Search Frictions, Financial Frictions, and Labor Market Fluctuations in Emerging Markets

ABSTRACT

This article examines the role of the extensive and intensive margins of labor input in the context of a business cycle model with a financial friction. We document significant variation in the hours worked per worker for many emerging-market economies using manufacturing data. Both employment and hours worked per worker are positively correlated with each other and with output. We show that a search-theoretic context in a small open-economy model requires a small wealth effect to explain these regularities at the expense of a smaller wage response. On the other hand, introducing a financial friction in the form of a working capital requirement can explain the observed movements of labor market variables such as employment and hours worked per worker, as well as other distinguishable business cycle characteristics of emerging economies. These include highly volatile and cyclical real wages, labor share, and consumption.

KEY WORDS:

• business cycles
• emerging markets
• intensive margin
• search frictions
• working capital

JEL CLASSIFICATION:

• F41
• E44
• J40

Notes

1. See Fan, Titman, and Twite (2012) for the importance of short-term debt in emerging economies.

2. Barth and Ramey (2002) notes that working capital, including the value of inventories and trade receivables, is 17 months of final sales, on average, over the period 1959 to 2000 in the US nonfinancial corporate business. When trade payables are net out, working capital becomes 11 months of final sales, which still suggests a considerable amount of time between production and cashing out of sales.

3. Instead of introducing imperfect credit markets, we concentrate on the interaction between search and short-term credit in an environment with countercyclical interest rates, as is the case for emerging markets.

4. Specifically, a scheme that allows for partial compensation when hours are reduced—as practiced by many European economies—may yield larger variations in the intensive margin relative to a U.S.-type scheme in which workers are compensated conditional on being employed or not.

5. A key feature of the model is that the self-employed must rely on relationship-based trade credit which is provided by firms in the salaried sector. Self-employed firms are also subject to capital matching frictions that impede the creation of new self-employed ventures. Thus, during periods of capital idleness or downturns, individuals that wish to set up self-employed firms find easier access to capital from the salaried sector firms. This tends to mitigate the decline in wages in the salaried sector during downturns and to put less pressure on wages during upturns. This feature of the model yields a negative correlation between the persistence of output and the share of self-employed in an economy, and helps to rationalize the countercyclical entry into self-employment. One problem with the approach in Finklestein Shapiro (2014) is that it is silent about the structure of informal credit markets that allow the self-employed sector to flourish. Arguably, the mechanism of securing such relation-based trade credit (from input suppliers, family, and friends) is the key feature of the model that would explain differences in the behavior of developed versus emerging economies.

6. Some countries report household/labor survey results for the overall economy (see the International Labour Organization website for this); however, they are often not comparable across countries and over time. To give a few examples: (i) many countries report hours paid or normal (usual) hours rather than those worked, (ii) some conduct these types of surveys in only one month during the year, (iii) labor surveys include many break points, which makes it difficult to calculate cyclical components around these points, and (iv) workers surveyed tend to report overwork (see Mellow and Sider 1983), which is potentially more problematic during recessions, when hours might be cut. This is why we choose to work on industrial (establishment) surveys in manufacturing.

7. Merkl and Wesselbaum (2011) also document that variation in the extensive margin accounts more for the overall variability in aggregate hours in the United States and Germany.

8. At the 95% and 90% level of significance, we cannot reject a hypothesis that hours per worker is as volatile as employment in emerging markets, where the alternative is that hours per worker is less volatile than employment.

9. Note that the volatility of the intensive margin might be even higher than presented here for both groups when we allow for labor utilization, since the effort that each worker puts in during recessions may be lower than in boom times. On the other hand, this could be offset by the potential upward bias coming from the use of data on manufacturing, which tends to be more volatile than the aggregate economy. Nevertheless, these results still justify a model in which the intensive margin varies endogenously, rather than a model in which it is assumed fixed.

