# Do firms react to monetary policy in developing countries?

Djeneba Dramé, Florian Léon

<figure><img src="image_1.png" /></figure>

## To cite this version:

Djeneba Dramé, Florian Léon. Do firms react to monetary policy in developing countries?. European Economic Review, 2025, 178, pp.105102. 10.1016/j.euroecorev.2025.105102. hal-05172185

HAL Id: hal-05172185

https://hal.science/hal-05172185v1

Submitted on 21 Jul 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
<figure><img src="image_3.png" /></figure>

ELSEVIER

Contents lists available at ScienceDirect

European Economic Review

journal homepage: www.elsevier.com/locate/eer

<figure><img src="image_4.png" /></figure>

# Do firms react to monetary policy in developing countries?

Djeneba Dramé<sup>a, b</sup>, Florian Léon<sup>b, c, d, e</sup>

## ARTICLE INFO

Dataset link: Data are attached to the title of the article (Original data)

JEL classification:

D4

E52

G32

L1

O16

Keywords:

Monetary policy

Financial constraints

Firms

Developing countries

## ABSTRACT

This paper examines how firms in developing countries respond to monetary policy changes, focusing on both their perceptions of credit constraints and their borrowing behavior. Using firm-level data from the World Bank Enterprise Surveys (WBES) and a newly constructed database of monetary policy changes, we employ an event study approach to analyze how managers adjust their expectations of credit access in the days following a policy intervention. We complement this with a broader analysis of how annual policy rate changes affect firms' credit applications. Our results show that firms perceive credit access as more restrictive after a policy rate hike, but do not significantly reduce their credit applications. Instead, credit demand increases after a rate cut, highlighting an asymmetric response to monetary policy. We also find substantial heterogeneity, with firms' sensitivity depending not only on their proximity to banks, but also on the degree of liquidity of the banking market and the degree of independence of the central bank. These results provide new insights into the transmission of monetary policy in developing countries.

## 1. Introduction

The recurrence of economic crises has led to a global resurgence of inflation, forcing policymakers to find ways to contain inflation without stifling economic activity. Monetary policy is a key instrument in achieving this balance, but its effectiveness in developing countries remains an open question. While a large body of empirical work has examined the impact of monetary policy in developing countries, results remain mixed.

This paper takes a novel approach to assessing the effectiveness of monetary policy in developing countries by examining how firms respond to monetary policy decisions. Using firm-level data from the World Bank Enterprise Surveys (WBES) and a newly constructed database of monetary policy changes, we employ two complementary approaches. First, we use an event study to assess how managers adjust their expectations of credit access in the days following a policy intervention. Second, we analyze whether these shifts in perception translate into actual borrowing decisions by examining how firms' credit applications respond to changes in policy rates.

Our findings reveal that firm managers adjust their perceptions and behavior in response to monetary policy. Using a sample of over 10,000 firms in 39 developing countries, we show that managers surveyed within 3 to 12 days of a policy rate increase are more likely to report access to credit as a serious constraint than those interviewed just before the event. In addition, using annual data on over 36,000 firms from 76 developing countries, we find that loan applications increase significantly following a policy rate cut.

https://doi.org/10.1016/j.euroecorev.2025.105102

Received 10 September 2024; Received in revised form 30 June 2025; Accepted 1 July 2025

Available online 17 July 2025

0014-2921/© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

<footnote><sup>a</sup> Université Paris-Nanterre, Economix, France</footnote>

<footnote><sup>b</sup> Foundation for Studies and Research on International Development (FERDI), France</footnote>

<footnote><sup>c</sup> Université Clermont Auvergne, CNRS, IRD, CERDI, France</footnote>

<footnote>* Corresponding author at: Foundation for Studies and Research on International Development (FERDI), France.
E-mail addresses: djenebadrame@gmail.com (D. Dramé), florian.leon1@ferdi.fr (F. Léon).</footnote>
While both approaches confirm that companies react to monetary policy in developing countries, the responses are asymmetric: perceptions are more sensitive to monetary tightening, while credit applications are more responsive to monetary easing. This discrepancy likely reflects differences in how firms process monetary policy signals - managers internalize the risks of tighter credit conditions immediately, but actual borrowing decisions depend more on the availability of financing. In addition, methodological constraints may limit the ability to fully capture the effects of monetary tightening on loan applications.

Finally, we exploit the richness of our database to examine heterogeneity in firms' responses to monetary policy. We exploit both differences across firms (size, age, etc.) and across countries. The results document that firms with a pre-existing banking relationship are less sensitive to monetary policy. This result suggests that prior access to credit helps buffer against interest rate fluctuations. At the country level, firms in economies with independent central banks, inflation targeting regimes, and less liquid banking systems exhibit stronger responses to policy changes. These findings underscore the importance of institutional quality and financial system characteristics in shaping monetary policy transmission in developing countries.

Our study makes three key contributions to the literature on monetary policy in developing countries.

First, we offer a novel approach that balances internal and external validity<fnref n="1" /> in assessing the effectiveness of monetary policy in low- and middle-income countries. Identifying the causal effects of monetary policy is challenging due to the endogeneity of policy decisions. While structural models are commonly used in developing countries (Mishra et al., 2012, 2014; Barajas et al., 2018; Brandao-Marques et al., 2020), they face limitations due to data constraints that prevent accurate estimation (Mishra and Montiel, 2013; Li et al., 2019). Recent studies have improved identification by using narrative approaches (Berg et al., 2019), unexpected monetary shocks (Willems, 2020), or bank-level or credit registry data to track the effects of monetary policy (Bhaumik et al., 2011; Abuka et al., 2019; Bhardwaj et al., 2022). However, these approaches often sacrifice external validity by focusing on a limited set of (dramatic) monetary events. Our study bridges this gap by combining a short-term event study of firm perceptions, modeled on the discontinuity approach of Nakamura and Steinsson (2018a), with a broader behavioral analysis of credit applications. By reconstructing high-frequency data<fnref n="2" /> from firm interviews conducted within days of monetary policy changes, we provide a novel identification strategy that captures short-term adjustments in expectations while maintaining external validity by covering a wide range of policy events of different magnitudes.

Second, we contribute to the growing literature on how monetary policy affects firms' expectations and decisions. Existing research has primarily focused on the supply side of monetary transmission, analyzing how banks adjust their lending in response to policy changes while attempting to control for credit demand shocks (Jiménez et al., 2012, 2014). However, a significant part of monetary transmission may occur through changes in firm behavior, particularly borrower discouragement, which has been identified as a major source of credit constraints in developing countries (Brown et al., 2011; Léon, 2015). While a burgeoning literature explores how monetary policy shapes firms' macroeconomic perceptions (Enders et al., 2019; Pinter and Kočenda, 2023) and how these expectations influence real decisions (Coibion et al., 2020; Enders et al., 2022), most studies focus on developed economies. The closest work to ours is Ferrando et al. (2022), which shows that monetary policy in Europe affects firms' investment and employment growth by shaping expectations of future credit availability. We extend this analysis to developing countries, where financial frictions are more severe, and further explore how institutional factors influence firms' sensitivity to monetary policy.

Third, we provide new evidence on firm-level heterogeneity in monetary policy transmission. Much of the literature on developing countries has examined country-level determinants of monetary policy effectiveness (Mishra et al., 2014; Brandao-Marques et al., 2020), but few studies have investigated firm-level heterogeneity in responses to monetary policy. Existing studies on firm-level differences largely focus on advanced economies, analyzing how monetary policy affects firms differently based on size, leverage, or financial constraints (Gertler and Gilchrist, 1994; Ottonello and Winberry, 2020; Durante et al., 2022; Cloyne et al., 2023). The study most relevant to ours is (Choi et al., 2024), which examines how monetary policy transmission varies across industries using a sample of both developed and developing countries. We go further by showing that even within the same industry, firms with prior banking relationships are significantly less sensitive to monetary policy changes. This suggests that, beyond industry-level effects, firm-specific characteristics play a crucial role in shaping monetary policy transmission in developing countries, a dimension that remains underexplored in the literature. We also confirm that banking system characteristics and institutional factors are crucial.

Our work contributes to the literature on credit access in developing countries, where research on the direct effects of short-term monetary policy remains limited. While several studies examine the crowding out effects of fiscal policy (Cavallo and Daude, 2011; Huang et al., 2020), few examine how monetary policy affects firms' ability to secure financing. We find that monetary policy affects both managers' perceptions of credit constraints and their borrowing decisions. Tightening increases concerns about credit availability, while easing encourages firms to apply for credit. By influencing borrower discouragement, a key obstacle to financing in developing countries, monetary policy plays a crucial role in determining access to credit.

The remainder of the paper is organized as follows. Section 2 describes the data. Section 3 presents the perception-based analysis. Section 4 examines the impact on behavior (loan application). Section 5 presents the heterogeneity analysis. The last section concludes.

<footnote n="1">¹ Internal validity can be defined as the degree of confidence that the causal relationship being tested is reliable and not confounded by other factors or variables. External validity refers to the extent to which the results of a study can be generalized to other contexts.</footnote>

<footnote n="2">² The definition of high-frequency data differs between our study and previous research, initiated by Nakamura and Steinsson (2018a), due to the data limitations we face. Unlike (Nakamura and Steinsson, 2018a) and forthcoming studies, which focus on intraday variations within a single country, our study considers a longer time horizon (several days). Our paper also differs from this work in other dimensions, as we consider events occurring in several countries and use monetary policy directly as the treatment. In other words, our parallel with this stream of research is mainly based on the general framework rather than the specific application.</footnote>
## 2. Data

This paper investigates how firm managers react to monetary policy changes in developing countries. We combine firm-level data with information on monetary policy changes.

### 2.1. Reactions of the managers

Firm-level data are extracted from the World Bank Enterprise Surveys (hereafter WBES). The WBES is a firm-level survey of a representative sample of private enterprises, covering more than 180,000 firms operating in 154 countries (at the time of extraction in October 2022). The WBES provides harmonized questionnaires across countries. The firm data extracted from the WBES allow us to measure two aspects of managers’ responses: perceptions of financial constraint and credit application behavior.

In the first part of the analysis, we examine how monetary policy events affect managers’ perceptions of credit constraints. We use the following question in the WBES: “To what extent is access to finance an obstacle to the current operations of this establishment?” (question k30 in the WBES). Responses range from 0 (not an obstacle) to 4 (very serious obstacle). The score increases as the perception of credit constraints as an obstacle increases. The dependent variable captures both expected changes in the extensive margin and the intensive margin. Indeed, a manager may adjust her perception after a monetary policy tightening because she expects to be unable to obtain a new loan in the future (extensive margin), but also because credit conditions will be less favorable (intensive margin).

In the second step of the analysis, we examine the behavior of managers in terms of credit application. To do so, we exploit information on credit experience of managers in the year previous to the survey. The dependent variable (Application) is a dummy equal to one if a firm applied for a loan (regardless of the outcome of the application) and zero if the firm was discouraged from applying despite needing funds. To construct the credit application measure, we follow the approach of Léon (2015) using questions k16 (Referring again to the last complete financial year, did this establishment apply for any lines of credit or loans?) and k17 (What was the main reason why this establishment did not apply for any lines of credit or loans?) in the WBES. Firms are divided into three groups: applicants, non-applicants, and firms with no need for credit. The latter group of firms is excluded from the analysis.

