Credit Decomposition and Business Cycles in Emerging Market Economies

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1 Credit Decomposition and Business Cycles in Emerging Market Economies Berrak Bahadir y University of Georgia Inci Gumus z Sabanci University July 24, 214 Abstract This paper analyzes di erent types of private sector credit in relation to business cycles in emerging market economies. We rst provide evidence that the results found in the literature on credit expansions being associated with economic expansions, real exchange rate appreciations and current account de cits hold more strongly for household credit than business credit. Then, using a two-sector real business cycle model of a small open economy, we study the model dynamics generated by shocks to household credit and business credit in tradable and nontradable sectors. We show that the three types of credit shocks generate di erent macroeconomic dynamics in sectoral output and input levels as well as the real exchange rate. We also show that the credit shocks are important in matching key business cycle properties observed in the data, especially the moments related to credit. JEL classi cation: E32, E44, F34, F41 Key Words: Household Credit, Business Credit, Business Cycles, Credit Shocks We would like to thank participants at the Society for Computational Economics 212 Meeting, the SNDE 212 Meeting, the LACEA-LAMES 212 Meeting, the EEA-ESEM 213 meeting, the North American Summer Meeting of the Econometric Society 214 and seminar participants at the University of Georgia, Federal Reserve Bank of Atlanta and Central European University for valuable comments and suggestions. All remaining errors are our own. y Department of Economics, Terry College of Business, 516 Brooks Hall, Athens, GA bbahadir@uga.edu, tel: z Corresponding author. Sabanci University, Faculty of Arts and Social Sciences, Orhanli, Tuzla, Istanbul, 34956, Turkey. incigumus@sabanciuniv.edu, tel:

2 1 Introduction The literature on credit cycles, including Mendoza and Terrones (28, 212) and Tornell and Westermann (22, 23), has established that credit expansions in emerging market economies are associated with large macroeconomic expansions, widening current account de cits and real exchange rate appreciations. The standard measure of credit used in this literature is total private credit which includes all types of credit to the private sector without di erentiating between lending to households and lending to rms. These two types of credit may generate di erent business cycle patterns given that household credit expansions are likely to a ect the economy through an increase in consumption and demand for goods and services whereas business credit has the potential to increase investment and labor demand, and thereby increase output. Furthermore, the allocation of business credit between tradable and nontradable sectors is another factor that may a ect the business cycle dynamics. Recent developments in credit markets underline the importance of distinguishing between household and business credit in emerging markets. Figure 1 shows the evolvement of household and business credit-to-gdp ratios for nine emerging market economies and Table 1 shows the average values of these ratios for the sample period. The two types of credit exhibit di erent patterns: while lending to households has grown substantially over the period we analyze, the growth in business credit has been much slower in most cases. As a result, household credit has become an important component of overall private credit with potentially important consequences for business cycles. On average, household credit constitutes 33 percent of total private credit in our sample. Despite the increased importance of household credit, existing models that study emerging market business cycles abstract from household credit dynamics. 2

3 .9 Household credit/gdp 1.4 Business credit/gdp Brazil Chile Czech Republic Hungary Korea Poland South Africa Thailand Turkey Figure 1. Credit-to-GDP ratios in emerging market economies Table 1. Household and business credit Countries HC/GDP BC/GDP HC/TC Sample Period Brazil Chile Czech Republic Hungary Korea Poland South Africa Thailand Turkey Average Note: HC, BC and TC denote household, business and total credit, respectively. In this paper, we distinguish between household and business credit, as well as the sectoral allocation of business credit, and study the macroeconomic dynamics generated by di erent credit types. To this end, rst we re-examine the documented stylized facts 3

4 regarding credit cycles di erentiating between household and business credit. Using data from a group of emerging market economies, we show that the patterns documented in the literature with respect to credit and key macroeconomic variables are much stronger for household credit: household credit exhibits a strong positive comovement with output, consumption, investment and real exchange rate appreciation, and a strong negative correlation with the trade balance. Business credit, on the other hand, has weaker correlations with all of these variables. These correlations show that the empirical ndings of the literature on credit cycles are mostly driven by household credit. We then construct a two-sector small open economy real business cycle model to assess quantitatively the role of di erent types of credit shocks in driving business cycles. In our model households and both the tradable and nontradable sectors are credit constrained, and rms have a working capital requirement. The model allows us to disaggregate total credit into three subcomponents: (i) household credit, (ii) nontradable sector business credit, and (iii) tradable sector business credit. We choose to model all sectors as credit constrained since this allows us to study the model dynamics for each type of credit expansion and analyze the e ects in each sector separately. We analyze the model under productivity shocks in tradable and nontradable sectors and three types of credit shocks. The credit shocks are modeled as stochastic processes that a ect the borrowing limits of the agents. The shocks to credit are similar to the nancial sector shocks studied in Jermann and Quadrini (212) who analyze the e ects of these shocks in a closed economy model. We calibrate our model to Turkey for the period 1999Q1-211Q4. The reason we choose to calibrate the model to Turkey is that we are able to obtain detailed sectoral data on credit variables at quarterly frequency for a relatively long time period. 1 For our analysis, we not only need data on household and business credit separately, but also business credit data at the sectoral level. To the best of our knowledge, most of the emerging economies for which household credit and business credit data are separately available do not provide the sectoral credit classi cation. 1 Turkey is a representative emerging market economy that features the standard business cycle properties observed in emerging economies documented by Aguiar and Gopinath (27) and Neumeyer and Perri (25): consumption is more volatile than output, trade balance is countercyclical and business cycles are very volatile (see Table 4 for more details). With respect to the correlations of di erent types of credit with the key macroeconomic aggregates, Turkey displays the same pattern as the average of the emerging economies in Table 2. 4

