*Corresponding Author DOLLARISATION AND MACROECONOMIC PERFORMANCE: AN EMPIRICAL INVESTIGATION FROM VIETNAM Thi Thu Hieu Nguyen Mien Trung University of Civil Engineering, Faculty of Economics and Construction Management, Vietnam nguyenthithuhieu@muce.edu.vn Do Thi Thanh Nhan* Ton Duc Thang University, Faculty of Finance and Banking, Ho Chi Minh City, Vietnam dothithanhnhan@tdtu.edu.vn Le Kieu Oanh Dao Banking of University of Ho Chi Minh City, Faculty of Banking, Vietnam oanhdlk@buh.edu.vn Quynh Nga Duong Ho Chi Minh City Open University, Faculty of Finance and Banking, Vietnam nga.dq@ou.edu.vn Abstract The paper examines the relationship between dollarisation and economic performance, focusing on the effects of dollarisation on macro variables for the Vietnamese economy. Using the Vector Error Correlation Model (VECM) model, the paper exhibits two key relationships: (1) the relation between the dollarisation of deposits and the monetary variables under the impact of ceiling policy of deposit interest rates, (2) the relation between the dollarisation of loans and economic growth and exports. The paper concludes by offering some recommendations for the control dollarisation in the economy. Key Words Dollarization; currency; international trade; macroeconomics. Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 2 INTRODUCTION Transition economies have attracted a considerable amount of foreign currency through various channels. These sources are essential resources in seeking to boost economic growth. However, these countries have thereby encountered a dollarised economy, which is the phenomenon of currency substitution. The attraction of dollarisation can reduce transaction costs and eliminate exchange rate risk (Dornbusch, 2001; Fischer, 1982; De Grauwe and Polan, 2000). Thereby promoting international trade and global economic integration (Baliño et al., 1999; Edwards, 2001; Gruben and McLeod, 2004); as well as controlling hyperinflation, and thereby mitigating against crises (Goldfajn et al., 2001; Beckerman and Cortés Douglas, 2002; Solimano, 2002; Pasara, 2020). Nevertheless, dollarisation leads to difficulties concerning the foreign exchange market for the central bank's regulatory procedure of money supply (Yinusa, 2008). Vietnam has had a history of using the US dollar parallel with the Vietnamese currency since the 1960s. In South Vietnam, the US dollar was widely stored and used, and, by contrast, in North Vietnam, the government banned foreign currencies under Decree 102/CP dated July 6, 1963. After the country's reunification in 1975, the Vietnamese economy went through a long period of difficulties and failures in domestic currency and monetary policies. The outcome was a loss in confidence in the Vietnam Dong, increased gold and foreign currency attractiveness, and complex control of dollarisation. The rate of foreign currency deposit out of M2 was officially announced in 1991 as 41.2% (without globalisation data), and from here, the issue of dollarisation became a concern of researchers Dodsworth (1996); Nguyen (2002); Hauskrecht and Nguyen (2004); Goujon (2006) and Watanabe (2006). The paper investigates the effects of dollarisation on the real economy with the economic variables of growth, employment, and volatility. According to its supporters, dollarisation will positively affect change through two channels: firstly, dollarisation will result in lower interest rates, higher investment, and faster growth (Dornbusch, 2001). Secondly, by eliminating currency risk, a common currency encourages international trade; this, in turn, results in more rapid growth. In contrast, following a view that goes back at least to Meade (1951), countries with a hard peg – including dollarised countries – will have difficulties accommodating external shocks. This, in turn, will be translated into greater volatility and may even lead to lower economic growth (Parrado and Velasco, 2002, Broda, 2001). At a general level, dollarisation has been presented to achieve credibility, growth, and prosperity. Following this view, countries that give up their currencies will be unable to engage in macroeconomic mismanagement, with the outcome that their public finances stay in balance and their external accounts move within reasonable bounds. Dollarisation-imposed Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 3 macroeconomic stability leads to lower interest rates, higher investment, and superior economic performance. Current arguments favoring dollarisation have gone beyond traditional discussions on optimal currency areas. Indeed, dollarisation proponents have recently argued that giving up the national currency is the right option for the vast majority – if not for all – of the emerging nations. Most researches on dollarisation in Vietnam focused on analysing and evaluating the status of dollarisation with common theories, including (i) replacing assets with assets in foreign currencies and (ii) replacing currency positions in various economic sectors, households, businesses, and commercial banks. The question then becomes: How to limit this phenomenon to an acceptable level while exploiting the positive effects of dollarisation? In Vietnam's socio-economy, the reality of dollarisation has increased due to the complicated developments of the past years. Therefore, a concern must be how this will impact the stability and economic growth in the integration process in the Vietnamese economy. Determining the relationship between dollarisation and macroeconomic indicators represents a necessary research direction aimed at optimizing the situation in the economy. The remains of the paper are organised as follows. Section 2 presents a literature review of the phenomenon of dollarisation. Section 3 presents an analysis of how dollarisation affects economic performance. Section 4 presents the results of our investigation, and Section 5 provides some concluding remarks. THEORETICAL AND LITERATURE REVIEW Literature review The issue of dollarisation has attracted many pieces of research on both the causes and impacts of foreign currency holdings and macroeconomic indicators and management policies. Edwards (2001), Edwards and Magendzo (2003) provide empirical evidence that dollarised economies have lower inflation rates, lower GDP growth rates, and more significant variation in output than economies using local currency. Nicolo et al. (2003) argue that dollarisation directs the financial system of developing countries in the condition of an inflationary economy. Reinhart et al. (2003) demonstrate that dollarisation can partly curb inflation and create currency imbalances in developing countries. Ize and Yeyati (2003) argue that the only way to limit dollarisation is to discourage the use of the dollar and increase the attractiveness of the local currency. Neanidis and Savva (2009) used monthly data for 11 transitional economies in Central and Eastern Europe (Armenia, Bulgaria, the Czech Republic, Estonia, Georgia, Kyrgyz, Lativia, Poland, Romania, Russia and Ukraine) to reveal the influence of the interest rate differential between local and foreign currencies. Kamin and Ericsson (2003) for Argentina, Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 4 Clements and Schwartz (1993) for Bolivia; and Mueller (1994) for Lebanon, provide empirical evidence that the need to hold foreign currency will be higher when inflation is high and prolonged. In addition, Menon (2007) states that for transition economies in Southeast Asia such as Cambodia, Laos and Vietnam, dollarisation is a "symptom" of macroeconomic instability, political instability, an underdeveloped monetary and financial system, and a lax legal system on foreign exchange management. Carranza et al. (2009), using data from 124 countries (including Vietnam), analyse empirically the exchange rate pass- through mechanism in economically affected economies and find that the higher the level of dollarisation, the greater the pass-through effect of exchange rate fluctuations on inflation. However, the focus of this study is the transmission mechanism of the exchange rate in dollarized economies. Musoke (2017) used a GARCH model for Tanzania, concluded that an increase in dollarisation leads to an increase in exchange rate volatility. Brown et al. (2018) use