







































American Research Journal of Economics, Finance and Management 

Volume 10 Issue 2, April-June 2022 

ISSN: 2836-9416 

Impact Factor: 4.85 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

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43 | P a g e  

THE DYNAMICS OF TRADE CREDIT TERMS IN RESPONSE TO 
INTEREST RATE CHANGES 

 
 

1Dr. William S. Lim and 2Dr. Mohammad A. Rashid 
1School of Administrative Studies, York University, Toronto, Canada 

2Faculty of Business Administration, University of New Brunswick, Fredericton, Canada 
 

Abstract: This study investigates the impact of trade credit on the credit channel of monetary policy 
transmission. Despite the growing interest in monetary policy effectiveness, a comprehensive 
theoretical model explaining the empirical observation that trade credit mitigates the effects of 
central bank actions has been lacking. In this paper, we introduce a partial equilibrium model 
incorporating third-degree price discrimination with menu costs. Our key finding suggests that, in 
low-inflation periods, the increase in net present value (NPV) resulting from optimizing credit terms 
and product prices is insufficient to outweigh even minimal menu costs associated with short-term 
interest rate changes. As a result, credit terms and product prices remain stable over time. This 
outcome aligns with empirical evidence from Ng, Smith, and Smith (1999) and Mateut (2005) and 
provides a plausible explanation for the Meltzer (1960) hypothesis, which posits that trade credit 
fluctuates less than bank credit in the credit channel transmission of monetary policy. Furthermore, 
we draw parallels between this phenomenon and exchange rate pass-throughs and discuss the 
effectiveness of monetary easing during a pandemic. 
Keywords: Trade Credit, Credit Channel, Monetary Policy, NPV Optimization, Menu Costs. 
 
 
1. Introduction 
How does trade credit affect the credit channel of monetary policy transmission? This interest in 
monetary policy effectiveness has increased in recent years. However, there has not been a satisfactory 
theoretical model to explain the empirical stylized fact that trade credit dampens the impact of central 
bank actions. In this paper, we present a partial equilibrium model of third-degree price discrimination 
with menu costs. The main result is that the increase in NPV from optimizing credit terms and product 
prices is less than even miniscule menu costs for short-term interest rate changes in low-inflation 
periods. Therefore, credit terms and product prices are stable over time. This finding is consistent with 
Ng, Smith and Smith’s (1999) and Mateut’s (2005) empirical evidence and may also explain the Meltzer 
(1960) hypothesis on credit channel transmission of monetary policy, i.e., trade credit fluctuates less 
than bank credit. In short, certain empirical phenomena related to the credit channel could be 
rationalized by assuming that firms set trade credit terms to maximize NPV and take menu costs into 
account. Finally, similarities to exchange rate pass-throughs and the effectiveness of monetary easing 
in a pandemic are discussed.  
The rest of the paper is organized as follows: Extant literature is reviewed in Section 2, while Section 3 
performs the analysis. In Section 4, numerical results show credit terms and product prices are stable 
even with large changes in interest rates. Menu costs are considered and the Meltzer (1960) effect is 

