A Data Appendix
The dates in square brackets are the beginning date of a data series and the end date is 2007 unless otherwise stated. Our sample period is 1976-2007. All detrending is done using the HP filter with a smoothing parameter of 1600 and deseasonalizations are done using the U.S. Census Bureau’s X-12. Only those series that seemed to have seasonality were deseasonalized.
All data are from the International Financial Statistics (IFS). Availability varies across countries. Countries that have the data for the entire sample: Australia, Austria, Canada, Finland, France, Israel, Japan, Korea, Norway, Spain, Sweden.40
Countries with shorter samples: Belgium , Brazil , Chile , Denmark , Ecuador , Hungary , Ireland , Malaysia , Mexico , the Netherlands , New Zealand [1982Q2], the Philippines , Portugal , Turkey .
Manufacturing output data are in real terms and from Haver Analytics. Data for the entire sample are available for Australia, Austria, Chile, Norway and Spain. For others, the availability varies: Belgium , Brazil , Canada , Denmark , Ecuador , Finland , Hungary , Ireland , Israel , Mexico , New Zealand [1987Q2], Philippines , Portugal , Sweden , Turkey .
Aggregate Hours, Manufacturing Hours, Employment
All countries: Hours worked data are available only for the manufacturing sector. We report a set of statistics using those data. In addition, we approximate aggregate hours worked by multiplying the hours worked per worker in manufacturing by total employment. Some countries do not report hours worked per worker but only total hours worked in manufacturing. In that case, we divide total hours in manufacturing by the number of employees in manufacturing to approximate hours worked per worker.
Australia, Austria, Canada, Finland, Japan: All series are from OECD and available for the entire sample.
Brazil: Monthly hours worked in manufacturing is from OECD  and 1987Q1-1991Q4 was calculated using data from the Confederation of Industries. Civilian employment data are from Neumeyer and Perri (2005) for 1991-2002 and 2003-2007 was extrapolated using “formal employment” series from Ministerio do Trabalho e Emprego (MTE) assuming that civilian employment grows at the same rate with formal employment. Employment in manufacturing are from MTE.
Chile, Malaysia, The Netherlands, Philippines: No data is available on hours worked. Employment data is from IFS and start at different years for these counties, Netherlands , Chile [1983Q3], the Philippines , Malaysia .
Ecuador: No data is available on hours worked. Employment data is short in quarterly frequency, available in IFS only for 2000-2003 and therefore was not included in the employment statistics.
Hungary: All series are from OECD. Hours worked per worker in manufacturing are available starting 1984, civilian employment and employment in manufacturing from 1992.
Ireland: Weekly hours worked in manufacturing per worker is from OECD (entire sample). However, the number of employees in manufacturing data start in 1998 and therefore, total hours worked in manufacturing could be calculated only starting in 1998. The number of employees in manufacturing and civilian employment are also from OECD .
Israel: Hours worked in manufacturing data are from International Labor Organization (ILO) and covers the entire sample (series named weekly hours actually worked in non-agricultural activities per worker). Employment is from IFS . Number of employees in manufacturing was not available.
Korea: Monthly hours worked in manufacturing is from OECD . Number of employees in manufacturing and total employment are from the Korean Statistical Institute .
Mexico: Employment data are from Neumeyer and Perri (2005) covering 1987-2001. We used two different data for monthly hours worked in manufacturing, from INEGI  and from OECD . The data from OECD is an index (2000=100) and is used in the calculation of the statistics related to the hours worked in manufacturing. The series from INEGI is in units of hours and is used to compute the approximate aggregate hours worked as explained above. Number of employees in manufacturing data are from INEGI .
New Zealand: Hours worked in manufacturing data are from ILO  (series called weekly hours actually worked in non-agricultural activities per worker). Employment and employment in manufacturing are from OECD [1985Q4].
Norway: All series are from OECD and available for the entire sample except hours worked manufacturing that starts in 1988Q2.
