Dependent variable

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In this paper, there are four main financial instability variables (𝐶𝑅𝐼𝑆𝐼𝑆_!"): financial crisis indicator (𝐹𝐶_!"), a currency crisis indicator (𝐶𝐶_!"), a systematic banking crisis indicator (𝐵𝐶!") and a debt crisis indictor (𝐷𝐶!"). Each indicator was constructed as a binary measure where ‘1’ means that the crisis happened in a particular year in a country otherwise it is ‘0’. The crisis indicators are from Honohan and Laeven (2005) and Luc, Laeven Fabian Valendia’s (2010) databases, which were updated extending the data to include 2011. This covers systemic banking, debt and currency crisis episodes in the period 1970 –2005. For the period between 2003 and 2005, used are publications from the IMF, World Bank, Moody’s and Fitch Ratings, and the Financial Times information used to identify crisis episodes.

3.3.1.3 Dependent variable

In practice, various indicators of capital account restrictions are available across a wide cross-section of countries. As the discussion in Chapter 2 shows, the measures are divided into two main categories, qualitative and rules-based, although there has been some attempt to go beyond and on/off categorization by reflecting the intensity with which controls are imposed, following analyses as Lane and Milesi-Ferretti’s (2007), Chinn and Ito (2008).

To capture capital account openness, this chapter has utilized six measures, which represent different features of measures, e.g. on/off features and continuous- intensity features. In order to investigate if a Capital Account Liberalization event happened, binary indicators are employed as an on-off capital transaction index (𝑖𝑚𝑓_!,!) and the new measure proposed in Chapter 2 is based on Chinn and Ito’s (2008) index. These indicators are defined as “1” when liberalization of capital control exists or “0” when the country imposes restrictions on capital control.

An alternative is to use continuous measures of capital account liberalization. These measures assess the intensity of capital controls by the usage of information published in IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER) or

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by analysing the changes in economic variables such as, capital flows or changes in interest rates etc. The higher the value of measures the more liberalized this country is.

Chinn and Ito (2008) compiled data for 182 countries for the period 1970–2006 based on IMF’s AREAER database and created an intensity measure of financial openness. Chinn and Ito’s (2008) Intensity indicator ranges from -1.7 to +2.6 and, is focused on four categories for IMF AREAR such as, the existence of multiple exchange rates, requirement to surrender export proceeds, restrictions on current accounts and on capital transactions, which compute the significance of each of them ending up being a continuous variable. To obtain the comparison between intensive rule-based measures, it seems logical to use the new Capital Transaction measure which was proposed in Chapter 2.

Lane and Milesi-Ferretti (2007) established, on the contrary, a measure of capital control as the ratio of external capital stock to GDP. The authors estimated external assets and liabilities for 145 industrial and developing countries using international-investment position figures published by national central banks and governments. Also, Lane and Milesi-Ferretti defined, external assets and liabilities with respect to the type of capital flow. In order for that, the authors determined two indicators as

1) 𝐼𝐹𝐼𝐺𝐷𝑃_!,! is defined as the sum of foreign direct assets and foreign direct liabilities as a ratio to GDP.

2) 𝐺𝐸𝑂𝐺𝐷𝑃_!,! is defined as a ratio of the sum of foreign direct assets, foreign direct liabilities, foreign portfolio assets and foreign portfolio liabilities to GDP. The last measure, which acquires fluctuations in capital control regulation as changes in interest rate, is Chanda’s index. This index was compiled based on these two indices from the Economic Freedom of the World known as, i) freedom of citizens to own foreign currency bank accounts domestically and abroad and ii) difference between the Official Exchange Rate and the Black Market Rate, the data period used is between 1980 and 2005. Each empirical result for these models is presented as an outcome of benchmark and augmented specification estimations. The benchmark equation explains a fact if a country liberalized capital accounts as the result of changes in the set of control variables.

67 3.3.2 Descriptive Summary Statistics

In this research, the main interest is the relation between CAL, economic growth and financial instability. As described in section 3.2, CAL might have a direct positive effect on economic growth, or vice versa. In this section, we discuss the summary statistics and then present a descriptive analysis on financial crises frequencies, conditional and unconditional on Capital Account Liberalization.

