The EconDash is a set of interactive data visualizations created by the Frederick S. Pardee Center for International Futures. The purpose of this visualization is to allow the user to explore relevant indicators of financial and economic instability and resilience. The EconDash uses both monadic and dyadic data across time, and includes some forecasted variables from the International Futures (IFs) system. There is currently one public user interface available from EconDash, Trade Networks. A new economic vulnerability interface will be available by the fall of 2017.
- 1 EconDash: Trade Networks Interface
- 2 Defining the Variables
- 2.1 Dependent Variable:Types of Crises
- 2.2 Independent Variable: Drivers of Crises
- 3 Trade Data and Centrality Scores
EconDash: Trade Networks Interface
This dashboard focuses on trade networks from 1960 to 2014 and the centrality of countries in these networks. It also contains data on financial crises over this same time period. To access the dashboard click here.
The EconDash interface allows users to display and explore financial and economic crises and global trade networks along a variety of dimensions. Figure 1 is an main display page with its default settings. It provides definitions and instructions on each of the page's functions. On this page, the user can select the independent variables that determine: 1) the size of the bubbles that represent each country ("Select country size"); 2) the bubbles' color scheme ("Select country color"); 3) the network that is represented by the links (grey lines) between countries ("Select network"); and 4) the value threshold over which network links should be displayed for the selected network variable ("Show connections"). One can also select the year of the data that will be displayed ("Select year"). Currently, data is available from 1960 to 2014. In addition, the interface provides information about the selected independent variables in the textual display on the right and in graphs at the bottom of the screen. One can access country-specific information about mousing over each country bubble (see Figure 2). Information about the network variable for two countries (country dyads) can be viewed by mousing over the grey line linking them (see Figure 3). Generally, the most meaningful stories emerge when two or more of the selected variables represents the same category of information. For example, one could gain a better understanding of the world's energy trade networks and how they relate to economic sophisitaction (as measured by GDP per capita) by selecting "GDPPCP" for country size, "centrality score energy (percent)" for country color, "total energy trade" for network, and experimenting with connection thresholds.
Example Exploration: Financial Crises Across TimeSay you want to better understand the occurrence and movement of financial crises across time. Select "GDP at MER" for the country
- Between 1960 and 1980 financial crises were limited to the Global South
- After 1980, more countries in the Global North began to experience crisis, and the US had its first post-1960 crisis in 1988
- Crises occur in geographically contiguous country clusters relatively often (e.g. western South America 1981, Scandanavia 1991, Eastern Europe 1992, east and southeast Asia 1997 and 1998)
- When countries with large economies are involved in a crisis, it can affect a region and/or trading partners in subsequent years; this cascading effect can be seen in the map view and in the bar graph displayed at the bottom of the page (e.g. Asian financial crisis with Japan as the epicenter in 1996 and 1997 and the Global Financial Crisis with the US and UK as epicenters in 2007-2009)
Defining the Variables
The different categories of relevant indicators are listed below, with a justification for their inclusion in the EconDash visualization.
Dependent Variable:Types of Crises
The dependent variable is defined as an economic crisis that occurs as a result of strictly economic phenomena. This excludes economic instability resulting from political instability or natural disasters. Economic crises are classified according to the following IMF data.
The IMF Systemic Banking Crises Database was originally published in 2008 by Luc Laeven and Fabián Valencia, and updated in 2012. The IMF Systemic Banking Crises Database covers 431 crisis events are identified from 1970 to 2011, of which 134 are identified as systemic banking crises, 13 borderline systemic banking crises, 218 currency crises, and 66 sovereign debt crises. For the 147 systemic or borderline systemic banking crises, the database also track the mixture of policy responses to each of these systemic banking crises. The authors of the database classify each of the crisis events per the following criteria:
Financial crises are analyzed as binary variables from the IMF's banking crises database. They observe the occurence of any one of the following types of financial crises:
- Systemic Banking Crisis
- Currency Crisis
- Sovereign Debt Crisis
Systemic Banking Crisis
Systemic banking crises are contingent upon satisfying the following two conditions:
1) Significant signs of financial distress in the banking system (as indicated by significant bank runs, losses in the banking system, and/or bank liquidations)
2) Significant banking policy intervention measures in response to significant losses in the banking system. The first year that both conditions are satisfied is considered the onset year.