5 The impulse response analysis shows that the three types of credit shocks generate di erent macroeconomic dynamics. The starkest di erence appears for the real exchange rate response. We observe that an increase in the supply of credit to the nontradable sector leads to a real exchange rate depreciation whereas credit increase to households and tradable sector generates an appreciation. The credit shocks generate di erent responses in sectoral output and input levels as well. Nontradable output expands after each credit shock whereas tradable output only increases as a result of a positive credit shock to that sector. Investment in each sector increases as a response to the sector-speci c credit shocks whereas consumption increases on impact after each shock. Finally, trade balance declines in all cases. The extent to which trade balance responds to credit shocks depends on the share of each type of credit in the overall economy as analyzed in Section The quantitative results from the model with productivity and credit shocks can account for most of the empirical regularities of the Turkish economy. In particular, the model is successful at matching the key business cycle moments such as the relative volatilities of consumption, labor and trade balance. The model also generates a countercyclical trade balance and a strong negative correlation between real exchange rate and trade balance. More importantly, the model can account for the comovement between the key macroeconomic variables and di erent credit types. Household credit exhibits a strong positive correlation with output and real exchange rate, and a strong negative correlation with the trade balance whereas for business credit the correlations are weaker. In the absence of credit shocks, the model performs poorly in all aspects but especially with respect to moments related to credit. When we replace the credit shocks with an interest rate shock, the model still underperforms relative to the baseline model. Therefore we conclude that credit shocks are important in driving the business cycles in emerging market economies. The role of credit shocks for macroeconomic uctuations has been recently investigated primarily in closed economy models. This literature, including Christiano, Motto and Rostagno (21), Jermann and Quadrini (212) and Khan and Thomas (213), nd that nancial shocks are important for the dynamics of real and nancial variables. One recent study by Perri and Quadrini (214) studies the implications of credit shocks in a twocountry model and focuses on international comovement in real and nancial variables. The contribution of the current paper is to show that shocks to di erent types of credit 5

6 are important to understand business cycles, as well as the real exchange rate behavior, in emerging market economies. Our paper is further related to the empirical studies on the distinction between household and business credit, such as Büyükkarabacak and Krause (29), Büyükkarabacak and Valev (21), and Beck et al. (212) who underline the importance of di erentiating between the types of borrowers. The main conclusion of these papers is that the two types of credit serve di erent purposes and have distinct e ects on the economy. 2 Our paper complements these empirical studies by providing a general equilibrium model that helps understand the transmission mechanism through which each type of credit a ects the business cycle properties in emerging market economies. 2 Credit and Business Cycles in Emerging Economies This section documents business cycle regularities in relation to household and business credit for a sample of emerging market economies for which we could obtain time series data of su cient length. The data for Czech Republic, Hungary, Korea, Poland, Thailand and Turkey are obtained from the Bank for International Settlements (BIS). 3 For Brazil, Chile and South Africa we obtain the data from their respective central banks. Due to the lack of credit data at the sectoral level for several countries in our sample, here we analyze total business credit rather than tradable and nontradable sector credit separately. Table 2 shows the correlations between macroeconomic aggregates and the creditto-gdp ratios for household and business credit. These correlations overall show that household credit is more strongly correlated with the business cycle compared to business credit. The correlation between household credit and output is, on average,.55 whereas for business credit this correlation is only.14. The same pattern holds for consumption 2 Büyükkarabacak and Krause (29) show that household credit leads to a deterioration in the trade balance, whereas business credit has a small but positive e ect. Büyükkarabacak and Valev (21) nd that household credit expansions have been a signi cant predictor of banking crises. Business credit expansions are also associated with banking crises but their e ect is weaker. Beck et al. (212) show that bank lending to rms is positively associated with growth, while the relationship between household credit and growth is insigni cant. 3 BIS also provides data for Indonesia, Mexico, Singapore and Hong Kong. For Indonesia the data starts from 21, which does not give a su ciently long time series. We opt to exclude Hong Kong, Mexico and Singapore because of the strong negative correlation between output and private credit, which contradicts with the general pattern documented in the literature. 6