Table 3.1 shows variable definitions, data sources and corresponding summary statistics.

The variables that are included in Table 3.1 are the ones, which were used in the actual econometrical estimation. The overall average value of on/off measures of Capital Account Liberalization is approximately 0.4 in the scale between ‘1’ and ‘0’. In the case of Milsesi-Ferretti’s indicators, there are some significant differences with respect to the average and standard deviation, suggesting that there is more volatility if the indicator included the value of portfolio capital flows. The average value of financial instability indicators for liberalized and non-liberalized countries was between 0.08 and 0.01, which meant that a probability of a crisis was even low for period between 1995 and 2005. The average value of the economic growth indicator, on the contrary, shows that one third of country-year observations experienced substantial economic growth periods. Moreover, the table below in the column summarizes the expected sign of coefficients for both estimation equations as described in section 3.3.

Table 3.2 presents the frequencies for on/off measures of the CAL process. Showing, that there is no significant variation over time for the dependent variables. A majority of changes in the CAL process happened as cross-country variations. In this case, it was sensible, to use linear probability estimation with implementation of a yearly fixed effect.

To investigate the changes in the capital control process, as there are only a few country switchers with respect to Capital Account Liberalization, We adapted a country-fixed effect in this estimation, which however might not allow for the observation of the impact of liberalization of capital flows, also any variations of a country switcher will be caught by

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the country fixed effect as a result of further economic analysis that will include a yearly dummy variable to capture variation over time.

67 Table 3.1: Variables description and Summary Statistics

Expected sign Variables Descriptions Statistics

Control Variables : Macroeconomic variables Obs Mean SD

(+/-) CA/GDP Lagged variable of Current Account as percentage of GDP. Source: Trade Sift WDI 979 -1.65 11.11

(+/-)

Economic growth indicator is defined as “1” is an economic growth or, alternatively, “0” is no economic growth. If (𝐺𝐷𝑃!,!− 𝐺𝐷𝑃!,!!!)

𝐺𝐷𝑃!,!!!> 5% then 𝐺𝑅𝑂𝑊𝑇𝐻!,! is 1 and vice

Constructed based 4A and 4B where each of the indices has a 50% weight. A range between 0-10. Source: Economic Freedom of the World 712 7.67 2.63

Capital

Transaction Index Sum of subcategories in “Capital Transaction” category of IMF’s AREAER. Source: IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER) 712 5.36 3.52

Note: ‘+’ positive impact on dependent variable, ‘-’- negative impact on dependent variables, ’new’ measures are in bold letters which was introduced in Chapter 2

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Table 3.2: Frequency of CAL indicators

Year

IMF index A new CAL index based Chinn and Ito's (2008) index

Liberalization Non-Liberalization Liberalization

Non-Liberalization Total

1995 33 56 . . 89

1996 40 49 . . 89

1997 39 50 . . 89

1998 37 52 40 49 89

1999 38 51 41 48 89

2000 38 51 41 48 89

2001 37 52 42 47 89

2002 38 51 41 48 89

2003 39 50 41 48 89

2004 38 51 43 46 89

2005 39 50 37 52 89

Note: Liberalization is a number of country liberalizing capital flow regulations, so on/off indicator is ‘1’ for this year. Non-Liberalization is a number of countries that did not liberalize capital flow regulations. Source:

IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER)

3.4. Empirical Methodology

The empirical method employed in this chapter to model the Capital Account Liberalization determinants, exploits Fixed Effect Linear Probability analysis and Fixed Effect Panel. As described in section 3.3.1 estimation variables are divided into three main groups: control variables, relevant independent variables: economic growth and financial instability measures and lastly, explanatory variables: as capital account liberalization measures for these models. As capital control measures are expressed in two forms, on/off measures and continuous intensive indexes, there are two estimation equations the Fixed Effect Linear Probability model equation and for the Fixed Effect Panel model equation.

To measure the impact of the binary outcome for the CAL variable on interested independent variables, a Fixed Effect Linear Probability model was used with a yearly fixed effect. Then, to analyse an effect of continue CAL measures on economic growth and