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Can access to FinTech credit truly catalyze entrepreneurial growth and customer satisfaction in the digital economy, as evidenced by Alibaba’s two million vendors experiencing increased sales and better customer ratings?

Image courtesy of interviewee. November 25, 2024

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This video discusses challenges and solutions surrounding the availability of credit for small firms in emerging economies, particularly focusing on the role of FinTech. It highlights that many small businesses struggle to access necessary credit, which is crucial for their growth and, consequently, economic productivity. Traditional banks face difficulties in providing this credit due to high information costs, risks of default, and contract enforcement issues.

FinTech emerges as a potential solution by utilizing innovative data sources, reducing distribution costs, and offering better contract enforcement methods through online platforms. This video examines the complex relationship between credit availability and economic growth, arguing that while growth can lead to more credit access, providing credit can also spur growth—a concept known as reverse causality.

The study specifically points to China as a case example, noting its rapidly developed FinTech sector that caters to underbanked small firms. The research methodology included analyzing automated credit approval processes based on sales data, revealing substantial growth effects—up to 40%—for firms that gained access to FinTech credit.

Additionally, the analysis identifies various factors influencing credit access and usage, such as firm size, the need for collateral, and distribution costs. Smaller firms, particularly those with fewer than ten employees, benefit significantly from FinTech credit due to lower reliance on collateral and reduced transaction costs.

While the text acknowledges that FinTech credit is not poised to replace traditional banking entirely, it anticipates a market segmentation where FinTech thrives in niches underserved by traditional banks. Finally, the evolution of China's credit market through FinTech is viewed as a leveling force that can enhance equitable access to credit for entrepreneurs across the country.

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Here are five key points from the video regarding small firm credit, particularly in emerging economies and the role of FinTech:

1. **Credit Shortage for Small Firms**: There is a widespread belief that small firms in emerging economies face significant challenges in accessing adequate credit. Research, including surveys by organizations like the World Bank and International Finance Corporation, suggests that many small businesses lack the capital they need for growth, which is essential given that these firms employ a large percentage of the workforce.

2. **Challenges Facing Traditional Banks**: Traditional banks struggle to provide credit to small firms due to various high costs and challenges, including information frictions (difficulty in assessing creditworthiness), high distribution costs, and complex contract enforcement in emerging markets. These obstacles result in a substantial undersupply of credit to small businesses.

3. **FinTech Innovations**: FinTech has emerged as a solution by leveraging new information sources and technologies that reduce the cost of credit distribution. Online platforms can quickly assess creditworthiness through sales data analytics, thus improving access to credit for small and underserved firms. These innovations allow for better enforcement mechanisms, as defaulting borrowers can be excluded from these platforms.

4. **Causality Between Credit and Growth**: The relationship between credit access and economic growth is complex, displaying reverse causality. While improved credit access can spur economic growth, increased growth can also lead to greater credit availability. Understanding this relationship is crucial for policymakers and researchers aiming to determine whether new credit opportunities truly trigger growth.

5. **Impact and Advantages of FinTech Credit**: Research in China indicates that access to FinTech credit can significantly boost growth for small firms, particularly those with fewer employees and limited collateral. The data shows that companies near the credit threshold experienced growth rates of up to 40%. Additionally, smaller firms benefit more from FinTech due to lower collateral requirements and reduced transaction costs inherent in online credit systems. Hence, FinTech credit provides a lifeline to small businesses facing liquidity issues, even if they do not fully utilize the loan.

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Here are some key quotes extracted from the video about small firm credit and the advent of FinTech in emerging economies:

1. **On Credit Supply and Demand:**
"Small firm credit has often been deemed to be in short supply, particularly in emerging economies."

2. **Impact on Employment and Economy:**
"If a lot of firms don't get the needed capital that they seek, that means that the economy overall will be very unproductive... many developing countries... have too many people working in unproductive firms, small firms."

3. **Challenges for Banks:**
"Big problem with banks not being able to supply credit to small firms is that it's very costly to do so... there are information frictions... distribution costs are very high."

4. **The Role of FinTech:**
"FinTech is pioneering new information sources to a certain credit worthiness... online distribution is very inexpensive... enforcement technology might be better than those available to traditional banks."

5. **Causality between Credit and Growth:**
"The question... is whether actually access to credit is an impediment to growth... there’s a two-way relationship."

