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This paper investigates how land size measurements vary across three common land measurement methods (farmer estimated, Global Positioning System (GPS), and compass and rope), and the effect of land size measurement error on the inverse farm size relationship and input demand functions. The analysis utilizes plot-level ata from the second wave of the Nigeria General Household Survey Panel, as well as a supplementary land validation survey covering a subsample of General Household Survey Panel plots. Using this data, both GPS and self-reported farmer estimates can be compared with the gold standard compass and rope measurements on the same plots. The findings indicate that GPS measurements are more reliable than farmer estimates, where self-reported measurement bias leads to over-reporting land sizes of small plots and under-reporting of large plots. The error observed across land measurement methods is nonlinear and results in biased estimates of the inverse land size relationship. Input emand functions that rely on self-reported land measures significantly underestimate the effect of land on input utilization, including fertilizer and household labor.

Agriculture. --- E-Business. --- Education. --- Land Measurement. --- Private Sector Development. --- Rural Development Knowledge and Information Systems. --- Rural Development. --- Science and Technology Development. --- Science Education. --- Scientific Research and Science Parks. --- Standards and Technical Regulations. --- Survey Methods.

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A recent survey of rigorous impact evaluations of programs to help small and medium-size firms to formalize indicates that the programs do not seem to work for most informal firms. One of the few exceptions finds large effects of a tax simplification program in Brazil called SIMPLES on firms' formalization rates and performance indicators. Using the same data set but a different identification strategy, another study concludes that the program had limited effect on formalization rates. The aim of this paper is twofold. First, it revisits the two studies to reconcile their conflicting conclusions. Second, it investigates the validity of the identification strategy of both studies. The findings suggest that the conflicting results between the two studies are caused by the dates each used to identify when the program was put into effect. A robustness check indicates that data heaping and seasonality around November cast doubts on the identification strategy used in both studies to estimate the effect of this particular program.

Business Environment. --- Business Taxation. --- E-Business. --- Education. --- Finance and Financial Sector Development. --- Private Sector Development. --- Regression Discontinuity Design. --- Science and Technology Development. --- Science Education. --- Scientific Research and Science Parks. --- Simples Program. --- Small Scale Enterprise. --- Sme Development.