the inflation index (CPI) in 71 regions of Russia and apply the OLS method to examine the relationship between the inflation index and financial dollarisation. Then, the results confirm that higher inflation leads to an increase in deposit dollarisation and a reduction in loan dollarisation. Bannister et al. (2018) analyse panel data following a GMM method on 77 developing countries from 1996 to 2015 to examine the relationship between dollarisation and financial development and determine that dollarisation impedes financial development, leading to slow economic growth in developing countries. Recently, Tweneboah et al. (2019) examined the macro variables that determine the state of dollarisation in Ghana and, based on an ARDL model with a data set from January 2002 to March 2016, affirm that a low inflation rate and stable exchange rate lead to a reduction in the dollarisation. Edy-Ewoh and Binuyo (2019) provide empirical evidence with data series from 1972 to 2017 showing that dollarisation in Nigeria does not positively impact macroeconomic variables such as lending rates, inflation, unemployment, and GDP growth. Hauskrecht and Nguyen (2004) use a qualitative analysis method to evaluate the status of the dollarisation in Vietnam based on the ratio of foreign currency deposits to total deposits. The study shows that there are two main drivers of dollarisation in Vietnam: firstly, the loss of credibility of monetary policy due to a very high and unstable inflation rate in the long run, which combined with exchange rate decline, leads to an increase the riskiness of nominal assets in VND; secondly, the level of savings in the form of local currency assets is low and relatively short term. However, this study only assessed the dollarisation of Vietnam from 1988 to 2003. In this period, the Vietnamese economy was standing on the threshold of the World Trade Organization (WTO) and still not yet profoundly integrated into the world economy and even without sources of foreign currency transfers into the country. Goujon (2006) argues that the Vietnamese economy suffered from Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 5 dollarisation in 1991-1999 due to the necessity to control the exchange rate and money supply M2 to control inflation. Nevertheless, this study explains the relationship between inflation and exchange rate fluctuations and the M2 money supply in the economy suffering. It does not focus on the relationship between dollarisation and exchange rate instability. Also, this study shows that countries with a foreign exchange market ineffectively operate on a large scale. The tendency to suffer from dollarisation is higher. The research indicates the government makes an appreciation changes in Vietnam in implementing the Foreign Exchange Law in the period 1996-2005. Nguyen (2002), using the method of integrated research and analysis, synthesizes the dollarisation picture in Vietnam in the period 1991-2001, pointing out the main influences such as international trade and financial integration, ineffective coordination between exchange rate policy and interest rate policy. Most research in this area has been conducted before the WTO 2007 and the global financial crisis of 2008. Moreover, very few articles using econometric models determine the relationship between dollarisation and macroeconomic indicators. Thus, this paper finds out the impact of dollarisation on macro variables using econometric models. To date, most cross-country studies have been restricted to “independent currency unions” and have included very few observations on strictly dollarised countries. More comprehensively, this paper seeks to enlighten the impact of dollarisation on Vietnam’s economic performance. The relationship between dollarisation and macroeconomic indicators The relationship between deposit dollarisation and the macro indicators High inflation increased the interest rate because inflation reduces the purchasing power of the local currency. Then, Vietnamese tend to switch to gold or foreign currencies, this has been confirmed in the researches of Calvo, and Végh Gramont (1992), Clements and Schwartz (1993), Mueller (1994), Kamin and Ericsson (2003), Bahmani-Oskoee and Domac (2003), Yeyati (2006) found out. In contrast, Kurasawa and Marty (2007), Payne (2009), and Kim et al. (2004) argue that the dollarization leads to lower inflation. The reason is that in these countries with a history of instability currencies, they rely on a strong foreign currency with low inflation to control their inflation For deposit rate and exchange rate, Uncovered Interest Rate Parity (UIP) ( , r interest rate of the local currency, r * interest rate of the foreign currency, E spot exchange rate, ∆E expected change in the exchange rate) only occurs when two currencies have the same credit rating. In the case of countries with weak currencies, the trade balance is often in deficit, leading to abnormal exchange rate fluctuations, especially after exchange rate shocks. r r* E    Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 6 Thus, deposits dollarisation has a positive relationship with exchange rate fluctuations. This result has been proved by Girton and Roper (1981), Corrado (2008), Akçay et al. (1997), Bahmani-Oskoee, Domac (2003), Lay et al. (2012), Oomes (2003), Honohan (2007). In addition, Yeyati and Ize (2005) indicated a positive relationship between deposit dollarisation and exchange rate volatility in a stable environment when inflation is controlled at a low level for the developing countries. Regarding the interest rate, Oomes (2003) demonstrated that the current exchange rate is stable, the expected interest rate of the domestic currency may decrease, making foreign currencies more attractive. Bofinger et al. (2001), Vetlov (2001), Civcir (2005), Yeyati (2006), Kessy (2011), Lay et al. (2012) provided the evidence on this relationship, the result that the deposit dollarisation has a positive relationship with foreign currency interest rates and negative with domestic currency interest rates. For deposit dollarization and parallel market profit, Bahmani-Oskooee et al. (2002) presented the positive correlation between parallel market profit and depost dollarization in 27 developing countries. The result is similar to the studies from Reinhart and Rogoff (2004) and Bahmani-Oskooee and Tanku (2006). The relationship between loan dollarisation and macroeconomic indicators Regarding loan dollarisation and exchange rate, According to the law of interest rate parity, the foreign currency borrowers have to pay r*+∆E while local currency borrowers only cost r. Therefore, if the exchange rate fluctuates continuously, foreign currency borrowers will be at risk and vice versa. It can be said that the exchange rate is a factor that has a negative impact on the decision to borrow foreign currency. This is confirmed by the research result of Barajas and Morales (2003), Luca and Petrova (2008), Rosenberg and Tirpák (2008), Neanidis and Savva (2009), Steiner (2012). In addition, Basso et al. (2007) found that in the short run, loan dollarisation is more likely to cause exchange rate shocks than in the long run. Loan dollarisation and interest rate, the countries with weak currencies had higher domestic interest rate (r) than the foreign currency interest rate (r*). In this case, if the financial market is perfect, investors will borrow foreign currency, invest domestically to enjoy profits, thereby increasing loan dollarisation. Barajas and Morales (2003) show an important factor promoting the loans dollarisation is the difference in interest rates between domestic and foreign currencies. This is also the same finding of Basso et al. (2007, 2011), Rosenberg and Tirpak (2008), Brown and De Haas (2010). Loan dollarisation and deposit dollarisation have the same direction because the commercial banks must balance to avoid currency deviations to ensure liquidity and making profits from foreign currency trading. The relationship between loan dollarisation and deposit dollarisation is found a lot in the studies: Yeyati and Ize (2005), Basso et al. (2007), Brown et al. Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 7 (2011), Luca and Petrova (2008), Neanidis and Savva (2009), Rosenberg and Tirpák (2008), Steiner (2012). The connection between loan dollarisation and export, Dalgic's study (2018) provided evidence that most large firms with foreign currency revenues borrow in foreign currencies in emerging economies. Alp and Yalcin (2015) and Dalgic (2018) prove that foreign currency borrowing has a positive impact on the export growth of firms. METHODOLOGY VECM model The paper chooses the Vector Error Correlation Model (VECM) because all the variables included are macroeconomic indicators with time-series data that are often correlated. Furthermore, VECM is useful in studying the relationships from the previous period that have affected the demand for foreign currency holding of individuals. Moreover, many previous studies use the VECM model to measure the relationship between dollarisation and macroeconomic variables. Studies on deposit dollarisation are from Vetlov (2001), Civcir (2005), Kessy (2011), Mengesha and Holmes (2015), Krupkina and Ponomarenko (2017), Fabris and Vujanović (2017). The studies on loan dollarisation are Arteta (2005), Luca and Petrova (2008), Rosenberg and Tirpák (2008), Neanidis and Savva (2009), Zettelmeyer et al. (2010). Besides, VAR/VECM models are applied to solve the exogenous and endogenous variables. This method is suitable for available data series from general to specific econometric models, simple in use, and high reliability. The VECM (Vector Error Correlation Model) model proposed by Johansen and Juselius (1990) and Johansen (1995) is used in the case that the data series is non-stationary at the original order I(0), stops at the order difference I(1) and contains a cointegration relationship. In fact, VECM is a general form of VAR model, using Error Correlation Model (ECM) method. The overall regression equation for the time series Yt and Xt has the following form (t is time): 01    t t t Y X u  (1) and, thus, 01    t t t u Y X  (2) If Yt and Xt are time series that do not stop at the origin I(0) and stop at the first difference I(1), the remainder from (2) is also stationary. It contains r cointegration relationships, then the model VECM form: 0 1 1        t t t t Y X u     (3) Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 8 Here  is the first difference 1; α1 is the short-run effect that measures the direct effect when a change in Xt will change Yt; φ is the adjustment effect representing how much of the imbalance will be properly corrected; t  error; ut-1 is one-stage delay value of the error correction term (error correction term - ECT), 1 1 0 1 1       t t t u Y X  (β1 indicates the long- term effect of Xt on Yt). Methodology for deposit dollarisation Data Data for the study were collected monthly from January 2008 to December 2017 from reliable sources such as the State Bank of Vietnam (SBV), the International Monetary Fund (IMF), and the State Bank of Vietnam. The study selects this period because of the period (from 2008 to 2017) when Vietnam's economy is heavily affected after joining the WTO and the 2008 global financial crisis. Moreover, deposit dollarisation in Vietnam has decreased sharply since the State Bank applied the policy of ceiling deposit dollarisation. The selected variables are as follows: - Deposit Dollarisation (DDI): the two general indicators that researchers used to measure the deposit dollarisation status in the economy are: the rate of deposits in foreign currencies in total deposits (DDI) and the rate of deposits in foreign currency in money supply (M2 DDI). The study uses the ratio of deposits in foreign currencies to total deposits (DDI). In the Vietnamese economy, the number of foreign currencies is statistically recorded in the form of deposits in the commercial banking system. Besides that, the foreign currency also exits considerably as the cash holding, but for which there have no accurate statistics for measuring M2 DDI. - Inflation: is measured by the consumer price index, CPI. The higher the inflation rate, the more the devaluation of the domestic currency, and the more the tendency to change to holding foreign currencies. - USD/VND exchange rate (ER): reflecting the increase or decrease in the value of VND against the USD, is a signaling tool to regulate the exchange rate policy and monetary policy of the SBV. The exchange rate used in the model is the official rate (from January 1, 2016, the central rate) announced by the SBV. - Deposit interest rate VND (R_VND) and deposit interest rate USD (R_USD) are two crucial variables measuring the return when holding VND or USD, showing the attractiveness of that type of asset in the investment portfolio. These are two variables that directly affect the deposit dollarization status. - Parallel market profit (PERF): is the percentage difference between the selling rate of USD/VND on the free market (ER F) and the official bank (ERC selling rate of commercial banks). Unofficial payments coexist with the authorized bank with higher regular exchange rates. Therefore, this is a factor affecting people's decisions to hold foreign currency: Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 9 FC C ER ER PERT 100% ER   - Foreign Exchange Reserve (RES): reflects the government's ability to intervene to keep the foreign exchange market stable and are a measure of public confidence in macroeconomic stability and the value of the VND. - The distance between the ceiling rate of the interest rate (DIF_CE): is the difference between the ceiling deposit of the interest rate of the domestic currency ( ce VND R ) and the ceiling deposit of the interest rate of the foreign currency ( ce USD R ). This variable reflects the limit of nominal profit of domestic currency against foreign currency. It simultaneously transmits a signal to regulate the monetary policy of the SBV in a certain period. ce VND R applied by the SBV from April 2011, thus, in the period from January, 2008 to March, 2011, the study uses the US dollar deposit interest rate with a term of less than 6 months by commercial banks as the ceiling deposit for foreign currency interest rates: ce ce VND USD DIF_CE R R  The variables of deposit dollarisation (DDI), foreign exchange reserves (RESt), the exchange rate (ERt), and inflation (CPI t) are trend variables without standard division, for which the right deviation is very high. The study converts to the natural logarithmic base to reduce the right-skew and approximate a normal distribution. Model for deposit dollarization The State Bank has applied the policy of ceiling interest rate since 2008 until now; divided into 2 phases: from January 2008 to March 2011 – using the ceiling of VND; from April 2011 to present – applied on both VND and USD. The purpose of the article is to find out the relationship between dollarization and macro variables in-ceiling deposit interest rate policy. Therefore, the study selects the data series starting from January 1/. 2008 to December 2017 and divided model (4) into 2 phases to assess the role of the operating mechanism with a ceiling of VND (from January 2008 to March 2011 – referred to as phases 1) compared with the working mechanism of ceilings on both VND and USD interest rates (from April 2011 to present - referred as phases 2). The VECM model is used to determine the relationship between the deposit dollarization and macroeconomic variables: Y t = [DDI t , RES t , DIF_CE t , PERF t , R_USD t , R_VND t , ER t , CPI t ] (4) Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 10 Methodology for loan dollarization Data The SBV and the IMF publish the official data on foreign currency loans by year (from 2015 to 2017, IFM statistics every six months). Therefore, the research period has been extended from 1992 to 2017. The data is collected from the SBV and the IMF to get a larger sample. In addition, in 1992 Vietnam officially opened the economy and entered the world economy. The selected variables are defined as follows: - Loan dollarisation (LDI): is the rate of credit in foreign currency (FCL) in the total credit of commercial banks (TL) - Deposit Dollarisation (DDI): is the rate of deposits in foreign currencies (FCD) in the total deposits of commercial banks (TD) - Payable cost difference (IRD): is the difference in the cost of paying when borrowing VND versus borrowing USD. This ratio is calculated as follows: vnd usd IRD LSCV LSCV ER     vnd