Sweden: All series are from OECD and available for the entire sample except hours worked manufacturing that starts in 1987.
Turkey: Total hours worked in manufacturing is from OECD . Number of employees in manufacturing and total employment are from TURKSTAT [1988Q4] and are semi-annual (April and October) in 1988Q4-1999Q4 and quarterly afterwards. The semi-annual series were used to calculate quarterly series using linear interpolation.
From OECD: Austria , Belgium , Brazil , Canada , France , Japan , Korea , Mexico , the Netherlands , Norway , Sweden , Turkey .
From the Economist Intelligence Unit: Australia , Chile , Ecuador , Finland , Hungary , Ireland [1997Q4], Israel , Malaysia , New Zealand , the Philippines , Spain .
From OECD: Brazil and Mexico’s earnings statistics are reported in OECD both in real and nominal terms, and we deflate all other countries’ data by the corresponding countries’ CPI. For Australia, Brazil, New Zealand, and Spain, earnings statistic captures all activities, for Belgium and France it captures the private sector. For all other countries, it is the earnings only for manufacturing. Data for the entire sample is available for Canada, Denmark, Finland, Germany, Ireland, Japan, Norway, and Sweden. For other countries, availability varies: Australia [1983Q4], Belgium , Brazil , France , Hungary , Korea , Mexico , New Zealand , Spain , and Turkey . Nominal earnings data for Brazil and Mexico start in 1994Q3 and 1980Q1, respectively.
From ILO: Chile , Israel , the Philippines .
From IEO: Ecuador .
All CPI data used to deflate earnings are from IFS and are available for the entire sample period with the exception of Brazil .
All PPI data used to deflate earnings are from Haver Analytics. Data for the entire sample are available for Australia, Canada, Korea, Spain, Sweden. For others, the availability varies: Austria , Belgium , Brazil [1991Q4], Chile [2003Q2], Denmark , Ecuador , Finland , Hungary , Ireland , Israel , Mexico , New Zealand [1977Q4], Norway , Philippines , Portugal , Turkey .
B TFP computation
Assume that output (Yt) can be represented by the following Cobb-Douglas production function:
where Kt is the capital stock in year t, Lt is labor which is augmented its relative efficiency due to schooling (ht), and At is TFP.
We constructed the capital stock series using the perpetual inventory approach following Easterly and Levine (2001). In particular, the law of motion for the capital stock is given by:
where It denotes investment and the rate of depreciation of the capital stock which is set equal to 0.07. In steady state, the initial capital-output ratio is:
where i is the steady state investment-output ratio and g the steady state growth rate. We use annual investment data from the Penn World Tables, version 6.2. In order to compute k, we approximate i by the country’s average investment-output ratio in the first ten years of the sample and g by a weighted average between world growth (75 percent) and the country’s average growth in the first ten years of the sample. The initial capital level K0 is obtained by multiplying the three-year average output at the beginning of the sample.
For labor, we use the labor force implied by the real GDP per worker and real GDP (chain) series from the Penn World Tables. Follow Hall and Jones (1999) and consider human capital h to be the relative efficiency of a unit of labor with E years of schooling. In particular, h is constructed by:
where φ(⋅)is a function that maps the years of schooling into efficiency of labor with φ(0) = 0 and φ’(E) equal to the Mincerian return to schooling. We assume the same rates of return to schooling for all countries: 13.4 percent for the first four years, 10.1 percent for the next four, and 6.8 percent for all years of schooling above eight years (following Psacharopoulos, 1994). The data on years of schooling is obtained from the Barro-Lee database and linear extrapolations are used to complete the five-year data.
Output per worker is given by:
The log TFP is thus calculated according to
C Decentralized Economy
In this section, we characterize a decentralized world, where the sequence of wage rates is determined by the Nash Bargaining between workers and firms.41 In our setting, the markets for aggregate shocks are incomplete but the household may partially insure against these shocks through precautionary savings.
