Real wage rigidity has been one of the essential characteristics of many modern macroeconomic models, especially those used for macroeconomic policy evaluations. The class of sticky wage models, originally tailored to mature industrial countries, has been adopted to study a variety of optimal policy rules in the developing world. In contrast to developed economies, emerging economies have lower but faster growing per capita income, higher aggregate uncertainty, and less developed financial sectors. Given these economic differences and the importance of wage stickiness in monetary policy evaluation, a natural question to ask is whether emerging markets share the same characteristics of rigid wages with developed economies.
This paper is motivated by the above observations and has two objectives. The first objective is to document the cyclical behavior of real wages in a sample of emerging economies and contrast it with the wage behavior in developed ones using aggregate data. The second goal is to present a model with contractual arrangements between employers and workers that can reconcile this differential wage behavior in emerging markets versus developed economies. The model highlights the role of counter cyclical interest rates (e.g. negative and significant contemporaneous correlation between interest rates and output) and less developed financial markets in emerging markets for explaining the different wage movements. In emerging markets, because of the increasing default risk, interest rates rise when output falls. The increase in interest rates reduces equilibrium consumption through the intertemporal substitution effect and also raises the financial cost of workers’ wage compensation, making it more costly for employers to smooth workers’ income/consumption. Consequently, wages are volatile and positively correlated with output. In contrast, in developed countries, interest rates rise during economic expansion, offsetting the cycle and making consumption smoothing easier for employers. Therefore, wages display more stability.
Estradiol stimulates protein, RNA, and ultimately DNA synthesis in the uterus. The earliest uterine responses seen after estrogen administration occur within minutes. Some of these responses are incorporation of labelled uridine into RNA, histone acetylation, eosinophil infiltration into the uterus, synthesis of mRNA for induced protein, RNA polymerase activity, a decrease in arginine-rich histone content, an increase in glucose uptake, and an increase in amino acid uptake. Among later uterine responses to estrogen requiring 6 hours or more are generalized protein synthesis, and the increase in the activity of the hexose monophosphate shunt enzymes.
The classical model of induction of the response of the uterus to estradiol is the two-site model proposed by Gorski et al. (11) and Jensen et al. (12). This model is based on studies of the binding of radioactive estradiol to receptor in various cell fractions. According to this model, estradiol enters the cytoplasm and binds to the receptor. The receptor upon estradiol binding has a sedimentation coefficent which changes from 4S to 5S and is then translocated to the nucleus. Once in the nucleus, the receptor-estradiol complex binds to chromatin. It is through this interaction that estradiol induces changes in cellular activity. This model, however, is inconsistent with the findings of Welshons et al. who showed that cytoplasts (cells enucleated with cytochalasin B) showed little estrogen binding activity while the nucleoplasts showed considerable estrogen binding activity. In addition, the studies of King and Greene, using a monoclonal antibody against the estrogen receptor and immunochemical staining, showed that specific staining was confined to the nucleus. These last two studies support the conclusion that the estradiol receptor resides in the nucleus and not in the cytoplasm and that the cytoplasmic receptor may be an artifact of the fractionation procedures.
The volatility of financial assets is an important parameter in risk management, portfolio trading and option pricing. The variability in price changes displays the uncertainty of the market and thus volatility is mainly determined by trading itself. There is much literature on volatility models mainly on stochastic models but during the last years more and more work is done on implied volatilities resulting from the well known Black-Scholes (BS) formula for option pricing, when the option price is known.