LSCV is a short-term lending rate of commercial banks usd LSCV : Short-term USD lending interest rate of commercial banks ER  : USD / VND exchange rate fluctuations of the SBV: t t 1 t1 ER ER ER 100 ER       - GDP economic growth: GDP growth index (%) - Export (EX): USD Export Price Indexes are calculated in U.S. dollar terms The variables take a natural logarithm (except for the variable IRDt because this variable has a negative number period) to ensure stability. Model for loan dollarisation Previous studies using the VECM model find a relationship between loan volatility status and macroeconomic variables (Arteta, 2005; Luca and Petrova, 2008; Rosenberg and Tirpák, 2008; Neanidis and Savva, 2009; Zettelmeyer et al., 2010). Therefore, the model is applied to determine the relationship between the status of loan dollarization and macroeconomic variables: Yt = [LDIt, GDPt, DDIt, EXt, IRDt] (5) Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 11 FINDINGS In this part, we used the VECM estimation model (4) and (5) with the following sequence (model (4) estimation in two stages): (1) Checking the VECM estimation conditions, including (i) Testing the stationarity of the data series, (ii) Choosing the optimal delay based on the reduced VAR model estimation results, (iii) Testing the optimal lagged cointegration relationship by Johanson method, (iv) (1) Test to remove the variable; (2) Estimating the VECM model; (3) The residual test of VECM includes: (i) Normal distribution of residuals, (ii) Series correlation of residuals, (iii) Overall stability of the model to ensure reliable estimation results; (4) Analysis of VECM estimation results. Deposit dollarization Verify the VECM estimation conditions Stationary test We use Augmented Dickey Fuller (ADF for one unit root and Phillips - Person (PP)) method for a unit root to detect the stationarity in time series data. The results of the Table 1 test show that all variables do not stop at the original order I(0), but all stop at the first difference I(1) with the significance level of 1% and 5%. Table 1: Results of detecting the stationarity and variance of DDI model data Variable t-statistic Variable t-statistic Stage 1 Stage 2 Stage 1 Stage 2 ADF test PP test ADF test PP test ADF test PP test ADF test PP test LDDIt 0.3511 -0.1413 -2.6336 -2.6336 D(LDDIt) - 3.0673*** - 3.2267*** - 7.3921*** - 7.3101*** LRESt 0.1839 0.1347 -1.4244 -1.8232 D(LRESt) - 5.6380*** - 5.6356*** - 5.3209*** - 5.3354*** DIF_CEt -2.2400 -1.8110 -1.9979 -1.8970 D(DIF_CEt) -3.4221** - 4.4884*** - 6.4011*** - 6.5780*** PERFt -1.5683 -1.4601 - 3.4367* - 3.5252* D(PERFt) - 6.2268*** - 7.1712*** - 9.3335*** - 9.3635*** R_USDt -0.7487 -0.4014 -1.9402 -1.9582 D(R_USDt) - 4.6209*** - 4.5778*** - 9.3220*** - 9.3220*** R_VNDt -0.4546 -0.0387 -0.7678 -1.0767 D(R_VNDt) - 3.2798*** - 3.2876*** - 6.3666*** - 6.4520*** LERt 0.5685 1.2015 0.7874 0.6475 D(LERt) - 6.6845*** - 6.7349*** - 7.4985*** - 7.4844*** LCPIt 0.2996 -1.2903 -0.6122 -1.1598 D(LCPIt) -3.0557** -3.0063** - 6.7185*** - 12.099*** *, **, *** denote rejection of null hypothesis at the 10%, 5% and 1% level of significance. Source: Own survey. Lag Determination The optimal model lag is selected according to SC standards and based on a consideration of model stability. Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 12 Table 2: Lag Determination of DDI model Lag LogL LR FPE AIC SC HQ Stage 1 0 -30.37291 NA 1.10e-09 2.074211 2.422518 2.197006 1 240.1156 409.3880 1.69e-14 -9.087329 -5.952570* -7.982179 2 346.0451 114.5184* 2.93e-15* -11.35379* -5.432579 -9.266286* Stage 2 0 372.1353 NA 9.52e-15 -9.582507 -9.337167 -9.484457 1 1059.037 1211.116 7.30e-22* -25.97466 -23.76659* -25.09221* 2 1120.177 94.92775 8.30e-22 -25.89939 -21.72860 -24.23254 Source: Own survey. Cointegration test By Johansen's method, the selected research results are passed by both Trace and Maximum Eigenvalue tests: at least 3 cointegrating equations are shown in Table 3. Table 3: Cointegration test results of DDI model Stage 1 Unrestricted Cointegration Rank Test (Trace) Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.885037 324.3050 187.4701 0.0000 At most 1 * 0.848635 244.2686 150.5585 0.0000 At most 2 * 0.840989 174.4104 117.7082 0.0000 At most 3 * 0.604208 106.3754 88.80380 0.0015 At most 4 * 0.546829 72.08132 63.87610 0.0087 At most 5 0.397267 42.79638 42.91525 0.0514 At most 6 0.370121 24.06396 25.87211 0.0826 At most 7 0.171510 6.961560 12.51798 0.3486 Trace test indicates 5 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Hypothesized Max- Eigen 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.885037 80.03645 56.70519 0.0001 At most 1 * 0.848635 69.85823 50.59985 0.0002 At most 2 * 0.840989 68.03498 44.49720 0.0000 At most 3 0.604208 34.29407 38.33101 0.1355 At most 4 0.546829 29.28494 32.11832 0.1068 At most 5 0.397267 18.73241 25.82321 0.3236 At most 6 0.370121 17.10240 19.38704 0.1041 At most 7 0.171510 6.961560 12.51798 0.3486 Max-eigenvalue test indicates 3 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Stage 2 Unrestricted Cointegration Rank Test (Trace) Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.770508 327.0224 187.4701 0.0000 At most 1 * 0.498580 212.2151 150.5585 0.0000 At most 2 * 0.478208 158.3709 117.7082 0.0000 At most 3 * 0.386365 107.6329 88.80380 0.0012 At most 4 * 0.295872 69.54120 63.87610 0.0155 Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Hypothesized Max-Eigen 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.770508 114.8073 56.70519 0.0000 At most 1 * 0.498580 53.84424 50.59985 0.0223 At most 2 * 0.478208 50.73797 44.49720 0.0093 At most 3 0.386365 38.09170 38.33101 0.0532 At most 4 0.295872 27.36199 32.11832 0.1707 Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 13 At most 5 0.203023 42.17920 42.91525 0.0591 At most 6 0.178074 24.47874 25.87211 0.0738 At most 7 0.111059 9.182530 12.51798 0.1694 Trace test indicates 5 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values At most 5 0.203023 17.70046 25.82321 0.4005 At most 6 0.178074 15.29621 19.38704 0.1781 At most 7 0.111059 9.182530 12.51798 0.1694 Max-eigenvalue test indicates 3 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Source: Own survey. Variable elimination test Variables rejection tests have no long-run impact With three cointegration equations with long-run relationship coefficient matrix β, the study examines whether each variable has a long-run relationship according to the following model hypotheses: The hypothesis H0 is rejected if the statistic 𝑋 𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐 2 > 𝑋 𝑐𝑟𝑖𝑡𝑖𝑐𝑎𝑙 2 with the degree of freedom = 3 (number of cointegrating equations) or the p-value is < 5%. The testing results of each of the above hypotheses are summarized as follows. Table 4: The results of the test of variable elimination have no long-run impact on DDI model LDDI LRES DIF_CE PERF R_USD R_VND LER LCPI Stage1 Chi- square(3) 13.09*** 13.14*** 41.71*** 37.13*** 25.63*** 37.06*** 29.04*** 18.66*** Probability 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Stage 2 Chi- square(3) 68.33*** 22.37*** 18.09*** 8.71** 15.62*** 14.71*** 13.67*** 7.84** Probability 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.04 *, **, *** denote rejection of null hypothesis at the 10%, 5% and 1% level of significance X critical 2 with degree freedom 3 at the 10%, 5% and 1%: 6.251, 7.815, 11.345 Source: Own survey. The long-run variable rejection test results in Table 4 reveal that no variable was removed in the long-run relationship at the 5% significance level. Variable elimination test has no short-run impact                                                              LFCD 0 11 12 13 LRES 0 21 22 23 DIF_CE 0 31 32 33 PERF 0 41 42 43 R_USD 0 51 52 53 R_VND 0 61 62 63 LER 0 71 72 73 LCPI 0 81 82 83 H : 0 H : 0 H : 0 H : 0 H : 0 H : 0 H : 0 H : 0 Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 14 Although no variables are removed in the long-run relationship, the findings cannot conclude that there is a short-run effect on the correction to the long-run equilibrium after each stroke. Therefore, determining which variable has no short-term impact or has a very weak effect on the long-run balance in each period is essential to assess the relationship between deposit dollarization status and