price level, with both variables detrended using the same filter. The data tenet and sources are described in the appendix. Source: Authors' calculations based on I M F data. provide a clear indication of a positive relationship between output and lagged inflation, as Phillips curve-Hype models, for instance, would predict. Indeed, for some countries, such as Mexico and Turkey, we find negative correlations be- tween inflation and the cyclical component of output, indicating countercyclical variations in inflation. Aginor, McDermott, and Prasad 269 Table 7. Cross Correlations between Domestic Output and Inflation Country , Eight-quarter Four-quarter Zero Four-quarter Eight-quarter and filter ' lag lag lag lead lead Chile HP -0.08 0.49 0.09 -0.33 -0.14 BP 0.32 0.39 0.16 -0.20 -0.18 Colombia HP -0.02 -0.17 -0.23 0.48 -0.03 BP -0.50 -0.15 0.03 0.30 0.66 India HP -0.40 -0.02 0.10 0.14 0.12 BP -0.26 -0.42 0.04 0.36 034 Korea, Rep. of HP -0.16 -0.01 -0.19 0.05 0.40 BP 0.19 0.17 0.27 0.43 0.56 Malaysia HP -0.25 -0.23 0.10 -0.05 0.06 BP 0.14 -0.02 -0.17 -0.22 0.08 Mexico HP 0.23 -O.09 -0.48 0.11 0.39 BP 0.02 -0.52 -0.52 0.08 0J8 Morocco HP 0.07 -0.02 -0.13 0.23 0.21 BP -0.32 -0.02 0.00 0.31 0.35 Nigeria HP -0.02 0.08 -0.27 0.16 0.14 BP 0.00 -0.12 -0.08 0.10 0.04 Philippines HP -0.64 -0.14 0.76 0.04 -0.40 BP -0.49 -0.22 0.52 0.42 -0.05 Tunisia HP 0.23 0.12 -0.25 0.03 0.00 BP 0.58 0.42 -0.05 -0.26 -0.48 • HP 0.25 0.00 -0.39 0.15 0.24 BP 0.23 -0.30 -0.46 -0.04 0.49 Uruguay HP 0.18 0.49 -0.06 -0.56 -0.28 BP 0.44 0.36 0.00 -0.44 -0.08 Note: HF and B P refer to the stationary components derived using the modifed Hodrick-Prescott and band-pass filters, respectively. The correlations reported are between the contemporaneous values of domestic output and the /th lag or lead of inflation, with both variables detrended using the tame filter. The data series and sources are described in the appendix. Source: Authors' calculations based on I MF data. For countries like Mexico and Turkey the procydical behavior of real wages and the countercyclical behavior of both the price level and the inflation rate suggest that supply shocks may have been a key determinant of domestic macro- economic fluctuations over the past two decades. It is worth emphasizing that, for our sample of middle-income countries, the term "supply shocks" could have 2 70 THE 'WORID BANX ECONOMIC REVIEW, VOL. 14, NO. 2 a different connotation than it has for large industrial economies. In particular, these developing countries could be subject to large terms-of-trade shocks rather than prototypical productivity shocks, although terms-of-trade shocks could, in principle, have both supply-side and demand-side effects. Money and Credit In recent years it has become increasingly evident that equilibrium business cycle models often need to incorporate monetary variables to capture important business cycle phenomena. The relationship between monetary variables and the business cycle has, therefore, become a topic of increasing interest (see, for in- stance, Kydland and Prescott 1994). This relationship is particularly relevant to middle-income countries, where the monetary mechanism could play an impor- tant stabilizing role. A large literature has evolved around the question of whether monetary vari- ables influence output in industrial countries or, in more loaded terminology, whether money causes output. From a different perspective, King and Plosser (1984) argue that the positive correlation between money and the business cycle largely reflects the endogenous response of inside money to exogenous shocks that drive business cycle fluctuations rather than a causal relationship from money to output. Given this debate and uncertainties regarding the definition of money that theoretical models use, we examine money-output correlations using several definitions of monetary aggregates. We estimate correlations between industrial production and an index of broad money (M2). Broad money roughly corresponds to the definition for industrial economies. Although in some cases the sign (and statistical significance) of the correlations is affected by the detrending procedure, the contemporaneous corre- lations are broadly positive for a majority of the sample countries, including Chile, Colombia, India, Morocco, the Philippines, Tunisia, and Uruguay (table 8). Among the remaining countries, the contemporaneous correlations are often close to zero, although for Korea, Malaysia, and Mexico, there is some evidence of countercyclical variation in broad money. Of the countries that show positive correlations between money and output, the pattern of lead-lag correlations and, in particular, the lag at which the peak positive correlation