7 and investment. The starkest di erence between the two types of credit is observed in their correlations with the real exchange rate. There is a very strong comovement between the real exchange rate appreciation and household credit with an average correlation coe cient of.52. The business credit-real exchange rate correlation is lower than the correlation of household credit for all countries and negative for most of them, with an average of -.3. In the case of the ratio of trade balance to output, household credit has a negative correlation in all countries, except for Czech Republic, with an average correlation of The correlation between business credit and trade balance is also negative in six out of nine countries. However, the correlations are weaker and the average correlation drops to Figures 2-4 plot the time series for three key macroeconomic variables; GDP, real exchange rate, and the ratio of trade balance to output; together with the credit-to-gdp ratios for the two types of credit. These gures also reveal the di erences between the credit types in relation to the cycle. To summarize, the empirical regularities presented in this section show that the stylized facts documented in the literature with respect to private credit are to a large extent driven by household credit rather than business credit in emerging markets. Table 2. Business cycles and credit types Household Credit/GDP Business Credit/GDP GDP C I RER TB/GDP GDP C I RER TB/GDP Brazil Chile Korea S. Africa Thailand Turkey Czech Republic Poland Hungary Average Notes: Annual data ltered using Hodrick-Prescott lter with smoothing parameter 1. 7

8 .4 Brazil.3.8 Chile.3.8 Czech Republic Hungary.8, Korea Poland South Africa.6, Thailand Turkey , Output (left axis).2 Household credit/gdp (right axis) ,8.4 Brazil.2.8 Chile.2.8 Czech Republic Hungary Korea Poland South Africa Thailand Turkey Output (left axis).3 Business credit/gdp (right axis) Figure 2. Output and credit-to-gdp ratios 8

9 .3 Brazil.3.2 Chile.3.2 Czech Republic Hungary Korea Poland South Africa.4, Thailand Turkey Real exchange rate (left axis).2 Household credit/gdp (right axis) Brazil.2.2 Chile.2.2 Czech Republic Hungary Korea Poland South Africa Thailand Turkey Real exchange rate (left axis).3 Business credit/gdp (right axis) Figure 3. Real exchange rate and credit-to-gdp ratios 9

10 2.8 Brazil.3 8 Chile.3 3 Czech Republic Hungary 4, Korea Poland South Africa 4, Thailand Turkey Trade balance/gdp (left axis).2 Household credit/gdp (right axis) Brazil.2 8 Chile.2 3 Czech Republic Hungary Korea Poland South Africa Thailand Turkey , , Trade balance/gdp (left axis).3 Business credit/gdp (right axis) Figure 4. Trade balance-to-gdp and credit-to-gdp ratios 1

11 3 The Model 3.1 Households Households choose consumption and labor to maximize their expected lifetime utility given by E 1 X t= c h ( h ) t t (c h t;n ; ch t;t ) l t 1 where h 2 (; 1) is the discount factor, c h t 1 ; > 1; > ; (1) is the consumption aggregator, l t represents labor, is the risk aversion parameter, is the parameter that governs the intertemporal elasticity of substitution in labor supply, and is the measure of disutility from working. Consumption is an aggregate of the consumption of nontradable goods, c h t;n ; and the consumption of tradable goods, c h t;t. The budget constraint of households is given by c h t;t + p t;n c h t;n + Rb h t 1 = b h t + w t;t l t;t + p t;n w t;n l t;n ; (2) where b h t denotes the amount borrowed at time t, R = (1 + r) is the gross interest rate and r is the net real interest rate, which is taken as constant. The variables l t;t and l t;n denote labor supplied to tradable and nontradable sectors, respectively, w t;t and w t;n denote the wage rates in the two sectors and p t;n is the relative price of the nontradable good, where the price of the tradable good is normalized to one. Households face a credit constraint in every period. The total value of their debt including both interest and principal cannot exceed a fraction of their expected income in the next period. As in Ludvigson (1999), we choose to tie borrowing to income because many banks require income statements before they provide funds to the borrowers since income is associated with some observable measure of the borrower s nancial health. The credit constraint of households is of the form Rb h t m h t E t (w t+1;t l t+1;t + p t+1;n w t+1;n l t+1;n ) : (3) 11

