DOI: 10.5061/dryad.8931zcrp9
title: Considering institutional type: a varieties of capitalism approach to the economic growth resource curse

description: Defined by North (1994) as the “rules of the game”, over the last few
 decades, many scholars have sought to understand whether quality
 institutions can alleviate the Resource Curse (the idea that natural
 resource abundance hinders rather than promotes economic growth). However,
 with this focus on quality, few papers have addressed the question of
 institutional type, and its Curse mitigating properties.
 This paper, via utilising the associated
 data, contributes towards filling this gap. Using the
 Varieties of Capitalism framework, we test whether
 certain institutional typologies possess the ability to
 mitigate the Resource Curse and perhaps even turn it into a blessing.
 Specifically, Rougier and Combarnous’ cluster analysis, “The Diversity of
 Emerging Capitalisms in Developing Countries” (2017), whereby nations are
 assigned various institutional typologies is used to create our primary
 (dummy) independent variables of interest in this study. The remaining
 control variables essential for economic growth analysis are collected via
 the World Bank and Polity datasets.
description: The main independent variables of interest are those that
 represent which institutional typology a nation belongs to. Consequently,
 five dummy variables are created: Liberal, Coordinated, Informal,
 Paternalistic and Idiosyncratic (columns B-F in the dataset). These
 variables take the value of 1 when a nation belongs to the relevant
 cluster and 0 if not. Regarding our measure of resource
 abundance, this paper creates a Resource Abundance variable (column J in
 the dataset) based on the World Bank’s measure of total resource rents as
 a percentage of GDP. Such a measure defines total natural resource rents
 as “the sum of oil rents, natural gas rents, coal rents (hard and soft),
 mineral rents, and forest rents.” The Resource
 Abundance variable, as well as any other variable aside from the dummy
 variables that are not restricted to a single year is, for any given
 nation, an average taken from 1996-2017. The end date is chosen because,
 at the time of writing, this is the latest year provided by the World Bank
 for the aforementioned measure of natural resource rents. The start date
 is chosen because this is the earliest year the World Bank provides the
 data that is used to create our measure of institutional
 quality. Four of the six World Bank Worldwide
 Governance Indicators are utilised as controls in this study. These are:
 Control of Corruption (column P in the dataset), Rule of Law (column Q in
 the dataset), Government Effectiveness (column N in the dataset) and
 Regulatory Quality (column O in the dataset). Additionally, an aggregate
 measure of Institutional Quality is calculated (column R in the dataset).
 This is done by, for a given nation, averaging the abovementioned four
 measures individually between 1996-2017, before finally averaging this
 calculated figure. Three additional baseline controls
 are included. The data for each arises from the World Bank and are:
 Openness (an average from 1996-2017 of “the sum of exports and imports of
 goods and services measured as a share of gross domestic product”),
 Investment (an average from 1996-2017 of “the total value of the gross
 fixed capital formation and changes in inventories and acquisitions less
 disposals of valuables for a unit or sector” as a percentage of GDP) and
 Initial Income (the log of 1996 GDP per capita). Each variable (columns K,
 L and AH, respectively in the dataset) is derived from World Bank
 data. Finally, our dependent variable of interest,
 Growth (column M in the dataset), is a measure of GDP per capita provided
 by the World Bank, averaged from 1996-2017. Specifically, this measure
 captures the “annual percentage growth rate of GDP per capita based on
 constant local currency… based on constant 2010 U.S. dollars… without
 making deductions for depreciation of fabricated assets or for depletion
 and degradation of natural resources”. For all variables derived from
 averages, missing data was resolved via a method of average imputation
 whereby said missing data was replaced by the period average.
 Further variables are included in the dataset to test the
 robustness of results. These are:  measures of ethnic (column G in the
 dataset), linguistic (column H in the dataset) and religious (column I in
 the dataset) fractionalisation as defined by Alesina, et al (2003), a
 dummy variable (column S in the dataset), arising from the Polity IV
 (2019) dataset surrounding whether a nation experienced a regime
 transition or violent change during the sample period (Change), the log of
 nations’ population  in 1996-column T in the dataset- (Population), the
 sample period average of nations’ mortality rate-column U in the dataset
 (Mortality), government consumption as a percentage of GDP-column V in the
 dataset- (Government Consumption) and mineral rents as a percentage of
 GDP-column W in the dataset (Mineral Abundance). Additionally, legal
 origin dummies of Socialist, French, German and Scandinavian (columns Y-AB
 in the dataset) as defined by La Porta, et al (1999) where the omitted
 category is English (column X in the dataset) , as well as the regional
 dummies of  North America, Central and South America, Africa and Middle
 East and Asia and Oceania (columns AD-AG in the dataset) ,where the
 omitted category is Europe and Central Asia (column AC in the dataset),
 are included. Aside from the regional dummies, unless stated otherwise,
 all of the above variables are collected via World Bank
 datasets. Where any of the metrics' data is
 unavaible for a nation, the relevant cell is presented as
 "N/A"
description: To replicate this dataset, interested parties will need access to
 the following sources of data: 1) The World
 Bank 2) Polity IV 3) Rougier, E. and
 Combarnous, F., 2017. <i>The Diversity of Emerging Captialism in
 Developing Countries: Globalization, Institutional Convergence and
 Experimentation. </i>Cham: Palgrave Macmillan (p. 111).
 4) La Porta, R., Lopez-de-Silanes, F., Shleifer, A. and Vishny,
 R., 1999. The Quality of Government. <i>Journal of Law, Economics
 and Organization, </i>15(1), pp. 222-279 (pp. 268-276
 specifically). Additionally, it is noteworthy that in
 calculating averages, missing data was resolved via a method of average
 imputation whereby said missing data was replaced by the period
 average. To reanalyse the data, one can take the
 following steps: Create interaction terms
 between institutional typology variables and the Resource Abundance
 variable as well as interaction terms between institutional quality
 variables and the Resource Abundance variable. The latter of these acts as
 a control variable. This is
 essential to test this paper’s hypothesis that it is institutional
 typology’s interaction with resource abundance that impacts economic
 growth. Using the other
 control variables outlined in the above “Methods” section, create OLS
 multiple linear regression models of the following form: 
 <i>Y<sub>i</sub> =
 </i><i>β</i><sub><i>0</i></sub><i>+ </i><i>β</i><sub><i>1</i></sub><i>Q<sub>i </sub>+</i><i>β</i><sub><i>2</i></sub><i>W<sub>i</sub> + </i><i>β</i><sub><i>3</i></sub><i>X<sub>i </sub>+</i><i>β</i><sub><i>4</i></sub><i>Z<sub>i</sub></i><i> +</i><i>ε</i><i><sub>I </sub></i> Here, Y<sub>i</sub> is our dependent variable, Growth, Q<sub>i</sub> is a vector of our included institutional type variables, W<sub>i</sub> is a vector of our included institutional type variables’ interaction with Resource Abundance, X<sub>i</sub> is Resource Abundance, and Z<sub>i</sub> is a vector of our included controls. Researchers can vary the omitted institutional typology variable depending on differing institutional type comparison desires. To test the robustness of results, add to the regression, both individually and collectively, the variables outlined above in the “Methods” section intended for such purpose, paying attention to whether the independent variables of interest change sign, magnitude or statistical significance.