the macro variables. The study can then compare the impact of the policy of two ceiling interest rates: how to change the position of deposit dollarization compared to the policy of one ceiling interest rate. From the short-term relationship coefficient matrix, α is obtained in the test of three cointegration equations, and the study examines the elimination of variables with no short-term effects established for each specific variable: Similar to the long-run variable rejection test, the hypothesis H0 will be rejected if the statistic 𝑋 𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐 2 > 𝑋 𝑐𝑟𝑖𝑡𝑖𝑐𝑎𝑙 2 with the degree of freedom = 3 or the p-value is < 5%. The inspection results are summarized as follows. Table 5: The results of the test of variable elimination have no short-run impact on DDI model LDDI LRES DIF_CE PERF R_USD R_VND LER LCPI Satge 1 Chi- square(3) 6.28* 9.63** 5.19 27.53*** 10.84** 21.62*** 19.94*** 7.69* Probability 0.09 0.021 0.15 0.00 0.01 0.00 0.00 0.05 Stage 2 Chi- square(3) 12.23*** 11.73*** 25.25*** 7.63* 74.16*** 8.61** 8.53** 11.04** Probability 0.00 0.00 0.00 0.05 0.00 0.03 0.03 0.01 *, **, *** denote rejection of null hypothesis at the 10%, 5%, and 1% level of significance X critical 2 with degree freedom 3 at the 10%, 5% and 1%: 6.251, 7.815, 11.345 Source: Own survey. Looking at Table 5, it is clear that, in both periods, the variables are statistically significant at 10%, except for the variable DIF_CE in phase 1 (𝑋 𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐 2 = 5.19 < 𝑋 𝑐𝑟𝑖𝑡𝑖𝑐𝑎𝑙 2 = 6.251). This result shows that, in the short-run, deposit rate ceiling gap in the phase 1 does not affect DDI; in other words, the deposit dollarization is affected by the policy of two ceiling interest rates (VND and USD) more substantial than the policy of one ceiling interest rate in VND. VECM Estimation                                      LFCD 0 11 12 13 LRES 0 21 22 23 DIF_CE 0 31 32 33 PERF 0 41 42 43 R_USD 0 51 52 53 R_VND 0 61 62 63 LER 0 71 72 73 LCPI 0 81 82 83 H : 0 H : 0 H : 0 H : 0 H : 0 H : 0 H : 0 H : 0                         Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 15 Model (4) has met the conditions for VECM estimation, and the study has estimated and obtained the results of regression, pulse response function, and variance decomposition, precisely: Regression Results Table 6 and Table 7 shows only the regression results that are statistically significant in phase 1 and phase 2. Table 6: Vector Error Correction Estimates of DDI model (Phase 1) Vector Error Correction Estimates Sample (adjusted): 2008M03 2011M03 Included observations: 37 after adjustments Standard errors in ( ) and t-statistics in [ ] Cointegrating Eq: CointEq1 CointEq2 CointEq3 LDDI(-1) 1.000000 0.000000 0.000000 LRES(-1) 0.000000 1.000000 0.000000 DIF_CE(-1) 0.000000 0.000000 1.000000 PERF(-1) 0.031611*** -0.089233*** 0.671988*** (0.00916) (0.01165) (0.11433) [ 3.45040] [-7.65844] [ 5.87780] R_USD(-1) 0.234022*** -0.268941*** 3.273722*** (0.03358) (0.04271) (0.41906) [ 6.96891] [-6.29716] [ 7.81214] R_VND(-1) -0.156490*** 0.183494*** -2.344740*** (0.01734) (0.02206) (0.21644) [-9.02248] [ 8.31840] [-10.8331] LER(-1) -9.173265*** 4.380193* (1.80960) (2.30146) [-5.06922] [ 1.90323] LCPI(-1) 3.296831** (1.49535) [ 2.20472] @TREND(08 M01) 0.040389*** 0.368934*** (0.01081) (0.13495) [ 3.73474] [ 2.73380] C 71.35708 -42.09609 299.2841 Error Correction: D(LDDI) D(LRES) D(DIF_CE) D(PERF) D(R_USD) D(R_VND) D(LER) D(LCPI) CointEq1 -0.177649* 24.73035*** 0.114329*** (0.09394) (5.49243) (0.03565) Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 16 [-1.89111] [ 4.50263] [ 3.20687] CointEq2 29.11585*** -2.319055** -0.051762* -0.036239** (4.57528) (0.98769) (0.02970) (0.01524) [ 6.36373] [-2.34797] [-1.74294] [-2.37790] CointEq3 -0.013339** -0.550901** 1.036653** -0.226107** -0.012645*** (0.00586) (0.25298) (0.44735) (0.09657) (0.00290) [-2.27596] [-2.17761] [ 2.31733] [-2.34136] [-4.35479] D(LDDI(-1)) 0.395730* 0.255354** (0.22907) (0.11349) [ 1.72756] [ 2.25001] D(LRES(-1)) 0.169126* (0.08024) [ 2.10763] D(DIF_CE(- 1)) -0.013706* 0.838459* 0.249764** 0.480361** * (0.00754) (0.44067) (0.09513) (0.16325) [-1.81852] [ 1.90268] [ 2.62551] [ 2.94243] D(PERF(-1)) 0.281166* -0.097608* 0.002184** (0.15147) (0.05611) (0.00098) [ 1.85630] [-1.73951] [ 2.22106] D(R_USD(-1)) 2.113304** (0.96247) [ 2.19571] D(R_VND(-1)) -0.011327* 1.214964** -0.012229*** (0.00673) (0.51340) (0.00333) [-1.69401] [ 2.36652] [-3.66956] D(LER(-1)) 0.716516** -1.341521** 11.89107* (0.39646) (0.51756) (6.53256) [ 1.80728] [-2.59199] [ 1.82028] D(LCPI(-1)) 154.5411*** 0.481218*** (49.6965) (0.16553) [ 3.10970] [ 2.90706] C -2.597500 0.005930 (0.75132) (0.00250) [-3.45725] [ 2.36940] R-squared 0.532933 0.376597 0.442661 0.767438 0.499118 0.755667 0.609222 0.674718 Adj. R- squared 0.327424 0.102299 0.197431 0.665111 0.278729 0.648160 0.437279 0.531593 Sum sq. resids 0.019280 0.032857 35.92181 112.3211 5.234356 15.41532 0.004732 0.001246 S.E. equation 0.027770 0.036253 1.198696 2.119633 0.457574 0.785247 0.013759 0.007060 F-statistic 2.593233 1.372950 1.805088 7.499832 2.264720 7.029032 3.543173 4.714209 Log likelihood 87.35243 77.48976 -51.95362 -73.04394 -16.32076 -36.30293 113.3377 138.0229 Akaike AIC -4.073104 -3.539987 3.456952 4.596969 1.530852 2.610969 -5.477713 -6.812051 Schwarz SC -3.550644 -3.017527 3.979412 5.119429 2.053312 3.133429 -4.955253 -6.289591 Mean dependent 0.000620 -0.021189 0.070270 -0.043987 -0.016216 0.136054 0.006880 0.009631 Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 17 S.D. dependent 0.033862 0.038263 1.338036 3.662772 0.538781 1.323835 0.018341 0.010316 Determinant resid covariance (dof adj.) 1.38E-16 Determinant resid covariance 5.99E-18 Log likelihood 313.6281 Akaike information criterion -10.30422 Schwarz criterion -4.949007 Value [ ] is t-statistical; (***),(**), (*) statistical significance level 1%, 5% and 10% Source: Own survey. Table 7: Vector Error Correction Estimates of DDI model (Phase 2) Vector Error Correction Estimates Sample (adjusted): 2011M07 2017M12 Included observations: 78 after adjustments Standard errors in ( ) & t-statistics in [ ] Cointegrating Eq: CointEq1 CointEq2 CointEq3 LDDI(-1) 1.000000 0.000000 0.000000 LRES(-1) 0.000000 1.000000 0.000000 DIF_CE(-1) 0.000000 0.000000 1.000000 PERF(-1) 0.160766*** -0.302652*** (0.02896) (0.08425) [ 5.55116] [-3.59237] R_USD(-1) 0.148998*** 0.762172*** (0.01773) (0.25674) [ 8.40328] [ 2.96864] R_VND(-1) -0.007682* -0.863359*** (0.00443) (0.06421) [-1.73238] [-13.4456] LER(-1) -5.576675*** (1.37062) [-4.06872] LCPI(-1) -9.013048*** 17.08450** (2.44878) (7.12364) [-3.68064] [ 2.39828] @TREND(11M04) 0.021382*** (0.00308) [ 6.95351] C 56.09303 83.74925 -102.1323 Error Correction: D(LDDI) D(LRES) D(DIF_CE) D(PERF) D(R_USD) D(R_VND) D(LER) D(LCPI) CointEq1 -0.230712*** 5.193282*** 4.094139* - 3.738149*** 1.936894* -0.013169* (0.07866) (1.03380) (2.42166) (0.31305) (1.01701) (0.00775) Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 18 [-2.93292] [ 5.02351] [ 1.69064] [-11.9412] [ 1.90450] [-1.69820] CointEq2 -0.307505*** 0.812886*** 0.012740*** -0.014659** (0.07223) (0.15096) (0.00374) (0.00631) [-4.25740] [ 5.38493] [ 3.40712] [-2.32262] CointEq3 -0.082100*** 0.405961*** 0.004417*** - 0.009608*** (0.02648) (0.05533) (0.00137) (0.00231) [-3.10095] [ 7.33660] [ 3.22246] [-4.15277] D(LDDI(-1)) 3.284083*** 3.902471** (0.57472) (1.86712) [ 5.71424] [ 2.09010] D(LDDI(-2)) -3.613014** 1.964383*** (1.72592) (0.52263) [-2.09338] [ 3.75863] D(LRES(-1)) -0.194637** 0.322399** 0.039660*** (0.07418) (0.14126) (0.01234) [-2.62373] [ 2.28238] [ 3.21303] D(LRES(-2)) -0.133764* -1.908130* 4.041740* - 2.351661** (0.07485) (0.98368) (2.30426) (0.96771) [-1.78710] [-1.93978] [ 1.75403] [-2.43013] D(DIF_CE(-1)) -0.183708** 0.007051* (0.08752) (0.00366) [-2.09908] [ 1.92679] D(DIF_CE(-2)) 0.006613** (0.00328) [ 2.01759] D(PERF(-1)) 0.010207** 0.015068* -0.043134** (0.00463) (0.00882) (0.01843) [ 2.20367] [ 1.70844] [-2.34000] D(PERF(-2)) -0.032818* (0.01820) [-1.80369] D(R_USD(-2)) -0.684504** - 2.091646** - 0.686095** (0.34177) (0.80060) (0.33623) [-2.00279] [-2.61259] [-2.04058] D(R_VND(-1)) 0.226740*** (0.08110) [ 2.79574] D(LER(-1)) - 37.85836*** (5.81301) [-6.51270] D(LER(-2)) - 22.96778*** Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 