12 In the calibration of the model, h is chosen such that h < 1=R. This condition guarantees that the credit constraint is binding in and around the steady state. The loanto-income (LTI) ratio, denoted by m h t, is modeled as a stochastic process. 3.2 Entrepreneurs Tradable sector Entrepreneurs who produce tradable goods combine households labor services with capital, k t 1;T. Output is produced by a Cobb-Douglas technology: y t;t = e A t;t k t 1;T l 1 t;t ; (4) where A t;t is an exogenous stochastic productivity shock. The capital accumulation decision is made by the entrepreneurs and the capital accumulation equation for the tradable sector is given by i t;t = k t;t (1 )k t 1;T ; (5) where i t;t denotes investment in the tradable sector. The investment good used in both sectors is assumed to be tradable and is the common depreciation rate. Firms in both sectors have to pay a fraction of the wages before output becomes available and they need working capital loans from foreign lenders. Thus, tradable sector rms borrow w t;t l t;t at the beginning of period t and repay Rw t;t l t;t at the end of the period as in Neumeyer and Perri (25). As households, entrepreneurs are also restricted in their borrowing due to enforceability problems. Following Mendoza (21), we assume that the entrepreneur s total debt, which includes intertemporal debt, b et t ; and within-period working capital loans, cannot exceed a fraction of the collateral assets, which are capital holdings in our model. In the case of the tradable sector, the credit constraint takes the form Rb et t + Rw t;t l t;t m et t E t q k t+1;t k t;t : (6) 12

13 q k t+1;t The loan-to-capital (LTC) ratio, denoted by m et t ; is modeled as a stochastic process, and is the price of capital at time t + 1: We use adjustment costs for capital accumulation to reduce the volatility of investment. Therefore, the price of capital in terms of tradable consumption di ers from one and is given by where (k t q k t;t = 1 t 1;T ; i t;t t;t ; (7) 1;T ; i t;t ) is the capital adjustment cost function. The entrepreneur s problem is to maximize her expected utility E 1 X t= ( et ) t (cet t (c et t;n ; cet t;t ))1 1 (8) subject to technology and borrowing constraints, and the following ow of funds constraint c et t;t + p t;n c et t;n + w t;t l t;t + i t;t + (k t 1;T ; i t;t ) + Rb et t 1 + (R 1)w t;t l t;t = y t;t + b et t : (9) As in the case of households, consumption of the tradable sector entrepreneur, c et t ; is an aggregate of the consumption of nontradable and tradable goods, c et t;n and cet t;t ; respectively. Similar to household s discount factor, we assume that et constraint is binding in and around the steady state. < 1=R so that the credit Nontradable sector Entrepreneurs in the nontradable sector also produce output with a Cobb-Douglas technology: y t;n = e A t;n k t 1;N l1 t;n ; (1) where A t;n is an exogenous stochastic productivity shock and k t 1;N denotes capital used in the production of the nontradable good. Capital is accumulated by the entrepreneur and the equation for capital accumulation is given by i t;n = k t;n (1 )k t 1;N ; (11) where i t;n denotes investment in the nontradable sector. 13

14 Similar to the tradable sector, rms in the nontradable sector also have a working capital requirement and face a credit constraint. The entrepreneur s total value of debt including the interest payments cannot exceed a fraction of the expected value of the capital holdings: Rb en t + Rw t;n l t;n m en t E t q k t+1;nk t;n ; (12) where b en t denotes the intertemporal debt issued at time t by the nontradable sector entrepreneur. The loan-to-capital ratio, denoted by m en t ; is modeled as a stochastic process. We use adjustment costs for capital accumulation in the nontradable sector as well, in order to reduce the volatility of investment. The price of capital in terms of tradable consumption, qt;n k ; is given by q where (k t k t;n = 1 t 1;N; i t;n t;n ; (13) 1;N ; i t;n ) is the capital adjustment cost function. The entrepreneur in the nontradable sector maximizes her expected utility E 1 X t= ( en ) t (cen t c en t;n ; cen t;t ) 1 1 (14) subject to technology and borrowing constraints, as well as the following ow of funds constraint c en t;t +p t;n c en t;n +p t;n w t;n l t;n +i t;n +(k t 1;N ; i t;n )+Rb en t 1+(R 1)w t;n l t;n = p t;n y t:n +b en t : Consumption of the nontradable sector entrepreneur is also an aggregate of the consumption of nontradable and tradable goods, c en t;n and cen t;t, respectively. We also assume for the nontradable sector entrepreneur that en < 1=R to make sure that the credit constraint is binding in and around the steady state. (15) 3.3 Equilibrium Given initial conditions b h ; b et ; b en ; k ;T ; k ;N ; the constant interest rate r; the sequence of shocks to sectoral productivity levels, the loan-to-income ratio of the household and the 14