6. **China as a Case Study:**
"China is sort of one of the biggest countries for data on firm credit and also one of the biggest users of FinTech credit."

7. **Methodological Insights:**
"Once you're above a certain threshold, then credit is automatically ... this discontinuity can be used to disentangle the direction of causality."

8. **Effects of Credit Access:**
"We find growth effects of 20%,... over the next month... an average effect of roughly 40% growth."

9. **Importance of Small Firms:**
"Small entrepreneurial firms... were the most sensitive to the availability of online credit. 40% growth over two months."

10. **Collateral and Risk:**
"For FinTech lenders... you’re much less reliant on the collateral channel... firms that have no collateral... were relatively more willing to take on FinTech credit."

11. **Understanding Loan Utilization:**
"Many entrepreneurs actually never took out the loan... you use the loan like a credit line... it freed up existing capital."

12. **Market Segmentation in Credit:**
"FinTech credit is particular in its information advantage... I don't think banks are in a good position to compete on their turf."

13. **Financial System Integration:**
"What FinTech credit does, it kind of levels... online credit is a way of unifying the credit market in China and creating much fairer and equal credit access conditions."

These quotes encapsulate the main themes regarding the challenges faced by small firms in accessing credit, the innovative role of FinTech, especially in emerging markets like China, and the implications of credit access on economic growth.

X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:90000

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Think small firm credit has often been deemed

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to be in short supply, particularly in emerging economies.

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There are sort of estimates out there by the World Bank and

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or by the International Finance Corporation that

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a large amount of small firms do not have the amount

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of credit they need to develop their, their business.

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And that's mostly based on survey evidence.

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Entrepreneurs are asked,

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do you have the credit necessary?

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And often we find that the surveys hint

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to important undersupply of credit,

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and that's very important because small, small

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and medium sized firms amount

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to a large percentage of the employment.

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So if a lot of firms don't get the needed capital

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that they seek, that means

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that the economy overall will be very unproductive.

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And that's kind of the curse of many developing countries,

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that they have too many people working in unproductive

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firms, small firms.

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And that's why it's an important question.

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Big problem with banks not being able to supply credit

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to small firms is that it's very costly to do so.

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I mean, first of all, there are information frictions.

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It's very hard to verify whether a small firm is actually

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solvent and often they lack collateral.

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So one can sort of highlight different problems,

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and one is certainly the information problem.

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The second is distribution costs are very high,

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and then contract enforcement

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and often in emerging market content

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can also be very difficult.

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So for all these reasons, we believe

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that particularly in emerging countries

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with an underdeveloped banking system,

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small farm credit is a big obstacle.

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And FinTech is kind of like, looks like a solution

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to all these problems.

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First of all, FinTech is pioneering new information sources

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to a certain credit worth in this, it has a cheap technology

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and distributing, namely online distribution is very

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inexpensive and therefore can be used for small credit,

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for small amounts of loans.

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And often, and that's maybe an aspect not so widely known,

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if it comes with, if it's the credit is bundled

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with online distribution platforms, there are also ways

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of enforcing contracts.

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So for example, a FinTech lender could exclude a,

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a defaulting creditor from the platform and so on.

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So the enforcement technology might be better than those

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available to traditional banks.

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Sort of a, a question whether actually access

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to credit is an impediment to growth.

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This is the question that bothers development

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economists for a long time.

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And here's why it's sort of actually difficult to address,

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because we are dealing with a two-way relationship.

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On the one hand, growth is making credit more available.

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If you have a firm that has actually great growth,

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growth prospects, you will be more likely

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to get credit, right?

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So the causality runs from, opportunities to credit,

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but, there's also the reverse causality

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and that's actually the one we are more interested in,

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which is, which is if you just provide new credit

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opportunities through FinTech,

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does it actually trigger growth?

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Right? Is a is a better credit technology growth engine.

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That's sort of the reverse causality,

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and it's very hard to just entangle the two causalities one

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run running from one way to the other and vice versa.

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And this is what we call a reverse causality problem.

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So we have to find a relatively sophisticated ways to,

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to filter, one channel

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and focus only on the causal channel from credit to growth

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and not vice versa.

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China is sort of one of the biggest countries for

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data on firm credit

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and also one of the biggest users of FinTech credit.