### 2.2. Monetary policy data

Information on monetary policy combines existing databases and manual data collection. The first source of data on monetary policy is extracted from the International Financial Statistics (IFS) compiled by the IMF. The IFS report the key policy rate in each country at the end of the year or of the month. We exploit these data in the second step of the analysis (behavior) as we examine whether the likelihood for a firm to apply in year *t* was related to monetary policy changes from year *t* − 1 to year *t*. Using the interest rate on December 31 of each year, we calculate the difference in basis points during the year *t*.

However, this approach is no longer feasible for the analysis of perceptions, as we study how managers adjust their expectations in a few days after a monetary policy change (also called an event in this paper). To do so, we need precise information on the day of the event. Unfortunately, the IFS data do not provide such information, and we are unable to identify cross-country databases that report these events for a large number of low- and middle-income countries. We filled this gap by manually collecting monetary policy changes in developing countries over the period 2006–2020. We constructed a new database that identifies an event (i.e., a monetary policy change) as a change in the central bank’s policy rate. To build the database, we consulted central bank documentation (press releases, monetary policy bulletins, etc.) to check whether the central bank’s policy rate changed during the period. For each event, we collected the following four pieces of information: the exact date of the policy intervention, the new policy rate, the date of the previous rate change, and the previous policy rate. We describe the data collection procedures in Appendix A and the list of events used in the analysis is shown in Table A1.

Before using the hand-collected database on monetary policy changes, we compare hand-collected data on events with monthly IFS. We document that our database is in perfect agreement with the IFS for 94 out of 130 of the events considered (72%). For the remaining 36 events, we find a discrepancy, which is mainly explained by a difference in the key policy rate considered (23 events) and an absence of data in the IFS (11 events). Procedures and detailed results are provided in Appendix A2.

It should be noted that we focus on policy rates rather than other instruments (money supply, reserve requirements) for two main reasons. First, a growing number of central banks in developing countries are now using market-based instruments (Brandao-Marques et al., 2020). Second, measuring the intensity of treatment is simplified by using a single measure. It will be complex to find a common measure of intensity with different instruments. By using the difference in the policy rate before and after the change, we can have a comparable measure. But this choice has shortcomings. We may understate the importance of a change if a central bank not only changes its policy rate but also activates other instruments. In addition, we may miss some changes if a central bank uses non-price instruments but does not change its policy rate. Nevertheless, focusing on the policy rate is a clear, relevant, and transparent way to measure monetary policy events, which explains its use in cross-country papers (Barajas et al., 2018; Brandao-Marques et al., 2020; Willems, 2020).
## 2.3. Other data

We extract from the WBES a rich set of firm-level variables that serve as controls in regressions in both approaches (perceptions and behaviors). These variables control for firm size (number of employees, in logarithms) and firm age (number of years between the date of the interview and the year of establishment reported in the WBES, in logarithms). We also include the manager's experience in years (Experience). We also add a set of dummies for whether the firm is owned by a woman (Female); whether it is foreign-owned (Foreign) or state-owned (State); whether it is in manufacturing (Manufacturing); whether it is part of a multi-plant firm (Multi-plant). We also control for exporters (Export), defined as firms that send part of their production abroad. We also add three dummies to control for different legal statuses: Listed, Partnership, Sole proprietorship.

For the perception analysis, we also use two additional pieces of information. First, we extract the exact date of the interview to measure the interval between the previous/next monetary policy change and the interview. Second, we also control for the average value of other obstacles reported by the manager. Some managers are naturally more pessimistic and are more likely to report facing high obstacles on each dimension (finance, electricity, corruption, etc.). There is no reason to believe that the proportion of pessimists is correlated with the monetary policy calendar. However, to avoid such a problem, we control for the average of other obstacles in order to have a proxy for the degree of individual pessimism.<fnref n="3" /> This approach allows us to obtain a measure of financial constraints net of other constraints.

In addition, we consider country-level data in the analysis of the application decision. We include the level of GDP growth and inflation to control for macroeconomic activity. We also control for the level of financial development using the ratio of private credit to GDP and the level of income using GDP per capita (in constant USD).

Definitions of all variables are provided in Tables D1 (perception analysis) and E1 (behavior analysis) in Appendix.

# 3. Perception analysis

The first step of the analysis consists of examining how perception of firms' managers of credit constraints is affected by a monetary policy change. To do so, we employ an event study with survey data.

## 3.1. Empirical strategy

The approach employed here consists of comparing units surveyed shortly before and after a given event, in line with model initiated by Depetris-Chauvin et al. (2020). In our study, we not only exploit the timing of the event, but also the intensity of the treatment (the amplitude of the change in the policy rate). The estimated model is a difference-in-difference with continuous treatment model as follows:

$$Y_{i,e,d} = \delta_e + \beta_0 Post_{e,d} + \beta_1 Post_{e,d} \times \Delta(IR_e) + \gamma X_i + \epsilon_{i,e,d} \quad (1)$$

where i, e, and d denote the firm, the event (defined as a change in the policy rate), and the date of the interview (day), respectively. The dependent variable ($Y_{i,e,d}$) is the manager's perception of access to finance as an obstacle to the current operation of the establishment, ranging from 0 (no obstacle) to 4 (very serious obstacle).

The empirical model is a difference-in-differences framework with a continuous treatment. $Post_{e,d}$ is an indicator variable that takes the value of one if a firm is surveyed after the event and zero otherwise. The amplitude of the monetary policy change ($\Delta(IR_e)$) is measured by the change in basis points of the central bank's policy rate. Our main variable of interest is the interaction between the $Post_{e,d}$ dummy and the amplitude of the monetary policy change ($\Delta(IR_e)$).<fnref n="4" /> To restrict the comparison to respondents interviewed before a monetary policy change with those in the same country but interviewed a few days later, we add monetary policy fixed effects ($\delta_e$). The inclusion of monetary policy dummies is essential for our identification strategy as it will allow us to compare firms operating in the same environment.

Another important decision to ensure identification is the choice of the relevant windows around the event. In this paper, we consider four different windows: 3, 6, 9, and 12 days. We balance data availability with the need to capture the shortest possible time around an event to isolate the effect of the monetary policy change. Indeed, the duration of the window is crucial for identifying causal effects in high-frequency event studies (Casini and McCloskey, 2024).<fnref n="5" /> Unlike previous high-frequency event studies, such as Nakamura and Steinsson (2018a), which focus on intraday variations using market data, our analysis is based on survey data, necessitating a longer time window. By comparing firms observed a few days before and just after a policy change, we aim to

<footnote n="3">³ We acknowledge that the concept of pessimism is not necessarily the most appropriate one. To measure the average of other constraints, we use 15 perceptions to calculate the average index: business license, corruption, court, crime, electricity, informal sector competition, political instability, labor regulation, land, tax administration, tax rate, telecommunications, trade regulation, transportation, and labor skills. All constraints are constructed using the same Likert scale (from 0 'no constraint' to 4 'very serious constraint'). Note that four business constraints are not included due to lack of observations: macroeconomic instability, zoning restrictions, restrictions on opening hours, and restrictions on pricing and markups.</footnote>

<footnote n="4">⁴ The variable $\Delta(IR_e)$ is absorbed by event fixed effects $\delta_e$ and does not enter in level in Eq. (1).</footnote>

<footnote n="5">⁵ Casini and McCloskey (2024) document that the ability of high-frequency event studies to identify causal effects requires that the variance of the policy shock must dominate that of other variables within the event window. According to the authors, this condition is more likely to hold as the window is shorter. We describe how we adjust and implement the test proposed by Casini and McCloskey (2024) in Appendix C for a 3-day window. Results of the test confirm that our framework does not suffer from a major bias.</footnote>
attribute any differences primarily to that event. The timing of monetary policy changes is rarely immediate and is often influenced by institutional factors, such as the schedule of the Monetary Policy Committee. The timing of our interviews is determined weeks in advance, and there is no reason to believe that monetary policy interventions would influence this scheduling. Thus, we expect that the differences observed are due to the policy change itself. Appendix B describes in detail how we proceed to select firms included in the analysis.

We also control for heterogeneity across firms by including a set of individual-level control variables ($X_i$). The list of control variables is provided in Table D1 in Appendix D.<fnref n="6" /> Finally, the baseline analysis uses a linear approach even though the dependent variable is an ordered variable ranging from 0 to 4. The decision to rely on a linear model is twofold. First, the inclusion of many fixed effects induces an incidental parameter risk in a non-linear model. Second, the interpretation of interactions in a non-linear model is complex. Standard errors are clustered at the treatment level, which is the event here.

We expect the coefficient associated with the interaction ($\beta_1$) to be positive: when the policy rate rises, firms are more likely to face (or expect to face) difficulties in accessing finance. The opposite is true when the central bank eases monetary policy stance by lowering its policy rate. Furthermore, we expect that the effect of a change in monetary policy depends also on the intensity of the change. For small changes in policy rates, we do not expect managers to react strongly. However, the larger the change, the larger the effect (both up and down). The absence of a significant coefficient reflects that a change in the transmission of monetary policy is not transmitted to the perceptions of managers. It is important to note that a decision by the Monetary Policy Committee not only affects the money market, but may also convey information about the economy (Nakamura and Steinsson, 2018a; Bauer and Swanson, 2023). As a result, the estimated coefficient associated with $\beta_1$ could be attenuated or even reversed if the information channel dominates. Meanwhile, we expect $\beta_0$ to be zero. The coefficient associated with $Post_{e,d}$ merely reflects the effect of being interviewed after an event when the monetary policy change is zero (which is only a hypothetical case in our empirical framework, as we focus exclusively on interest rate changes).

## 3.2. Descriptive statistics

The analysis is based on four different windows: 3, 6, 9, and 12 days. The number of firms, events and countries considered increases with the length of the window, and we discuss in detail how we construct the different samples based on the windows in Appendix B. The sample includes 1264 firms from 26 events in 13 countries for the 3-day window; 4156 firms from 81 events in 32 countries for the 6-day window; 7794 firms from 116 events in 38 countries for the 9-day window; and 10,128 firms from 130 events in 39 countries for the 12-day window (see Appendix, Table D2, for detail per country).

The descriptive statistics reported in Table 1 show that the average perception score is 1.36 (sd = 1.27). One-third of the enterprises report that access to credit is not an obstacle to their activity, while one-fifth report that it is a serious or very serious obstacle. The distribution of firms by time of event is fairly balanced, with between 51% and 61% of firms interviewed after a monetary policy change (variable *Post*, depending on the window considered). The distribution of firms by type of change is also fairly balanced, with about 40% of firms referring to a restrictive monetary policy (variable *Hike*). On average, we see an interest rate change of minus 11 basis points. However, the overall average is not meaningful as it combines both cuts and hikes. We take a closer look at the distribution of policy rate changes (at the event level) in Appendix (Table D3). There are a total of 130 events. Of these, 45 are monetary tightening events, with an average increase in the policy rate of 72 basis points. There are more easing events (85), but they are of smaller amplitude (the average is minus 61 basis points). Almost half (45%) of the events are of limited amplitude (less than 50 basis points in absolute terms). On the other hand, a quarter of the events exceed 100 basis points.