15 loan-to-capital ratios of the entrepreneurs, the competitive equilibrium is de ned as a set of allocations and prices y t;t ; y t;n ; l t;t ; l t;n ; k t;t ; k t;n ; i t;t ; i t;n ; c h t ; c h t;t ; ch t;n ; cet t ; c et t;t ; cet t;n ; cen t ; c en t;t ; cen t;n ; bh t ; b et t ; b en t ; p t;n ; w t;t ; w t;n such that (i) the allocations solve the problems of households, and entrepreneurs in the tradable and nontradable sectors at the equilibrium prices, (ii) factor markets clear, and (iii) the resource constraints for the tradable and nontradable sectors hold: c h t;t + c et t;t + c en t;t + i t;t + i t;n + (k t 1;T ; i t;t ) + (k t 1;N ; i t;n ) + tb t = y t;t (16) c h t;n + c et t;n + c en t;n = y t;n (17) where the trade balance is de ned as tb t = R b h t 1 + b et t 1 + b en t 1 + (R 1)wt;T l t;t + (R 1)w t;n l t;n b h t + b et t + b en t : (18) 4 Calibration The model is solved using quarterly Turkish data for the period 1999Q1-211Q4. The construction of the series used in the model solution is explained in detail in the Appendix. The parameter values of the model are summarized in Table 3. We take the real interest rate as constant and set it equal to the average real interest rate in Turkey, which equals.117. We set the discount factors such that the credit constraints bind in and around the steady state. The values for h and en are set to.94, and the value for et is set to.97, which are the highest possible values that guarantee binding credit constraints in the solution of the model. The value of ; which determines the intertemporal elasticity of substitution in labor supply, is set to 1.7 following Correia et al. (1995). The coe cient of relative risk aversion is set to 1, which corresponds to log-utility. The annual depreciation rate is set to.8 following Meza and Quintin (27). 15

16 Table 3. Parameter values of the benchmark model Parameter Value Description h.94 Discount factor of households en.94 Discount factor of nontradable sector entrepreneurs et.97 Discount factor of tradable sector entrepreneurs 1 Relative risk aversion coe cient 1.7 Labor curvature Labor weight in utility.54 Nontradable weight in the consumption aggregator.35 Capital exponent in the tradable sector.25 Capital exponent in the nontradable sector.8 Annual depreciation rate r.117 Real interest rate ' T 5.21 Capital adjustment cost coe cient in the tradable sector ' N 18.3 Capital adjustment cost coe cient in the nontradable sector.25 Working capital coe cient m h.423 Loan-to-income ratio m en.266 Loan-to-capital ratio in the nontradable sector m et.12 Loan-to-capital ratio in the tradable sector Stochastic processes A T.662 h T.697 (" A T ).283 A N.777 h N.62 (" A N ).148 h.817 et T.153 (" h ).35 et.656 en N.362 (" et ).287 en.83 (" en ).27 We cannot calibrate the capital share parameters in the tradable and nontradable sectors for Turkey due to unavailability of data. Di erent values have been used in the literature for these parameters and the general consensus is that the tradable sector is more capital intensive than the nontradable sector. Therefore, we set the capital s share of income equal 16

17 to.35 in the tradable sector and.25 in the nontradable sector, which are close to the values used in the literature. 4 The value of is set to so that the steady state labor supply equals.17, which is the average value of time spent working as a percentage of total discretionary time in Turkey. The share of nontradable goods in the consumption aggregator, ; is set equal to the average share of nontradable consumption in total consumption in Turkey. The steadystate value of the loan-to-capital ratio in the nontradable and tradable sectors, m en and m et ; are set to match the average value of business credit in each sector as a ratio of GDP for the sample period, which are 11.2% and 9.6%, respectively. Likewise, the steady-state value of the loan-to-income ratio, m h ; is set to match the average value of the ratio of household credit to GDP in the data, which is 7.2%. For the calibration of the parameter ; we use data on short-term bank loans from the Company Accounts database of the Central Bank of Turkey. Total liabilities of rms are composed of intertemporal loans and working capital loans in our model, and the loans for working capital have a shorter duration compared to the other loans. Therefore, we choose to approximate the working capital loans with short-term bank loans. We calibrate by taking the average of the ratio of short-term loans to the compensation of employees, which is equal to.25. Since the data on short-term bank loans are not available at the sectoral level, we use the same value for both tradable and nontradable sectors. The consumption aggregator is assumed to be of the following form for all agents: c j t c j t;n ; cj t;t = (c j t;n ) (c j t;t )1 ; < < 1; for j = h; et; en: (19) The form of the capital adjustment cost functions is given by (k t 1;s ; i t;s ) = ' 2 s 2 k it;s t 1;s ; for s = T; N: k t 1;s The parameters that determine the size of the adjustment costs, ' s ; are set to match the volatility of investment relative to output in each sector. 4 As a robustness check, we tried two di erent combinations for capital shares: in one we set both shares equal to.3 and in the other we increased the share of capital to.45 in the tradable sector and decreased it to.15 in the nontradable sector. Our results mainly remained the same and are avalaibale upon request. 17