occurs could be interpreted as an indication of the speed with which innovations in monetary variables are transmitted to real activity. For these countries the peak positive correlations generally occur at very short lags, suggesting that the transmission of monetary shocks to real activity is fairly rapid. Of course, as noted earlier, this could simply reflect the endogenous response of money to output fluctuations that are driven by nonmonetary shocks. Indeed, when we run bivariate Granger-causality tests on these two variables, we find little evidence that money Granger-causes output, even in those countries where the correlations between the two variables are strongly positive. The patterns of correlations are similar when we use two alternative monetary aggregates—reserve (or base) money and narrow money (currency in circulation Agbior, McDermott, and Prasad 271 Table 8. Cross Correlations between Output and Broad Money (M2) Country t Eight-quarter Four-quarter Zero Four-quarter Eight-quarter and filter lag lag lag lead lead Chile HP -0.51 -0.20 0.37 0.32 0.08 BP -0.22 -0.25 0.23 0.50 0.24 Colombia HP -0.23 -0.44 0.18 0.24 0.21 BP -0.36 -0.24 0.06 0.54 0.45 India HP 0.15 0.14 0.07 -0.41 -0.01 BP 0.13 0.25 0.35 -0.24 -0.10 Korea, Rep. of HP -0.27 0.17 0.03 -0.15 -0.11 BP -0.65 -0.39 -0.27 -0.34 -0.20 Malaysia HP -0.10 -0.04 -0.26 0.05 0.20 BP -0.05 -0.29 -0.32 -0.14 -0.04 Mexico HP 0.04 0.08 -0.09 -0.25 0.28 BP 0.32 -0.03 -0.24 0.00 0.15 Morocco HP -0.29 -0.07 0.24 -0.07 0.21 BP -0.37 0.06 0.21 0.17 0.53 Nigeria HP -0.05 -0.18 -0.14 0.22 0.00 BP -0.53 -0.38 -0.09 0.07 0.36 Phtlipptnes HP -0.45 -0.21 0.48 0.25 -0.22 BP -0.56 -0.34 034 " 0.61 0.21 Tunisia HP -0.15 -0.08 0.19 0.34 -0.16 BP -0.09 0.20 0.42 0.26 -0.45 Turkey HP -0.04 -0.07 0.17 -0.39 0.00 BP 0.01 -0.03 -0.18 -0.24 0.18 Uruguay HP 0.03 -0.02 -0.04 0.04 0.04 BP -0.09 0.26 0.45 0.37 0.25 Note: HP and B P refer to the stationary components derived using the modifed Hodrick-Prescott and band-pass filters, respectively. The correlations reported are between the contemporaneous values of domestic output and the /th lag or lead of broad money, with both variables detrended using the same filter. The data series and sources are described in the appendix. Source: Authors' calculations based on I M F data. plus sight deposits in the banking system).20 The main features of the results derived from using broad money are preserved when using the other monetary aggregates. The contemporaneous correlations are positive for about half of the countries in the sample, generally statistically insignificant for many of the oth- ers, and, in the case of Nigeria, clearly negative. Overall, therefore, we find lim- 20. These results are available on request. 272 THE •WORLD BANK ECONOMIC REVIEW, VOL. 14, NO. 2 ited evidence in our sample of the type of procyclical behavior of monetary ag- gregates that has been documented for many industrial countries (see, for in- stance, Backus and Kehoe 1992). More important, we are unable to detect evi- dence of Granger causality from money to output. These results may indicate the need to develop a different analytical framework for studying the relationship between monetary policy and macroeconomic fluctuations in developing coun- tries. "We discuss this issue below. We also examine the cyclical behavior of measures of velocity corresponding to the alternative definitions of monetary aggregates discussed above. Again, to conserve space, we present only the results for the measure of velocity based on broad money.21 These correlations are striking (table 9). For 11 of the 12 coun- tries in our sample (Mexico being the exception) and with both filters, the con- temporaneous correlations between the velocity of broad money and industrial output are strongly negative. From a quantity theory perspective, of course, the countercyclical behavior of velocity is be expected, given the procyclical behav- ior of broad money and countercyclical variation in the aggregate price level in a majority of the sample countries. This result stands in sharp contrast to the weakly procyclical behavior of velocity in the G-7 countries, as documented by Fiorito and Kollintzas (1994). Finally, we consider another monetary variable that could have a significant influence on economic activity—domestic private sector credit. This variable is especially relevant for middle-income countries, where equity markets tend to be weakly capitalized relative to industrial-country markets and private sector credit typically plays an important role in determining investment and the financing of working capital needs—and thus overall economic activity, especially in the in- dustrial sector.22 Note that changes