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Since its introduction by Chari, Kehoe, and McGrattan (2007), Business Cycle Accounting (BCA) exercises have become widespread. Much attention has been devoted to the results of such exercises and to methodological departures from the baseline methodology. Little attention has been paid to identification issues within these classes of models. In this chapter, the authors investigate whether such issues are of concern in the original methodology and in an extension proposed by Šustek (2011) called Monetary Business Cycle Accounting. The authors resort to two types of identification tests in population. One concerns strict identification as theorized by Komunjer and Ng (2011) while the other deals both with strict and weak identification as in Iskrev (2010). Most importantly, the authors explore the extent to which these weak identification problems affect the main economic takeaways and find that the identification deficiencies are not relevant for the standard BCA model. Finally, the authors compute some statistics of interest to practitioners of the BCA methodology.

Wages have been spreading out across workers over time – or in other words, the 90th/50th wage ratio has risen over time. A key question is, has the productivity distribution also spread out across worker skill levels over time? Using our calculations of productivity by skill level for the United States, we show that the distributions of both wages and productivity have spread out over time, as the right tail lengthens for both. We add Organization for Economic Co-Operation and Development (OECD) countries, showing that the wage–productivity correlation exists, such that gains in aggregate productivity, or GDP per person, have resulted in higher wages for workers at the top and bottom of the wage distribution. However, across countries, those workers in the upper-income ranks have seen their wages rise the most over time. The most likely international factor explaining these wage increases is the skill-biased technological change of the digital revolution. The new artificial intelligence (AI) revolution that has just begun seems to be having similar skill-biased effects on wages. But this current AI, called “supervised learning,” is relatively similar to past technological change. The AI of the distant future will be “unsupervised learning,” and it could eventually have an effect on the jobs of the most highly skilled.

This chapter examines the limit properties of information criteria (such as AIC, BIC, and HQIC) for distinguishing between the unit-root (UR) model and the various kinds of explosive models. The explosive models include the local-to-unit-root model from the explosive side the mildly explosive (ME) model, and the regular explosive model. Initial conditions with different orders of magnitude are considered. Both the OLS estimator and the indirect inference estimator are studied. It is found that BIC and HQIC, but not AIC, consistently select the UR model when data come from the UR model. When data come from the local-to-unit-root model from the explosive side, both BIC and HQIC select the wrong model with probability approaching 1 while AIC has a positive probability of selecting the right model in the limit. When data come from the regular explosive model or from the ME model in the form of 1 + n^α/n with α ∈ (0, 1), all three information criteria consistently select the true model. Indirect inference estimation can increase or decrease the probability for information criteria to select the right model asymptotically relative to OLS, depending on the information criteria and the true model. Simulation results confirm our asymptotic results in finite sample.

This chapter proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other covariates and common trends for a panel of 23 OECD countries observed over the period 1971–2015. The observed differentiated behaviors by country reveal strong heterogeneity. This is the motivation behind using a mixed fixed- and random coefficients model to estimate this relationship. In particular, this chapter uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), the authors estimate a mean field variational Bayes semiparametric model with random coefficients for this panel of countries. Results reveal nonparametric specifications for the common trends. The use of this flexible methodology may enrich the empirical growth literature underlining a large diversity of responses across variables and countries.

The paper investigates whether stock liquidity of firms is valued by lending banks revealing that firms with higher liquidity in the capital market pay lower spreads for the loans they obtain. This relationship is causal as evidenced by using the decimalization of tick size as an exogenous shock-to-stock liquidity in a difference-in-differences setting. Reduction in financial constraint and improvement in corporate governance induced by higher stock liquidity are potential mechanisms through which liquidity impacts loan spreads. These higher liquidity firms also receive less stringent nonprice loan terms, for example, longer loan maturity and less required collateral.

We examine the use of relative performance evaluation (RPE), asymmetry in pay for skill/luck, and compensation benchmarking for a sample of firms involved in a spinoff. The spinoff affects firm characteristics that influence the use of the identified compensation practices. We test for differences in the compensation practices for the pre- and post-spinoff firms. We find that RPE is used for post-spinoff CEOs, but not pre-spinoff CEOs. Post-spinoff CEOs are also paid asymmetrically for luck where they are rewarded for good luck but not punished for bad luck. Both pre- and post-spinoff CEOs receive similar levels of compensation benchmarking. The study provides additional evidence on factors that influence compensation practices. Our spinoff sample allows us to examine how compensation practices are affected by changes in firm characteristics while keeping other determinants of compensation constant (i.e., the board and, in many cases, the CEO). Our findings contribute to the understanding of how the identified compensation practices are used.

In this paper, we update and extend “Is There a Glass Ceiling in Sweden?” (Albrecht, Björklund, & Vroman, 2003) by documenting the extent to which the gender log wage gap across the distribution in Sweden has changed over the period 1998–2008. We then examine the Swedish glass ceiling in 2008 in more detail by documenting how it differs for white-collar versus blue-collar workers and for private- versus public-sector workers. We also examine when in the life cycle the glass ceiling effect arises and how this effect develops around the birth of the first child. Finally, we investigate the possible connection between the glass ceiling and the parental leave system in Sweden by linking wage data with data on parental leave from different Swedish registers.

This chapter develops a structural framework for the analysis of scoring procurement auctions where bidder’s quality and bid are taken into account. With exogenous quality, the authors characterize the optimal mechanism whether the buyer is private or public and show that the optimal scoring rule need not be linear in the bid. The model primitives include the buyer benefit function, the bidders’ cost inefficiencies distribution and cost function, and potentially the cost of public funds. We show that the model primitives are nonparametrically identified under mild functional assumptions from the buyer’s choice, firms’ bids and qualities. The authors then develop a multistep kernel-based procedure to estimate the model primitives and provide their convergence rates. Our identification and estimation results are general as they apply to other scoring rules including quasi-linear ones.