We conclude the discussion of descriptive statistics by reporting balance tests. Indeed, a crucial step in validating the empirical strategy is to test whether the units surveyed before and after the event are similar in terms of their characteristics. We expect the firms to be similar in terms of their observable characteristics. In line with Depetris-Chauvin et al. (2020), to ensure that we are comparing firms related to the same event, we regress each of these variables on the $Post_{i,e,d}$ dummy, controlling for monetary policy fixed effects ($\delta_e$) and clustering the standard errors at the event level. We report the p-value of each characteristic for the four windows considered in Table 2, indicating that the sample is well balanced.

## 3.3. Empirical results

Table 3 presents the baseline results. In the different columns, we show the econometric results for different windows considered: 3, 6, 9, and 12 days, respectively. Panel A shows the baseline model as described in Eq. (1), where we interact the *Post* dummy with $\Delta(IR)$. All models include control variables and event fixed effects (full results are shown in Table D4 in Appendix).<fnref n="7" />

The different specifications show a positive and statistically significant coefficient associated with the interaction between the $Post_{e,d}$ dummy and $\Delta(IR_e)$, which is quite stable across different windows. According to this result, firms are more likely to report greater difficulties in accessing credit after a significant change in monetary policy (and vice versa).

However, the results so far represent an average effect, combining both tightening and easing. Firms are unlikely to respond symmetrically to increases and decreases in the policy rate. To test this hypothesis, in Panel B of Table 3 we replace the variation

<footnote n="6">⁶ All additional tables and figures related to this section are presented in Appendix D.</footnote>

<footnote n="7">⁷ We document that the perception of credit constraints is higher for small and local firms, those in manufacturing, and those with a single plant. We also document that financial constraints are highly correlated with other constraints.</footnote>
<table><caption>**Table 1**</caption><caption>Perception analysis - descriptive statistics.</caption><thead><tr><th rowspan="2"></th><th colspan="2">3-day</th><th colspan="2">6-day</th><th colspan="2">9-day</th><th colspan="2">12-day</th><th colspan="2">All</th></tr><tr><th>Mean</th><th>Std. dev</th><th>Mean</th><th>Std. dev</th><th>Mean</th><th>Std. dev</th><th>Mean</th><th>Std. dev</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>Perception</td><td>1.186</td><td>1.215</td><td>1.365</td><td>1.275</td><td>1.358</td><td>1.274</td><td>1.369</td><td>1.283</td><td>0</td><td>4</td></tr><tr><td>Post</td><td>0.606</td><td>0.489</td><td>0.563</td><td>0.496</td><td>0.529</td><td>0.499</td><td>0.516</td><td>0.500</td><td>0</td><td>1</td></tr><tr><td>Hike</td><td>0.415</td><td>0.493</td><td>0.404</td><td>0.491</td><td>0.384</td><td>0.486</td><td>0.385</td><td>0.487</td><td>0</td><td>1</td></tr><tr><td>Δ(IR)</td><td>-6.226</td><td>147.2</td><td>-10.49</td><td>115.7</td><td>-11.62</td><td>110.6</td><td>-11.63</td><td>107.7</td><td>-300</td><td>550</td></tr><tr><td>Size</td><td>99.80</td><td>254.6</td><td>92.13</td><td>261.6</td><td>91.20</td><td>262.7</td><td>90.23</td><td>256.6</td><td>1</td><td>4700</td></tr><tr><td>Age</td><td>17.35</td><td>13.65</td><td>18.33</td><td>14.78</td><td>18.58</td><td>14.97</td><td>18.66</td><td>15.19</td><td>0</td><td>220</td></tr><tr><td>Female</td><td>0.309</td><td>0.462</td><td>0.352</td><td>0.478</td><td>0.349</td><td>0.477</td><td>0.345</td><td>0.475</td><td>0</td><td>1</td></tr><tr><td>Manag. exp.</td><td>16.63</td><td>10.77</td><td>17.51</td><td>11.00</td><td>17.81</td><td>10.88</td><td>17.82</td><td>10.89</td><td>0</td><td>50</td></tr><tr><td>Foreign</td><td>0.055</td><td>0.227</td><td>0.062</td><td>0.241</td><td>0.068</td><td>0.252</td><td>0.066</td><td>0.249</td><td>0</td><td>1</td></tr><tr><td>State</td><td>0.006</td><td>0.074</td><td>0.011</td><td>0.102</td><td>0.011</td><td>0.106</td><td>0.011</td><td>0.102</td><td>0</td><td>1</td></tr><tr><td>Listed</td><td>0.025</td><td>0.155</td><td>0.047</td><td>0.211</td><td>0.042</td><td>0.201</td><td>0.042</td><td>0.201</td><td>0</td><td>1</td></tr><tr><td>Partnership</td><td>0.206</td><td>0.404</td><td>0.183</td><td>0.387</td><td>0.169</td><td>0.375</td><td>0.171</td><td>0.377</td><td>0</td><td>1</td></tr><tr><td>Sole Prop.</td><td>0.320</td><td>0.467</td><td>0.278</td><td>0.448</td><td>0.257</td><td>0.437</td><td>0.262</td><td>0.440</td><td>0</td><td>1</td></tr><tr><td>Multiplant</td><td>0.146</td><td>0.353</td><td>0.144</td><td>0.351</td><td>0.148</td><td>0.355</td><td>0.145</td><td>0.352</td><td>0</td><td>1</td></tr><tr><td>Export</td><td>0.226</td><td>0.419</td><td>0.236</td><td>0.425</td><td>0.248</td><td>0.432</td><td>0.245</td><td>0.430</td><td>0</td><td>1</td></tr><tr><td>Manufacturing</td><td>0.679</td><td>0.467</td><td>0.627</td><td>0.484</td><td>0.614</td><td>0.487</td><td>0.615</td><td>0.487</td><td>0</td><td>1</td></tr><tr><td>Other Const.</td><td>1.120</td><td>0.775</td><td>1.229</td><td>0.800</td><td>1.227</td><td>0.797</td><td>1.240</td><td>0.803</td><td>0</td><td>4</td></tr><tr><td>Obs.</td><td colspan="2">1,264</td><td colspan="2">4,156</td><td colspan="2">7,794</td><td colspan="2">10,128</td><td colspan="2"></td></tr></tbody></table>

The table reports summary statistics of variables used in the baseline model. See Table D1 for the variable definitions. Firms data come from the World Bank Enterprise Survey (WBSE). Data on monetary policy are hand-collected from reports of Central banks.

<caption>**Table 2**</caption>

Perception analysis - balance test.

<table><thead><tr><th>Firm characteristic</th><th>3 days</th><th>6 days</th><th>9 days</th><th>12 days</th></tr></thead><tbody><tr><td>Size</td><td>0.652</td><td>0.604</td><td>0.744</td><td>0.972</td></tr><tr><td>Age</td><td>0.229</td><td>0.342</td><td>0.128</td><td>0.128</td></tr><tr><td>Female</td><td>0.889</td><td>0.361</td><td>0.428</td><td>0.619</td></tr><tr><td>Manag. Exp.</td><td>0.947</td><td>0.341</td><td>0.339</td><td>0.327</td></tr><tr><td>Foreign</td><td>0.111</td><td>0.912</td><td>0.975</td><td>0.868</td></tr><tr><td>State</td><td>0.869</td><td>0.976</td><td>0.813</td><td>0.830</td></tr><tr><td>Listed</td><td>0.830</td><td>0.805</td><td>0.838</td><td>0.939</td></tr><tr><td>Partnership</td><td>0.423</td><td>0.177</td><td>0.511</td><td>0.135</td></tr><tr><td>Sole Prop.</td><td>0.753</td><td>0.371</td><td>0.424</td><td>0.917</td></tr><tr><td>Multiplant</td><td>0.393</td><td>0.827</td><td>0.450</td><td>0.501</td></tr><tr><td>Export</td><td>0.900</td><td>0.126</td><td>0.646</td><td>0.365</td></tr><tr><td>Manufacturing</td><td>0.416</td><td>0.142</td><td>0.537</td><td>0.443</td></tr><tr><td>Other constraints</td><td>0.214</td><td>0.895</td><td>0.905</td><td>0.645</td></tr></tbody></table>

The table presents the balance tests comparing pre- and post-event respondents. The *p*-value reported are obtained from the regressions of each variable on the treatment dummy, *Post*, while controlling for event fixed effects and clustering the standard errors at the event level. We display *p*-value for each window considered (3, 6, 9, 12 days) in the four columns.

in the policy rate ($\Delta(IR)$) with a dummy that takes the value one when monetary policy is tightened ($Hike = 1$ if $\Delta(IR) > 0$, and 0 otherwise). In this specification, the coefficients associated with the *Post* dummy refer to the effect of monetary easing (when $Hike = 0$) and the interaction between *Post* and *Hike* is the effect of monetary tightening. Results in Panel B are in line with expectations. Coefficients associated with monetary easing are negative (a rate decline reduces the perception of credit constraints) and those with monetary tightening are positive (a rate increase raises the perception of credit constraints). However, we also see that managers do not react symmetrically. They are more likely to adjust their perceptions to an increase in the policy rate. The economic effect is also significant. The level of perceptions increases between 0.12 and 0.19 (corresponding to between 0.09 and 0.15 standard deviations of the dependent variable). However, the adjustments are smaller and statistically insignificant at the usual threshold for expansionary monetary policy. Even disregarding statistical significance, the economic impact of monetary easing is lower, ranging from -0.03 to -0.08.

In the final part of Table 3, we examine whether the relationship between variation in the policy rate differs between hikes and cuts. To do so, we run a triple interaction model combining the measure of policy rate variation and the dummy for monetary tightening (*Hike*). In the model shown in Panel C, the coefficients associated with *Post* report the (hypothetical) value of the change in managers' perception when there is a cut, and the double interaction *Post*×$\Delta(IR)$ shows how managers adjust their perception as the rate cut increases. As expected, we see that both coefficients are negative, indicating that managers adjust their perceptions to monetary easing, especially when cuts are important. However, both coefficients, as well as their combination for cuts ranging from 0 to 200 basis points, are never statistically significant. For example, as documented in Table D5 in Appendix, a 100 basis point rate cut reduces perceptions between 0.05 and 0.09 (but is never statistically significant). We do the same exercise for monetary
<table><caption>**Table 3**
Perception analysis - baseline model.</caption><thead><tr><th>Window →</th><th>3</th><th>6</th><th>9</th><th>12</th></tr></thead><tbody><tr><td colspan="5">Panel A: Baseline model</td></tr><tr><td>Post</td><td>-0.00627<br>[-0.08]</td><td>0.03312<br>[0.72]</td><td>0.02247<br>[0.62]</td><td>0.02457<br>[0.77]</td></tr><tr><td>Post × Δ(IR)</td><td>0.00051**<br>[2.15]</td><td>0.00058**<br>[2.10]</td><td>0.00063**<br>[2.48]</td><td>0.00042*<br>[1.73]</td></tr><tr><td colspan="5">Panel B: Tightening vs. easing</td></tr><tr><td>Post</td><td>-0.08449<br>[-0.92]</td><td>-0.03948<br>[-0.92]</td><td>-0.05409<br>[-1.46]</td><td>-0.02635<br>[-0.67]</td></tr><tr><td>Post × Hike</td><td>0.19056<br>[1.30]</td><td>0.16502*<br>[1.87]</td><td>0.17801**<br>[2.61]</td><td>0.11799*<br>[1.97]</td></tr><tr><td colspan="5">Panel C: Non-linearities between tightening and easing</td></tr><tr><td>Post</td><td>-0.07433<br>[-0.61]</td><td>-0.04264<br>[-0.81]</td><td>-0.06693<br>[-1.37]</td><td>-0.04702<br>[-0.89]</td></tr><tr><td>Post × Δ(IR)</td><td>0.00013<br>[0.20]</td><td>-0.00005<br>[-0.08]</td><td>-0.00020<br>[-0.38]</td><td>-0.00031<br>[-0.76]</td></tr><tr><td>Post × Hike</td><td>0.15070<br>[0.83]</td><td>0.13662<br>[1.26]</td><td>0.15076*<br>[1.76]</td><td>0.10369<br>[1.35]</td></tr><tr><td>Post × Δ(IR) × Hike</td><td>0.00015<br>[0.21]</td><td>0.00046<br>[0.74]</td><td>0.00073<br>[1.33]</td><td>0.00079*<br>[1.73]</td></tr><tr><td>Obs.</td><td>1264</td><td>4156</td><td>7794</td><td>10 128</td></tr><tr><td>Event</td><td>26</td><td>81</td><td>116</td><td>130</td></tr><tr><td>R2</td><td>0.26</td><td>0.27</td><td>0.27</td><td>0.28</td></tr></tbody></table>