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44 | P a g e  

shown to hold in Section 5. Section 6 concludes with a discussion of exchange rate pass-throughs and 
the effectiveness of monetary easing in a pandemic.  
2. Review of Factors affecting Trade Credit Terms   
Many theories have been proposed to explain the use of trade credit by vendors and the determination 
of credit terms. Credit terms specify when invoiced amounts are due and whether a cash discount could 
be taken for earlier payment. The credit period is the length of time allowable for payment of the invoice 
amount. The cash discount is the percentage amount that can be subtracted from the invoice if the 
customer pays within the discount period. Smith (1987) argues that a supplier provides trade credit in 
order to protect non-salvageable investment in the client’s relationship. Mian and Smith (1992) focus 
on the information advantage of trade credit over traditional financing. Emery (1984) proposes trade 
credit as a means of alleviating credit market imperfections, while Emery (1987) emphasizes that trade 
credit provides the means for the vendor to manage fluctuations in product demand. Brick and Fung 
(1984) consider the differential of tax rates between a supplier and its buyer as the reason for the 
provision of trade credit. Petersen and Rajan (1994) suggest credit rationing as a reason, while Schwartz 
and Whitcomb (1980) and Petersen and Rajan (1997, p.664) suggest price discrimination as a motive 
for trade credit. 
Equally, if not more, important as the abovementioned theories of trade credit are those that integrate 
credit policy with other policy decisions. It has been recognized (Kim and Atkins, 1978; Kim and Chung, 
1990) that suboptimal results will occur whenever interrelated policy variables are modeled 
independently. Therefore, it is desirable that credit management decisions be made jointly with other 
policy decisions. Perhaps the most important area of integration is the integration of a firm’s credit 
policy with its product pricing as recognized by Kim and Atkin (1978, p.403) who state that “it is 
conceptually incorrect to analyze credit programs in isolation of pricing schemes.” Their paper, along 
with Atkins and Kim (1977), use wealth-maximizing frameworks in their integrating efforts.  
The determination of an optimal cash discount from a theoretical perspective originated with Lieber 
and Orgler (1975) who developed expressions for the expected net present value or NPV of accounts 
receivable and implicit form solutions of the optimal discount. Later, Hill and Riener (1979) derived an 
explicit form solution of an optimal discount in a situation where the firm has no bad-debt exposure 
and the fraction of buyers discounting is known with certainty. Beranek (1991) provided analysis of 
behavioral factors determining the optimal cash discount. Recognizing that the provision of a cash 
discount is equivalent to a reduction in price, Rashid and Mitra (1999) linked it to the price elasticity of 
demand. Further recognizing that a cash discount for early repayment separates buyers with respect to 
their borrowing costs, Lim and Rashid (2002, 2008) introduce a partial equilibrium model of third-
degree price discrimination where the firm sets two prices to maximize NPV: a product price, and a 
cash discount (which determines the effective price in the second market). Setting two prices then 
requires two elasticities: a cash discount elasticity of demand (which measures the sensitivity of sales 
to the cash discount or credit terms in general), and the product price elasticity of demand (which 
measures the sensitivity of sales to the product price). The main conclusion of their paper is that the 
effect of the cash discount elasticity is mainly on the optimal cash discount, while the effect of product 
price elasticity is mainly on the optimal product price.  

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3. Theoretical Analysis     
In order to solve for the product price, P, and the cash discount, d, Lim and Rashid (2002, 2008) needed 
two separate elasticities of demand, Q: (a) the cash discount elasticity of demand, denoted by ηd, where:  

.Q d 
ηd  =                            (1)  

 
 d Q  

and (b) the product price elasticity of demand, denoted by ηp, where:  
  

 
.Q P 

ηp =                              (2)  