in credit could partly reflect the derived de- mand for credit, which in turn could be affected by exogenous shocks that influ- ence the level of industrial activity. Nevertheless, even in these circumstances changes in the availability of credit could dampen the effects of these shocks on industrial output. Thus the pattern of these correlations is still of considerable analytical value. A number of countries, including Colombia, India, Mexico, and Turkey, show a positive contemporaneous association between domestic credit and industrial output (table 10). Chile and Uruguay, in contrast, show a negative correlation. In the countries where the association is positive, the correlations peak at or close to a zero lag, indicating that the availability of domestic credit affects activity in the industrial sector fairly rapidly. However, this could simply reflect cyclical fluc- 21. Measures of velocity corresponding to the reserve-money and narrow-money aggregates yield velocity-output correlations that are broadly similar to the results discussed in this paragraph. These results are available on request. 22. Although the role of private sector credit in many developing countries is well documented, few studies have quantitatively assessed the relative importance of money and credit in the transmission of monetary policy. We intend to pursue this issue in future work. Agenor, McDermott, and Prasad 273 Table 9. Cross Correlations between Domestic Output and M2 Velocity Country Eight-quarter Four-quarter Zero Four-quarter Eight-quarter r and filter lag lag lag lead lead Chile HP 0.26 -0.26 -0.76 0.34 0.56 BP -0.26 -0.62 -0.70 -0.08 038 Colombia HP -0.04 -0.42 -0.27 0.16 0J9 BP -0.06 -0.34 -0.38 0.28 035 India HP 0.48 0.11 -0.73 -0.22 0.21 BP 0.25 0.13 -0.68 -0.34 0.01 Korea, Rep. of HP 0.13 0.25 -0.69 0.13 0.07 BP -0.08 -0.29 -0.65 -0.24 0.03 Malaysia HP 0.09 0.13 -0.82 0.19 030 BP 0.18 -0.32 -0.80 -0.09 0.33 Mexico HP -0.14 0.01 0.05 -0.02 0.28 BP -0.52 -0.21 0.17 0.42 0.32 Morocco HP -0.13 0.22 -0.48 0.22 0.18 BP -0.02 0.21 -0.44 0.14 0.43 Nigeria HP 0.15 -0.10 -0.48 0.21 -0.02 BP -0.23 -0.38 -0.53 -0.19 0.02 Philippines HP 0.43 0.43 -0.71 0.07 0.46 BP 0.19 0.16 -0.60 0.18 031 Tunisia HP 0.21 -0.18 -0.62 0.20 0.19 BP 0.04 -0.38 -0.61 -0.26 -0.25 Turkey HP -0.11 -0.17 -0.10 -0.12 0.05 BP -0.09 -0.16 -0.43 0.05 0.33 Uruguay HP 0.14 -0.24 -0.59 0.39 0.47 BP -0.37 -0.50 -0.51 0.04 0.51 Note: H P and B P refer to the stationary components derived using the modifed Hodrick-Prescott and band-pass filters, respectively. The correlations reported are between the contemporaneous values of domestic output and the /th lag or lead of M2 velocity, with both variables detrended using the same filter. The data series and sources are described in the appendix. Source: Authors' calculations based on I M F data. tuations in the demand for private sector credit, where the demand for loans is determined primarily by other factors. To test this hypothesis, we run bivariate Granger-causality tests between the stationary components of private sector credit and industrial output. For some of the countries with positive correlations between these two variables, we do find that private sector credit has predictive power for industrial output in the Granger- 2 74 THE WORLD BANK ECONOMIC REVIEW, VOL. 14, NO. 2 Table 10. Cross Correlations between Domestic Output and Private Sector Credit Country Eight-quarter Four-quarter Zero Four-quarter Eight-quarter and filter lag lag lag lead lead Chile HP 0.05 -0.43 -0.26 0.32 0.18 BP -0.54 -0.66 -0.50 -0.35 -0.32 Colombia HP -0.45 -0.31 0.28 0.37 0.18 BP -0.62 -0.11 0.36 0.64 0.34 India HP 0.17 0.29 0.21 -0.17 -0.19 BP 0.01 0.55 0.47 -0.07 -0.27 Korea, Rep. of HP -0.24 -0.01 0.12 0.12 -0.19 BP 0.02 0.03 0.12 0.04 0.19 Malaysia HP 0.07 0.08 -O.04 0.00 0.08 BP -0.46 -0.32 0.13 . 0.41 0.10 Mexico HP -0.39 -0.15 0.65 0.09 -0.31 BP -0.45 0.38 0.84 0.48 -0.11 Morocco HP -0.34 -0.09 0.09 0.02 031 BP -0.38 -0.22 0.04 0.27 0.54 Nigeria HP -0.13 -0.07 0.14 0.06 -0.01 BP -0.17 -0.05 0.10 0.02 -0.29 Philippines HP 0.05 0.56 0.00 -0.20 -0.07 BP -0.35 0.32 0.55 0.12 -0.38 Tunisia HP 0.14 -0.03 -0.10 0.25 0.18 BP -0.10 0.23 0.53 0.69 0.29 Turkey HP -0.32 -0.25 0.44 0.05 -0.28 BP -0.52 0.01 0.52 0.37 -0.37 Uruguay HP 0.08 -0.07 -0.27 0.18 0.39 BP -0.14 -0.38 -0.34 -0.04 0.18 Note: H P and BP refer to the stationary components derived using the modifed Hodrick-Prescott and band-past filters, respectively. The correlations reported are between the contemporaneous values of domestic output and the /th lag or lead of private sector credit, with both variables detrended using the same filter. The data series and sources are described in the appendix. Source: Authors' calculations