The dependent variable is the perception of access to finance as an obstacle by the manager. The table reports the estimates of our baseline model (Eq. (1)) in Panel A. *Post* is a dummy equal to one if the firm was surveyed after the event and *Δ(IR)* is the change of key policy rate in basis points. In Panel B, we replace *Δ(IR)* by a dummy equal to one if *Δ(IR)* > 0 (Hike). In Panel C, we employ a triple interaction model with *Post*, *Δ(IR)*, and the dummy for monetary tightening (Hike). Other variables are described in Table D1. All estimates are based on OLS regressions technique and include event fixed effects (defined as a monetary policy change). Robust *t*-value in parentheses are clustered at the event level. *, ** and *** refer to statistical significance at 10%, 5% and 1% respectively.

tightening, with coefficients associated with *Post × Hike* and *Post × Δ(IR) × Hike*. As before, we document that both coefficients have the expected sign (here positive). In other words, not only do managers react to a hike in the policy rate, but their reaction is stronger as the hike increases. For example, a 100 basis point increase in the policy rate increases perceptions by between 0.17 and 0.22 (and is often statistically significant).

We continue the analysis of possible non-linear effects by looking at the time since the last change in monetary policy. Considering that a similar change will be more effective if the central bank has not changed its interest rate for a long time, we take into account the time since the previous event. To do so, we adapt the model presented in Panel C of Table 3. We replace the amplitude of the shock (*Δ(IR)*) by a dummy equal to one if the time since the last event is above a given threshold ranging from 60 days (2 months) to 240 days (8 months).<fnref n="8" /> The results, displayed in Table D6, are quite complex to interpret. For monetary easing, the distance since the last change seems to matter only when the previous change was relatively old (more than 150 days). For monetary tightening, the econometric results are not consistent across different windows. In short, we document that, in contrast to the amplitude of the change, the time since the last event is not so crucial.

We then look at how monetary policy affects perceptions over time. So far, we have looked at the period before and after the event as a whole. However, the effect of monetary policy could be delayed or disappear shortly after the event. In addition, there is a concern about the possible anticipation effect, which introduces a damping bias. To address these different issues, we examine the effects of monetary policy changes before and after the event. Figure D1 in the Appendix plots the results for the two largest windows (it is rather impossible to plot results for shorter windows). We document that monetary policy changes take little time to materialize. The effect of monetary policy occurs mainly after three days. One possible explanation is the diffusion of information in the media. Moreover, we do not find an anticipation effect (Miller, 2023). In fact, we document the opposite for the 12-day window, as perceptions decrease (instead of increasing).

We conclude this section with some robustness checks. Table D7 in Appendix presents a series of sensitivity tests in which we change the measurement of the dependent and interest variables, the econometric method used, and the sample considered.<fnref n="9" />

<footnote n="8">⁸ Including both the rate change and the duration in the baseline framework is not feasible because we are comparing firms that refer to the same event, and both variables are perfectly collinear for the same event.</footnote>

<footnote n="9">⁹ Specifically, we replace the absolute value of perception with a relative measure, as done by *Cazals and Léon* (2023). For the dependent variable, we consider a relative change in the interest rate instead of an absolute change, and we also consider a measure of the monetary policy surprise index (*MPS*).</footnote>
The results are robust, except when we consider alternative treatment variables. In this case, the estimated coefficients are always positive, as expected, but not statistically significant. Regardless of the statistical significance, one standard deviation of MPS or relative measure of Δ(IR) causes a 20% increase in perception.<fnref n="10" />

In a final part, we conduct a falsification test, which is the ultimate robustness check. A change in monetary policy should primarily, if not exclusively, affect the perception of access to credit as a barrier. We do not expect monetary policy to affect the level of perceived corruption, access to electricity, or political instability. We present results for falsification tests for 15 other obstacles available in the WBES,<fnref n="11" /> and the average of all other obstacles. We present the results in Table D8 for the four windows considered. As expected, a change in monetary policy does not have a robust effect on other barriers (across the all windows), either collectively or individually.

To sum up, the analysis shows that managers adjust their perception of their credit constraint in the days following a change in monetary policy. This is particularly true of monetary policy tightening. There is no clear effect on monetary policy easing. Finally, we do not find any evidence of an anticipation effect.

# 4. Behavior analysis

The previous analysis documents that managers quickly adjust their perceptions of credit constraints after a change in monetary policy. A closely related question is whether monetary policy affects not only managers’ perceptions but also their behaviors. We address this issue in the following by examining how monetary policy changes affect managers’ decision to apply for loan.

## 4.1. Empirical strategy

To examine how monetary policy affects the manager’s decision to borrow, we run the following regressions:

$$Y_{ict} = \alpha_c + \beta\Delta(IR_{ct}) + \Gamma C_{ct} + \Omega F_i + \varepsilon_{ict} \quad (2)$$

where i, c, and t refer to firm i in country c in year t (since the questions on credit experience refer to the past year, we identify the past year as the year of interest).

The dependent variable (Application) is a dummy equal to one if a firm applied for a loan (regardless of the outcome of the application) and zero if the firm was discouraged from applying despite needing funds. We exclude all firms that did not express a need for funds.

The interest rate variable - Δ(IR<sub>ct</sub>) - is the change in the policy rate in country c from the end of year t-1 to the end of year t. The data are taken from the International Financial Statistics of the IMF. Using the interest rate on December 31 of each year, we calculate the difference in basis points during the year t.

The empirical model also includes country fixed effects (α<sub>c</sub>), which allow us to compare firms that operate in the same country but refer to different years. We also control for time-varying macroeconomic variables (C<sub>ct</sub>) to proxy for economic conditions due to the endogeneity of monetary policy. Thus, we include GDP growth and inflation. We also include the usual proxies for economic development with the logarithm of income per capita and the ratio of domestic credit to the private sector to GDP. These variables are taken from the World Development Indicators. Then, we control for the same set of firm-level control variables (F<sub>i</sub>) that in the previous section to account for observable heterogeneity. All variables are described in Table E1 in Appendix E.<fnref n="12" /> Standard errors are clustered at the treatment level (country-year).

We expect that the loan application to decrease after an increase in the policy rate and the opposite after a monetary easing. In other words, estimated coefficient β is expected to be negative.

This methodology provides a structured approach to studying the behavioral effects of monetary policy, but it has two limitations due to the data structure of the dependent variable.<fnref n="13" /> First, the dataset only indicates whether a firm applied for a loan in the past year, but does not specify the exact timing of the application. This lack of granularity makes it difficult to determine whether a loan

<footnote>computed by Choi et al. (2024). Choi et al. (2024) exploit the degree of surprise in the anchor currency. The authors estimate the degree of monetary policy surprise by multiplying the initial shock in the country (high-frequency identification) by the degree of capital account openness. Positive values of the MPS indicate monetary contraction, and the index rises with the degree of surprise. We then consider an ordered probit model in the fourth column, since the dependent variable is an ordered discrete variable. Finally, we change the sample by considering a sample with overlapping firms (see Appendix B for details about what is called “overlapping firms”) and by excluding abnormal periods. Specifically, we remove firms operating in countries where GDP growth deviates by more than two standard deviations from its historical trend, either in a boom (increase) or bust (decrease) scenario. The results, presented in column 6 of Table D7 remain consistent with the baseline model sample. In an unreported analysis (available upon request), we dropped country one by one.</footnote>

<footnote n="10">¹⁰ The lack of statistical significance for the monetary policy surprises may lie in the sharp reduction in the number of observations (only 20% of our initial sample remains).</footnote>

<footnote n="11">¹¹ The WBES survey asks business owners about 20 different obstacles. One of these is our main dependent variable (access to credit). Four obstacles are not included in our falsification analysis because they are poorly documented (zoning restrictions, macroeconomic instability, hours of operation, and prices).</footnote>

<footnote n="12">¹² All additional tables related to this section are displayed in Appendix E.</footnote>

<footnote n="13">¹³ This approach also has another, albeit minor, limitation. The variable representing the monetary policy change (Δ(IR)) is a simplified measure that captures only the net difference in the policy rate between the beginning and the end of the year. While this approach provides a straightforward summary of monetary policy movements, it overlooks the nuances of intermediate rate changes, such as the number of increases or decreases and their respective magnitudes. Alternative constructions of this variable, such as counting the number of significant rate changes or including their amplitude, could provide additional insights but introduce complexity without fully resolving the main issue discussed in this paragraph.</footnote>
<table><caption>**Table 4**
Behavior analysis - descriptive statistics.</caption><thead><tr><th>Variable</th><th>Obs.</th><th>Mean</th><th>Std. dev</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>Application</td><td>36,067</td><td>0.371</td><td>0.483</td><td>0</td><td>1</td></tr><tr><td>Δ(IR)</td><td>36,067</td><td>-20.03</td><td>330.1</td><td>-1750</td><td>3450</td></tr><tr><td>Size</td><td>36,067</td><td>93.35</td><td>479.7</td><td>0</td><td>64 000</td></tr><tr><td>Age</td><td>36,067</td><td>16.81</td><td>13.99</td><td>0</td><td>195</td></tr><tr><td>Female</td><td>36,067</td><td>0.314</td><td>0.464</td><td>0</td><td>1</td></tr><tr><td>Manag Exp</td><td>36,067</td><td>17.16</td><td>10.46</td><td>0</td><td>50</td></tr><tr><td>Foreign</td><td>36,067</td><td>0.046</td><td>0.211</td><td>0</td><td>1</td></tr><tr><td>State</td><td>36,067</td><td>0.006</td><td>0.074</td><td>0</td><td>1</td></tr><tr><td>Listed</td><td>36,067</td><td>0.049</td><td>0.216</td><td>0</td><td>1</td></tr><tr><td>Partnership</td><td>36,067</td><td>0.196</td><td>0.397</td><td>0</td><td>1</td></tr><tr><td>Sole Proprietorship</td><td>36,067</td><td>0.348</td><td>0.476</td><td>0</td><td>1</td></tr><tr><td>Multiplant</td><td>36,067</td><td>0.143</td><td>0.350</td><td>0</td><td>1</td></tr><tr><td>Export</td><td>36,067</td><td>0.220</td><td>0.414</td><td>0</td><td>1</td></tr><tr><td>Manufacturing</td><td>36,067</td><td>0.592</td><td>0.492</td><td>0</td><td>1</td></tr><tr><td>GDP GROWTH</td><td>36,067</td><td>5.074</td><td>2.700</td><td>-5.534</td><td>25</td></tr><tr><td>INFLATION</td><td>36,067</td><td>7.423</td><td>5.352</td><td>-1.537</td><td>59.22</td></tr><tr><td>PRIVATE CREDIT</td><td>36,067</td><td>40.16</td><td>23.83</td><td>2.180</td><td>134.1</td></tr><tr><td>GDP PER CAPITA (LOG)</td><td>36,067</td><td>8.058</td><td>0.833</td><td>5.721</td><td>9.388</td></tr></tbody></table>

The table reports summary statistics of variables used in the baseline model. See Table E1 for the definitions and sources of each variable.

application occurred before or after a monetary policy change within the same year. Second, the analysis focuses exclusively on the extensive margin (i.e., whether firms apply for loans) and does not consider the intensive margin (which includes the terms of the loans obtained). This is an important limitation, as monetary policy is likely to affect not only the decision to seek credit, but also the availability and affordability of credit, such as interest rates and loan amounts. By excluding the intensive margin, the analysis provides an incomplete picture of how monetary policy shapes credit outcomes.