18 The stochastic processes used in the model are for total factor productivity in the two sectors and the LTI and LTC ratios. The processes for the productivity shocks are estimated using the Solow residuals for the tradable and nontradable sectors in Turkey as A t;s = As A t 1;s + " As t ; where s = T; N and " As t are normally distributed and serially uncorrelated innovations. The LTI and LTC ratios are characterized by the following law of motion m j t = m j exp( ~m j t); for j = h; et; en. The stochastic process for the LTI and LTC ratios are as follows ~m h t = h ~m h t 1 + h T A t;t + h NA t;n + " h t ; ~m et t ~m en t = et ~m et t 1 + et T A t;t + " et t ; = en ~m en t 1 + en N A t;n + " en t ; where the innovations " j t are normally distributed and serially uncorrelated for j = h; et; en. We model the shocks to credit availability as being a ected by productivity shocks. It is a well-documented fact that emerging market economies borrow more when their output level is high and have limited access to international nancial markets in low-output episodes. Based on this observation, we choose to incorporate the interaction between the productivity shocks, which are the main determinant of output uctuations, and credit access. This formulation is similar to the way the country risk component of interest rates is modeled in Neumeyer and Perri (25), as a decreasing function of expected productivity. Since there are two productivity shocks in our model, we assume that the credit limits of the rms depend on the productivity shock of their own sector and household s credit access depends on both shocks. 18

19 5 Results 5.1 Impulse Response Analysis Credit Shocks Figure 5 shows the response of the economy to positive one percent shocks to household credit, tradable sector credit and nontradable sector credit, i.e. increases in m h t ; m et t m en t ; respectively. Credit shocks lead to di erent responses in sectoral output and input levels as well as the real exchange rate. For all types of shocks, higher credit availability leads to increased demand for both tradable and nontradable goods. After household credit and tradable sector credit shocks, higher demand for the nontradable good leads to an appreciation of the real exchange rate. Higher credit to the nontradable sector, on the other hand, raises production in this sector and real exchange rate depreciates. The response of tradable and nontradable sector output to credit shocks depends mainly on the labor supply response. With a shock to household credit, total labor supply stays almost constant on impact. The appreciation of the real exchange rate raises the return to labor in the nontradable sector relative to the tradable sector and labor moves from the tradable sector to the nontradable sector. Therefore, tradable output decreases and nontradable output increases on impact. The combined e ect of the sectoral output levels with the real exchange rate response leads to an increase in total output measured in terms of tradables, followed by a decline in the second period. This decline is due to the e ect of the borrowing constraint on the supply of labor. With the borrowing limit of the household tied to next period s labor income, labor supply has the additional bene t of enabling a higher level of borrowing. and Therefore, labor supply response is not only determined by the wage rate, but also by changes in credit availability. The increase in m h t raises the direct return to working, while at the same time the credit constraint becomes less binding, which reduces the bene t of working. The decline in the Lagrange multiplier of the credit constraint dominates the positive e ect of an increase in m h t : As a result, total labor supply decreases in the second period leading to a decline in labor in both sectors. 19

20 Positive shocks to business credit in the two sectors lead to an increase in total labor on impact, with the nontradable sector credit having a stronger e ect. Since borrowing is required for part of the labor payments, higher credit availability to rms raises the demand for labor. In the case of the tradable sector credit, labor and output increase in both sectors. Higher availability of credit raises the demand for labor in the tradable sector by reducing its cost. On the other hand, the real appreciation raises the return to labor in the nontradable sector, which leads to an increase in labor supply to this sector as well. Hence, increased credit in the tradable sector leads to an expansion in both sectors. In the case of the nontradable sector credit shock, while total labor supply increases, the increase in labor demand in the nontradable sector is strong enough to lead to a decline in labor in the tradable sector. Hence, nontradable output expands while tradable sector labor and output decrease. Overall, nontradable sector expands in response to all types of credit shocks, although the magnitude of the expansion di ers with the nontradable sector credit shock having the strongest e ect. Production of the tradable good, on the other hand, only increases after an expansion in credit to that sector, while the increase of other two types of credit lead to a contraction. After each shock, consumption of the tradable good increases on impact. This response is reversed in the second period as the credit constraints become more binding and the initial period s debt has to be repaid as well. The consumption decline in the second period is bigger for the household credit shock since income level decreases more with this shock in the second period. Consumption of the nontradable good follows a similar pattern, while the magnitudes of the responses di er depending on the shock. Investment in each sector increases signi cantly with an increase in business credit to that sector, as expected. Positive shocks to the credit limits raise the borrowing of the respective agent and lead to a decline in the trade balance on impact. The decline in the trade balance is of similar magnitude for the business credit shocks and smaller for the household credit shock. This is mainly due to the credit limit of the household, determined by m h t ; being less than those of the rms since the household credit is lower than the other two types of credit in the data. Hence, the change in debt as a ratio of output, which is the main determinant of the trade balance-to-output ratio, is smaller in the case of a household credit shock. 2

21 x Total output x Tradable output x Nontradable output x Total labor x Tradable labor x Nontradable labor x Tradable consumption x Nontradable consumption x Real exchange rate Tradable investment.4 Nontradable investment x Trade balance/output Household T entrepreneur NT entrepreneur Figure 5. Positive shocks to credit: Percent deviation of variables from their steady-state values Productivity Shocks Figure 6 shows the response of the economy to positive one percent shocks to productivity in the two sectors. The productivity shocks have the standard positive e ects on output, labor and investment in the sector where productivity increases. Since higher productivity also raises the credit limits through the stochastic processes, higher borrowing of the agents a ects the dynamics of the economy as well. The productivity shocks raise the demand for both goods, through both higher income and higher credit availability. In the case of a tradable sector productivity shock, the increased demand for the nontradable good leads to a real exchange rate appreciation, whereas a positive productivity shock in the nontradable sector leads to a real depreciation due to increasing supply of the nontradable good. 21