There are externalities generated by each side of the labor market, for both employers and firms. When the number of vacancies posted by the firms increases, there is a positive externality for workers who are actively seeking a job and a negative externality for firms that are trying to fill up a position. More specifically, an individual household takes the probability of being hired, M/u = ϕ(θ) as given without considering the impact of its own employment on the general market tightness. Similarly, the individual firm takes the probability of filling a vacancy, M/v = ϕ(θ)/θ, as given.
We can obtain the marginal value associated with an additional job from the following household optimization problem. Given the wage rate, interest rate and the prevailing probability of finding a job, the household solves the following optimization problem:
With a similar interpretation as in the Social Planner’s problem, we can lay out the Envelope condition as follows:
Firms. Firms are owned by the household and therefore discount expected future profits according to the same stochastic discount factor as the household, ρt,t+1 = β(ct)U’ (ct+1)/U’(ct). Given the wage rate and the probability of filling a vacancy, the firms choose the optimal number of vacancies to be posted to maximize their profits:
subject to the law of motion that governs employment:
The Envelope theorem implies the standard job-creation condition
and the first order condition w.r.t. vt implies
Putting the above two equations together, one can equivalently write
That is, the marginal value the firms associate with filling one more position is given by the marginal product of an extra worker net of the wage cost plus the asset value of activating one more job and enjoying a pre-exiting relationship with a worker in the next period. Let J denote the value of filling a position and Q the value of posting a vacancy Then,
Nash Bargaining. Assuming that employed workers’ bargaining power is ξ ∊ (0, 1), the matched worker-firm pair negotiates over wage by solving the following Nash Bargaining problem:
At the optimum, the firms and the household divide the total matching surplus according to the Nash Bargaining power of each party.
which is the same as Equation (10).
D Canonical SOE-RBC
The recursive representation of the social planner’s problem in the canonical SOE-RBC model is:
where lt stands for labor supply. Per-period utility is takes the GHH form,
We use the same values for all preference related parameters as in our baseline search-matching model as documented in Table 4. The only additional parameter that appears in the canonical SOE-RBC is η which we set to 1.6 following Mendoza (1991) and Aguiar and Gopinath (2007). As mentioned in the text, we feed in the same TFP and interest rate shocks as in our baseline search-matching framework.
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An early version of this paper was circulated with the title “Labor Market Search in Emerging Economies.” We would like to thank Mark Aguiar, Cristina Arellano, Ariel Burstein, Giancarlo Corsetti, Charles Engel, Gita Gopinath, David Kaplan, Enrique Mendoza, Carmen Pages, Fabrizio Perri, Andrea Raffo, Pedro Silos, Vivian Yue and seminar participants at the Bank of Canada, Federal Reserve Board, Federal Reserve Banks of Dallas, Philadelphia, and New York, Georgetown, International Monetary Fund, World Bank, Institute for Advanced Studies, Ohio State University, Singapore Management University, National University of Singapore, World Bank, EEA meetings, Midwest Macro Meetings, North American Summer Meetings of the Econometric Society, and SED Meetings for valuable comments and suggestions. All remaining errors are exclusively our responsibility. Correspondence:EBoz@imf.org, Bora.Durdu@frb.gov, NanLi@mail.econ.ohio-state.edu. The views expressed in this paper are those of the authors and should not be attributed to the International Monetary Fund, or the Board of Governors of the Federal Reserve System.
Perhaps one exception is Neumeyer and Perri (2005) which do not focus on labor markets per se but document labor market statistics such as the variability and cyclicality of employment and total hours worked.
These empirical findings have important implications on welfare and monetary policy. For example, high wage volatility suggests that workers bear more risks than firms, and monetary policy may not have a large aggregate impact if prices are relatively flexible. Albeit interesting, we leave these issues for further research.
