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Do macroeconomic and financial governance matter? Evidence from Germany, 1950–2019

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Abstract

This study hypothesises that economic governance matters for economic performance; neglecting its role in creating positive synergies between macro- and microeconomic institutions has underlain significant coordination failures and costs. This study examines economic governance in the context of mutual feedback between macro-financial governance (FGV), macro-non-financial governance (NFGV), and micro-financial development (FND) in Germany in the period 1950–2019. The study uses an institutionalist approach, introducing two modes of economic governance based on institutional complementarities and tests its hypotheses using both an exhaustive structuralist analysis and a time-series quantitative technique based on the Autoregressive Distributed Lag cointegration model and the Vector Error Correction Mechanism. The study concludes that (i) the German model of economic governance based on the positive complementarities between FGV, NFGV and FND in the period 1950–1982 significantly enhanced real economic performance, that (ii) the fragmentation of the model became a key determinant of the country’s weak economic performance in the periods 1983–2019 and 1990–2019, and that (iii) the path-dependence of coordinational mechanisms and underlying institutional dynamics, though fragmented, prevented the genesis and embedding of an irrational exuberance in the country.

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Fig. 1
Fig. 2

Source: Bundesbank (2020)

Fig. 3

Source: Bundesbank (2020)

Fig. 4
Fig. 5

Source: Bundesbank (2020)

Fig. 6
Fig. 7
Fig. 8
Fig. 9

Source: Bundesbank (2021). *German contribution to Euro-Area M3

Fig. 10

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Change history

Notes

  1. Irrational exuberance caused finance sector to turn into a growth sector in itself (Beck 2012), underlying the de-linkage between growth and finance. Because (i) as a result of rapidly rising prices of financial assets, the profitability of financial investment outpaced the profitability of non-financial investment in the face of rapidly rising international competition in the market for goods and services, (ii) finance sector allocated an increasing proportion of available financial funds to short-run speculative investment rather than to productive industrial investment with deepening financial deregulation and the introduction of sophisticated financial technologies, and (iii) non-financial investors started using an increasing proportion of their financial resources for financial investment rather than for non-financial investment (Palley 2013).

  2. Synergy-creating governance is neither a ‘hieararchy’ nor ‘a centrally-planned model’ in Hayekian sense. It denotes that policy coordination driven by positive complementarities can mediate a stronger and more sustainable economic performance.

  3. There are five points that should be clarified in understanding this intermediary role of institutions. First, the nature and the direction of the actors’ preferences are expressed as policy choices, rather than institutions themselves. For example, governments can increase or decrease the size or quantity of their investment in the market for goods and services. Second, by increasing or decreasing the size of investment, public investment policy determines the scale and direction of the intermediary role of investment or how investment is used in enforcing a policy choice. Third, investment as an institution here intermediates the relationship between the policy choice and the structure affected by the policy—that is, the market for goods and services. Fourth, macroeconomic governance is the collective ordering of micro-macro-policy choices in using the financial and non-financial institutions illustrated in Fig. 1. Five, macro-economic indicators illustrate not the macroeconomic institutions themselves such as investment, consumption, and trade but the quantity of these institutions or the change in their quantities in time.

  4. As there is a well-established literature on finance-growth nexus, we did not delve into its details for saving space in this paper. See Cournède, B., & Denk, O. (2015). Finance and economic growth in OECD and G20 countries. Available at SSRN 2649935. for a detailed documentation of (i) the varieties of positive and negative relationships between growth and finance and (ii) the relevant literature.

  5. Output gap data should be approached prudentially as AMECO’s, OECD’s and IMF’s calculations are different. OECD and IMF calculated negative one percent and positive one percent output gap for the country for the period 2008–2019 (OECD, 2021; IMF, 2021).

  6. Hellwig (2018: 2) calculates this amount by “adding up numbers that have been publicly given for various institutions”, as precise data on fiscal costs were not available.

  7. Output gap in this period amounted to −0.4 percent of the potential output on an average according to IMF’s calculation (see IMF, 2021), which is much higher than AMECO’s calculation.

  8. F-test estimates two types of cointegration relationships simultaneously. First is the joint relationships between dependent and all independent variables. Second is the separate long-run relationship between dependent and each independent variable. That F-test is statistically significant implies the first type of cointegration relationship for all cases but this does not require that there be the second type of cointegration relationship as well. We are looking at the cointegration coefficients to infer the existence or the absence of second type of cointegration relationship.

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Authors and Affiliations

  1. Department of Economics, Istanbul University, Istanbul, Turkey

    Taner Akan

  2. Department of Economics, Tubingen University, Tubingen, Germany

    Tim Solle

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  1. Taner Akan
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Appendix

Appendix

1.1 Appendix 1

1. PCA for NFGV (Annual, 1950–2019)

Eigenvalues: (Sum = 5, Average = 1)

Number

Value

Difference

Proportion

Cumulative value

Cumulative proportion

1

4.950702

4.917328

0.9901

4.950702

0.9901

2

0.033375

0.023097

0.0067

4.984077

0.9968

3

0.010278

0.005250

0.0021

4.994355

0.9989

4

0.005027

0.004409

0.0010

4.999382

0.9999

5

0.000618

–

0.0001

5.000000

1.0000

figure a

2. PCA for FGV (Annual, 1950–2019)

Eigenvalues: (Sum = 4, Average = 1)

Number

Value

Difference

Proportion

Cumulative value

Cumulative proportion

1

3.967095

3.947157

0.9918

3.967095

0.9918

2

0.019938

0.010432

0.0050

3.987033

0.9968

3

0.009506

0.006046

0.0024

3.996540

0.9991

4

0.003460

–

0.0009

4.000000

1.0000

figure b

3. PCA for NFGV (Quarterly, 2008Q1–2019Q4)

Eigenvalues: (Sum = 5, Average = 1)

Number

Value

Difference

Proportion

Cumulative value

Cumulative proportion

1

4.112832

3.422619

0.8226

4.112832

0.8226

2

0.690213

0.539033

0.1380

4.803045

0.9606

3

0.151180

0.117529

0.0302

4.954224

0.9908

4

0.033650

0.021525

0.0067

4.987875

0.9976

5

0.012125

–

0.0024

5.000000

1.0000

figure c

1.2 Appendix 2: The plots of CUSUM and CUSUMSQ stability tests

1950–1982

Model 1.1.

figure d

Model 1.2.

figure e

Model 1.3

figure f

1950–1989

Model 1.1.

figure g

Model 1.2.

figure h

Model 1.3.

figure i

1983–2019

Model 1.1.

figure j

Model 1.2.

figure k

Model 1.3.

figure l

Model 2.1.

figure m

Model 2.2.

figure n

Model 2.3.

figure o

1990–2019

Model 1.1.

figure p

Model 1.2.

figure q

Model 1.3.

figure r

Model 2.1.

figure s

Model 2.2.

figure t

Model 2.3.

figure u

2008Q1-2019Q1

Model 3.1.

figure v

Model 3.2.

figure w

Model 3.3.

figure x

Model 4.1.

figure y

Model 4.2.

figure z

Model 4.3.

figure aa

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Akan, T., Solle, T. Do macroeconomic and financial governance matter? Evidence from Germany, 1950–2019. J Econ Interact Coord 17, 993–1045 (2022). https://doi.org/10.1007/s11403-022-00356-7

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