Improved Productivity Measurement in New Zealand's Longitudinal Business Database

Abstract

results in this paper have been confidentialised to protect these groups from identification and to keep their data safe. Careful consideration has been given to the privacy, security, and confidentiality issues associated with using administrative and survey data in the IDI. Further detail can be found in the Privacy impact assessment for the Integrated Data Infrastructure available from www.stats.govt.nz. The results are based in part on tax data supplied by Inland Revenue to Stats NZ under the Tax Administration Act 1994. This tax data must be used only for statistical purposes, and no individual information may be published or disclosed in any other form, or provided to Inland Revenue for administrative or regulatory purposes. Any person who has had access to the unit record data has certified that they have been shown, have read, and have understood section 81 of the Tax Administration Act 1994, which relates to secrecy. Any discussion of data limitations or weaknesses is in the context of using the IDI for statistical purposes, and is not related to the data's ability to support Inland Revenue's core operational requirements.

Valuing Cultural Diversity of Cities

“…The utility function specified in equation(1)implies an elasticity of substitution of one and cannot account for housing taking a larger share of expenditure in areas where prices and rents are high, which would imply a utility function with an elasticity of substitution less than one. Rather than increasing the mathematical complexity by introducing more flexible utility functions, we investigate the sensitivity of our findings to alternative values of in section 5.1 and specifically inTable 8.4 Reliable estimates of the value of are not available Fabling and Maré (2019). report aggregate Cobb Douglas production function estimates.…”

Of interest? Estimating the average interest rate on debt across firms and over time

“…Figure 3 plots this retained data rate by year. Consistent with the productivity dataset cleaning process, new IR10 form data quality appears to be higher resulting in less dropped observations in more recent years (Fabling and Maré 2019). 8…”

“…We start by using the Fabling-Maré labour and productivity datasets available in the LBD, and currently covering the 2001 to 2018 (March) financial years (Fabling 2011;Fabling and Maré 2015a, 2015b, 2019. These data contain standard production function variables -output (Y ), intermediate consumption (M ), capital services (K) and labour (L).…”

Living on the edge: An anatomy of New Zealand’s most productive firms

“…The bottom row of each panel (bolded) reflects the final analysis sample. Y and M are deflated using the relevant official Stats NZ producer price index, while K is deflated using an industry-specific weighting of official asset-type deflators as described in Fabling and Maré (2019). 1 0.085 0.014 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 2 0.011 0.060 0.023 0.004 0.001 0.000 0.000 0.000 0.000 0.000 3 0.002 0.017 0.046 0.026 0.006 0.002 0.001 0.000 0.000 0.000 4 0.001 0.005 0.019 0.039 0.027 0.007 0.002 0.001 0.000 0.000 5 0.001 0.002 0.006 0.019 0.036 0.027 0.007 0.002 0.001 0.000 6 0.000 0.001 0.003 0.007 0.019 0.035 0.027 0.006 0.001 0.000 7 0.000 0.001 0.001 0.003 0.008 0.019 0.037 0.026 0.004 0.001 8 0.000 0.000 0.001 0.001 0.003 0.007 0.020 0.042 0.024 0.002 9 0.000 0.000 0.000 0.000 0.001 0.002 0.005 0.020 0.054 0.016 10 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.003 0.015 0.080…”

“…Table 1 shows the impact of these restrictions on the coverage of the productivity population broken down into sequential, cumulative steps. We make use of productivity dataset weights based on industry-firm-size cells to account for missing productivity data, using full coverage employment data to infer missing productivity population observations (Fabling and Maré 2019). The top panel of table 1 shows the proportion of productivity components (and firm-year observations) that are captured by the population, and the bottom panel shows how well the productivity data covers the population of interest (ie, the proportion of data that is actually observed, rather than accounted for by weighting).…”