When we refer to business cycle characteristics through out our analysis, we not only focus on the cyclical fluctuations of output but also those of consumption, current account, and wages as well.
Our result that a setting with search-matching frictions contributing to higher wage variability might sound puzzling at first. Because, earlier research such as Andolfatto (1996) and Merz (1995) found that incorporating search-matching frictions in a setup with TFP shocks only would lower the wage variability. (A version of our model with TFP shocks only also preserves this feature). However, when the model economy is subject to countercyclical interest rate shocks along with TFP shocks, as we consider here, search-matching frictions contribute significantly to the variability of wages due to the mechanism highlighted above.
This is in sharp contrast with the developed economies’ experiences where interest rates tend to be acyclical. The detailed discussions on interest rate shocks in emerging economies are in Neumeyer and Perri (2005), Uribe and Yue (2006).
In other words, countercyclical interest rate shocks help curb the standard consumption smoothing result in the business cycles literature that cause savings to decrease in bad times.
Our results do not undermine the importance of financial frictions, terms of trade shocks or trend shocks highlighted by earlier studies. On the contrary, our findings complement these studies by proposing an additional amplification/transmission mechanism through which external shocks can feed into variability in consumption and real wages.
“Sudden Stop”, a term coined by Calvo (1998), refers to sudden reversals in capital inflows that are typically accompanied by sharp declines in output, asset prices and the price of nontradable goods relative to tradables.
In more recent work, Garcia-Cicco, Pancrazzi and Uribe (forthcoming) find that trend growth shocks are not important if one uses longer time series data. Boz, Daude and Durdu (2008) emphasize that severe informational frictions in EMEs can explain the stylized facts without resorting to large trend growth shocks.
Among the recent contributions in the search literature is Hall (2005) who considers an alternative wage setting where sticky or even constant wages can be sustained in the equilibrium. Hall and Milgrom (2008) introduce an alternating offer bargaining model which leads to weaker feedback from current unemployment to wage. Both models can generate greater variation in the employer surplus, vacancy postings and employment than the Nash bargaining model. However, given the objective of this paper and volatile wage observations in EMEs, we choose to use the simple flexible wage bargain setup.
We use data at quarterly frequency and our sample selection criterion for EMEs is availability of quarterly data. For example, we excluded Argentina from our sample because the data are available only at semi-annual frequency. U.S. is excluded since we focus only on small open economies. All series are deseasonalized using U.S. Census Bureau’s X-12 ARIMA, and then HP-filtered with a smoothing parameter of 1600 after taking the logarithm when appropriate. See the Data Appendix for more details on sources and calculations.
Note that the difference between the two country groups remains.
We analyze employment data in addition to the unemployment rate because unemployment statistics suffer from several deficiencies including the inaccurate measurement of discouraged workers.
We report a set of statistics for hours worked in manufacturing sector in columns 1-3 of Table 3 and approximate aggregate hours worked in columns 4-6 of the same table. Hours worked in manufacturing normalized by the standard deviation of output has a variability of 1.58. Similarly, aggregate hours worked variability approximated in the aforementioned fashion when normalized by output variability is 1.24. Also in terms of correlations with output, these two statistics yield similar results, 0.57 and 0.47.
Our statistics are not fully comparable with those reported by Neumeyer and Perri (2005) because they report labor market statistics using semi-annualized data to make all other countries comparable with Argentina. Our statistics are based on quarterly data.
The data source is TURKSTAT.
This is based on the informal employees defined as those not registered for mandatory social security.
Although not exactly comparable with these ratios, OECD (2004) documents that the “black hours worked as a portion of white working hours” for Denmark, Norway, and Sweden are 3.8 percent, 2.6 percent and 2.3 percent, respectively, significantly smaller than the aforementioned figures for EMEs.
Another interpretation is that markets for the idiosyncratic unemployment risk are complete so that family members can fully diversify this risk using state-contingent claims.