The second condition can be met when three of the following six policy intervention measures have been implemented:
- Extensive liquidity support- Liquidity support is extensive when the ratio of central bank claims on the financial sector to deposits and foreign liabilities exceeds five percent and more than doubles relative to its pre-crisis level. The authors also included any liquidity support extended directly from the treasury. But liquidity support to subsidiaries of foreign banks is not included in the ratio of the foreign country, only the domestic ratio.
- Bank restructuring gross costs- Bank restructuring costs are defined as gross fiscal outlays directed to the restructuring of the financial sector. The authors exclude liquidity assistance from the treasury captured by the first intervention to avoid potentially double counting. Bank restructuring costs are considered significant if they compose at least 3% of GDP
- Significant bank nationalizations- Significant nationalizations are takeovers by the government of systemically important financial institutions and include cases where the government takes a majority stake in the capital of those financial institutions.
- Significant guarantees put in place- Significant guarantee on bank liabilities indicate that either a full protection of liabilities has been issued or that guarantees have been extended to non-deposit liabilities of banks. However, policy interventions that only target the level of deposit insurance coverage are excluded.
- Significant asset purchases- Significant asset purchases from financial institution by the central bank or the treasury exceeding five percent of GDP.
- Deposit freezes and/or bank holidays
Outside of these criteria, a crisis can be deemed systemic if 1) a country’s banking system exhibits significant losses resulting in a share of nonperforming loans above 20 percent, or bank closures of at least 20 percent of banking system assets; or 2) fiscal restructuring costs of the banking sector are sufficiently high exceeding 5 percent of GDP
Currency crises occur when the national currency experiences a nominal depreciation of the currency against the U.S. dollar of at least 30 percent and is also at least 10 percentage points greater than the rate of depreciation in the year before. The authors use the bilateral dollar exchange rate from the World Economic Outlook database from the IMF. In cases where countries meet the currency criteria for several continuous years, the authors use the first year of each 5-year window to identify the crisis. Using this approach the authors identify 218 currency crises from 1970 to 2011, of which, 10 occur from 2008 to 2011.
Sovereign Debt Crisis and Debt Restructuring Years
Sovereign debt crises occur when countries default on their sovereign debt to private creditors. The authors identify 66 sovereign debt crises using data taken from a Beim and Calomiris 2001 paper, the World Bank, a Sturzenegger and Zettelmeyer 2006 paper, IMF staff reports, and reports from rating agencies. Similarly, the year of debt restructuring is the year a country restructures their debt. It is possible to have multiple crises and debt restructurings in a single year, see Greece 2012.
Independent Variable: Drivers of Crises
The independent variables in this dataset describe countries' internal economic conditions and their networked relationships, i.e. centrality scores. Table 1 lists each independent variable and provides its category, source, and definition.
All dyadic trade data comes from COMTRADE and UN Trade Statistics. CEPII cleans the COMTRADE data, so data has been pulled from there. CEPII does not have dyadic trade data for the services sector, so data from UN Trade Statistics has been blended with the CEPII data to get a complete trade balance. CEPii data
Table 1: Variable List
|Variable name||Source Institution(s)||Source Database(s)||Definition|
|GDP Growth Rate||IFs & International Monetary Fund (IMF)||IMF's World Economic Outlook (WEO)||GDP growth rate (percent), Market Exchange Rate (MER), 2011 constant prices|
|GDP at MER||IFs & IMF||WEO|| GDP at Market Exchange Rates (billion USD), 2011 constant prices|
|GDPPCP||IFs & IMF||WEO|| GDP per capita at Purchasing Power Parity (PPP) (thousand USD), 2011 constant prices|
|Centrality Score Ag||Pardee Center, United Nations Trade Statistics (UNTS) & CEPii|| UN Comtrade Database (Comtrade) & CEPii's BACI
||Centrality of country in the agricultural trade network when the network is calcuated in absolute terms (million USD)|
|Centrality Score Ag (Percent)|| Pardee Center, UNTS, & CEPii
||Comtrade & BACI||Centrality of country in the agricultural trade network when the network is calculated in relative terms (as a percent of GDP)|
| Centrality Score Energy
|| Pardee Center, UNTS, & CEPii
||Comtrade & BACI||Centrality of country in the energy trade network when the network is calcuated in absolute terms (million USD)|
|Centrality Score Energy (Percent)|| Pardee Center, UNTS, & CEPii
||Comtrade & BACI||Centrality of country in the energy trade network when the network is calculated in relative terms (as a percent of GDP)|
|Centrality Score ICT||Pardee Center, UNTS, & CEPii||Comtrade & BACI||Centrality of country in the ICT trade network when the network is calcuated in absolute terms (million USD)|