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Maybe it has to do to some extent with the underdevelopment

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of the traditional banking system,

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which was mostly geared towards pushing credit

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to state-owned enterprises.

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So the private sector has always been underserved

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by the banking system, and

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therefore FinTech credit was kind of the opportunity

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and combined with the rapid expansion

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of internet trading platforms and retail platforms, it also

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provided the data to have new forms of credit based on,

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analytics, sales analytics from these platforms.

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So kind of China emerged as the biggest,

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laboratory in the world.

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For, for, exploring

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this research question I'm interested in, let me first

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explain a little bit what is the methodological sort

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of angle we, we look at.

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So most approved automated approval platforms,

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they are often run on a, a sort of credit,

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no credit threshold,

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meaning you have a credit model which analyzes your sales

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data and then comes to a conclusion whether this is

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sufficiently solid in order to be credit worthy, right?

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It's a credit scoring system like we have for households.

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You can run similar things for small firms

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or for online vendors.

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And so you create a credit rating.

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And the principle is then once you're

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above a certain threshold,

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then credit is automatically provided.

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And this threshold is very, very important,

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from a methodological perspective

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because it provides kind of a difference between treated

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and non-treated firms.

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So if you look at the firms just around the threshold, just

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around the discontinuity, so to see,

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say in the decision process, we have very similar firms just

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above and below the threshold.

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They don't differ very much.

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So the reverse causality is kind

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of excluded at the same time.

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There's a rapid difference in treatment.

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So meaning you are, you are getting all of a sudden

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credit if you are just a minimal amount above the

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threshold value, which was in the case of the arm financial

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state, we studied at a certain number, right?

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480. If you were at 481, you would get credit.

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If you were at 479, you would not get it right.

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So this discontinuity can be used to disentangle

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the direction of causality,

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namely have similar firms being treated differently.

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It's a bit like in medical research, right?

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You have, sort of treated patients

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and placebo, medicine,

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and then it's all about identifying the difference, right?

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And the more similar the firm, the the patients are,

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or the more similar the, the firms are, the better.

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And the more precise is this identification of causality.

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So that's basically our research idea,

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and it's now a widely used method.

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It's called, discontinuity design

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in making, sort

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of causal inference on certain economic effects, right?

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And this mechanism was just there. We just used it.

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Basically, the internal algorithm was programmed in such a

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way that it, provided this discontinuity,

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which we then explored it in our,

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in our statistical approach.

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Identification of the causal effect is now all

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about the difference, right?

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Between these firms that were just about above the threshold

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and slightly below what, which we, which we assume

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to be very, very similar nature.

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And here we still have basically, millions

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of observations because this platform

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that we looked at on financial is gigantic.

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It has basically on its platform, 30 million

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online inventors, and roughly 3 million of them were in kind

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of this critical zone, which is interesting for us just

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around the discontinuity.

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So we had basically millions of observations

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to study the effect,

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and these millions of firmware spread all over China.

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And so to, provides immense statistical power

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that the previous literature could not reach, right?

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If you're a development economist, you deal deal with

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a sample of maybe a few hundred observations most,

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and then often the data is very bad

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because you have to deal with small firms.

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There's not good data available them.

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There are many issues with data collection.

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Here we had millions of observation

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and they were kind of perfectly measured

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because they came from the electronic system.

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So data quality was, by order

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of magnitude higher than any, any previous study

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and development economics could reach.

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So we are actually able then to come up

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with relatively precise numbers

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and, relatively exact estimates

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of the beneficiary effects of red access.

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So here's what we found.

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So we looked at a relatively quick response.

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So one or two months, right?

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Over the first month,

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the reaction was already quite enormous.

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We find growth effects of 20%,

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and which is quite substantial between those firms

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that were similar except

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for a minor difference in the discontinuity, right?

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And that's quite enormous.

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And over the next month discontinued

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and over two months we could easily find an average effect

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of roughly 40% growth.

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So discredit access in China was a major boost

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to these online vendors.

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So that's sort of the first thing to keep to, to

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register is the,

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the growth effects was quite substantial

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economically, very, very significant.

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And it's very precisely estimated.

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Also, we looked at different channels

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through which they actually pushed their sales, right?

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There were different things that they did.

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They used the money to improve their product quality,

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they improved their website, their improved service quality,

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they increased their number of products on the platform.