based on I M F data. causal sense. However, for some of these countries there is also evidence of re- verse causation from output to credit. Thus we do not find robust evidence of a unidirectional causal relationship from credit to economic activity. Nevertheless, the strong positive association between private sector credit and the domestic business cycle in some of the sample countries has important implications for the Aginor, McDermott, and Prasad 27S design of stabilization programs. Ignoring this link may exacerbate the output cost of a restrictive monetary policy aimed at lowering inflation. Foreign Trade and the Business Cycle In this subsection we explore the relationship between domestic business cycle fluctuations and fluctuations in price and quantity variables that are relevant to international trade. In particular, we examine correlations of output fluctuations with fluctuations in merchandise trade and measures of both nominal and real effective exchange rates. An adequate measure of foreign trade transactions is the trade balance, con- structed as the difference between real exports and real imports and divided by real GDP in order to control for scale effects. In the absence of reliable data on price deflators for exports and imports, many authors use the ratio of the sum of nominal exports and imports to output. Unfortunately, we are even more con- strained because we have only real industrial output data for most of the coun- tries in our sample. Hence we use the ratio of exports to imports at current prices as a rough measure of the trade balance. Because changes in the terms of trade could be large and important for these countries, we return to that issue later. For Chile, Mexico, Turkey, and Uruguay, the contemporaneous correlations between our proxy for trade balance movements and industrial output are nega- tive irrespective of the filter used (table 11). This pattern is similar to that found for industrial countries, as reported by several authors—see Fiorito and Kollintzas (1994), Prasad and Gable (1998), and the references therein. However, for cer- tain countries—including Morocco and Nigeria—the contemporaneous correla- tions are strongly positive. This result may reflect a strong link between changes in industrial output and exports of manufactures, or it may reflect the fact that merchandise imports are not highly sensitive to fluctuations in domestic demand. In addition, where we do find significant correlations between the trade ratio and domestic output, these correlations peak at (or near) lag zero. We interpret this as indicative of the close relationship between international trade and industrial output in these middle-income economies, with industrial output being a good proxy for output in the traded goods sector (other than primary commodities). We were able to obtain unit values of imports and exports and to construct a quarterly index of the terms of trade for only three of the countries in our sample— Colombia, Korea, and Mexico. These three countries show a strong positive cor- relation between the cyclical components of industrial production and the terms- of-trade index (table 12). For Colombia and Korea the BP-filtered data yield the strongest correlations. This suggests that the positive relationship between out- put and the terms of trade might be obscured when using the HP filter because of the large amount of high-frequency variation in the terms-of-trade data. Because these middle-income countries are unlikely to affect the world price of any industrial commodity, the positive correlations could be seen as consistent with demand shifts that lead to simultaneous increases in world prices and in the export demand for the industrial sector output of these countries. For the three 276 THE WORLD BANK ECONOMIC REVIEW, VOL. 14, NO. 2 Table 11. Cross Correlations between Domestic Output and the Trade Balance Country ' Eight-quarter Four-quarter Zero Four-quarter Eight-quarter and filter lag lag lag lead lead Chile HP 0.23 0.54 -0.54 -0.49 0.28 BP 0.46 0.32 -0.48 -0.48 0.18 Colombia HP 0.30 0.05 -0.20 -0.16 0.25 BP 0.23 0.02 -0.08 -0.15 0.05 India HP 0.10 -0.05 -0.10 0.08 -0.03 BP 0.18 0.19 -0.10 0.10 0.24 Korea, Rep. of HP 0.00 0.40 0.04 -0.09 -0.15 BP 0.13 0.43 0.39 -0.03 -034 Malaysia HP -0.15 0.17 0.16 -0.02 -0.19 BP -0.15 0.04 0.15 0.10 0.16 Mexico HP 0.42 -0.09 -0.71 0.08 0.53 BP 0.24 -0.49 -0.60 0.17 0.75 Morocco HP 0.04 -0.28 0.31 -0.16 -0.30 BP 0.15 -0.12 0.23 -0.14 -0.22 Nigeria HP 0.00 -0.02 0.46 -0.19 -0.32 BP -0.03 0.20 0.20 -0.47 -0.18 Philippines HP -0.34 -0.27 0.24 0.05 0.12 BP -0.23 -0.35 -0.09 0.02 0.62 Tunisia HP 0.07 -0.06 0.00 0.04 0.04 BP 0.08 -0.03 -0.08 0.10 0.04 HP -0.06 0.09 -0.49 