These limitations suggest that the results should be interpreted with caution. The inability to identify the timing of loan applications within a year and the fact that the intensive margin is ignored may lead to a downward bias in the estimated effect of monetary policy, especially during tightening episodes. Firms that applied for credit early in the year, before a monetary policy tightening, may be misclassified as affected by the policy change. This misclassification could dilute the observed effect of interest rate increases on loan applications, leading to a downward bias in the estimated impact of tightening. This problem is less pronounced for monetary easing, as firms in need of credit may decide to apply after a rate cut. Despite these challenges, the results remain valuable because they show that monetary policy affects not only managers' perceptions of credit access but also their actual borrowing behavior. This is consistent with and strengthens the broader evidence on the real effects of monetary policy.

## 4.2. Descriptive statistics

The analysis is based on all developing countries for which the WBES provide data on loan applications and policy rates are provided in the IFS. After excluding firms without a need for credit and no information on controls, the sample considers 36,067 firms operating in 76 countries from 2006 to 2020. Details about the composition of the sample by country are given in Table E2.

The descriptive statistics in Table 4 show that 37% of firms applied for a loan in the year prior to the survey. It is interesting to note that behavior (application) and perceptions are not perfectly correlated and therefore capture different aspects. For example, the proportion of managers who say that access to credit is a serious or very serious obstacle to their business is 32% for non-applicants and 27% for applicants. Conversely, the proportion of managers who say it is not an obstacle is 20% for non-applicants and 29% for applicants.

The average value of $\Delta(IR)$ is negative ($-20$ bps). However, as before, we examine the distribution of the interest rate variable in detail. In Table E3, we refer to 203 available country years (which is more or less the same as the previous event). We document that there are 56 cases of monetary tightening (the rate at the end of the year is higher than at the end of the previous year), 91 cases of monetary easing, and 56 cases where there is no difference. We document an average increase of 261 bps for hikes and a decrease of 245 bps for cuts. These figures are higher than the events used previously because they refer to cumulative changes within the year.

## 4.3. Empirical results

Table 5 reports the main empirical results. Columns (1) and (2) report the results from the baseline model using a linear and a probit model, respectively. Column (3) presents the results from the linear model after excluding the country-year pairs with no variation in the policy rate.

Results from columns (1) to (3) document that an increase of $\Delta(IR)$ decreases the likelihood that a firm applied for a loan in the previous year. As expected, the coefficient associated to a change in policy rates is negative and statistically significant at the
<table><caption>**Table 5**
Behavior analysis - baseline model.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Δ(IR)</td><td>-0.00008**<br>[-2.52]</td><td>-0.00007**<br>[-2.49]</td><td>-0.00010**<br>[-2.20]</td><td></td><td></td></tr><tr><td>Cut</td><td></td><td></td><td></td><td>0.170***<br>[4.92]</td><td>0.201***<br>[6.25]</td></tr><tr><td>Hike</td><td></td><td></td><td></td><td>0.066<br>[1.41]</td><td>0.103<br>[1.40]</td></tr><tr><td>Cut × Δ(IR)</td><td></td><td></td><td></td><td></td><td>0.00028***<br>[3.36]</td></tr><tr><td>Hike × Δ(IR)</td><td></td><td></td><td></td><td></td><td>-0.0001***<br>[-2.77]</td></tr><tr><td>Country FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm CV</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country CV</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Obs.</td><td>36,067</td><td>34,748</td><td>27,861</td><td>36,067</td><td>36,067</td></tr><tr><td>Country-year</td><td>203</td><td>203</td><td>147</td><td>203</td><td>203</td></tr><tr><td>R<sup>2</sup></td><td>0.265</td><td></td><td>0.285</td><td>0.270</td><td>0.271</td></tr></tbody></table>

The dependent variable is a dummy equal to one if a firm applied for a loan in the year previous the survey. The table reports the estimates of our baseline model in columns (1) to (3) using linear model on the full sample (column 1), probit model on the full sample (column 2) and linear model on restricted sample to positive change in column (3). In column (4), we replace $\Delta(IR)$ by the dummy Hike equal to one if $\Delta(IR) > 0$ and the dummy Cut equal to 1 if $\Delta(IR) < 0$. In the last column, we employ an interaction model with $\Delta(IR)$ and dummies for monetary tightening (Hike) and monetary easing (Cut). Other firm- and country-level variables are described in Table E1. All estimates are based on OLS regressions technique and include country fixed effects (defined as a monetary policy change). Robust t-value in parentheses are clustered at the event level. *, ** and *** refer to statistical significance at 10%, 5% and 1% respectively.

5% level. However, the magnitude of the effect is rather limited. A 100 basis point increase in the policy rate reduces the decision to apply for a loan by between 0.8 and 1 percentage point (the average is 37%).<fnref n="14" />

As with perceptions, we are interested in the possible asymmetric response to monetary tightening and easing. Therefore, in column (4) of Table 5, we first include a dummy (Hike) that equals one if the country experienced an increase in the policy rate over the year and a second dummy (Cut) that equals one for countries that experienced a cut in the policy rate over the year. This approach allows us to examine the possible heterogeneous effect between tightening and easing of monetary policy. In the last column of the same table, we follow the same approach as in the perception analysis by interacting dummies with the amplitude of the change in order to document how managers react not only to the direction of the change but also to its amplitude.

The result in column (4) of Table 5 suggests that the decision to apply is driven by expansionary rather than contractionary monetary policy. The effect is also economically significant. A reduction in the key rate over the course of the year increases the probability of applying by about 17 percentage points (the fraction of applicants is 37%). The last column allows us to examine the possible different trends in the response to tightening and easing. We document that the response to a cut increases as the value of the decline in the monetary policy rate increases. For example, the probability of a hike increases by 22 percentage points after a 50 bps contraction and by 26 percentage points after a 200 bps contraction. For monetary tightening, we document a slight decrease as the amplitude of rate hikes increases. However, the net effect on loan applications is always statistically insignificant.

Finally, we conclude the analysis by carrying out a series of sensitivity tests, as shown in Table E5. As an alternative, we create a dummy that equals one if the firm purchased a fixed asset (using external funds<fnref n="15" />). Second, we use the relative variation of the change in the policy rate instead of the absolute change. Third, we play with the sample. We restrict the analysis to the sample used in the perception analysis to check that the results are not driven by the sample.<fnref n="16" /> As before, we also restrict the analysis to non-crisis periods. Finally, we conclude the robustness checks by performing falsification tests in columns (5) to (8) of the same table. As before, the objective is to identify managerial decisions that are not expected to be directly related to monetary policy.<fnref n="17" /> We consider applications for an electricity connection (column 5), a water connection (6), a telephone connection (7) and whether the firm paid for its security (8). As expected, changes in monetary policy do not affect these decisions.

<footnote n="14">¹⁴ Note that the interest rate changes are higher than in our baseline analysis because we are looking at annual changes, as described above. In addition, the full results are shown in table E4 in the appendix. We document that the demand for credit is higher for large and old firms and for firms run by a female and an experienced manager. In addition, exporters and manufacturing firms with multiple plants are more likely to request a loan. However, country-level variables are rarely significant, even if the signs are as expected (increased demand during growth episodes, stable inflation, in financially and economically developed countries).</footnote>

<footnote n="15">¹⁵ In other words, the dummy equals zero if a firm purchased a fixed asset using only its own funds.</footnote>

<footnote n="16">¹⁶ We only present results for the most restrictive sample (3-day window), but results for other samples (6, 9 and 12 days) are similar (available on request).</footnote>

<footnote n="17">¹⁷ For this reason, we do not consider variables such as the export decision or the application for a building permit, as they may be related to access to finance.</footnote>
To sum up, the analysis shows that managers are more inclined to apply for credit after a reduction in the policy rate. However, we do not find a symmetrical effect when monetary policy is tightened.

### 4.4. Discussion of differences in results between perceptions and behaviors

The above results suggest that managers are sensitive to monetary policy by adjusting not only their perceptions but also their behavior (decision to apply). However, the results in Tables 3 and 5 are rather contradictory. Indeed, the empirical analysis shows that perceptions of credit constraints are more sensitive to monetary tightening. On the other hand, borrowing behavior is more affected by policy rate cuts. Several arguments can be advanced to justify this apparent contraction between perceptions and behaviors.

A first explanation takes roots in the limitations of the behavior approach described above. A key methodological limitation of the loan application analysis is that it only captures firms that have applied for credit, potentially introducing bias. During monetary easing, many previously unprofitable projects become viable, leading to a surge in applications and amplifying the observed sensitivity to interest rate cuts. In addition, some firms may delay applications in anticipation of further rate cuts, leading to a more concentrated and rapid response to easing. In contrast, if firms anticipate rate hikes, they may preemptively apply early in the year to lock in lower borrowing costs, obscuring the full effect of tightening as later declines in applications go unmeasured. This timing asymmetry can distort the comparison between the effects of monetary tightening and easing.

A second explanation lies in the difference between the two approaches: perception analysis captures both the intensive (e.g., changes in loan terms or borrowing conditions) and the extensive margin (e.g., whether firms have access to credit at all), thus providing a more comprehensive view of the effects of monetary policy. In contrast, the analysis of loan applications focuses exclusively on the extensive margin, examining only whether firms apply for credit or not. Perhaps the decision to apply for credit is only marginally affected after an increase, even if borrowers expect credit conditions to deteriorate. This narrower focus may miss significant adjustments in borrowing conditions or restrictions that affect firms' perceptions without necessarily translating into observable application behavior.