22 .15 Total output.15 Tradable output.2 Nontradable output x Total labor x Tradable labor.2 Nontradable labor Tradable consumption.2 Nontradable consumption.2 Real exchange rate Tradable investment.4 Nontradable investment x Trade balance/output T productivity NT productivity Figure 6. Positive shocks to productivity: Percent deviation of variables from their steady-state values For both shocks, higher productivity raises the demand for labor in the respective sector. Increased credit limits through higher productivity and increased capital levels through higher investment relax the borrowing constraints of rms. This increases the labor demand further as a result of increased availability of working capital loans. In the case of a tradable productivity shock, the real appreciation raises the return to labor in the nontradable sector and labor increases in this sector as well as the tradable sector, resulting in an increase in nontradable output. Likewise, after a nontradable productivity shock, labor increases in the tradable sector due to the reduction in the return to labor in the nontradable sector with the real depreciation. While investment levels increase with higher productivity in each sector, nontradable sector investment increases considerably with a tradable productivity shock since the real appreciation raises the return to capital in this sector. 22

23 Total output measured in units of tradables increases in both cases but the increase is bigger with the tradable sector shock. Output of both goods considerably increase after a shock to the tradable sector, whereas a shock to the nontradable sector has a small e ect on tradable output. Since the value of nontradable output measured in terms of tradables also decreases due to the real depreciation with a nontradable sector shock, the total e ect on output is smaller. For each shock, the borrowing of agents increase, which leads to a decline in the trade balance. Higher borrowing results from increased LTI and LTC ratios as well as a higher collateral for rms and a higher income for the household. 5.2 Business Cycle Statistics In this section, we examine the ability of the model to match the main characteristics of business cycles observed in Turkey in the period 1999Q1-211Q4. Table 4 documents the key business cycle moments obtained from the data and the model. The model is loglinearized around the steady state and the moments are calculated using HP- ltered series. The model dynamics are generated by productivity and credit shocks. The correlations of di erent types of credit with output, real exchange rate and the trade balance-to-output ratio are in general consistent with patterns observed in the data. The model generates a stronger correlation between household credit and output compared to the two types of business credit, which is in line with the empirical regularity observed in emerging economies as illustrated in Section 2. The di erences between household credit and the two types of business credit in terms of their correlations with output are lower in the data than the model, even though household credit has the highest correlation in both. 5 The credit-real exchange rate correlations are in line with the data as well. The correlation between the real exchange rate and the change in household credit as a ratio of output is strongly positive and very close to the data. This correlation is higher than 5 In the statistics reported in Table 2 household credit has a much higher correlation with output than business credit in Turkey. The main reason for the di erence between the statistics reported in the two tables is that output is measured in tradable units in Table 4 to make the data comparable with the model. The correlations in the data for real GDP and change in credit-to-gdp ratios are.69,.38 and.46 for household credit, nontradable credit and tradable credit, respectively, for the time period considered in Table 4. 23

24 the correlations between the real exchange rate and the two types of business credit, as observed in the data. Table 4. Business cycle properties Standard Deviations Data Model Data Model ( ) (C)=( ) ( T ) (L)=( ).6.58 ( N) (RER)=( ) (IT )=( T ) T B (IN)=( N) Correlations Data Model Data Model (C; ) T B ; RER (L; ).4.7 HC ; RER (I; ) BCN ; RER T B ; BCT ; RER HC ; HC BCN BCT ; BCN ;.5.34 BCT (RER; ).4.72 ; T B ; T B ; T B Note: Trade balance (TB) is exports minus imports. Changes in household credit (HC), nontradable sector credit (BCN) and tradable sector credit (BCT) are HC t -HC t 1 ; BCN t -BCN t 1 and BCT t -BCT t 1, respectively. Output (), tradable output (T), nontradable output (N), consumption (C), labor (L), sectoral investment (IT and IN) and real exchange rate (RER) are in logarithms. All series are HP ltered. The standard deviations are reported in percentage terms. The trade balance-to-gdp ratio has a much stronger negative correlation with household credit than with the other two types of credit. The model matches this feature, albeit less strongly than the data. The e ect of changes in a certain type of credit on the trade balance is closely tied to the LTI and LTC ratios of the agents. Since household credit is 24

