This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in Low Inflation Bends the Phillips Curve, PIIE Working Paper 19-6.

If you use the data, please cite as: Gagnon, Joseph E., and Christopher G. Collins. (2019). Low Inflation Bends the Phillips Curve. PIIE Working Paper 19-6. Peterson Institute for International Economics.

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This project includes codes and data to replicate "Supply Disruptions, Economic Slack, and the State-Dependent Phillips Curve."Abstract:This paper investigates how supply disruptions and economic slack affect the responsiveness of inflation to changes in aggregate demand. We propose and estimate a nonlinear Phillips curve, whereby the sensitivity of inflation to changes in demand varies with supply conditions and the amount of slack in the economy. We find evidence that supply disruptions and low unemployment both steepen the Phillips curve, thereby amplifying the inflationary effects of increased demand. Our results suggest that the effects of fiscal policy on inflation depend on the prevailing supply conditions and the level of unemployment in the economy.

Sustainable intensification of agriculture requires understanding of the effect of soil characteristics and nutrient supply on crop growth. As farms are increasing in size by acquiring small fields from various farmers, the soil characteristics and nutrient supply might be very different from field to field, while at the same time specific soil properties might limit the nutrient uptake. As a result, there might be a large number of heterogeneous reasons why crop growth varies significantly. New data analysis techniques can help to explain variability in crop growth among fields. This paper introduces Exceptional Growth Mining (EGM) as a first contribution. EGM instantiates the data mining framework Exceptional Model Mining (EMM) such that subgroups of fields can be found that grow exceptionally in terms of three growth parameters (high/low maximum growth, steep/flat linear growth and early/late midpoint of maximum growth). As second contribution, we apply EGM to a case study by analyzing the dataset of a potato farm in the south of the Netherlands. EGM consists of (i) estimating growth curves by applying nonlinear mixed models, (ii) investigating the correlation between the estimated growth parameters, and (iii) applying EMM on these growth curve parameters using a growth curve-specific quality measure. By applying EGM on the data of the potato farm, we obtain the following results: 1) the estimated growth curves represent the variability in potato tuber growth very well (R2 of 0.92), 2) the steepness of the growth curve has a strong correlation with the maximum growth and the midpoint of maximum growth, and the correlation between the midpoint of maximum growth and maximum growth is weak, 3) the subgroup analyses indicate that: high values of K correspond to high maxima; low values of K correspond to low maxima, steep growth curves’, and a late midpoint of halfway growth; Mg influences the midpoint of the growth curve; values of B are higher on dry soils with high tuber growth, while low values of B are found on wet soils with high tuber growth; high values of Zn, Mn, and Fe are found in subgroups with low tuber weight, probably related to the soil’s low pH. In summary, this paper introduces EGM to obtain understanding in crop response to soil properties and nutrient supply. In addition, EGM provides a way to analyze only small parts of a large dataset, such that the impact of soil factors on growth can be analyzed on a more detailed level than existing methods.

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