 
Q 

 p  
Since this paper builds on the model of Lim and Rashid (2002, 2008), it is essential that the 
assumptions, notation and the model be presented briefly.  
A single period framework is assumed. At the beginning of the period, both production and sale of Q 
quantity of output takes place, with the variable cost per unit, v, assumed to be constant. Given the 
length of credit period N2 days, the firm considers providing a cash discount rate, d, for early repayment 
of invoices by customers. If a cash discount is provided, we denote the discount period as N1 days. The 
sales are assumed to be uniformly distributed among customers. We assume that p fraction of 
customers take the cash discount, and of the (1-p) fraction that decline the cash discount, a λ fraction 
of these customers pay on day N2. Thus, (1-p)(1-λ) fraction of customers are those who do not take the 
cash discount and do not pay on day N2. This (1-p)(1-λ) fraction therefore default and become a bad 
debt loss. The firm sets not only the value of d but also the level of P . Assuming that the annual cost of 
short-term funds, k, is initially constant, the net present value of accounts receivable is given by: 
V = p (1-d) PQ(1+k)-N1/360 + (1-p)λPQ(1+k)-N2/360 – vQ     (3)  
The first term represents the present value of payments by customers who take the cash discount, while 
the second term represents the present value of payments by customers who do not take the cash 
discount and pay on day N2. The last term gives the variable cost of production, Q. The firm’s problem 
is to optimally choose the cash discount rate, d, and the product price, P. The optimal cash discount 
rate and product price will be denoted by d* and P* respectively. The separation of customers to those 
taking the cash discount and those not taking the cash discount makes the model one of third-degree 
price discrimination, similar to the model in Layson (1998). Layson denotes each market by 1 and 2, 
and denotes price and quantity by p and q. Equation (3) above would then be a special case of Layson’s 
(1998) profit function, π(p1,p2)=p1q1+p2q2-C(Q), where p1=P(1-d)(1+k)-N1/360, p2=P(1+k)N2/360, q1=pQ, 
q2=(1-p)λQ and C(Q)=vQ. As our model is one of price discrimination, we would also require the three 
conditions for price discrimination to exist as postulated by Carroll and Coates (1999): (i) the firm must 
have some market power; (ii) there can be at best imperfect arbitrage opportunities; and (iii) customers 
must have different price elasticities of demand. The imperfect arbitrage opportunities result from 

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imperfect financial markets (Emery, 1984). Behavioral specifications of p, λ and Q are given in 
Appendix 1. The theoretical derivation of the simultaneous equations determining the optimal cash 
discount rate and product price is shown in Appendix 2. The simultaneous equations are: 

Z 
d 

  

 p    p d 
 

P 

 0 
  

 (4)  

  (1  d )  p  d   
 

d  

d    (1  d ) d  
d 

Z 
 
 P  p (1  d ) (1     

)  (1  
P 

  
p) (1     )   P   

 0 
P 
P   

  

   (5)  

 
 
 

 
 
 

  
 where Z = V (1+k)N2/360, θ = (1+k)(N2-N1)/360 and σ =  v (1+k)N2/360. Z is then the NPV of accounts 
receivable at the end of the credit period N2. Equations (4) and (5) constitute a system of simultaneous 
equations in d* and P* where ηd and ηP play an important role. In equation (4), the effect of ηd is 
embodied in the last two terms as the first two terms simply represent a trade-off between the time 
value of money of early receipt of payment and the cash discount expense. In equation (5), if ηP=-1, the 
first order condition cannot be satisfied because the first two terms become zero while the last term is 
positive. For 0<|ηP|<1, all three terms in equation (5) are positive, again making it impossible for this 
condition to hold. 
4. Numerical Results 
For the solution of the simultaneous system in equations (4) and (5), a specific relationship between p 
and d has to be assumed. Following Rashid and Mitra (1999), we assume p=Bd where B is a positive 
constant. Instead of recursive substitution used by Rashid and Mitra (1999) and Lim and Rashid (2002, 
2008), we solve equations (4) and (5) using the “Solver” tool in MS Excel. Using empirical estimates 
from the 1993, 1998 and 2003 National Survey of Small Business Finances as reported in Lim, Rashid 
and Mitra (2006) and bad debt estimates found in Scherr (1989), the model is calibrated as follows: λ 
= 0.99; k = 10% per annum; v = $0.8 per unit of output; N1 = 10 days; N2 = 30 days; B = 10. For a 
selected pairs of values of ηd and ηp, Table 1 presents optimal cash discount rates and optimal product 
prices.  
Table 1 Optimal Cash Discount Rates and Optimal Product Price at Various Demand 
Elasticities  
  

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ηd   

ηP  

  
  

d* (top of each cell), P* (bottom of 
each cell )  

        
    -1.5    -2.0    -2.5    -3.0  
  
  
  0.005  

  
  0.0172  
  $2.444  

  
  0.0155  
  $1.629  

  
  0.0144  
  $1.357  

  
  0.0137  
  $1.222  

  
  
  0.01  

  
  0.0222  
  $2.447  

  
  0.0199  
  $1.631  

  
  0.0183  
  $1.358  

  
  0.0171  
  $1.222  

  
  