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So there are a variety of channels, that that

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improved their sales performance

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that we can actually also observe from the data

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that we had supplementary data on their, on their management

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and their, investment activity.

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One additional question is then, okay,

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was it all the same across China

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across very different firms?

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And the answer to that question is actually no.

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It depends on a lot of factors,

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how big exactly the growth effect was

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for a particular online vendor or an entrepreneur.

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And here we can kind of distinguish very,

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very different channels that are themselves insightful.

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So the first channel, going back

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to the comparative advantage of

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FinTech credit has to do with information.

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We know from traditional finance that small firms,

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the smaller you are, the more information sensitive you are.

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And in fact, we find that small entrepreneurial firms,

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meaning less than 10 employees, they were the most sensitive

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to, to the availability of online credit.

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For them, the effect was even bigger than

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40% over two months.

231
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So that's, that's a very important variable.

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And, and obviously those smaller firms needed smaller

233
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amounts of credit, and

234
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therefore the, the comparative advantage

235
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of FinTech credit was bigger, right?

236
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So this is kind of the, the information advantage

237
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of fintechs of a traditional banks increases

238
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with the small size of the, of the firm involved, right?

239
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Smaller firms have a relatively bigger, advantage

240
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to being served by FinTech lenders.

241
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So the other effect is also, also if you think about

242
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how banks give credit, well,

243
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an important consideration is of course, collateral.

244
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In case of default, will you recover your loan, right?

245
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That's a prime concern for, for any bank.

246
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Now, for FinTech lenders, given

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that I have better information, you're much less

248
00:11:29.935 --> 00:11:32.985
reliant on the collateral channel, meaning,

249
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securities don't matter so much if you can

250
00:11:36.175 --> 00:11:39.305
correctly predict default profitability based on online

251
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monitoring of sales.

252
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So, this is the second, channel,

253
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which we clearly see firms that have no collateral

254
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or very little were relatively, more willing to

255
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Take on FinTech credit and boosted their sales even more.

256
00:11:57.405 --> 00:12:01.785
So collateral, poor entrepreneurs benefit relatively more

257
00:12:01.815 --> 00:12:05.545
from, from this type of, of new, credit, right?

258
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It's, we call it the collateral channel.

259
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And I think the third going

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to the issue of distribution cost.

261
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That's also very, very important.

262
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Traditional banks have high costs, right?

263
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You have to pay a credit officer.

264
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There's also transactional costs.

265
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The entrepreneur has to go to your bank and so on.

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Online, automated online credit

267
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dispenses with all these costs.

268
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So the marginal costs essentially zero, right?

269
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And the smaller loan is the higher the fixed cost

270
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per unit landed, right?

271
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And so there's a clear benefit to having small loans,

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served via, FinTech credit.

273
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So the, the effect is bigger for entrepreneurs

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that just needed very, very small amounts.

275
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So these are three dimensions that

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support the information advantage to support the collateral,

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the lower dependence on collateral.

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And thirdly, also the,

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just the transaction cost channel is very, very important.

280
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All three, kind of mean that those firms

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that were afflicted

282
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by all three problems had the biggest growth effect.

283
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That was a thing that also puzzled us a little bit,

284
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because the cost of this online credit is relatively high.

285
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It's 16% debt's still lower than often bank credit

286
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or loan charts that would charge 20 or 25%,

287
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but it doesn't seem to be a good source

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of financing since it's so high.

289
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Now, one thing to keep in mind is what we,

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what we discovered by talking more to the entrepreneurs and

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and looking into this deeper, is

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that many entrepreneurs actually never took out the loan,

293
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which you might ask,

294
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how can it be beneficial if I'm not taking out the loan?

295
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Well, what you do is you use the loan like a credit line,

296
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like a, a fallback option.

297
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You kind of free up existing capital,

298
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which you previously used as kind of rainy day savings.

299
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You use this one

300
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and you have now the, online credit as a credit line

301
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that you can tap in, in case of liquidity problems, right?

302
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So it's kind of, the entrepreneurs used them just in second

303
00:14:07.415 --> 00:14:11.165
place, and it freed up existing capital then to be used

304
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and deployed and invested.

305
00:14:12.465 --> 00:14:15.845
So it is actually not, not often that.

306
00:14:15.865 --> 00:14:18.645
So, so the, the paradoxical thing is the online credit

307
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availability as such created growth effects even though the

308
00:14:22.855 --> 00:14:25.055
loan was not taken out, right?