In the Mexican data, 81 percent of the variance in manufacturing total hours is accounted for by changes in employment rather than hours per worker.
Similar to the previous case where working hours are fixed, workers and employers only bargain over wage. Optimal working hours are pinned down by the efficiency condition of labor supply.
See Schmitt-Grohe and Uribe (2003) for detailed discussions on other methods that also induce stationarity in small open economy models.
EMBI yields for Mexico cover 1993Q4:2008Q4.
In the data from International Financial Statistics, the unemployment rate for Mexico is 3.65 percent. Notice that due to precautionary savings incentives, the unemployment rate in the stochastic steady state is lower than that in the deterministic steady state.
See the appendix for TFP calculation for further details.
As pointed out by Neumeyer and Perri (2005), large swings in local inflation sometime make it difficult to interpret and construct sensible real interest rates using local currency nominal interest rates. Moreover, EMEs typically borrow a significant amount from the external market and the emerging market bonds—which reflect the intertemporal terms of trade faced by locals—are denominated in dollars, and can be used to construct real interest rates without using domestic expected inflation.
The noise-like fluctuations in unemployment and vacancy rates at the top panel of Figure 3 are due to the discretization of unemployment with relatively low number of nodes while solving the model.
The decline in output as a result of interest rate shocks only being minuscule is very much in line with the earlier literature. Mendoza (1991), for instance, found that interest rate shocks alone would lead to very little movements in output. Findings of Neumeyer and Perri (2005) and Uribe and Yue (2006) also imply that without additional amplification such as due to working capital constraints, interest rate shocks would lead to small output fluctuations.
Note that when we refer to business cycle features, we not only focus on the cyclical fluctuations of output, but also those of consumption, the current account and the labor market as well.
As discussed earlier, this is very much in line with the earlier literature. Mendoza (1991), for instance, found that interest rate shocks alone would lead to very little movements in output. Findings of Neumeyer and Perri (2005) and Uribe and Yue (2006) also imply that without additional amplification such as due to working capital constraints, interest rate shocks would lead to small output fluctuations.
Our search-matching model implies a negative and small autocorrelation for vacancies. Since we do not have vacancy data for any EMEs, we cannot judge whether this is an inability of the model to account for emerging economies’ labor market dynamics. However, the evidence provided by Shimer (2005) (Table 1) suggests a strong positive autocorrelation (0.94) for vacancies in the case of the U.S. labor markets.
In this case, the matching function would become M(ut, vt) = min(ut, vt) and as long as u and vdo not substantially deviate from each other, the probability of finding a job or filling a vacancy is close to one.
When calibrated to the U.S., Andolfatto (1996) finds that including matching efficiency shocks changes the variability of wage in the opposite direction than desired.
We date the Sudden Stops using the definition by Gallego and Tessada (2008): a period that a. annual capital flow falls at least two standard deviation below its sample mean at least once; b. begins as the first time the annual drop in capital flow is one standard deviation below the sample mean and c. ends when it rises one standard deviation above the mean. The employment data is obtained from International Labour Organization. We categorize the agriculture, mining, manufacturing and utility supply as the tradable sector and construction and services as the nontradable sector.
Net job creations also display similar dynamics. Using the sectoral job creation and destruction data provided by Haltiwanger et al (2004), we find that the ratio of net job creations in the tradable sector relative to that in the nontradable sectors increases in both Mexico and Brazil in the aftermath of crises.
We also analyzed a case when the correlation of matching efficiency shocks and TFP is set to zero.
See Marcus and Manovskii (2005) and Nakajima (2008) for a detailed explanation.
In the calculation of the GDP standard deviation for Israel, we excluded 1976-1980 because of large fluctuations observed in this period.
Important deviations of our model from the standard Mortensen-Pissarides type of search models (e.g., Shimer, 2005) are that the production technology has curvature, the household is risk-averse with access to international financial markets and the economy is subject to shocks to the interest rate at which they can borrow from the rest of world.