The Maastricht Convergence Criteria and Optimal Monetary Policy for the EMU Accession Countries.

The EMU accession countries are obliged to fulfill the Maastricht convergence criteria prior to entering the EMU. This paper uses a DSGE model of a two-sector small open economy, to address the following question: How do the Maastricht convergence criteria modify optimal monetary policy in an economy facing domestic and external shocks? First, we derive the micro founded loss function that represents the objective function of the optimal monetary policy not constrained to satisfy the criteria. We find that the optimal monetary policy should not only target inflation rates in the domestic sectors and aggregate output fluctuations but also domestic and international terms of trade. Second, we show how the loss function changes when the monetary policy is constrained to satisfy the Maastricht criteria. The loss function of such a constrained policy is characterized by additional elements penalizing fluctuations of the CPI inflation rate, the nominal interest rate and the nominal exchange rate around the new targets which are potentially different from the steady state of the unconstrained optimal monetary policy. Under the chosen parameterization, the unconstrained optimal monetary policy violates two criteria: concerning the CPI inflation rate and the nominal interest rate. The constrained optimal policy results in targeting the CPI inflation rate and the nominal interest rate that are 0.7% lower (in annual terms) than the CPI inflation rate and the nominal interest rate in the countries taken as a reference. The welfare costs associated with these constraints need to be offset against credibility gains and other benefits related to the compliance with the Maastricht criteria that are not modelled. JEL Classification: F41, E52, E58, E61EMU accession countries, Maastricht convergence criteria, optimal monetary policy

Can Matching Frictions Explain the Increase in Mexican Unemployment After 2008?

We use a novel data set on firm vacancies and job seekers from a Mexican government job placement service to analyze whether changes in matching frictions can explain the large and persistent increase in Mexican unemployment after the 2008 global financial crisis. We find evidence of a statistically significant reduction in the efficiency of the matching function during the crisis. The estimated effect explains about 70 basis points of the 233 basis points observed increase in the unemployment rate. Hence, these results suggest that changes in matching frictions cannot explain most of the increase in unemployment

Employment fluctuations in a dual labor market

In light of the huge cross-country differences in job losses during the recent crisis, we study how labor market duality - meaning the coexistence of "temporary" contracts with low firing costs and "permanent" contracts with high firing costs - affects labor market volatility. In a model of job creation and destruction based on Mortensen and Pissarides (1994), we show that a labor market with these two contract types is more volatile than an otherwise-identical economy with a single contract type. Calibrating our model to Spain, we find that unemployment fluctuates 21% more under duality than it would in a unified economy with the same average firing cost, and 33% more than it would in a unified economy with the same average unemployment rate. In our setup, employment grows gradually in booms, due to matching frictions, whereas the onset of a recession causes a burst of firing of "fragile" low-productivity jobs. Unlike permanent jobs, some newly-created temporary jobs are already near the firing margin, which makes temporary jobs more likely to be fragile and means they play a disproportionate role in employment fluctuation

Essays on Estimating Production Functions

In the first essay I estimate production functions of multiproduct firms when technologies are product-specific but inputs are observable only at the firm-level. I provide an estimation strategy that solves for the unobservable inputs while correcting for the well-known simultaneity, collinearity and omitted price problems in production function estimation. The key insights of the estimation strategy are, first, using output demand estimates in identifying the product-level input allocations and production functions, and second, using an inverse of the production function to control for endogeneity. The second essay describes the biases that arise when production functions are estimated under the standard assumption of a firm-level technology, while the true technologies are product-specific. The assumption of a firm-level technology implies that the technology parameters are identical across the various goods produced in the industry, and that a multiproduct firm produces all of its output with a single technology. To examine the implications of these simplifying assumptions, I estimate a firm-level production function on a dataset generated of an industry where two types of goods are produced with product-specific Cobb-Douglas production functions. I find that the biases in the estimated firm-level parameters are substantial even when the true product-specific technologies are very similar. The directions and the magnitudes of the biases are determined by intricate functions of the true product-specific technologies and the product scopes of the firms in the industry. The estimated productivity levels have a relatively low correlation with the true firm-level productivity levels when the firms' product scopes are heterogeneous, as they usually are. The third essay estimates the range of productivity gains achieved by information technology investments in the Finnish manufacturing sector. The contribution is to provide estimates of IT's productivity effects while accounting for some of the key characteristics of IT, i.e., that returns to IT depend on previous IT or complementary investments, come with lags, and, due to the aforementioned factors, are heterogeneous across firms and over time. I find that the productivity effects of IT range from negative to positive. For example, most firms obtain a negative productivity effect in the first year after the investment, which may be due to disruption in the production process caused by the implementation of the IT investment. Two years after the IT investment was made, most firms attain a positive productivity effect. In the third year after the investment, almost all firms gain a positive productivity effect. The estimation results suggest that the common practice of estimating a single output elasticity for an IT stock that is constructed as a linear function of the IT investments is unlikely to provide a truthful description of the productivity effects of IT