|Centrality Score ICT (Percent)|| Pardee Center, UNTS, & CEPii
||Comtrade & BACI||Centrality of country in the energy trade network when the network is calculated in relative terms (as a percent of GDP)|
|Credits to the non-financial sector||BIS||
||Outstanding amount of credit to the non-financial sector as a percent of nominal GDP. Credit includes credit provided by banks, all other sectors of the economy and non-residents. Credit covers core debt, defined as loans, debt securities, currency and deposits.|
|Debt service ratio (private sector)||BIS||
|| The Debt Service Ratio (private sector) is the ratio of interest payments plus amortizations to income. The DSR provides a flow-to-flow comparison of the flow of debt service payments to the flow of income.|
|Debt service ratio (households)||BIS||
|| The Debt Service Ratio (households) is the ratio of interest payments plus amortizations to income. The DSR provides a flow-to-flow comparison of the flow of debt service payments to the flow of income.|
|| Economic freedom indicator from the Fraser Institute on a scale of 1 to 10 (least free to most free). The indicator measures personal choice, voluntary exchange coordinated by markets, freedom to enter and compete in markets and protection of persons and their property from aggression from others.|
|Socio-Political Freedom Score||IFs||
|| Civil and political freedom score from Freedom House on a scale of 2 to 14 (higher is more democratic).|
|Capital Account Balance||IMF||
||The capital account data from the IMF shows credit and debit entries for nonproduced nonfinancial assets and capital transfers between residents and nonresidents. It records acquisitions and disposals of nonproduced nonfinancial assets, as well as capital transfers and the provision of resources for capital transfers.|
Size of Financial Sector Relative to Non-Financial Sector
The financial crisis of 2008 occurred at a time of vast deregulation. Flawed institutions and practices of the New Financial Architecture (NFA) and light government regulation are seen as the cause of the financial aspects of the crisis. These factors combined with rapid financial innovation and moral hazard resulting from periodic government bailouts contributed to creating conditions that led to the crisis. Rapid financial innovation manifested itself in the form of inflated financial markets relative to the real economy. This means that asset prices were highly overvalued, a sign that a crash was bound to occur at any moment. Debt to GDP rose from 22% in 1981 to 117% in 2008. Corporate profits rose from 10% to 40% in the financial sector in roughly the same period. Data on debt service ratio and credit to the non-financial sector reveal the size of the financial sector relative to the non-financial sector by highlighting the ratio between the value of the real economy and the value of financial markets .
Exchange rate data reveals currency value fluctuations by exposing a currency’s value relative to another. As a result, this data is helpful for revealing currency shocks. Depreciation of the Thai baht is believed to have led to the East Asian currency crisis. Some models have shown that a speculative attack resulting in one country devaluing their currency might threaten the competitiveness of a trading partner. The significance of this risk lies in the exchange rate regime that a country pursues and how deeply their economic system is integrated into the global economy. This is because certain exchange rate mechanisms can impede monetary policy attempts at stabilizing economic instability.
GDP growth rates are used to identify levels of economic growth due to the fact that dramatic changes in GDP are strong signifiers of economic health. For instance, a study of 77 countries, found that low GDP growth, high real interest rates, and high inflation strongly correlate with banking crises. This study reveals that a combination of periods of weak economic growth and loss of monetary control are large contributors to economic crises. They also find that banking fragility can result from real interest rate risk. This is associated with the idea that, during the 1980’s and 1990’s, more volatile interest rates may have contributed to banking crises. While low GDP growth can be a sign of economic downturn, increased risk can result from economic booms. At some point in the cycle risks materialize, reversing financial agents’ risk taking behavior, triggering deleveraging and, consequently, financial turmoil in the form of huge stocks of accumulated debt. 
BIS consumer prices are used to chart inflation due to the fact that changes in consumer prices are strong indicators for inflationary pressures. Real effective exchange rates are incorporated in order to identify inflation through relative currency values. Economic data from 20 countries was analyzed to find what drives economic crises. The study found that governments’ attempts at spurring economic growth through expanding the money supply, known as expansionary monetary policy, often result in currency crises. More specifically, these countries attempt monetary policies that cause high inflation and reserve losses in an attempt to try and remedy domestic economic problems such as unemployment.