0.28 0.22 BP -0.33 -0.18 -0.18 0.37 0.52 Uruguay HP -0.12 -0.15 -0.30 -0.11 0.46 BP -0.25 -0.35 -036 -0.11 0.28 Note: HP and BP refer to the stationary components derived using the modifed Hodrick-Prescott and band-pass fitters, respectively. The correlations reported are between the contemporaneous values of domestic output and the /th lag or lead of the trade balance, with both variables detrended using the same filter. The data series and sources are described in the appendix. Source: Authors' calculations based on IMF data. countries for which we have tenns-of-trade data, the strong positive correlations between lagged values of the tenns-of-trade index and contemporaneous output provide further support for this interpretation. Overall, our results are consistent with those of Rodrfguez-Mata (1997) for Costa Rica, Kose and Riezman (1998) for Sub-Saharan Africa, and Mendoza (1995), who suggest that almost half of the output fluctuations in developing countries can be explained by terms-of- Aginor, McDermott, and Prasad 277 Table 12. Cross Correlations between Domestic Output and Terms of Trade Country Eight-quarter Four-quarter Zero Four-quarter Eight-quarter and filter ' lag lag lag lead lead Colombia HP 0.18 0.15 0.10 0.15 0.08 BP 0.57 0.42 0.34 0.06 -0.29 Korea, Rep. of HP 0.08 0.08 0.41 -0.17 -0.14 BP 0.00 0.38 0.62 0.50 0.26 Mexico HP -0.28 0.13 0.46 -0.30 -0.24 BP -0.10 0.49 0.46 0.14 0.32 Note: HP and BP refer to the stationary components derived using the modifed Hodrick-Prescott and band-pass filters, respectively. The correlations reported are between the contemporaneous values of domestic output and the /th lag or lead of terms of trade, with both variables detrended using the same filter. The data series and sources are described in the appendix. Source: Authors' calculations based on IMF data. trade disturbances. Our results are also consistent with those of Deaton and Miller (1995), who find evidence that export price shocks have had substantial contem- poraneous effects on output in Sub-Saharan Africa. Cyclical Behavior of Exchange Rates The interpretation of the unconditional correlations between industrial out- put and measures of nominal and real effective exchange rates is complicated by the fact that their short-run relationship depends crucially on the sources of mac- roeconomic fluctuations.23 Nonetheless, it is useful to look at unconditional cor- relations for two reasons. First, the sign and magnitude of these correlations could indicate the types of shocks that have dominated fluctuations over a period of time. Second, these correlations could help in interpreting the correlations between output and trade variables. In India, Morocco, Tunisia, and Turkey, there is some evidence of a positive relationship between nominal effective exchange rates and industrial output, while the correlations are generally negative for Chile, Nigeria, and Uruguay (table 13). The correlations between output and real effective exchange rates show a similar pattern, but with a few important differences (table 14). The contempo- raneous correlations for Mexico and Uruguay are positive, while the correlations for Morocco and the Philippines are close to zero. However, many of the con- temporaneous correlations are not significantly different from zero. The absence 23. There are, of course, important differences in the exchange rate arrangements of the sample countries. However, since we use trade-weighted measures of both real and nominal effective exchange rates, the fact that certain bilateral f-rrhangf rates could be fixed does not, in principle, affect the interpretation of our results. We also define the effective exchange rates such that an increase in the exchange rate implies an appreciation of the currency (in real or nominal terms, as the case may be). Thus a positive correlation indicates that the exchange rate tends to appreciate when the cyclical component of output rises. 2 78 THE WORLD BANK ECONOMIC REVIEW, VOL. 14, NO. 2 Table 13. Cross Correlations between Domestic Output and the Nominal Effective Exchange Rate Country Eight-quarter Four-quarter Zero Four-quarter Eight-quarter and filter lag lag lag lead lead Chile HP 0.13 -0.57 -0.05 0.32 -0.11 BP -0.04 -0.40 -0.36 -0.13 0.00 Colombia HP -0.29 -0.21 0.19 0.13 -0.23 BP -0.14 -0.40 -0.40 -0.27 -0.08 India HP 0.03 0.45 0.25 -0.19 -0.32 BP -0.26 0.21 0.18 -037 -0.48 Korea, Rep. of HP 0.11- -0.43 -0.02 0.45 0.06 BP -0.39 -0.55 -0.09 0.45 0.56 Malaysia HP -0.03 0.17 0.10 0.12 -0.51 BP -0.25 0.14 0.19 -0.06 -0.34 Mexico HP 0.02 0.69 -0.05 -0.41 -0.10 BP -0.05 0.52 0.38 0.10 0.36 Morocco HP 0.04 0.06 0.17 0.04 -0.12 BP -0.05 0.18 0.33 0.29 -0.02 Nigeria HP 0.06 -0.13 -0.04 -0.13 -0.14 BP -0.41 -0.47 -039 -0.25 0.10 Philippines HP 0.48 -0.01 -0.38 0.26 0.02 BP -0.06 0.29 0.25 -0.02 -0.46 Tunisia HP -0.32 -0.31 0.33 0.49 0.05 BP -0.21 0.14 0.67 0.79 031 Turkey HP 0.02 -0.26 0.36 -0.09 -0.22 BP 0.28 0.34 0.09 -0.15 -0.32 Uruguay HP 0.22 -0.22 -0.27 0.03 0.16 BP -0.18 -0.41 -0.57 -0.31 0.24 Note: H P and B P refer to the stationary components derived using the modifed Hodrick-Prescott and band-pan filters, respectively. The correlations reported are between the contemporaneous values of domestic output and the /th lag or lead of the nominal effective exchange rate, with both variables detrended using the tame filter. The data series and sources are described in the appendix. Source: Authors' calculations based on I M F data. of a systematic relationship between real exchange rates and the business cycle is consistent with the notion that this relationship is affected by an amalgam of supply, real demand, and nominal shocks, each of which could affect this corre- lation in different ways. One interesting aspect of these results is that, for many countries, the correla- tions are quite similar using either nominal or real measures of effective exchange Agenor, McDermott, and Prasad 279 Table 14. Cross Correlations between Domestic Output and the Real Effective Exchange Rate Country Eight-quarter Four-quarter Zero Four-quarter Eight-quarter and filter lag lag lag lead lead Chile HP -0.23 -0.55 0.26 0.36 -0.24 BP -032 -0.54 -0.41 -0.33 -031 Colombia HP -0.12 -0.03 0.06 0.01 -0.30 BP 0.01 -0.19 -0.36 -0.44 -0.46 India HP 0.02 0.47 0.19 -0.23 -0.30 BP -0.10 0.16 0.09 -0.45 -0.61 Korea, Rep. of HP 0.12 -0.47 0.10 0.27 0.06 BP -0.52 -0.62 -0.14 0.41 0.56 Malaysia HP 0.06 0.18 0.10 0.04 -0.49 BP -0.19 0.17 0.19 -0.14 -0.38 Mexico HP -0.47 0.24 0.59 -0.36 -039 BP -0.09 0.48 0.47 -0.12 0.01 Morocco HP -0.06 0.08 -0.01 0.01 -0.23 BP -0.10 -0.06 -0.01 0.09 -0.23 Nigeria HP 0.08 -0.06 -0.09 -0.19 -0.12 BP -0.32 -0.46 -0.45 -0.34 0.00 Philippines HP 0.07 -0.56 0.03 0.63 -0.19 BP -0.48 -0.46 0.21 0.61 0.06 Tunisia HP -031 -0.17 0.30 0.44 0.02 BP -0.27 0.11 0.67 0.76 0.22 Turkey HP 0.19 -0.13 0.30 -0.23 -0.22 BP 0.44 0.33 -0.15 -035 -0.21 Uruguay HP -0.17 -0.16 0.22 0.47 -0.05 BP -0.53 -0.25 0.16 0.31 -0.02 Note: HP and BP refer to the stationary components derived using the modifed Hodrick-Prescott and band-pass filters, respectively. The correlations reported are between the contemporaneous values of domestic output and the /th lag or lead of the real effective exchange rate, with both variables detrended using the same filter. The data series and sources are described in the appendix. Source: Authors' calculations based on IMF data. rates. This finding is in line with a substantial body of research showing that, for industrial countries, nominal and real exchange rates are strongly positively cor- related at business cycle frequencies (see, for instance, Mussa 1986 and Taylor 1995). Indeed, we find that the contemporaneous correlations between real and nominal effective exchange rates are strongly positive for all countries in our sample, irrespective of the filter used. 280 THE -WORLD BANK ECONOMIC REVIEW, VOL. 14, NO. 2 IV. SUMMARY OF THE FINDINGS I In this section we summarize the main findings of the paper. As noted in the discussion thus far, some of these results have previously been reported by other authors, using different data sets. • Output volatility, as measured by the standard deviations of the filtered cyclical components of industrial production, varies substantially across developing countries. However, on average, it is much higher than the level typically observed in industrial countries. Developing countries also show considerable persistence in output fluctuations. • Activity in industrial countries has a significantly positive influence on output in most developing countries. Real interest rates in industrial countries tend to be positively associated with output fluctuations in our sample of middle- income countries. • Government expenditures are countercyclical. Government revenue is acyclical in some countries and significantly countercyclical in others—a phenomenon that is difficult to explain. The fiscal impulse (defined as the ratio of government spending to government revenue) is negatively correlated with the business cycle. • The cyclical behavior of nominal wages varies markedly across countries and is not robust across filters. In contrast, the evidence strongly supports the assumption of procyclical real wages. • There is no consistent relationship between the stationary components of the levels of output and prices and the levels of output and inflation. Variations in die price level and inflation are countercyclical in some