309
00:14:25.055 --> 00:14:26.655
That's kind of a bit surprising.

310
00:14:26.675 --> 00:14:30.295
But if you understand the liquidity problems of small firms,

311
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you can understand that this is actually a channel

312
00:14:32.855 --> 00:14:36.475
that operates independently from the loan volumes, right?

313
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Just the opportunity. I can, I can be sure

314
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that I can meet any liquidity needs if it really,

315
00:14:41.815 --> 00:14:44.795
if I really need to, and maybe only over short term, right?

316
00:14:44.795 --> 00:14:46.355
Many of the,

317
00:14:46.695 --> 00:14:49.435
retail activities in China are very cyclical, right?

318
00:14:50.115 --> 00:14:53.565
A lot of the volume happens in on very few days, right?

319
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Single stay and other events, right?

320
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When you have huge capital needs

321
00:14:57.705 --> 00:14:59.805
and on these very, very short periods,

322
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this credit is actually very, very useful in spite

323
00:15:02.885 --> 00:15:05.215
of the high annualized rates, right?

324
00:15:05.215 --> 00:15:08.135
So this is kind of a, a bit counterintuitive, but,

325
00:15:08.995 --> 00:15:13.335
but this is what we kind of, understood

326
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to be the, an important,

327
00:15:15.135 --> 00:15:17.095
important aspect of, of FinTech credit.

328
00:15:17.395 --> 00:15:19.135
How do, how does traditional banking

329
00:15:19.275 --> 00:15:22.015
and traditional credit compete against

330
00:15:22.205 --> 00:15:23.455
this type of online credit?

331
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And I think there's gonna be more

332
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or less a market segmentation.

333
00:15:27.055 --> 00:15:28.175
We have to keep in mind

334
00:15:28.175 --> 00:15:31.135
that this FinTech credit mostly involves relatively small

335
00:15:31.135 --> 00:15:34.815
loans and also small loans to sectors

336
00:15:34.815 --> 00:15:37.375
that are very particular like retail that produces a lot

337
00:15:37.375 --> 00:15:39.775
of information without information.

338
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There's no FinTech credit, right?

339
00:15:41.315 --> 00:15:43.215
Not all kinds of businesses produce a lot

340
00:15:43.215 --> 00:15:46.095
of transaction data, in a public space.

341
00:15:46.155 --> 00:15:50.015
So it's limited to retail, small firm, a lot

342
00:15:50.015 --> 00:15:51.255
of consumer credit also.

343
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That's another big thing. 40% is roughly small firm credit.

344
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The other 60% is retail credit.

345
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That's where FinTech is, has a clear information advantage.

346
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And I, I don't think banks are in a good position

347
00:16:06.755 --> 00:16:09.655
to compete on in their turf, right?

348
00:16:09.655 --> 00:16:13.255
But then there are large areas of the economy,

349
00:16:13.305 --> 00:16:17.015
where I mean, medium-sized large firms where,

350
00:16:17.175 --> 00:16:18.855
FinTech has no competitive advantage.

351
00:16:18.995 --> 00:16:22.695
So it isn't that, we see all the banks perish

352
00:16:22.695 --> 00:16:24.335
because of FinTech credit.

353
00:16:24.735 --> 00:16:28.015
FinTech credit is particular in its information advantage.

354
00:16:28.195 --> 00:16:32.895
It concerns 5%, 10% of the loan sector, right?

355
00:16:33.035 --> 00:16:36.295
Um, it's not gonna abolish banks, right?

356
00:16:36.675 --> 00:16:40.855
So, and also banks, pioneer new information sources.

357
00:16:40.885 --> 00:16:44.095
They try to compete. Um, you can purchase data.

358
00:16:44.765 --> 00:16:47.135
It's a competitive market for financial data.

359
00:16:47.325 --> 00:16:49.575
They can get into this. They have other sources.

360
00:16:50.715 --> 00:16:53.615
And, so I think the banks, particularly in China

361
00:16:53.675 --> 00:16:56.175
and developing countries in general, have to evolve in order

362
00:16:56.195 --> 00:16:59.655
to, to on some segment in the middle right, to compete with,

363
00:17:00.125 --> 00:17:03.855
with other sources, with FinTech creditors.

