This paper develops a multi-industry general equilibrium model where entrepreneurs within each industry can decide to operate formally or informally. The model generates a rich set of predictions including productivity cut-offs for formal and informal firms to operate within different industries. In doing so, it matches empirical research that finds an overlap in the aggregate productivity distributions of formal and informal firms, while being consistent with theoretical predictions of strict duality within industries. Our explanation for this outcome is that it is natural result of fixed costs varying across industries. We offer evidence that the overlap between formal and informal firms in the aggregate is larger than the overlaps within industries for the case of Indian manufacturing establishments. Our model is also consistent with other features of the data in that it can explain high levels of competition between formal and informal firms that decrease with formal firm size.

This paper investigates how the ability to innovate affects firms' decisions to operate informally and the aggregate consequences of their sectoral choice. I embed a sectoral choice model, where firms choose to operate in the formal or informal economy, into a richer general equilibrium environment to analyze the aggregate effects of firm-level decisions in response to government taxation. I calibrate the model and conduct simulations to quantify the impacts on the aggregate economy. I find that a change in tax rates from 50% to 60% leads to a 20.9% reduction in the size of the formal sector. This change is accompanied by a 0.07 percentage point reduction in TFP growth per year. Given that countries like Mali, Mexico, and Sri Lanka impose total tax rates near 50%, these findings have significant and applicable policy implications across a broad range of lesser developed countries. Even at lower tax rates, for instance 10%, a 10% increase, decreases the size of the formal sector by more than 7.7%.

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This paper investigates the informational content of regular revisions to real GDP growth and its components. We perform a real-time forecasting exercise for the advance estimate of real GDP growth using dynamic regression models that include revisions to GDP and its components. Echoing other work in the literature, we find little evidence that including aggregate GDP growth revisions improves forecast accuracy relative to an AR(1) baseline model; however, models that include revisions to components of GDP improve forecast accuracy. The first revision to consumption is particularly relevant in that every model that includes the revision outperforms the baseline model. Measured by root mean squared forecasting error (RMSFE), improvements are quite sizable, with many models increasing forecasting performance by 5% or more, and with top-performing models forecasting 0.18 percentage points closer to the advance estimate of growth. We use Bayesian model averaging to underscore that our results are driven by the informational content of revisions. The posterior probability of models with the first revision to consumption is significantly higher than our baseline model, despite strong priors that the latter should be the preferred forecasting model. We thank Dean Croushore, Eric Gaus, C. Richard Higgins, and Jermy Piger for reading early drafts, and we thank anonymous referees for their helpful feedback on the completed draft. We also benefitted from comments at the Midwest Economics Association's Annual Meeting, the Liberal Arts Macro Conference, the Applied Probability and Statistics Workshop at St. Thomas, and a seminar at Drake University. We appreciate Mary Reichardt's editorial suggestions. All remaining errors are our own.

This paper develops a multi-industry general equilibrium model where entrepreneurs within each industry can decide to operate formally or informally. The model generates a rich set of predictions including productivity cutoffs for formal and informal firms to operate within different industries. In doing so, it matches empirical research that finds an overlap in the aggregate productivity distributions of formal and informal firms, while being consistent with theoretical predictions of strict duality within industries. Our explanation for this outcome is that it is natural result of fixed costs varying across industries. We offer evidence that the overlap between formal and informal firms in the aggregate is larger than the overlaps within industries for the case of Indian manufacturing establishments. Our model is also consistent with other features of the data in that it can explain high levels of competition between formal and informal firms that decrease with formal firm size.