10. For the case of Argentina and Mexico, the sample period includes the Tequila crisis of 1995 as well as the contagious effects of the 1998 Russian crisis. Moreover, Argentina experienced the sovereign debt default of 2002. For Turkey, there are two major financial crises, the 1994 exchange rate crisis and the 2000–2001 banking and financial crisis. For a further discussion of the timing of the recessions associated with such crises, see Altug and Bildirici (2012).

11. As discussed in the Introduction, one reason for the negative correlation between employment and hours per worker in European economies may be the existence of employment protection laws.

12. The average employment protection index is 0.45 in emerging markets, a value that is between the average index for the United States and Canada (0.24) and that for European countries (0.67).

13. In the 2008 global financial crisis, what may have also affected emerging economies is the illiquidity in international financial markets, suggesting a decreased capability to borrow at longer horizons. While we do not model features such as borrowing constraints or borrowing at different horizons, these features will just amplify our results by making effective cost of borrowing even more responsive during crisis.

14. This could be considered as the equivalent version of having to pay labor costs before the sales are cashed out in an economy where there is a lag between production and cashing out the sales.

15. See Christiano and Eichenbaum (1992) and Neumeyer and Perri (2005) for the macro implications of this type of friction.

16. Note that, for simplicity, we assume the firm borrows in advance for payments to labor and recruitment services; however, the results would stay the same if the firm were to use its internal sources to finance these upfront costs. In this case, the value of upfront costs at the time when output is produced or sales are cashed out would still include interest payments.

17. See, for example, Devereux, Gregory, and Smith (1992) and Hairault (2002).

18. We will also conduct sensitivity analysis using separable preferences since these preferences are commonly used in the search literature. See, for instance, Andolfatto (1996) and Boz, Durdu, and Li (2015).

19. See Schmitt-Grohe and Uribe (2003) for more details.

20. To understand these results, recall that the non-working population at time $t$, $u_t$, is defined as $u_t=1-\left(1-\psi \right)n_{t-1}$.

21. We use the Dynare routine to solve the log-linearized equilibrium conditions (see Adjemian et al. 2011). Note that under log-linear methods bond holdings do not deviate from steady-state too much, which possibly underestimate the effect of interest rate shocks especially during financial crises in emerging markets. This is why, in the quantitative analysis, we concentrate onbusiness cycle dynamics rather than sharp economic downturns, such as sudden stops.

22. See Hall and Milgrom (2008), Shimer (2009) and Hall (2009).

23. When $\gamma =0$, the parameter value for labor curvature implies a Frisch elasticity of the labor supply of 0.5, which is in the range of estimates reported in Blundell and MaCurdy (1999).

24. See, among others, Neumeyer and Perri (2005), Aguiar and Gopinath (2007), Li (2011), and Kabaca (2014).

25. Hagedorn and Manovskii (2008) argue that the search and matching model can account for the cyclical volatility of vacancies and unemployment for the US based on a high value for non-market work and a low value of the bargaining parameter for workers. Boz, Durdu, and Li (2015) introduce shocks to matching efficiency, $\omega$, which increases the volatility for this variable. In addition, labor supply shocks such as changes in labor force participation over the cycle could amplify the fluctuations for employment.

26. Additional financial frictions such as collateral constraints would potentially contribute to fluctuations of these variables (see Mendoza 2010).

27. The details on the calibration of parameters in the separable utility case can be found in the Supplemental Appendix B.

28. Note that we change only the value of the working capital requirement and correlation coefficient between shocks and so do not calibrate the whole model to developed markets since, for developed markets, a role for monetary policy through nominal rigidities might be necessary to understand short-term variations in interest rates rather than assuming exogenous variations in rates correlated with TFP shocks in a real-business-cycle environment. In addition, labor-market specific rigidities on wages, hiring and firing decisions are known to be important in these economies. By shutting off emerging-market features, our objective is not to explain labor market fluctuations in developed markets. Rather, we aim to discuss the direction of the results in the light of discussion in Section 2.