A third explanation could be related to the different (over)reactions to good (easing) and bad (tightening) news. As market participants, managers may adjust their expectations of credit constraints immediately after a tightening, while the response to monetary easing is often slower and less pronounced (Beckers and Bernoth, 2024; Blot et al., 2024).<fnref n="18" />

## 5. Heterogeneity analysis

In the final step of our analysis, we examine the factors that either strengthen or weaken the transmission of monetary policy to managers' perceptions and behaviors. The effectiveness of monetary policy on firms depends on the two transmission channels. First, banks, which are the primary lenders in developing economies, must adjust their lending behavior and credit conditions in response to changes in monetary policy. Second, firms must be sensitive to these changes. If either of these channels fails (either because banks are unresponsive to monetary policy changes or because firms are unresponsive) then monetary policy is unlikely to have a significant impact. Therefore, we examine the sensitivity of firms to monetary policy based on both channels.

We begin by assessing the responsiveness of firms to changes in credit conditions induced by monetary policy, assuming that banks adjust their lending behavior accordingly. In particular, we try to identify which types of firms are more affected by changes in monetary policy. To do so, we measure a firm's proximity to banks using the usual proxies for firm opacity (size and age), but also whether firms are audited or have a prior relationship with a bank. We also control for ownership structure, recognizing that foreign-owned firms may be less sensitive to local credit conditions and that male- and female-owned firms may experience differences in access to credit.

Theoretically, the link between these firm characteristics and monetary policy sensitivity is unclear. A restrictive monetary policy may disproportionately affect firms that are distant from the banking system if it limits access to credit. Conversely, if only a subset of firms can borrow, changes in credit conditions should primarily affect them. Our empirical analysis tests this ambiguity.

We then turn to the second channel of monetary policy transmission by examining the responsiveness of banks to changes in monetary policy. The literature has often discussed the reasons why monetary policy transmission is often limited in low- and middle-income countries. Previous studies suggest that the effectiveness of monetary policy in these economies is shaped by both the structural characteristics of the financial system and the broader monetary policy framework. We therefore focus on both possibilities.

With respect to financial system characteristics, several factors have been identified as potential impediments to monetary policy transmission in developing countries. These include the underdevelopment of financial markets (Mishra et al., 2012; Ma and Lin, 2016), the lack of competition in the banking sector (Olivero et al., 2011; Mishra et al., 2014), the prevalence of foreign banks (Bhaumik et al., 2011; Cetorelli and Goldberg, 2012), and the presence of excess liquidity in the banking sector (Chatelain et al., 2003; Saxegaard, 2006).

Beyond the characteristics of the financial system, the monetary policy framework itself plays a crucial role in shaping transmission mechanisms. The monetary policy framework refers to the institutional structures and rules that govern the conduct of

<footnote n="18">¹⁸ Even in the absence of data, this asymmetry may be exacerbated by the media, which tends to place greater emphasis on tightening, thereby amplifying the signal sent to managers.</footnote>
monetary policy. It includes legal provisions, the monetary policy regime, and central bank practices that influence the discretion and credibility of policymakers (Unsal et al., 2022). In our analysis, we focus on two key aspects of the monetary policy framework that may affect managers' perceptions of monetary policy: central bank independence and the monetary policy regime, in particular whether the country has adopted an inflation targeting framework.

## 5.1. Empirical strategy

To examine how firm- and country-level characteristics may affect the transmission of monetary policy, we use an interaction model between the moderators and the monetary policy variable. Since we consider both perceptions and behavioral analysis, we adjust the equations. (1)–(2) respectively.

For perceptions, we estimate a triple difference model as follows:

$$ 
\begin{aligned}
Y_{i,e,d} = \delta_e &+ \zeta_0 Post_{e,d} \\
&+ \zeta_1 Post_{e,d} \times \Delta(IR_e) \\
&+ \zeta_2 Post_{e,d} \times \Delta(IR_e) \times Z_{i/c} \\
&+ \zeta_3 Post_{e,d} \times Z_{i/c} + \zeta_4 \Delta(IR_e) \times Z_{i/c} + \gamma \mathbf{X}_i + \epsilon_{i,e,d}
\end{aligned} 
\quad (3) $$

where $Z_{i/c}$ is a firm-level (indexed i) or country-level (indexed c) moderator (described below). We control for firm-level characteristics in the set of unit-level control variables ($X_i$) and for country-level characteristics by adding event dummies ($\delta_e$). We are particularly interested in two coefficients: $\zeta_1$ and $\zeta_2$. The first coefficient ($\zeta_1$) provides the effect of the monetary policy change for firms surveyed after the event when the moderator ($Z_{i/c}$) equals zero. We expect the firm or country characteristics to mitigate (or exacerbate) the effect of monetary policy when $\zeta_2 < 0$ (or $\zeta_2 > 0$). We also include double interactions of the moderating variable with $Post_e$ and $\Delta(IR_e)$, respectively.<fnref n="19" />

For behaviors, the model is simpler as it is an interaction model.

$$ Y_{ict} = \alpha_c + \gamma_1 \Delta(IR_{ct}) + \gamma_2 \Delta(IR_{ct}) \times Z_{i/c} + \Gamma C_{ct} + \Omega F_i + \epsilon_{ict} \quad (4) $$

where $Z_{i/c}$ is the moderator and other variables have been defined previously. The coefficient associated with $\gamma_1$ indicates the effect of monetary policy when $Z_{i/c}$ equals 0 and $\gamma_2$ indicates whether the transmission of monetary policy is shaped as $Z_{i/c}$ increases.

The exact interpretation of the coefficients $\zeta_1$ and $\zeta_3$ on the one hand and $\gamma_1$ and $\gamma_2$ on the other hand depends on the measurement of the moderator (sign, binary or continuous variable).

We include a set of moderating variables ($Z_{i/c}$) in line with our conceptual framework outlined above. At the firm level, we control for firm size using two measures: the number of employees and a binary indicator equal to one if the firm has multiple establishments. Firm age is measured as the difference in years between the date the firm was founded and the date of the survey interview. To capture the proximity to banks, we include two binary indicators: one equal to one if the firm has undergone an external audit and another equal to one if the firm has previously secured a loan. Finally, as discussed earlier, we account for ownership structure by including dummy variables for foreign-owned and female-owned firms.

At the country level, we include several moderating variables that capture key characteristics of financial systems and monetary policy frameworks. First, we assess the characteristics of the financial system. To measure the size of the financial system, we use the ratio of private credit to GDP (Priv Credit). The degree of competition in the banking sector is proxied by the market concentration of the five largest banks, measured by their share of total assets (CR5). In addition, we account for the presence of foreign banks by considering the share of total assets held by foreign-owned banks (Foreign). To assess the extent of (excess) liquidity in the banking system, in the absence of a standardized international measure of bank excess liquidity, we construct two proxies.<fnref n="20" /> First, we approximate excess liquidity in the financial sector using broad money (M3).<fnref n="21" /> Second, following Barajas et al. (2018), we use remittance inflows as an alternative proxy, as countries with high levels of remittances tend to experience weaker monetary policy effectiveness due to remittance-induced excess liquidity. All financial system data come from the World Bank's Global Financial Development Database.

Second, we examine the role of the monetary policy framework, taking into account both central bank independence and the monetary policy regime. To measure central bank independence, we use both *de jure* and *de facto* indicators. For *de jure* independence, we use the Central Bank Independence - Extended (CBIE) index developed by Romelli (2022), where higher values indicate greater independence. To capture *de facto* central bank independence, we use two measures. First, we calculate the turnover rate, which is the average number of changes in central bank governors per year. Second, we include a dummy variable for irregular turnover, which takes the value of one if a central bank governor was replaced before the end of his or her official term. Both the turnover rate (TOR) and the irregular turnover (Ir. Turn) data come from the (Dreher et al., 2010) database.<fnref n="22" />

<footnote n="19">¹⁹ Note that the interaction between $\Delta(IR_e) \times Z_{i/c}$ is not included when using country-level characteristics, as it is absorbed by the event fixed effects (both $\Delta(IR_e)$ and $Z_e$ are time-invariant for firms surveyed for the same event $e$).</footnote>

<footnote n="20">²⁰ Since the initial attempt by Saxegaard (2006), a comprehensive global measure of bank excess liquidity has yet to be established.</footnote>

<footnote n="21">²¹ Instead of relying on its absolute value, which may be affected by long-run trends and cross-country differences, we extract its cyclical component by applying a Hodrick-Prescott filter to measure deviations from its long-run level.</footnote>

<footnote n="22">²² The turnover rate ranges from 0 (no turnover) to theoretically infinite values, with higher turnover rates typically indicating less independence. However, the turnover rate is an imperfect measure because it suffers from two major limitations. First, it is affected by legal constraints, such as term lengths and mandate limits, that are specific to each central bank. Second, a low turnover rate does not necessarily indicate high independence; instead, it may indicate that the central banker consistently aligns with the preferences of the political authorities, thereby securing a long tenure (Strong and Yayi, 2021).</footnote>
Finally, we examine the role of the monetary policy regime by including a dummy variable (IT) that takes the value of one if a country adopts an inflation-targeting regime in year t and zero otherwise. Data on the adoption of inflation targeting regimes are taken from Rose and Rose (2024).<fnref n="23" />

The list of all moderators is provided in Table F1.

## 5.2. Firm characteristics and monetary policy transmission

We begin our exploration of heterogeneity by examining whether firms' sensitivity to policy rate fluctuations varies according to their individual characteristics. The results are presented in Table 6. Panel A refers to econometric results using the perception analysis (12-day window<fnref n="24" />) and Panel B refers to the behavioral analysis (loan application).

The results presented in column (1) document a possible effect of size on the transmission of monetary policy. However, the effect of size is modest. On the one hand, the perception analysis in Panel A fails to identify size as a robust moderator. On the other hand, in Panel B, the coefficient associated with the interaction between $\Delta(IR)$ and size is statistically significant, but its economic effect is modest: the marginal impact of monetary policy increases by only 3% between a firm with no employees and a firm with 20 employees (the median).<fnref n="25" /> Moreover, the results from Panel A and Panel B are not consistent. Column (2) includes a dummy for firms with more than one establishment (another proxy for size). We find no effect of this moderator. In the third column of the same table, we also document that the age of the firm has no effect on the transmission of monetary policy.

The results in columns (4) and (5) suggest that the transmission of monetary policy is attenuated for foreign- and female-owned firms. Regardless of the specification retained (Panels A and B), we document that coefficients associated with interaction signal a reduced effect for foreign- and female-owned firms. Indeed, even when the coefficients associated with the interaction are not statistically significant, we document in Appendix Table F4 that foreign-owned and female-owned firms are less affected by monetary policy. However, this finding should be treated with caution, as there is no statistical difference between foreign and domestic firms on the one hand, and female- and male-owned firms on the other. Moreover, for the former, we document that the results are not consistent for different windows of perception analysis, as documented in Table F3.

In the last two columns, we consider measures of proximity to formal banks. We do not document a clear pattern in the difference between unaudited and audited firms. In fact, the results are not consistent across Panels A and B. The results in column (7) are more interesting. We document that firms with a prior lending relationship are less affected by changes in monetary policy, although the interaction is not statistically significant in Panel A. In addition, column (7) of Appendix Table F4 confirms that firms with a prior lending relationship tend to be rather insensitive to changes in monetary policy.