19 (6.11833) [-3.75393] D(LCPI(-2)) -4.903740* - 0.357499*** (2.60436) (0.10889) [-1.88290] [-3.28311] C -0.187825** - 0.479907** 0.113634*** 0.001586*** 0.003542*** (0.07981) (0.18696) (0.02417) (0.00060) (0.00101) [-2.35334] [-2.56691] [ 4.70180] [ 2.64879] [ 3.50561] R-squared 0.477786 0.540782 0.557150 0.434953 0.771341 0.482058 0.312866 0.530941 Adj. R-squared 0.306716 0.390349 0.412078 0.249852 0.696436 0.312387 0.087771 0.377284 Sum sq. resids 0.027210 0.098657 4.699606 25.78793 0.430934 4.548243 0.000264 0.000753 S.E. equation 0.021660 0.041243 0.284654 0.666798 0.086197 0.280032 0.002135 0.003604 F-statistic 2.792923 3.594830 3.840515 2.349808 10.29753 2.841136 1.389926 3.455358 Log likelihood 199.7967 149.5626 -1.117232 -67.51192 92.06467 0.159538 380.5151 339.6834 Akaike AIC -4.610172 -3.322118 0.541467 2.243895 -1.847812 0.508730 -9.243977 -8.197009 Schwarz SC -4.005887 -2.717834 1.145752 2.848180 -1.243528 1.113014 -8.639693 -7.592725 Mean dependent -0.008472 0.015263 -0.070513 0.011014 -0.038462 -0.118256 0.001082 0.003544 S.D. dependent 0.026013 0.052821 0.371242 0.769876 0.156447 0.337704 0.002236 0.004567 Determinant resid covariance (dof adj.) 1.19E-22 Determinant resid covariance 1.11E-23 Log likelihood 1175.873 Akaike information criterion -25.35571 Schwarz criterion -19.70565 Value [ ] is t-statistical; (***),(**), (*) statistical significance level 1%, 5% and 10% Source: Own survey. Results of decomposition of variance The details of the decomposition of variance result are presented in Table 8. Table 8: Decomposition of variance of DDI Period S.E. LDDI LRES DIF_CE PERF R_USD R_VND LER LCPI Stage 1 1 0.027770 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 6 0.125164 76.24983 0.977780 0.814130 8.265729 0.895367 1.903191 0.495861 10.39811 12 0.194637 57.70258 4.680728 0.397342 16.58884 3.265310 4.381352 0.339740 12.64411 18 0.236380 55.86788 5.136346 0.374577 17.33024 3.501780 4.763238 0.277985 12.74795 24 0.273699 54.76843 5.331833 0.347594 17.81935 3.621221 4.940965 0.256544 12.91406 Stage 2 1 0.021660 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 6 0.052690 64.03969 13.03996 6.742469 4.791721 1.580599 3.805531 4.944178 1.055848 12 0.076284 58.08687 8.468995 12.01313 7.053852 0.833253 8.377084 3.550828 1.615985 18 0.098187 54.48493 7.178807 18.11458 5.079157 0.505543 10.15376 2.623592 1.859640 Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 20 24 0.118687 51.15597 6.100142 22.88908 3.850554 0.378118 11.52954 2.033982 2.062618 Source: Own survey. Impulse response function results Impulse response function results of deposits dollarization before shock 1% of variables as Figure 1. Figure 1: Response of deposits dollarization before shock 1% of variables Phase 1 Phase 2 Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 21 Source: Own survey. VECM residue test The VECM's residual verification was tested. The Portmanteau and LM test results show that the residue of the VECM has no auto-correlation. The White test indicates that there is no variance of variance and that the characteristic solutions are in a single circle indicating that the model is stable (details in Table 9). Table 9: VECM residual tests of DDI model Stage 1 Stage 2 VEC Residual Portmanteau Tests for Autocorrelations Lags Q-Stat Prob. Adj Q-Stat Prob. df 1 45.50295 NA* 46.76692 NA* NA* 2 98.22657 0.7160 102.5033 0.6049 107 3 146.6941 0.9108 155.2474 0.8004 171 4 209.1341 0.8866 225.2559 0.6646 235 5 248.9899 0.9840 271.3392 0.8730 299 6 303.7962 0.9894 336.7532 0.8349 363 7 365.8395 0.9853 413.2732 0.6744 427 8 429.9724 0.9779 495.0980 0.4397 491 9 478.9580 0.9912 559.8289 0.4347 555 10 524.2777 0.9976 621.9337 0.4593 619 11 572.5260 0.9992 690.5947 0.4119 683 12 619.3715 0.9998 759.9260 0.3633 747 *The test is valid only for lags larger than the VAR lag order. df is degrees of freedom for (approximate) VEC Residual Portmanteau Tests for Autocorrelations Lags Q-Stat Prob. Adj Q-Stat Prob. df 1 19.86510 NA* 20.12309 NA* NA* 2 50.40402 NA* 51.46567 NA* NA* 3 119.5224 0.1922 123.3487 0.1334 107 4 195.0457 0.1004 202.9544 0.0478 171 5 242.4517 0.3554 253.6074 0.1929 235 6 303.4849 0.4169 319.7267 0.1960 299 7 368.4705 0.4103 391.1194 0.1486 363 8 428.7255 0.4674 458.2606 0.1429 427 9 485.2073 0.5652 522.1096 0.1602 491 10 555.8367 0.4820 603.1256 0.0771 555 11 597.3383 0.7271 651.4410 0.1774 619 12 661.2548 0.7179 726.9787 0.1184 683 *The test is valid only for lags larger than the VAR lag order. df is degrees of freedom for (approximate) chi- -.02 -.01 .00 .01 .02 .03 2 4 6 8 10 12 14 16 18 20 22 24 Response of LDDI to LDDI -.02 -.01 .00 .01 .02 .03 2 4 6 8 10 12 14 16 18 20 22 24 Response of LDDI to LRES -.02 -.01 .00 .01 .02 .03 2 4 6 8 10 12 14 16 18 20 22 24 Response of LDDI to DIF_CE -.02 -.01 .00 .01 .02 .03 2 4 6 8 10 12 14 16 18 20 22 24 Response of LDDI to PERF -.02 -.01 .00 .01 .02 .03 2 4 6 8 10 12 14 16 18 20 22 24 Response of LDDI to R_USD -.02 -.01 .00 .01 .02 .03 2 4 6 8 10 12 14 16 18 20 22 24 Response of LDDI to R_VND -.02 -.01 .00 .01 .02 .03 2 4 6 8 10 12 14 16 18 20 22 24 Response of LDDI to LER -.02 -.01 .00 .01 .02 .03 2 4 6 8 10 12 14 16 18 20 22 24 Response of LDDI to LCPI Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 22 chi-square distribution VEC Residual Serial Correlation LM Tests Lags LM-Stat Prob 1 59.16094 0.6479 2 61.53095 0.5643 3 47.40582 0.9402 4 76.04845 0.1440 5 42.66743 0.9816 6 60.68048 0.5946 7 91.10607 0.0146 8 83.32281 0.0528 9 58.36486 0.6751 10 68.59161 0.3245 11 51.65381 0.8666 12 60.24077 0.6102 Probs from chi-square with 64 df. VEC Residual Heteroskedasticity Tests: No Cross Terms (only levels and squares) Joint test: Chi-sq df Prob. 814.0000 792 0.2862 square distribution VEC Residual Serial Correlation LM Tests Lags LM-Stat Prob 1 65.92945 0.4099 2 82.36621 0.0609 3 80.76247 0.0768 4 73.93012 0.1856 5 44.42497 0.9704 6 64.90730 0.4448 7 65.89190 0.4112 8 59.12782 0.6490 9 62.53141 0.5286 10 79.25844 0.0947 11 39.69445 0.9927 12 69.72775 0.2910 Probs from chi-square with 64 df. VEC Residual Heteroskedasticity Tests: No Cross Terms (only levels and squares) Joint test: Chi-sq df Prob. 1344.175 1368 0.6720 Source: Own survey. VECM estimation’s discussion Factors affecting the status of deposit dollarization in phase 1 and phase 2 are determined through the estimation results of the VECM model, pulse response function, and variance decomposition of the model. The study determined the following outcomes. First - for foreign exchange reserves (RES) In both phases, the foreign exchange reserves play a considerable role in reducing the status of dollarization deposits. In Phase 2, the impact of RES on DDI is more clearly revealed through the estimated coefficients (Table 6 and Table 7), which are statistically significant at the 5% level. At the same time, the reaction of DDI after the shock of 1% RES in Figure 1 (Phase 1, column number two, row number one) shows that phase 1 takes 9 months for foreign exchange reserve to fully affect while Phase 2 takes only 3 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 Inverse Roots of AR Characteristic Polynomial -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 Inverse Roots of AR Characteristic Polynomial Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 23 months (Phase 2, column number two, row number one). This indicates that the larger the foreign exchange reserves, the faster and stronger the ability to intervene to stabilize the foreign exchange market, creating public confidence in the VND value and macroeconomic stability. Secondly - for operating interest rate ceiling gap (DIF_CE) Phase 1, DIF_CE either did not impact or had only a very weak impact on DDI, so that the estimated coefficient was not statistically significant (Table 6), while in phase 2, this variable significantly reduced DDI (Table 7). This indicates that the two-ceiling policy of deposit rate VND and USD effectively limits the status of dollarization deposit and is more significant than the policy of a one-ceiling deposit rate VND. The effect of the double ceiling interest rate policy on deposit guarantee status is clearly seen through the results of the cumulative reaction of DDI due to the shock of DIF_CE which has opposite manifestations in two phases in Figure 1 (Phase 1 and Phase 2, column