  0.015  

  
  0.0262  
  $2.451  

  
  0.0233  
  $1.632  

  
  0.0213  
  $1.359  

  
  0.0199  
  $1.223  

  
  
  0.02  

  
  0.0295  
  $2.454  

  
  0.0261  
  $1.634  

  
  0.0238  
  $1.360  

  
  0.0222  
  $1.224  

As noted in Lim and Rashid (2002, 2008), higher (lower) is the product price elasticity of demand, 
lower (higher) is the optimal product price. Also, higher (lower) is the cash discount elasticity of 
demand, higher (lower) is d*. As customers with higher cash discount elasticities have higher 
borrowing costs, this explains survey evidence that customers with higher borrowing costs are 
offered higher cash discounts. As ηd rises, the rate of increase in d* slows down. We also find what Lim 
and Rashid (2002, 2008) term a “simultaneity effect”, that is, the cash discount is directly related to 
the contribution margin, and results from the assumption of interdependent demands. The effect of the 
cash discount elasticity ηd on the optimal cash discount d* is much larger than the effect of the product 
price elasticity ηp on d*. This confirms Lim and Rashid’s (2002, 2008) main theoretical finding for a 
60-day credit period and is consistent with Lim, Rashid and Mitra’s (2006) empirical evidence as 
described in Section 1. For most grids of ηd and ηp, the numerical values of d* are around 2%. Lim, 
Rashid and Mitra (2006) examine buyer firms from the 1993, 1998 and 2003 National Survey of Small 
Business Finances and find that the median and mode discount rates are 2%. Ng, Smith and Smith 
(1999) examine supplier firms and find the same 2% discount. Maness and Zietlow (2005) present an 
overview of cash discount practices consistent with competitive suppliers offering a 2% discount. For 
the three pairs of the two elasticities, Table 2 illustrates the effect of k on d* and P*. Note that while k 
increases from 0% to 20%, with (ηd,ηP) = (0.005,-1.5), the optimal cash discount only increases from 
1.55% to only 1.87% while the optimal product price increases from $2.43 to only $2.46. Similar results 
are observed for other pairs of elasticities. Therefore, credit terms and product prices are stable even 
with large changes in macroeconomic interest rates. 
 
 
 
 

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Table 2: Effect of Variations in k on d* and  P* at three Selected Pairs of ηd and ηp  
d* (top of each cell), P* (bottom of each cell)  

           
k (%) 
ηd ,  

  
  

  
  

  
  

  
  

  
  