25 lower than the other two types of credit in the data, the credit limit of the household is lower in the model as well, which reduces the impact of household credit changes on the trade balance. When the credit-to-gdp ratios are equalized for all agents, the negative correlation of household credit with the trade balance increases relative to the other credit types, as illustrated in Table 6. The model also generates a strongly countercyclical trade balance, with a value very close to the data, and a consumption volatility higher than output volatility. Both of these are common features of emerging market economies and hard to match using standard real business cycle models Assessing the Relative E ects of Credit Shocks To understand the role of credit shocks in terms of business cycle properties, Table 5 documents the moments generated by the model with only productivity shocks and with productivity and interest rate shocks. For the shocks to the interest rate, the following stochastic process is estimated using real interest rate data from Turkey: R t = (1 r )R + r R t 1 + r T A t;t + r NA t;n + " r t ; where R is the steady-state real interest rate and " r t are normally distributed and serially uncorrelated innovations. Similar to Neumeyer and Perri (25), we assume that the real interest rate depends on the productivity shocks. This formulation is also in line with the stochastic processes that we use for the credit shocks. The calculation of the real interest rate series is explained in the Appendix. For R, the value of the average interest rate from the baseline model, 1.117, is used. The estimation of the above process gives.494, -.24, -.9 for r ; r T ; r N ; respectively and ("r t ) equals.23. The performance of the model with only productivity shocks and with productivity and interest rate shocks deteriorates signi cantly in several dimensions. Without the credit shocks, the model cannot match the relative volatility of consumption, generating a consumption volatility much lower than the data. The volatility of the trade balance-to-output ratio is very low as well. The credit constraints on agents limit the amounts that they can borrow. Even though the uctuations in output and capital lead to changes in the amount 25

26 of debt through the collaterals, these changes are not enough to generate the volatility observed in the trade balance. The credit constraints also limit the response of consumption to shocks and the lack of volatility in borrowing leads to a lower consumption volatility as well. The sectoral volatilities of investment relative to output are matched using capital adjustment costs in the baseline model. Without the credit shocks, the relative volatility of investment in the tradable sector is lower than the data even when the adjustment costs are set to zero. Overall, these results show that the volatilities generated by productivity and interest rate shocks are not su cient to match the volatilities observed in the data. Table 5. Business cycle properties - Other shocks Standard Deviations Data Baseline A shocks A and R shocks ( ) ( T ) ( N) (C)=( ) (IT )=( T ) (IN)=( N) T B Correlations Data Baseline A shocks A and R shocks T B ; HC ; BCN ; BCT ; HC ; RER BCN ; RER BCT HC BCN BCT ; RER ; T B ; T B ; T B 26

27 The correlations of the credit variables generated by the model also deteriorate considerably when the credit shocks are excluded. The model with credit shocks always has the correct ordering in terms of the correlations of di erent types of credit, with the household credit having a stronger correlation with output, real exchange rate and trade balance. In the simulation results without the credit shocks, the ordering of the correlations are inconsistent with the data and the correlations of the tradable sector credit are almost always higher than the correlations of the other types of credit. The magnitudes of the correlations are quite far from the data as well. These results show that the credit shocks are important in matching certain features of the data, especially the moments related to di erent credit types Equal Credit-to-GDP Ratios for All Agents In the benchmark model, the LTI and LTC ratios are set to match the credit-to-gdp ratios in the data for each credit type. The credit-to-gdp ratios equal 7.2%, 9.6% and 11.2 % for household credit, tradable sector credit and nontradable sector credit, respectively. The credit ratios being di erent for agents may be important in terms of some of the model statistics since the e ect of each type of credit on the model dynamics depends on their relative magnitudes. By setting the LTI and LTC ratios in a way to equalize the credit-to- GDP ratios for all agents, we analyze how the moments change when each agent borrows the same amount on average. For this analysis, the borrowing limits are changed such that the credit-to-gdp ratio for each sector is equal to 11.2% and the results are reported in Table 6. With borrowing levels of the household and the tradable sector increasing, both the relative volatility of consumption and the volatility of the trade balance-to-output ratio increase. When agents face higher credit limits, they increase their borrowing more in response to positive shocks, which leads to bigger uctuations in consumption and trade balance. While most of the correlations are not a ected, the correlations of di erent types of credit with the trade balance change. The e ect of an agent s borrowing on the trade balance increases with the borrowing limit, which leads to an increasing correlation. In the baseline analysis, the credit type that is most negatively correlated with the trade balance is the household credit, even though the credit limit of the household is the lowest as implied 27

28 by the household credit-to-gdp ratio. When the credit limits are adjusted such that all agents on average borrow equal amounts, the negative correlation between household credit and trade balance-to-gdp ratio gets stronger while the correlations of business credit get weaker. Table 6. Equal credit-to-gdp ratios Standard Deviations Data Baseline Equal Credit/GDP ( ) ( T ) ( N) (C)=( ) (IT )=( T ) (IN)=( N) T B Correlations Data Baseline Equal Credit/GDP T B ; HC ; BCN ; BCT ; HC ; RER BCN ; RER BCT HC BCN BCT ; RER ; T B ; T B ; T B 28

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