number three, row number one). Phase 1, the interest rate ceiling only applies to VND to prevent the deposit interest rates of commercial banks in the context of tight monetary policy to prevent inflation and exchange rate fluctuation, while R_VND is always in a close position. This shock of DIF_CE depends on the change of R_USD. When R_USD increases, DIF_CE decreases, and DDI increases as people tend to switch to hold foreign currencies. Inference in the opposite direction can be explained as in Figure 1 (Phase 1, column number three, row number one). The reaction of the DDI has the opposite effect in the period when the SBV applies two ceilings ce VND R and ce USD R , especially ce USD R = 0%, so DIF_CE is completely dependent on ce VND R . In theory, for ce VND R , a downward trend should stimulate investment and promote growth On the other hand, ce VND R will increase only in the case of economic instability. In this case, it becomes necessary to use a tighter monetary policy to control inflation. Thus, DIF_CE increases when the economy is unstable or inflation increases, leading to the tendency to switch to holding foreign currencies to avoid inflation, preserve the value of assets, and increase the dollarization of deposits in the economy (Figure 1, Phase 2, column number three, row number one). In recent years, especially from the beginning of 2016 up to the present, the macroeconomy has been stable, the exchange rate has been less volatile, and inflation has been low, ce USD R =0%, ce VND R deep decrease, as compared to the beginning of the ceiling deposit rate policy, since when the dollarization of deposits has dropped to a very low level. The cumulative reaction of DDI due to the DIF_CE shock in Figure 1 also shows that DIF_CE has a positive effect with DDI during the period of stable exchange rate and low inflation, ie DIF_CE > ∆ER + CPI (phase 2). Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 24 Otherwise, DDI has mixed reactions. It indicates that in the ce USD R =0%, ce VND R >∆ER + CPI, DDI will drop. Besides, the decomposition of variance results in Table 8 clarifies the ceiling interest rate policy's role in the status of dollarization of deposits. Before April of 2011, the gap of ceiling deposit rate does not explain the evolution of the dollarization deposit, but after April of 2011, the gap of ceiling interest rate is one of the vital determinants (after values of DDI in the past) to the evolution of deposit dollarization. Thirdly - for parallel market returns (PERF) The results in Table 7 show that parallel market profits impact the status of deposit dollarization. The impulse response function of DDI due to the PERF shock in Figure 1 shows that, in both phases, DDI increases after the PERF shock, reaching the highest level in the 8th month, then decreasing gradually to reach equilibrium in different levels. This result correctly reflects that the high rate of training is due to the existence of an informal foreign currency market. Because of the convenience, simple transactions, no cumbersome procedures, and the ability to fully respond, when there is a demand for foreign currencies, individuals and businesses still have a preference for trading on this market. Therefore, they are willing to accept transactions at a higher rate than commercial banks, making the rate on unofficial payment centers always higher than official payment centers. Therefore, when this difference increases, the psychology is that the holding of foreign currencies is expected to be more profitable from this market. The role of parallel market profit in explaining the evolution of deposit dollarization status also differs between the two phases. Table 8 shows that in the first stage (except for past values of DDI), the PERF fluctuation is the leading determinant of DDI's evolution; but in stage 2, the PERF's role is significantly reduced in explaining the DDI's volatility. This shows the holding of foreign currency due to the expectation of gaining profits from unofficial financial markets, although remaining, has been much reduced, indicating that the exchange rate difference between the two markets has gradually narrowed, denoting an initial success in the SBV's exchange rate management mechanism, especially the flexible central rate each day, as it closely follows the market rate. Fourthly - for foreign currency deposit rate (R_USD) In theory, the interest rate of a foreign currency indicates the return earned from holding that currency, so that R_USD should have a positive impact on DDI. Figure 1 (Phase 1 and 2, column number two, row number one) shows that a 1% shock of R_USD causes the DDI to increase, following two distinctly different reactions. Before April 2011, the DDI increased rapidly, reaching the highest level of 1.42% after 8 months. Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 25 After the SBV applied the policy ce USD R , the very weak DDI increased 0.06% in the second month, then decreased to 0.47% in the 4th month, and gradually increased before reaching a new balance after 11 months. The results of the variance decomposition in table 8 provide clear evidence of the difference in the impact of R_USD on the evolution of DDI in the two periods. When the SBV started to apply the ceiling interest rate for USD, the R_USD only explained about 1% of DDI movements after 12 months. Fifthly - for domestic currency deposit rate (R_VND) The cumulative response of DDI in Figure 1 shows that aftershock increases of 1% R_VND, in general, in both phases, DDI has the lowest decrease of about 0.1% after 7 months before reaching a new equilibrium. However, the decisive role of DDI evolution in the stage after April 2011 has increased significantly compared to the previous stage through the decomposition of variance in Table 8. Sixthly - for the exchange rate (ER) and inflation (CPI) Similar to previous studies such as Vegh and Sahay (1995), Basso et al. (2007), Kamin and Ericsson (2003), Clements and Schwartz (1993), Mueller (1994), Catão and Terrones (2016), the model provides additional evidence of the positive effects of exchange rates and inflation on deposit dollarization status based on impulse response function results (Figure 1). In addition, the results of the decomposition of variance in Table 8 reveal the decisive role changes in deposit dollarization status situation of inflation and exchange rate. This result once again confirms the problem: in order to restore confidence in VND and to limit the status deposits in foreign currencies, the exchange rate must be kept stable and inflation controlled at a low level. Loan dollarization Verify the VECM estimation conditions The study conducted a stationary test, determining delay and cointegration testing of the time series data of model (5). The results are summarized in Table 10. Table 10: Results of VECM estimation conditions of LDI model Item Details I Results of detecting the stationarity and variance of LDI model data Variable Statistical value t Variable Statistical value t ADF test PP test ADF test PP test Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 26 LLDI t 0.848273 0.536896 D(LLDI t) -3.495372** -3.436475** LGDP t -2.524268 -2.218291 D(LGDP t) -4.464323*** -4.518549*** LDDI t 0.927885 0.927885 D(LDDI t) -4.601406** -4.601499*** IRD t -1.177460 -2.113680** D(IRD t) -3.832400*** -7.392106*** LEX t -1.764633 -1.807598 D(LEX t) -4.141977*** -4.173269*** II Lag Determination Lag LogL LR FPE AIC SC HQ 0 -72.68135 NA 0.000446 6.473446 6.718873 6.538558 1 52.48667 187.7520* 1.12e-07 -1.873889 -0.401322* -1.483217 2 86.46901 36.81419 7.22e-08* -2.622417* 0.077290 -1.906184* III Cointegration test results Unrestricted Cointegration Rank Test (Trace) Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.950140 127.6620 69.81889 0.0000 At most 1 * 0.665829 55.69685 47.85613 0.0077 At most 2 0.583993 29.39041 29.79707 0.0556 At most 3 0.291750 8.341160 15.49471 0.4297 At most 4 0.002587 0.062173 3.841466 0.8031 Trace test indicates 2 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Hypothesized Max-Eigen 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.950140 71.96510 33.87687 0.0000 At most 1 0.665829 26.30644 27.58434 0.0721 At most 2 0.583993 21.04925 21.13162 0.0513 At most 3 0.291750 8.278987 14.26460 0.3510 