ηp    0    5    10    15    20  
0.005,  
 
 
-1.5  

  0.0155  
 
 $2.42
6  

 
 0.016
4  
 
 $2.43
6  

 
 0.017
2  
 
 $2.44
4  

 
 0.017
9  
 
 $2.45
3  

 
 0.018
7  
 
 $2.46
1  

0.02,  
 
 
-1.5  

 
 0.028
0  
 
 $2.43
7  

 
 0.028
7  
 
 $2.44
6  

 
 0.029
5  
 
 $2.45
4  

 
 0.030
2  
 
 $2.46
2  

0.0309  
 
 $2.47
0  

0.005,  
 
 
-3  

  0.0119  
  $1.212  

 
 0.012
8  
  $1.217  

 
 0.013
7  
 
 $1.22
2  

 
 0.014
5  
 
 $1.22
6  

 
 0.015
4  
 
 $1.23
0  

  
5. Adding Menu Costs to the Model.   
Menu costs refer to the direct costs of price adjustment (like changing a menu). Zbaracki et al. (2004) 
identify and measure three types of managerial costs (information gathering, decision-making, and 
communication costs) and two types of customer costs (communication and negotiation costs) related 
to price adjustment. They find that the managerial costs are more than six times, and customer costs 
more than twenty times, the menu costs, confirming McCallum’s (1988) notion that menu costs are of 
insignificant magnitude. In total, the price adjustment costs comprise 1.22% of the company’s revenue, 
with menu costs comprising just 3.57% of the total price adjustment costs (or 0.0435% = 3.57%*1.22% 
of the company’s revenue). Zbaracki et al. (2004, pp. 523 and 530) have excluded fixed costs from their 
calculations. Thus a company would incur menu costs of 0.0435% of revenues each time a price change 
is made. Also, the estimates of menu costs obtained from Zbaracki et al. (2004) are for a billion-dollar 
company. If there are economies of scale in menu costs, then a smaller company would incur menu 
costs larger than 0.0435% of revenues.   
A profit-maximizing firm would only change its credit terms when the increase in the NPV of its 
accounts receivables exceeds the menu costs. With the NPV of accounts receivables, V, given by 
equation (3), the revenues are given by the first two terms. The increase in NPV as a percentage of 

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revenues is calculated in Appendix 3. Suppose that k is initially at 5%. Also suppose (ηd,ηP)= (0.005,-
1.5). From Table 2, d* is 1.64% and P* is $2.436. Now if the central bank raises short-term interest rates 
such that k is now 10%, the firm must decide whether to change d and P to their new optimal levels of 
1.72% and $2.444 respectively. Assuming that k stays at 10% for the next 30 days, in our model, the 
menu costs at the end of the credit period is 0.0435%*1.1^(30/360) = 0.0438% of revenues. The firm 
will change d and P if ∆Z is greater than 0.0438%. Now Zd = 2.575% and Zp = 0.07%. Both Zd and Zp 
are evaluated at the old optimum of d=1.64% and P=$2.436, but with k=10% (i.e., at the new value of 
k which is exogenous). So ∆d = 1.72%-1.64% = 0.08% or 0.0008 and ∆P = $2.4444-$2.4356 = $0.0088. 
Thus Zd*∆d = 0.00207% of revenues, which is the increase in NPV when the firm optimizes its cash 
discount, and Zp*∆P = 0.00063% of revenues, which is the increase in NPV when the firm optimizes 
its product price. The total increase in NPV when the firm optimizes both d and P is ∆Z = 0.0027% of 
revenues, which is well below the menu costs of 0.0438% of revenues. Even though NPV increases when 
the firm optimizes d and P, the increase is much less than the menu costs. Therefore, the firm would 
optimally choose not to change d and P.  
Table 3 shows the menu costs (as a percentage of revenues) at the end of the credit period for different 
levels of k. These menu costs will be compared to the estimates of the increase in NPV from optimizing 
d and P, as shown in Tables 4 to 8, where each table is for different pairs of elasticities. Tables 4 to 6 
have the bad-debt loss falling proportionately as the cash discount, d, increases (∂λ/∂d=0), Table 7 has 
the bad-debt loss about constant (∂λ/∂d=0.1), and Table 8 has the bad-debt loss increasing as d 
increases (∂λ/∂d=-0.625). We assume that k=5% initially, and the central bank can increase k from 5% 
to the levels of k reported in the tables. For example, k=20% refers to the central bank raising k from 
5% to 20% and lists the optimal d* and P* for k=20% and computations of the NPV increase. The point 
is to find out how much k must increase from 5% in order for the increase in NPV from optimizing d 
and P to be larger than the menu costs.   
The second column in each table lists the optimal d* and P* for the respective k. The third column in 
each table lists the increase in NPV (as a percentage of revenues) when the firm optimizes d, which is 
Zd*∆d. In order to calculate Zd with ∂λ/∂d nonzero in Tables 7 and 8, we replace Zd with Zd', with Zd' 
= 100*[(∂Z/∂d)/d + (1-p) * ∂λ/∂d * d]/A where ∂Z/∂d is defined by equation (4).  The fourth column 
in each table lists the increase in NPV (as a percentage of revenues) when the firm optimizes P, which 
is Zp*∆P. Note that Zp is unaffected by ∂λ/∂d. The fifth column in each table lists the total increase in 
NPV (as a percentage of revenues) when the firm optimizes both d and P, which is ∆Z.  
Table 3: Menu Costs as a Percentage of Revenues at End of Credit Period (Day N2)  