countries and procyclical in a few. • Contemporaneous correlations between money (measured by various monetary aggregates) and output are broadly positive, but not very strong— in contrast to the evidence for many industrial countries. • The contemporaneous correlations between the velocity of broad money and industrial output are strongly negative for almost all the countries in our sample. This result is in contrast to the weakly procyclical behavior of velocity observed in most industrial countries. • Domestic credit and industrial output are positively associated in some countries. However, the strength of the relationship is not always robust to the choice of detrending procedure. Some countries show a negative correlation between these two variables. • There is no robust correlation between merchandise trade movements (as measured by the ratio of exports to imports) and output. For some countries the contemporaneous correlations are negative (irrespective of the filter used), whereas for others the contemporaneous correlations are strongly positive. The positive relationship may indicate that fluctuations in industrial output Aginor, McDermott, and Prasad 281 are driven by export demand and that merchandise imports are not as sensitive to domestic demand fluctuations as they are in industrial countries. • Cyclical movements in the terms of trade are strongly positively correlated with output fluctuations. • There are no systematic patterns in the contemporaneous correlations between nominal effective exchange rates and industrial output; the results are similar for real effective exchange rates. Fluctuations in real and nominal effective exchange rates are strongly positively correlated for the developing countries in our sample. V. CONCLUDING REMARKS In this article we studied the cyclical properties of a large number of (season- ally adjusted) macroeconomic time series for a group of 12 (mostly middle- income) developing countries, using two univariate detrending methods. We dis- cussed the cross-correlation patterns between output and the macroeconomic time series and attempted to identify a set of relatively robust regularities that can be used as a benchmark to guide theoretical research in development macro- economics. We also highlighted similarities and differences between our results and other studies on business cycle fluctuations in industrial and developing countries. We can make several remarks on the methodological and analytical implica- tions of our analysis. First, our results suggest that, although the correlations derived from different filters were often very similar, several quantitative (as well as qualitative) features of the data are not robust across detrending methods. This result is similar to that of Blackburn and Ravn (1991) and Canova (1998), among other authors. Because generally we cannot know ex ante when results will vary across filters, considering systematically an array of detrending meth- ods remains an important test of robustness in empirical research on business cycle regularities. Second, the unconditional correlations between different variables (such as exchange rates or prices) and domestic output may be small because they average the effects of different types of shocks. It is, therefore, important to develop and estimate structural models, along the lines, for instance, of Ahmed and Park (1994), Rogers and Wang (1995), Hoffmaister and Roldos (1997), and Prasad (1999), that attempt to separate out the effects of different types of macroeconomic shocks on variables such as prices, output, foreign trade, and exchange rates in develop- ing countries. However, existing methods remain controversial; we do not yet have models that convincingly isolate different types of shocks. Third, the analysis in this article ignores the possible effects of measurement errors in the raw data. This is a potentially serious problem. For instance, in our analysis of the correlations between domestic output and foreign interest rate shocks, we do not account for the risk premium that borrowers from developing 282 THE WORLD BANK ECONOMIC REVIEW, VOL 14, NO. 2 countries typically face on world capital markets. However, there is considerable evidence, that such premiums can be large on average (particularly for countries with a high ratio of external debt to output) and could change unpredictably in the short run as a result of sudden shifts in market sentiment. This measurement problem, which has not been adequately addressed in other studies, suggests that we should exercise caution in judging the strength and direction of correlations between domestic output and a measure of world interest rates that does not capture movements in