At most 4 0.002587 0.062173 3.841466 0.8031 Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Source: Own survey. VECM estimations The estimation results of the cointegration equation in Table 11 show that loan dollarization status, in the long run, is inversely related to economic growth, the same relationship with deposit dollarization and payable cost difference at the 1% significance level; export and loan dollarization have no long-term relationship. Considering the short-term relationship from Table 11 shows that the status of loan dollarization affects export value but has no impact or has only a very weak effect on economic growth (the estimated coefficient is not statistically significant). Table 11: Vector Error Correction Estimates of LDI model Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 27 Vector Error Correction Estimates Sample (adjusted): 1992 2017 Included observations: 26 after adjustments Standard errors in ( ) and t-statistics in [ ] Cointegrating Eq: CointEq1 LLDI(-1) 1.000000 LGDP(-1) 1.658370*** (0.14700) [ 11.2817] LDDI(-1) -1.030840*** (0.11159) [-9.23796] LEX(-1) -0.076727 (0.04475) [-1.71443] IRD(-1) -0.013747*** (0.00416) [-3.30706] C -2.070719 Error Correction: D(LLDI) D(LGDP) D(LDDI) D(LEX) D(IRD) CointEq1 0.094474 -0.272626** 0.087534 0.181678** -10.21177** (0.09764) (0.10743) (0.09266) (0.08316) (3.91625) [ 0.96758] [-2.53762] [ 0.94465] [ 2.18475] [-2.60753] D(LLDI(-1)) 0.414154** 0.353578 -0.166709 0.288957* 6.932820 (0.19712) (0.21690) (0.18708) (0.16789) (7.90649) [ 2.10098] [ 1.63017] [-0.89113] [ 1.72115] [ 0.87685] D(LGDP(-1)) 0.186365 0.125015 -0.118600 0.094007 -2.794130 (0.17049) (0.18759) (0.16180) (0.14520) (6.83823) [ 1.09311] [ 0.66642] [-0.73300] [ 0.64742] [-0.40860] D(LDDI(-1)) 0.108857 -0.233210 0.135593 -0.266499 -18.39190 (0.28284) (0.31121) (0.26843) (0.24089) (11.3446) [ 0.38486] [-0.74935] [ 0.50514] [-1.10630] [-1.62120] D(LEX(-1)) -0.730150** 0.109063 0.002875 -0.375976 8.716294 (0.27277) (0.30013) (0.25886) (0.23231) (10.9404) [-2.67683] [ 0.36339] [ 0.01111] [-1.61843] [ 0.79671] D(IRD(-1)) 0.002022 0.001045 0.005575 0.002188 -0.588921*** (0.00458) (0.00504) (0.00435) (0.00390) (0.18383) [ 0.44120] [ 0.20728] [ 1.28165] [ 0.56063] [-3.20364] C 0.100023 -0.019800 -0.058350 0.247128 -3.752492 (0.06004) (0.06607) (0.05698) (0.05114) (2.40827) [ 1.66586] [-0.29971] [-1.02401] [ 4.83265] [-1.55817] Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 28 R-squared 0.440023 0.471687 0.160587 0.556604 0.432083 Adj. R-squared 0.242384 0.285224 -0.135676 0.400111 0.231641 Sum sq. resids 0.208403 0.252307 0.187697 0.151165 335.2657 S.E. equation 0.110720 0.121826 0.105076 0.094298 4.440891 F-statistic 2.226396 2.529652 0.542043 3.556737 2.155655 Log likelihood 22.90152 20.60744 24.15726 26.75472 -65.69696 Akaike AIC -1.325127 -1.133953 -1.429772 -1.646226 6.058080 Schwarz SC -0.981528 -0.790354 -1.086173 -1.302627 6.401679 Mean dependent -0.056987 -0.007102 -0.058404 0.180516 -1.207187 S.D. dependent 0.127204 0.144097 0.098600 0.121749 5.066267 Determinant resid covariance (dof adj.) 2.92E-08 Determinant resid covariance 5.20E-09 Log likelihood 58.62058 Akaike information criterion -1.551715 Schwarz criterion 0.411708 Value [ ] is t-statistical; (***),(**), (*) statistical significance level 1%, 5% and 10% Source: Own survey. The cumulative response of economic growth and export value due to the 1% LDI shock in Figure 2 shows that EX increases, whereas GDP decreases. This indicates that foreign currency credit has a negative impact on economic growth because the businesses face difficulties when they borrow capital in foreign currency with exchange rate risk. With the contract's maturity, companies must buy foreign currencies at a high price on the free market to repay the banks. This can quickly occur, and the rate increases more than decreases. These risks affect the results of business activities, thereby affecting economic growth. Besides, although there are no official data, many businesses likely borrow foreign currency not to import goods or invest in production but to speculate or invest in real estate creating speculative "fever" in the market and bringing instability to the economy. Despite facing many risks, many businesses still choose to borrow foreign currencies because there are no transaction costs. More importantly, the cost of lending VND is higher than borrowing USD. On the other hand, commercial banks also find ways to "release" the foreign currency capital they have mobilized to avoid risks in forex trading. Estimating the long-term relationship (Table 11) and the increased cumulative response of loan dollarization under the shock of the difference of payables and the shock of dollarization deposit (Figure 2) reveal a positive relationship between these variables. Figure 2: Response of variables of LDI model due to 1% shock Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 29 .02 .04 .06 .08 .10 .12 2 4 6 8 10 12 14 16 18 20 22 24 Response of LEX to Cholesky One S.D. LLDI Innovation -.12 -.10 -.08 -.06 -.04 -.02 .00 .02 2 4 6 8 10 12 14 16 18 20 22 24 Response of LGDP to Cholesky One S.D. LLDI Innovation .00 .01 .02 .03 .04 .05 2 4 6 8 10 12 14 16 18 20 22 24 Response of LLDI to Cholesky One S.D. LDDI Innovation .000 .001 .002 .003 .004 .005 .006 2 4 6 8 10 12 14 16 18 20 22 24 Response of LLDI to Cholesky One S.D. IRD Innovation Source: Own survey. CONCLUSION The study has used the VECM model to confirm: (1) The relationship between the deposit dollarized and the monetary variables under the interest rate ceiling policy, and (2) the relationship between dollarization loans with economic growth and exports. For the relationship between the deposit dollarized and monetary Advances in Business-Related Scientific Research Journal, Volume 12, No. 2, 2021 30 variables with the ceiling interest rate policy, the USD deposit rate changed from 3% to 0%. The dollarization of deposits decreases when the macroeconomic economy is stable. This finding suggests that the State Bank of Vietnam needs to pay more attention to maintaining the U.S dollars deposit rate to limit dollarization. Additionally, the model finds empirical evidence that official and unofficial foreign currency market returns are vital factors influencing dollarization. This issue is consistent with observed reality. In particular, when the exchange rate difference between the official and the unofficial market is large, people tend to transfer their assets to a foreign currency. The paper also finds that the exchange rate and inflation positively correlate with the deposit dollarization rate. Thus, this finding recommends that Vietnam seek to stabilize the macroeconomic environment, control the exchange rate stable and control the inflation rate at a low level, thereby limiting dollarization in the economy. The paper provides empirical evidence that loans dollarization positively affects exports in the short term, and the interest payment for borrowing USD is more preferable to borrowing VND. However, loan dollarization in the long term has not brought any benefits for the economy, and the paper has concluded that there is a negative correlation with economic growth. This finding affirms the correct policy of the government to control dollarization. Moreover, the State bank of Vietnam should promote the ability of commercial banks to lend foreign currency to businesses when the enterprises need in the export and import activities. This finding shows that the State Bank of Vietnam wants to limit the loan dollarisation. They need to have supportive policies for businesses borrowing VND to export at a lower interest rate than borrowing foreign currency. The paper still has some limitations which can be improved in further researches. Firstly, there are no accurate statistics for measuring M2 DDI (the deposit dollarization index). The article used the ratio of deposits in foreign currencies to total deposits (DDI) to measure the foreign exchange rate is not yet thoroughly assessing the degree of foreign exchange in the economy. Because in Vietnam, besides the statistical amount of foreign currencies in the commercial banking system, there is a vast amount of foreign currency in cash that people are storing. 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