K  Menu Costs (% of Revenues)  k  Menu Costs (% of Revenues)  

5%  0.0437%  28%  0.0444%  

10%  0.0438%  29%  0.0444%  

15%  0.0440%  30%  0.0445%  

20%  0.0442%  31%  0.0445%  

21%  0.0442%  32%  0.0445%  

25%  0.0443%  36%  0.0446%  

27%  0.0444%  47%  0.0449%  

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Table 4: Increase in NPV as a Percentage of Revenues at End of Credit Period (Day N2)  
(ηd,ηp) =  (0.005,-1.5); ∂λ/∂d= 0  

k  d*, P*  Zd * ∆d  Zp * ∆P  ∆Z  

5%  0.0164,  $2.436  -  -  -  

10%  0.0172,  $2.444  0.00207%  0.00063%  0.00270%  

15%  0.0179,  $2.453  0.00805%  0.00239%  0.01044%  

20%  0.0187,  $2.461  0.0176%  0.0051%  0.0227%  

25%  0.0195,  $2.468  0.0304%  0.0088%  0.0392%  

27%  0.0198,  $2.471  0.0364%  0.0104%  0.0468% 2  

30%  0.0202,  $2.476  0.0461% 1  0.0132%  0.0593% 2  
1 NPV Increase from Change in Cash Discount Only Exceeds Menu Costs  
2 NPV Increase from Change in Cash Discount and Product Price Exceeds Menu Costs  
Table 5: Increase in NPV as a Percentage of Revenues at End of Credit Period (Day N2)  
(ηd,ηp) =  (0.02,-1.5); ∂λ/∂d= 0  
  

k  d*, P*  Zd * ∆d  Zp * ∆P  ∆Z  

5%  0.0287,  $2.446  -  -  -  

10%  0.0295,  $2.454  0.00185%  0.00055%  0.00240%  

15%  0.0302,  $2.462  0.00717%  0.00208%  0.00925%  

20%  0.0309,  $2.470  0.0156%  0.0045%  0.0201%  

25%  0.0310,  $2.477  0.0267%  0.0077%  0.0344%  

28%  0.0319,  $2.481  0.0347%  0.0099%  0.0446% 2  

32%  0.0324,  $2.487  0.0466% 1  0.0132%  0.0598% 2  
1 NPV Increase from Change in Cash Discount Only Exceeds Menu Costs  
2 NPV Increase from Change in Cash Discount and Product Price Exceeds Menu Costs  
Table 6: Increase in NPV as a Percentage of Revenues at End of Credit Period (Day N2)  
(ηd,ηp) = (0.005,-3); ∂λ/∂d= 0  
  

k  d*, P*  Zd * ∆d  Zp * ∆P  ∆Z  

5%  0.0128,  $1.217  -  -  -  

10%  0.0137,  $1.222  0.00223%  0.00260%  0.00483%  

15%  0.0145,  $1.226  0.00869%  0.00992%  0.01861%  

20%  0.0154,  $1.230  0.0190%  0.0214%  0.0404%  

21%  0.0155,  $1.231  0.0215%  0.0241%  0.0456% 2  

25%  0.0162,  $1,234  0.0329%  0.0364%  0.0693% 2  

29%  0.0169,  $1.237  0.0464% 1  0.0507%  0.0971% 2  

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NPV Increase from Change in Cash Discount Only Exceeds Menu Costs  
NPV Increase from Change in Cash Discount and Product Price Exceeds Menu Costs  
   