country-specific risk premiums. Finally, at the analytical level, a natural step forward is to build stochastic general equilibrium simulation models of small, open developing economies in order to assess if such models (properly calibrated) can reproduce the stylized facts highlighted here. Some of the correlations established (such as the countercyclical behavior of government spending) can indeed be explained within existing theoretical constructs. Building more general quantitative models that are capable of accounting for the other types of business cycle regularities high- lighted here could prove important for the design of stabilization policies and macroeconomic management in developing countries. APPENDIX: DATA SOURCES AND UNIT ROOT TESTS The primary sources of data used in this study are the International Monetary Fund's International Financial Statistics (iFS) and Information Notice System, supplemented by other sources. This appendix describes the series, together with their IFS codes. All data are available on request. • Real output is the industrial production index (series 66) for Mexico, Korea, India, Malaysia, and Tunisia and the manufacturing production index (se- ries 66ey) for Chile, Morocco, Nigeria, the Philippines, and Uruguay. We obtained the industrial production index from the International Monetary Fund (IMP) desk economist for Colombia and from the Organisation for Economic Co-operation and Development (OECD) database for Turkey. IMF desk economists also provided partial information for Turkey, Tunisia, and Uruguay. • The CPI is series 64 for all countries. The IMF desk economist for Tunisia filled in the data (for that country) missing from the IFS. • The nominal wage index is series 65 for Mexico, Chile, and the Philippines and series 65ey for Korea. We obtained data for Colombia from the IMF desk economist. We obtained data for Turkey from the OECD database and the IMF desk economist. We calculate the real wage index by deflating the nominal wage series by the CPI. • The monetary base (or reserve money) is series 14 for all countries. Narrow money is series 34, and broad money is the sum of series 34 and 35, again for all countries. We calculate velocity for each monetary indicator by first transforming the monetary aggregate into an index and then dividing by Agtnor, McDermott, and Prasad 283 the product of the CPI and the real output index, which is used as a proxy for nominal output. • Private sector credit is series 32d for all countries. We calculate the real credit variable by deflating the nominal aggregate by the CPI. The IMF desk economist for Tunisia filled in the data (for that country) missing from IPS. • Government expenditures in nominal terms is series 82 for Mexico, Korea, and the Philippines. We obtained data for Chile from Chile's Ministry of Finance. We derive the expenditure index by first transforming the nominal series into an index and then dividing by the same proxy for nominal output used to derive velocity indicators. • Government revenue in nominal terms is series 81. We derive the revenue index in the same way as the expenditure index. • We derive the fiscal impulse measure by dividing series 82 by series 81. • The trade ratio is the ratio of merchandise exports at current prices (series 70) to merchandise imports at current prices (series 71), with both variables measured in U.S. dollar terms. • We obtain trade-weighted measures of nominal and real effective exchange rates from the IMF's Information Notice System. • The terms of trade are the ratio of export unit values (series 74) to import unit values (series 75) for Colombia and Korea. For Mexico we obtain export and import price indexes from the OECD database. • World output is proxied by the industrial production index for industrial countries (series 66, code 110). The world real interest rate is proxied by the difference between the nominal euro-dollar rate in London (series 60d, country code 112) and the rate of inflation in consumer prices in industrial countries (series 64, code 110). We performed a set of standard unit root tests, including augmented Dickey- Fuller tests and Phillips-Perron tests, on our raw data series (all of which were converted into logarithms for the empirical work, except for the world real inter- est rate). These tests indicated that virtually all of the series were nonstationary in levels over the relevant sample period and that, therefore, computing correla- tions using the raw data would not be appropriate. We also used similar unit root tests to confirm that the cyclical components obtained with the filters em- ployed in this article were indeed stationary. 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