364
00:17:03.875 --> 00:17:06.295
And, but it, it's, the competition will be limited

365
00:17:06.475 --> 00:17:09.895
to a small segment, neither the small, firm credit

366
00:17:09.995 --> 00:17:12.695
or consumer credit, nor the large firms.

367
00:17:12.805 --> 00:17:15.695
It's sort of in some intermediate segment where we will see,

368
00:17:16.275 --> 00:17:18.175
stronger competition.

369
00:17:18.415 --> 00:17:21.775
I think regulation plays a very important role in,

370
00:17:22.635 --> 00:17:25.215
in actually pioneering FinTech credit.

371
00:17:25.895 --> 00:17:28.095
I mean, we have to think of China's credit

372
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and banking system very much like the US in the

373
00:17:32.215 --> 00:17:33.255
19th century, right?

374
00:17:33.385 --> 00:17:36.135
Where you have very localized segmented markets

375
00:17:36.475 --> 00:17:38.335
or local bank controls it.

376
00:17:38.435 --> 00:17:41.135
And, there can be big imperfections.

377
00:17:42.415 --> 00:17:45.315
The Chinese banking system, at least in the past,

378
00:17:45.495 --> 00:17:48.955
was very much like a so economy allocating credit through

379
00:17:49.865 --> 00:17:53.765
big banks, and it was a very non-market driven allocation.

380
00:17:54.265 --> 00:17:56.845
So you could have a lot of credit in some locations

381
00:17:56.865 --> 00:17:58.525
and very little somewhere else.

382
00:17:59.445 --> 00:18:02.705
In fact, the poorest places where those,

383
00:18:02.705 --> 00:18:04.065
where there are a lot of state banks,

384
00:18:04.065 --> 00:18:06.665
because they're often sucked up all the savings,

385
00:18:06.805 --> 00:18:10.905
the local savings, and gave it done to so e somewhere else,

386
00:18:11.345 --> 00:18:12.905
possibly in some other city or profits.

387
00:18:13.285 --> 00:18:17.105
So there, there are very, very large,

388
00:18:17.295 --> 00:18:20.225
distortions in China's credit market

389
00:18:20.255 --> 00:18:23.905
that creates a very uneven, supply of credit

390
00:18:24.445 --> 00:18:25.785
to local entrepreneurs, and

391
00:18:25.785 --> 00:18:28.025
therefore is also a, a very big distortion

392
00:18:28.085 --> 00:18:29.785
for the development of small firms.

393
00:18:30.365 --> 00:18:33.625
So what FinTech credit does, it kind of levels,

394
00:18:35.055 --> 00:18:36.225
playing field, right?

395
00:18:36.725 --> 00:18:39.745
Uh, and financial and FinTech lenders were the first that

396
00:18:39.985 --> 00:18:43.345
provided basically nationwide similar rates,

397
00:18:43.345 --> 00:18:46.705
because they offered it over the online, over the internet,

398
00:18:46.705 --> 00:18:48.145
and everyone got the same condition

399
00:18:48.385 --> 00:18:49.785
provided on these characteristics, right?

400
00:18:49.785 --> 00:18:53.105
There was no local discrimination by credit rich

401
00:18:53.105 --> 00:18:54.945
and credit poor cities.

402
00:18:55.085 --> 00:18:58.945
So this is actually a very, very big integrating feature.

403
00:18:59.485 --> 00:19:01.905
So online credit is a way

404
00:19:01.905 --> 00:19:04.345
of unifying the credit market in China

405
00:19:04.365 --> 00:19:08.385
and creating much fair on equal credit access conditions

406
00:19:08.385 --> 00:19:09.505
for Chinese entrepreneurs.

407
00:19:09.605 --> 00:19:11.465
In that sense, it's, it's very, very beneficial.

Challenges of Small Firm Credit in Emerging Economies

00:00:00 - Think small firm credit has often been deemed to be in short supply, particularly in emerging economies. There are sort of estimates out there by the World Bank and or by the International Finance Corporation that a large amount of small firms do not have the amount of credit they need to develop their, their business. And that's mostly based on survey evidence. Entrepreneurs are asked, do you have the credit necessary? And often we find that the surveys hint to important undersupply of credit, and that's very important because small, small

00:00:37 - to a large percentage of the employment. So if a lot of firms don't get the needed capital that they seek, that means that the economy overall will be very unproductive. And that's kind of the curse of many developing countries, that they have too many people working in unproductive firms, small firms. And that's why it's an important question. Big problem with banks not being able to supply credit to small firms is that it's very costly to do so. I mean, first of all, there are information frictions.