Table 7: Increase in NPV as a Percentage of Revenues at End of Credit Period (Day N2)  
(ηd,ηp) = (0.005,-1.5); ∂λ/∂d= -0.1  
  

k  d*, P*  Zd * ∆d  Zp * ∆P  ∆Z  

5%  0.0139,  $2.435  -  -  -  

10%  0.0146,  $2.443  0.0019%  0.0006%  0.0025%  

15%  0.0153,  $2.452  0.0074%  0.0024%  0.0098%  

20%  0.0161,  $2.460  0.0163%  0.0053%  0.0216%  

25%  0.0168,  $2.467  0.0282%  0.0090%  0.0372%  

27%  0.0171,  $2.470  0.0339%  0.0107%  0.0446% 2  

31%  0.0176,  $2.476  0.0464% 1  0.0144%  0.0608% 2  
1 NPV Increase from Change in Cash Discount Only Exceeds Menu Costs  
2 NPV Increase from Change in Cash Discount and Product Price Exceeds Menu Costs  
Table 8: Increase in NPV as a Percentage of Revenues at End of Credit Period (Day N2)  
(ηd,ηp) = (0.005,-1.5); ∂λ/∂d= -0.625  
  

k  d*, P*  Zd * ∆d  Zp * ∆P  ∆Z  

5%  0.0058,  $2.433  -  -  -  

10%  0.0060,  $2.442  0.0007%  0.0007%  0.0014%  

15%  0.0063,  $2.451  0.0027%  0.0027%  0.0054%  

20%  0.0065,  $2.459  0.0060%  0.0057%  0.0117%  

30%  0.0071,  $2.475  0.0164%  0.0147%  0.0311%  

36%  0.0074,  $2.484  0.0250%  0.0217%  0.0467% 2  

47%  0.0081,  $2.499  0.0452% 1  0.0367%  0.0819% 2  
Although uncommon in the United States in recent decades with historically low inflation, absolute 
shortterm rate changes over 15% could be recent phenomena in some countries, explaining World Bank 
survey evidence that trade credit fluctuates more in such countries. For example, the World Bank has 
survey evidence that trade credit fluctuated significantly after financial crises in developing countries 
when short-term interest rates fluctuated between 9 and 44 percentage points over a month.   
5. Conclusion  
In this paper, numerical computations showed that credit terms and product prices are stable even with 
large changes in macroeconomic interest rates. Using a menu cost estimate by Zbaracki et al. (2004) of 
just 0.0435% of revenues, it would require an increase in short-term interest rates from 5% to over 20% 
for the benefits of increasing trade credit interest rates and product prices to outweigh the menu costs.  
Although uncommon in the United States in recent decades with historically low inflation, absolute 

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short-term rate changes over 15% could be recent phenomena in some countries, explaining World 
Bank survey evidence that trade credit fluctuates more in such countries (e.g., Indonesia, South Korea 
and Thailand in the Asian financial crisis of the late 1990s). This finding led to our main conclusion 
that the empirical phenomena of credit terms and product prices being stable over time (in the United 
States in recent decades) and of the Meltzer (1960) effect could be rationalized by assuming that firms 
set credit terms to maximize NPV and take menu costs into account.  
Our results on credit policy have some commonality with the literature on exchange rate pass-throughs. 
In the early years of floating exchange rates, economists expected to find a close association between 
movements in exchange rates and national price levels. Based on purchasing-power parity, it was felt 
that control of domestic inflation would become more problematic in an environment of exchange rate 
volatility. However, a substantial literature, covering many countries, has documented that exchange 
rate changes are, at best, weakly associated with changes in domestic prices at the consumer level. The 
low-degree of “exchange rate pass-through” both at the disaggregated level, for individual traded goods 
prices, and more generally, in aggregate price indexes, has been extensively documented. (Devereux 
and Yetman, 2002, p.347). This led Devereux and Yetman (2002) to develop a simple theoretical model 
of endogenous exchange rate pass-through that focuses exclusively on the role of price rigidities that 
come about because of the presence of “menu costs”.  In their model calibration, they find that for 
annual rates of inflation higher than 25 percent, firms will adjust prices every period so price rigidity 
disappears completely. In that case, the pass-through from exchange rate changes to prices is complete. 
In short, in countries with very high inflation (or very high interest rates), prices become essentially 
flexible as the cost to firms of maintaining fixed prices fully offsets the menu costs. In our model, firms 
only adjust credit terms and product prices when short-term interest rates change more than 15 
percent. Pass-throughs to credit terms and product prices are higher in periods with higher nominal 
interest rates (and thus higher absolute interest rate changes).  
Many industrialized countries seemed to have experienced a decline in exchange rate pass-through to 
consumer prices in the 1990s, despite large exchange rate depreciations in many of them. Bailliu and 
Bouakez (2004) state the fact that this documented decline in exchange rate pass-throughs in recent 
years coincided with the lowinflation period that most industrialized countries have entered and that 
these two phenomena are correlated. Sekine (2006) finds that pass-throughs have declined over time 
for all his sample countries. The decline in second-stage passthrough (from import prices to consumer 
prices) is associated with the emergence of a low inflation environment as well as a rise in import 
penetration. These results are consistent with our model, which predicts that credit terms and product 
prices would remain stable in recent decades due to low inflation.  
Outside of the United States and other industrialized countries, however, credit terms have not been 
stable, especially in countries which suffered through financial crises. Devereux (2001) presents 
evidence that exchange rate passthrough is very rapid for emerging markets, but slow for advanced 
economies. He quotes the Governor of the Central Bank of Mexico, Guillermo Ortiz, who stated on 24 
June 1999 that: “The pass-through of exchange rates to inflation was much higher in Mexico than in 
Canada, Australia or New Zealand. And this has to do a lot with history, with credibility of monetary 
policies, and this is one of the big challenges that we are facing today in Mexico in the conduct of 

