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From modelmania to datanomics? The rise of mathematical and quantitative methods in three top economics journals

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Abstract

This article investigates the rise of mathematical and quantitative methods in three leading economics journals—American Economic Review, Journal of Political Economy, and Quarterly Journal of Economics—from 1940 to 2010. We show that the trajectories describing the evolution of mathematical and quantitative methods in these outlets are strikingly similar. Analyzing theoretical and applied research separately, the former follows an inverted-U path while the latter has been growing in importance throughout the period, especially after 1990. Moreover, cointegration methods are used to investigate the existence of a long-term relationship between the time series. Our results suggest that whereas the three journals mutually influenced one another, different dynamics emerge when theoretical and applied research are considered separately.

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Notes

  1. AER’s Papers and Proceedings was also included since dropping papers that use the expression papers and proceedings also excludes papers from AER’s regular editions that cite papers from the special edition. As a check we have excluded all papers using the expression papers and proceedings and the results only changed by roughly 1 p.p.

  2. The code to replicate the results is available under request.

  3. Supply bounds on the critical values for the asymptotic distribution of the F-statistic are provided by Pesaran et al. (2001) and Narayan (2005).

  4. See Weintraub (2002), Fourcade (2009), Milonakis (2017), and the supplemental issue to volume 30 of History of Political Economy.

  5. In their ‘Manifesto’, the new editors clearly point at the desire to change the direction of the journal: “[W]e hope to expand its scope. Given the breadth of interests of our Board of Associate Editors, we would like the QJE to publish more articles beyond its traditional, primarily microtheory domain, that is, to become more fully a general journal. Toward this end we would especially welcome submissions in macroeconomics, both empirical and theoretical” (Blanchard et al. 1985).

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Author information

Authors and Affiliations

  1. Faculty of Business and Economics, University of Basel, Basel, Switzerland

    Thiago Dumont Oliveira

  2. Department of Economics and Statistics, University of Siena, Siena, Italy

    Marwil J. Dávila-Fernández

Authors
  1. Thiago Dumont Oliveira
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  2. Marwil J. Dávila-Fernández
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Correspondence to Thiago Dumont Oliveira.

Appendices

Appendix 1

Table 8 reports unit root tests in levels for our aggregate measure of formalization. Unless explicitly stated otherwise, Schwarz informational criteria was used to determine the optimal number of lags in ADF tests. For the PP tests, we used Bartlett kernel spectral estimation method with Newey-West bandwidth. Breakpoint tests are of the additive outlier type with number of lags also following Schwarz informational criteria. The null hypothesis of non-stationarity cannot be rejected for most cases. However, although ADF and PP seem to suggest that series are non-stationary and controlling for a break in the intercept does not modify this result, once we allow for a break in trend the series become stationary. This last result is of particular importance because the increase in the use of mathematical and econometric techniques in economics consists indeed of a change in trends.

Table 8 Unit root tests (levels)
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We proceed applying the same tests for aggregate series in first differences (Table 9). All estimates indicate stationarity. Therefore, we conclude that series are either stationary or integrated of order one, but it is not possible to determine precisely if they are I(0) or I(1).

Table 9 Unit root tests (first differences)
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We repeat the tests to theoretical research (Table 10). JPE series are clearly stationary. For the remaining two journals we have a similar situation as in the aggregate case. Most tests indicate the presence of a unit root but once we control for a break in trend, we reject the null hypothesis of non-stationarity.

Table 10 Unit root tests (levels)
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Table 11 brings our unit-root tests for series now in first differences. We reject the null of non-stationarity in all cases. This allows us to conclude that series are either I(0) or I(1), though again it is not possible to determine precisely their order of integration.

Table 11 Unit root tests (first difference)
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We proceed testing the presence of unit-root for applied research. Table 12 reports the main outcomes. Results are overall inconclusive. Not including a trend suggests series are non-stationarity. However, once we take into account the presence of a trend we reject the presence of unit root in eight out of nine cases. A break in trend maintains the stationarity result.

Table 12 Unit root tests (levels)
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Finally, in Table 13 we test for unit root when series are differentiated. Data are found to be stationary in first differences. Therefore, we conclude that the ARDL bounds/cointegration approach is the most adequate to study the existence of a long-run relationship between these variables.

Table 13 Unit root tests (first difference)
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In each of the estimated ARDL models reported in this paper, the order of lags was obtained using the Akaike Informational Criteria (AIC) with automatic lag selection imposing a maximum of 4 lags for dependent and independent variables. When serial correlation was observed the maximum number of lags was increased to remove serial correlation. To control for structural breaks, a dummy variable was created that assumes value zero for years before the break and value one afterwards. Break dates were determined by the unit root tests in levels allowing for a break in trend and are reported in Table A7. To assess a valid inference and not spurious regressions, residuals of regressions were checked for serial correlation and are available under request (Table 14).

Table 14 Year of Structural Break
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Appendix 2

Figure 7 shows the evolution of MQM on the top three economics journals and on all other economics journals listed on JSTOR. On the early 1940s the difference was quite small, but between the 1940s and the 1970s the gap increased significantly. After 1970 the proportion of papers using MQM on the top journals remains fairly stable while it steadily increases on the other economics journals. Therefore, the rise of MQM happened faster on the top journals relative to the rest of the economics profession and the proportion of papers using MQM was greater on the top journals for the whole period.

Fig. 7
figure 7

MQM on the top three \(\times\) all other journals (%)

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Oliveira, T.D., Dávila-Fernández, M.J. From modelmania to datanomics? The rise of mathematical and quantitative methods in three top economics journals. Scientometrics 123, 51–70 (2020). https://doi.org/10.1007/s11192-020-03375-y

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