The East Asia financial crisis is a significant example of financial systems gone haywire. Three key points relating to the dynamics between micro and macroeconomic integration factors contributed to vulnerability in the region. First, the policies used to mitigate excess demand pressures, resulting from heavy capital inflows, highlighted incentives for superfluous borrowing, and for the build-up of risky liabilities. Second, financial sector weakness combined with improper financial sector liberalization and inadequate regulation led to risky lending and poor management of balance sheet risk by financial intermediaries. Third, poor governance and false guarantees from corporates spurred speculative excessive borrowing and lending. The combination of these factors fomented financial and macroeconomic susceptibility to volatility. The capital account balance reveals how much how much capital countries spend and receive.
Current Account Deficit
Preceding the Mexican Peso crisis, the Mexican current account deficit rose to 8 percent of GDP and Mexico’s international reserves declined by two-thirds, resulting in a depreciated peso. After attempting, to no avail, to stabilize the peso through devaluation, the Mexican authorities left the peso to float freely, resulting in a diminished external value of the currency. Current account and balance of payments data are used to analyze countries’ current account deficits.
Levels of Economic Freedom
Financial liberalization is seen by some researchers as an instigator of financial fragility ). This is because financial liberalization allows banks to take on greater risk without suffering from the potential negative effects of risky, short term lending. Countries that have liberalized financial systems are more likely to experience banking crises. It is important to note, however, that such crises are less likely if liberalization coincides with sufficient regulation and institutions in place to guarantee adequate supervision. The Economic freedom index from Freedom House and socio-political freedom scores are used to calculate levels of economic freedom by showing how liberalized an economy is. Some factors that play into the index include access to capital, tax rates, and tariffs.
Trade Data and Centrality Scores
Eigenvector centrality is used to analyze centrality of trade data. Centrality is defined as:
- Reach- Ability of entity to reach other vertices
- Flow-Quantity/ weight of walks passing through entity
- Vitality- Effect of removing entity from network
- Feedback- A recursive function of alter centralities
Eigenvector centrality is defined as the centrality of each vertex being proportional to the sum of the centralities of its neighbor. The basic idea behind eigenvector centrality is that a central actor is connected to other central actors. In EconDash, eigenvector centrality is used to analyze centrality of a country in a trade network in a particular year. The following dyadic trade data is used to analyze bilateral trade levels between countries in the following sectors. Each sector is analyzed as percent of partner country's GDP as well as total intrasector trade in millions of US dollars:
- Information and Communication Technology
- Total Trade
- Systemic Banking Crises Database : An Update. (n.d.). Retrieved March 20, 2017, from https://www.imf.org/en/Publications/WP/Issues/2016/12/31/Systemic-Banking-Crises-Database-An-Update-26015fckLR
- Crotty, J. (2009). Structural causes of the global financial crisis: a critical assessment of the “new financial architecture.” Cambridge Journal of Economics, 33(4), 563–580
- Khan, H. (2004). Global Markets and Financial Crises in Asia Towards a Theory for the 21st Century. Basingstoke: Palgrave Macmillan
- Gerlach, S., & Smets, F. (1995). Contagious speculative attacks. European Journal of Political Economy, 11(1), 45-63
- Demirgüç-Kunt, A., & Detragiache, E. (2005). CROSS-COUNTRY EMPIRICAL STUDIES OF SYSTEMIC BANK DISTRESS: A SURVEY. National Institute Economic Review, (192), 68–83
- Benlialper, Ahmet, and Hasan Cömert. "Implicit Asymmetric Exchange Rate Peg under Inflation Targeting Regimes: The Case of Turkey." Cambridge Journal Of Economics 40.6 (2016): 1553-580. Web.fckLR
- Eichengreen, B., Rose, A. K., Wyplosz, C., Dumas, B., & Weber, A. (1995). Exchange Market Mayhem: The Antecedents and Aftermath of Speculative Attacks. Economic Policy, 10(21), 249–312.
- Alba, Pedro, Bhattacharya, Amar, Claessens, Stijn, Ghosh, Swati, & Hernandez, Leonardo. (1999). ‘The role of macroeconomic and financial sector linkages in East Asia’s financial crisis’, The Asian financial crisis: causes, contagion and consequences. Cambridge ; New York: Cambridge University Press.
- Truman, E. M. (1996). The Mexican peso crisis: Implications for international finance. Federal Reserve Bulletin; Washington, 82(3), 199.
- Stiglitz, J. (1993). THE ROLE OF THE STATE IN FINANCIAL-MARKETS. World Bank Economic Review, 19–52.
- Demirgüç-Kunt, A., & Detragiache, E. (1998). The Determinants of Banking Crises in Developing and Developed Countries. International Monetary Fund, Staff Papers; Washington, 45(1), 81–109.