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monetary policy. And we have to really build sufficient credibility so that this pass-through from 
exchange rate movements to inflation ceases to be such an automatic reaction.”   
Our model suggests that the stability of credit terms in the United States is due to the credibility 
established by the Federal Reserve in maintaining a low and stable inflation environment where short-
term interest rate changes are gradual. The converse would, however, also be true. Monetary easing in 
times of recession (like during a pandemic) may not result in lower trade credit interest rates or more 
generous credit terms as the benefits of companies changing credit terms might be offset by menu costs. 
This explains the common observation that trade credit or credit card interest rates remain high even 
though the federal funds rate is near zero. 
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Appendix 1.  Behavioral Specifications in Theoretical Model 
A1a.  The Specification of p:  
 p = p (d, P),   where   δp/δd > 0   and    δp/δP= 0                  (A1)  
The excess of the opportunity cost of not taking the cash discount over individual borrowing rates of 
marginal customers after an increase in the cash discount explains why δp/δd > 0. It is this proportion, 
p, which reflects the demand interdependencies of the two markets. If a larger proportion of customers 
take the cash discount with an increase in d, demand in the other market (those not taking the cash 
discount) must fall. Next, with a reduction in the product price, the amount of borrowing needed 
declines, which may result in lower borrowing rates. If this happens, then the effect on p is positive. On 
the other hand, with a reduction in product price, quantity demanded rises, requiring more borrowing 
and perhaps higher borrowing rates. If this happens, then the effect on p is negative. It is difficult to 
know which of the two opposing effects is stronger. Therefore, we let δp/δP= 0 throughout.  
A1b.  The Specification of λ:  
 λ  = λ (d,P), where    δλ/δd ≤ 0   and    δλ/δP = 0                   (A2) 
δλ/δd = 0 requires that among the customers who are not taking the cash discount, the percentage of 
those who would have paid on day N2 and the percentage of those who would have defaulted is 
unchanged after an increase in the cash discount. δλ/δd > 0 implies that, with an increase in the cash 
discount, customers who would have defaulted as a fraction of the customers who do not take the cash 
discount decrease. This effect is quite unlikely. Thus, we postulate that δλ/δd is either negative or has 
the highest value of zero. Initially we would let δλ/δd=0. With δp/δP=0, we may expect the bad-debt 
loss (1-p)(1-λ) to increase when the product price increases (i.e., δλ/δP<0). However, with a uniform  

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