The results presented in this subsection suggest that company characteristics play a moderate role in explaining differences in reaction to changes in monetary policy. However, we find that companies with an existing banking relationship tend to be less affected by monetary policy. This could be explained by the ability of these companies to benefit from having a strong enough relationship with lenders not to be affected by changes in monetary conditions, or their ability to access other sources of financing. Interestingly, the effect is more pronounced when applied to loans. These companies are undoubtedly the first to be served, and therefore the most apt to apply, after a central bank interest rate cut.

## 5.3. Country characteristics and monetary policy

We then turn to the debate on the factors affecting the effectiveness of monetary policy in developing countries. First, we focus on the characteristics of banking systems in columns (1) to (5) of Table 7. We report results from the analysis of perception in Panel A<fnref n="26" /> and of behavior (loan application) in Panel B.

The results presented in column (1) document that the transmission of monetary policy is raised in the deepest banking systems, in line with expectations. Thus, according to the estimates, the effect of monetary policy on the decision to apply is statistically null if the country has a level of financial development below 34% (which is close to the median at 37%). From this value onwards, we note an effect that increases as the level of financial development increases. Thus, the effect of monetary policy is almost tripled when the level of financial development rises from the median to the third quartile (54%). However, we document an absence of impact of financial development in the perception analysis (Panel A).

Columns (2) and (3) show that the industrial structure of banking systems does not matter. Neither the degree of concentration (column 2) nor the share of foreign banks affects the transmission of monetary policy.

We now turn to excess liquidity, which is often cited as a possible explanation for the lack of monetary policy transmission in developing countries. We consider two proxies based on the excess volume of broad money and remittances. We confirm the central role of excess liquidity in explaining the strength of monetary policy transmission. For both specifications based on perception (Panel A) and application (Panel B), we document that transmission is reduced in countries with higher levels of liquidity. In Panel A, we document that the positive effect is attenuated when the level of money exceeds its long-run trend (column 4) and when the level of remittances increases (column 5). We find a similar conclusion for loan applications in Panel B. The negative relationship between

<footnote n="23">²³ In an unreported analysis, we also examined the effects of the adoption of fiscal rules and exchange rate regimes, but found no significant effects.</footnote>

<footnote n="24">²⁴ We only present results from the 12-day window in the main text, but results for other windows are reported in Appendix F.</footnote>

<footnote n="25">²⁵ In Appendix (Table F4), we compute the marginal effect of monetary policy change for firms without employee ($Z=0$) and firms with a median value of employees ($Z=1$). We document that marginal effects in Panel B are respectively of -0.000067 and -0.000069.</footnote>

<footnote n="26">²⁶ As previously, we consider the 12-day window but we report results for other windows in Table F5 in Appendix.</footnote>
<table><caption>**Table 6**
Heterogeneity analysis - firm characteristics.</caption><thead><tr><th>Z →<br>Var. type →</th><th>Size<br>Cont.<br>(1)</th><th>Multi-plant<br>Dummy<br>(2)</th><th>Age<br>Cont.<br>(3)</th><th>Foreign<br>Dummy<br>(4)</th><th>Female<br>Dummy<br>(5)</th><th>Audited<br>Dummy<br>(6)</th><th>Has a loan<br>Dummy<br>(7)</th></tr></thead><tbody><tr><td colspan="8"><strong>Panel A: Perception analysis</strong></td></tr><tr><td>Post * Δ(IR)</td><td>0.000473*<br>[1.72]</td><td>0.000346<br>[1.19]</td><td>0.000308<br>[0.73]</td><td>0.000434*<br>[1.79]</td><td>0.000554*<br>[1.69]</td><td>0.000399<br>[1.41]</td><td>0.000533*<br>[1.72]</td></tr><tr><td>Post * Δ(IR) * Z</td><td>-0.000001<br>[-0.60]</td><td>0.000544<br>[0.91]</td><td>0.000006<br>[0.42]</td><td>-0.000221<br>[-0.21]</td><td>-0.000560<br>[-1.25]</td><td>0.000009<br>[0.02]</td><td>-0.000426<br>[-0.88]</td></tr><tr><td>Observations</td><td>10,128</td><td>10,128</td><td>10,128</td><td>10,128</td><td>10,128</td><td>9,877</td><td>9,956</td></tr><tr><td colspan="8"><strong>Panel B: Behavior analysis</strong></td></tr><tr><td>Δ(IR)</td><td>-0.00007**<br>[-2.20]</td><td>-0.00008***<br>[-2.65]</td><td>-0.00008**<br>[-2.57]</td><td>-0.00008***<br>[-2.63]</td><td>-0.00008***<br>[-2.83]</td><td>-0.00008***<br>[-2.92]</td><td>-0.00011**<br>[-2.53]</td></tr><tr><td>Δ(IR) * Z</td><td>-0.000000**<br>[-2.35]</td><td>0.000001<br>[0.34]</td><td>0.000000<br>[0.06]</td><td>0.000033<br>[1.53]</td><td>0.000027<br>[1.56]</td><td>0.000021<br>[1.09]</td><td>0.000103**<br>[2.08]</td></tr><tr><td>Observations</td><td>36,037</td><td>36,037</td><td>36,037</td><td>36,037</td><td>36,067</td><td>35,416</td><td>36,010</td></tr></tbody></table>

The dependent variable is the perception of access to finance as an obstacle by the manager in Panel A and a dummy equal to one if a firm applied for a loan in the year previous the survey in Panel B. The table reports the coefficients $\zeta_1$ and $\zeta_3$ of Eq. (3) in Panel A and coefficients $\gamma_1$ and $\gamma_2$ of Eq. (4) in Panel B. Each column considers a specific firm-level moderator and we also indicate the type of the variable (continuous or binary). All estimates are based on OLS regressions technique and include other control variables, fixed effects (event in Panel A and country in Panel B) and additional interactions described in Eq. (3) in Panel A. Robust t-value in parentheses are clustered at the event/country level. *, ** and *** refer to statistical significance at 10%, 5% and 1% respectively.

<table><caption>**Table 7**
Heterogeneity analysis - country characteristics.</caption><thead><tr><th rowspan="2">Z →<br>Var. type</th><th colspan="5">Financial system</th><th colspan="4">Institution and credibility</th></tr><tr><th>Priv credit<br>Cont.<br>(1)</th><th>CR5<br>Cont.<br>(2)</th><th>Foreign<br>Cont.<br>(3)</th><th>Money (M3)<br>Cont.<br>(4)</th><th>Rem.<br>Cont.<br>(5)</th><th>CBIE<br>Cont.<br>(6)</th><th>TOR<br>Cont.<br>(7)</th><th>Irr. Turn.<br>Dummy<br>(8)</th><th>IT<br>Dummy<br>(9)</th></tr></thead><tbody><tr><td colspan="10"><strong>Panel A: Perception analysis</strong></td></tr><tr><td>Post * Δ(IR)</td><td>0.00032<br>[0.48]</td><td>0.00250*<br>[1.77]</td><td>0.00059<br>[0.93]</td><td>0.00053***<br>[3.12]</td><td>0.00048**<br>[2.61]</td><td>-0.0023<br>[-1.58]</td><td>0.0007***<br>[3.87]</td><td>0.0007***<br>[4.44]</td><td>0.0001<br>[0.16]</td></tr><tr><td>Post * Δ(IR) * Z</td><td>0.00000<br>[0.27]</td><td>-0.00003<br>[-1.45]</td><td>-0.00000<br>[-0.01]</td><td>-0.00029**<br>[-2.01]</td><td>-0.00022**<br>[-2.06]</td><td>0.0037*<br>[1.95]</td><td>-0.0012**<br>[-2.39]</td><td>-0.0069**<br>[-2.19]</td><td>0.0008**<br>[2.20]</td></tr><tr><td>Observations</td><td>10 087</td><td>8938</td><td>5913</td><td>10 128</td><td>10 128</td><td>9390</td><td>9382</td><td>8065</td><td>10 128</td></tr><tr><td colspan="10"><strong>Panel B: Behavior analysis</strong></td></tr><tr><td>Δ(IR)</td><td>0.000172***<br>[2.75]</td><td>-0.000307*<br>[-1.71]</td><td>-0.000069<br>[-0.43]</td><td>-0.000072***<br>[-3.32]</td><td>-0.000082**<br>[-2.42]</td><td>0.000094<br>[1.12]</td><td>-0.000132***<br>[-3.59]</td><td>-0.000098**<br>[-2.13]</td><td>-0.000007<br>[-0.23]</td></tr><tr><td>Δ(IR) * Z</td><td>-0.000006***<br>[-4.26]</td><td>-0.000003<br>[1.16]</td><td>0.000006<br>[0.27]</td><td>0.000021***<br>[2.79]</td><td>0.000003*<br>[1.80]</td><td>-0.000320**<br>[-2.34]</td><td>0.000165***<br>[2.86]</td><td>0.000142<br>[0.61]</td><td>-0.000156***<br>[-3.23]</td></tr><tr><td>Observations</td><td>30,067</td><td>34,058</td><td>20,338</td><td>36,067</td><td>36,067</td><td>33,890</td><td>33,283</td><td>32,252</td><td>36,067</td></tr></tbody></table>

The dependent variable is the perception of access to finance as an obstacle by the manager in Panel A and a dummy equal to one if a firm applied for a loan in the year previous the survey in Panel B. The table reports the coefficients $\zeta_1$ and $\zeta_3$ of Eq. (3) in Panel A and coefficients $\gamma_1$ and $\gamma_2$ of Eq. (4) in Panel B. Each column considers a specific firm-level moderator and we indicate also the type of the variable (continuous or binary). All estimates are based on OLS regressions technique and include other control variables, fixed effects (event in Panel A and country in Panel B) and additional interactions described in Eq. (3) in Panel A. Robust t-value in parentheses are clustered at the event/country level. *, ** and *** refer to statistical significance at 10%, 5% and 1% respectively.

changes in interest rates and applications is attenuated by the level of liquidity, regardless of the indicators considered. The marginal effects are also far from anecdotal. For example, an increase of one of the measures of excess liquidity based on broad money (one is more or less the value of the difference between the median and the first or third quartile) reduces the effect of monetary policy by between 30% (Panel B) and 55% (Panel A).

We conclude this section by focusing on how institutional factors, such as central bank independence and the monetary policy regime, affect the transmission of monetary policy. In columns (6) to (8), we document that central bank independence is central to ensuring the transmission of monetary policy. Regardless of the measure of independence considered (de facto or de jure) and the dependent variables considered (perceptions and applications), we find that monetary policy is more effective in countries with independent central banks. In detail, we document in column (6) that the effect of monetary policy is not different from zero when CBIE equals 0 (non-independent central banks). Meanwhile, the effect of monetary policy becomes zero when the turnover ratio or the irregular turnover dummy equals one (non-independent central banks) in columns (7) and (8).

In the last column of the same table, we also document that monetary policy transmission is more effective in inflation-targeting countries. For other countries, we do indeed find managers do not react to monetary policy.

This subsection documents that monetary policy transmission is enhanced when banks are not overly liquid and central bank decisions are credible because they are independent and operate under an inflation targeting regime.
## 6. Conclusion

The transmission of monetary policy in developing countries remains a controversial issue, particularly in the context of rising inflation. Many empirical studies using macroeconomic time series have struggled to identify significant effects, raising the question of whether this reflects genuine policy ineffectiveness or limitations in methodology and data (Mishra and Montiel, 2013; Li et al., 2019). Recent research using large monetary shocks or single country case studies has challenged the conventional wisdom (Abuka et al., 2019; Willems, 2020; Bhardwaj et al., 2022), but these studies often lack external validity due to their focus on specific events or contexts.

This paper takes a novel approach to assessing the effectiveness of monetary policy by focusing on how firms react to monetary policy. Using firm-level data from the World Bank Enterprise Surveys and a newly constructed database of monetary policy changes, we employ an event study methodology to compare managers surveyed just before and just after a policy change. We complement this short-term analysis with a broader behavioral study of how annual policy rate changes affect firms' borrowing.

Our results show that monetary policy affects both firms' perceptions and their borrowing behavior, but in an asymmetric manner. Using an event study, we find that managers in developing countries perceive credit constraints as more severe following a policy rate hike, with no evidence of anticipation effects. However, while firms adjust their expectations after a tightening, they do not significantly reduce their loan applications. Instead, analysis of annual loan application data shows that firms are more likely to apply for loans after a policy rate cut, suggesting that monetary easing has a stronger impact on actual borrowing decisions than tightening.

We also document substantial heterogeneity in firms' responses. Firms with pre-existing banking relationships are less sensitive to changes in monetary policy, as prior access to credit insulates them from fluctuations in borrowing conditions. At the macro level, policy transmission is stronger in countries with independent central banks, inflation targeting regimes, and less liquid banking systems, while banking depth and competition play a more limited role. These results highlight the importance of institutional quality and financial system characteristics in shaping the effectiveness of monetary policy in developing countries.

Our study provides new evidence on how monetary policy affects firms in developing countries, albeit unevenly. More broadly, this paper is among the first to use reconstructed high-frequency data to assess monetary policy transmission in these economies, an approach widely used in advanced economies (Nakamura and Steinsson, 2018b). We believe that this method holds great promise for future research on how short-term policy interventions affect low- and middle-income economies.

## Declaration of competing interest

The authors whose names are listed immediately below certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers' bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

## Acknowledgments

The authors thank two anonymous reviewers, the Editor (Luca Benati), Jean-Pierre Allegrี, Nicoletta Bérardi, Vincent Bouvatier, Alessandro Cassini, Cécile Couharde, Dramane Coulibaly, Dodo Vigninou Gammadigbe, Samuel Guérineau, Sylviane Guillaumont, Michael Goujon, Sébastien Lotz, Edouard Mien, Caroline Perrin, Issouf Soumaré, Eric Swanson, Camelia Turcu, and Laurent Weill and the participants to the ADRES Annual doctoral conference 2023, GDRE Money, Bank and Finance conference 2023, Banque de France - Ferdi Seminar, Rwanda Central Bank Seminar, CSAE 2024, ICDE 2024, MT2P Euroafrican conference for their useful comments. All errors are ours. This research was supported by the Banque de France through the Sponsorship Agreement between Ferdi and the Banque de France. Florian Léon thanks the Agence Nationale de la Recherche of the French government through the program "France 2030" (grant number ANR-16-IDEX-0001) for its financial support.

## Appendix A. Supplementary data

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.euroecorev.2025.105102.

## Data availability

Data are shared

Data are attached to the title of the article (Original data)
References

Abuka, C., Alinda, R.K., Minoiu, C., Peydró, J.-L., Presbitero, A.F., 2019. Monetary policy and bank lending in developing countries: Loan applications, rates, and real effects. J. Dev. Econ. 139, 185-202.

Barajas, A., Chami, R., Ebeke, C., Oeking, A., 2018. What's different about monetary policy transmission in remittance-dependent countries? J. Dev. Econ. 134, 272-288.

Bauer, M.D., Swanson, E.T., 2023. An alternative explanation for the "fed information effect". Am. Econ. Rev. 113 (3), 664-700.

Beckers, B., Bernoth, K., 2024. Monetary policy and mispricing in stock markets. J. Money, Credit. Bank. 56 (7), 1887-1904.

Berg, A., Charry, L., Portillo, R.A., Vlcek, J., 2019. The monetary transmission mechanism in the tropics: A narrative approach. J. Afr. Econ. 28 (3), 225-251.

Bhardwaj, A., Subramanian, K., Tantri, P., 2022. Relationship banking and monetary policy transmission: Evidence from India. J. Money, Credit. Bank. 54 (8), 2341-2375.

Bhaumik, S.K., Dang, V., Kutan, A.M., 2011. Implications of bank ownership for the credit channel of monetary policy transmission: Evidence from India. J. Bank. Financ. 35 (9), 2418-2428.

Blot, C., Hubert, P., Labondance, F., 2024. The asymmetric effects of monetary policy on stock price bubbles. Eur. Econ. Rev. 168, 104824.

Brandao-Marques, M.L., Gelos, M.R., Harjes, M.T., Sahay, M.R., Xue, Y., 2020. Monetary policy transmission in emerging markets and developing economies. IMF Work. Pap. 2020/035.

Brown, M., Ongena, S., Popov, A., Yeşin, P., 2011. Who needs credit and who gets credit in Eastern Europe? Econ. Policy 26 (65), 93-130.

Casini, A, McCloskey, A., 2024. Identification and estimation of causal effects in high-frequency event studies. arXiv preprint arXiv:2406.15667.

Cavalo, E., Daude, C., 2011. Public investment in developing countries: A blessing or a curse? J. Comp. Econ. 39 (1), 65-81.

Cazals, A., Léon, F., 2023. Perception of political instability in election periods: Evidence from African firms. J. Comp. Econ. 51 (1), 256-279.

Cetorelli, N., Goldberg, L.S., 2012. Banking globalization and monetary transmission. J. Financ. 67 (5), 1811-1843.

Chatelain, J.-B., Ehrmann, M., Generale, A., Martínez-Pagés, J., Vermeulen, P., Worms, A., 2003. Monetary policy transmission in the euro area: new evidence from micro data on firms and banks. J. Eur. Econ. Assoc. 1 (2-3), 731-742.

Choi, S., Willem, T., Yoo, S.Y., 2024. Revisiting the monetary transmission mechanism through an industry-level differential approach. J. Monet. Econ. 103556.

Cloyne, J., Ferreira, C., Froemel, M., Surico, P., 2023. Monetary policy, corporate finance, and investment. J. Eur. Econ. Assoc. 21 (6), 2586-2634.

Coibion, O., Gorodnichenko, Y., Ropele, T., 2020. Inflation expectations and firm decisions: New causal evidence. Q. J. Econ. 135 (1), 165-219.

Depetris-Chauvin, E., Durante, R., Campante, F., 2020. Building nations through shared experiences: Evidence from African football. Am. Econ. Rev. 110 (5), 1572-1602.

Dreher, A., Sturm, J.-E., De Haan, J., 2010. When is a central bank governor replaced? Evidence based on a new data set. J. Macroecon. 32 (3), 766-781.

Durante, E., Ferrando, A., Vermeulen, P., 2022. Monetary policy, investment and firm heterogeneity. Eur. Econ. Rev. 148, 104251.

Enders, Z., Hünnekes, F., Müller, G.J., 2019. Monetary policy announcements and expectations: Evidence from German firms. J. Monet. Econ. 108, 45-63.

Enders, Z., Hünnekes, F., Müller, G., 2022. Firm expectations and economic activity. J. Eur. Econ. Assoc. 20 (6), 2396-2439.

Ferrando, A., Popov, A., Udell, G.F., 2022. Unconventional monetary policy, funding expectations, and firm decisions. Eur. Econ. Rev. 149, 104268.

Gertler, M., Gilchrist, S., 1994. Monetary policy, business cycles, and the behavior of small manufacturing firms. Q. J. Econ. 109 (2), 309-340.

Huang, Y., Pagano, M., Panizza, U., 2020. Local crowding-out in China. J. Financ. 75 (6), 2855-2898.

Jiménez, G., Ongena, S., Peydró, J.-L., Saurina, J., 2012. Credit supply and monetary policy: Identifying the bank balance-sheet channel with loan applications. Am. Econ. Rev. 102 (5), 2301-2326.

Jiménez, G., Ongena, S., Peydró, J.-L., Saurina, J., 2014. Hazardous times for monetary policy: What do twenty-three million bank loans say about the effects of monetary policy on credit risk-taking? Econometrica 82 (2), 463-505.

Léon, F., 2015. Does bank competition alleviate credit constraints in developing countries? J. Bank. Financ. 57, 130-142.

Li, B.G., Adam, C., Berg, A., Montiel, P., O'Connell, S., 2019. Structural VARs and the monetary transmission mechanism in low-income African countries. J. Afr. Econ. 28 (4), 455-478.

Ma, Y., Lin, X., 2016. Financial development and the effectiveness of monetary policy. J. Bank. Financ. 68, 1-11.

Miller, D.L., 2023. An introductory guide to event study models. J. Econ. Perspect. 37 (2), 203-230.

Mishra, P., Montiel, P., 2013. How effective is monetary transmission in low-income countries? A survey of the empirical evidence. Econ. Syst. 37 (2), 187-216.

Mishra, P., Montiel, P., Pedroni, P., Spilimbergo, A., 2014. Monetary policy and bank lending rates in low-income countries: Heterogeneous panel estimates. J. Dev. Econ. 111, 117-131.

Mishra, P., Montiel, P.J., Spilimbergo, A., 2012. Monetary transmission in low-income countries: Effectiveness and policy implications. IMF Econ. Rev. 60 (2), 270-302.

Nakamura, E., Steinson, J., 2018a. High-frequency identification of monetary non-neutrality: The information effect. Q. J. Econ. 133 (3), 1283-1330.

Nakamura, E., Steinson, J., 2018b. Identification in macroeconomics. J. Econ. Perspect. 32 (3), 59-86.

Olivero, M.P., Li, Y., Jeon, B.N., 2011. Competition in banking and the lending channel: Evidence from bank-level data in Asia and Latin America. J. Bank. Financ. 35 (3), 560-571.

Ottonello, P., Winberry, T., 2020. Financial heterogeneity and the investment channel of monetary policy. Econometrica 88 (6), 2473-2502.

Pinter, J., Kočenda, E., 2023. Media treatment of monetary policy surprises and their impact on firms' and consumers' expectations. J. Money, Credit. Bank. (forthcoming).

Romelli, D., 2022. The political economy of reforms in Central Bank design: Evidence from a new dataset. Econ. Policy 37 (112), 641-688.

Rose, A., Rose, A., 2024. A presence of absence: The benign emergence of monetary stability. J. Int. Money Financ. 146, 103125.

Saxegaard, M., 2006. Excess liquidity and effectiveness of monetary policy: Evidence from sub-Saharan Africa. IMF Work. Pap. 06/115.

Strong, C., Yayi, C., 2021. Central bank independence, fiscal deficits and currency union: Lessons from Africa. J. Macroecon. 68, 103313.

Unsal, D.F., Papageorgiou, C., Garbers, H., 2022. Monetary policy frameworks: an index and new evidence. IMF Work. Pap. 2022/022.

Willems, T., 2020. What do monetary contractions do? Evidence from large tightenings. Rev. Econ. Dyn. 38, 41-58.