00:01:10 - solvent and often they lack collateral. So one can sort of highlight different problems, and one is certainly the information problem. The second is distribution costs are very high, and then contract enforcement and often in emerging market content can also be very difficult. So for all these reasons, we believe that particularly in emerging countries with an underdeveloped banking system, small farm credit is a big obstacle.

FinTech's Role in Credit Access and Economic Growth

00:01:41 - to all these problems. First of all, FinTech is pioneering new information sources to a certain credit worth in this, it has a cheap technology and distributing, namely online distribution is very inexpensive and therefore can be used for small credit, for small amounts of loans. And often, and that's maybe an aspect not so widely known, if it comes with, if it's the credit is bundled with online distribution platforms, there are also ways

00:02:11 - So for example, a FinTech lender could exclude a, a defaulting creditor from the platform and so on. So the enforcement technology might be better than those available to traditional banks. Sort of a, a question whether actually access to credit is an impediment to growth. This is the question that bothers development economists for a long time. And here's why it's sort of actually difficult to address, because we are dealing with a two-way relationship. On the one hand, growth is making credit more available.

00:02:43 - growth prospects, you will be more likely to get credit, right? So the causality runs from, opportunities to credit, but, there's also the reverse causality and that's actually the one we are more interested in, which is, which is if you just provide new credit opportunities through FinTech, does it actually trigger growth? Right? Is a is a better credit technology growth engine. That's sort of the reverse causality, and it's very hard to just entangle the two causalities one

Analyzing Causality: Credit Growth in China's FinTech Landscape

00:03:16 - And this is what we call a reverse causality problem. So we have to find a relatively sophisticated ways to, to filter, one channel and focus only on the causal channel from credit to growth and not vice versa. China is sort of one of the biggest countries for data on firm credit and also one of the biggest users of FinTech credit. Maybe it has to do to some extent with the underdevelopment

00:03:44 - which was mostly geared towards pushing credit to state-owned enterprises. So the private sector has always been underserved by the banking system, and therefore FinTech credit was kind of the opportunity and combined with the rapid expansion of internet trading platforms and retail platforms, it also provided the data to have new forms of credit based on, analytics, sales analytics from these platforms. So kind of China emerged as the biggest,

00:04:15 - For, for, exploring this research question I'm interested in, let me first explain a little bit what is the methodological sort of angle we, we look at. So most approved automated approval platforms, they are often run on a, a sort of credit, no credit threshold, meaning you have a credit model which analyzes your sales data and then comes to a conclusion whether this is sufficiently solid in order to be credit worthy, right?

Credit Discontinuity and Causal Inference

00:04:48 - You can run similar things for small firms or for online vendors. And so you create a credit rating. And the principle is then once you're above a certain threshold, then credit is automatically provided. And this threshold is very, very important, from a methodological perspective because it provides kind of a difference between treated and non-treated firms. So if you look at the firms just around the threshold, just around the discontinuity, so to see,

00:05:22 - above and below the threshold. They don't differ very much. So the reverse causality is kind of excluded at the same time. There's a rapid difference in treatment. So meaning you are, you are getting all of a sudden credit if you are just a minimal amount above the threshold value, which was in the case of the arm financial state, we studied at a certain number, right? 480. If you were at 481, you would get credit.

00:05:55 - So this discontinuity can be used to disentangle the direction of causality, namely have similar firms being treated differently. It's a bit like in medical research, right? You have, sort of treated patients and placebo, medicine, and then it's all about identifying the difference, right? And the more similar the firm, the the patients are, or the more similar the, the firms are, the better.

Utilizing Discontinuity Design in Economic Research

00:06:27 - So that's basically our research idea, and it's now a widely used method. It's called, discontinuity design in making, sort of causal inference on certain economic effects, right? And this mechanism was just there. We just used it. Basically, the internal algorithm was programmed in such a way that it, provided this discontinuity, which we then explored it in our, in our statistical approach.

00:06:57 - about the difference, right? Between these firms that were just about above the threshold and slightly below what, which we, which we assume to be very, very similar nature. And here we still have basically, millions of observations because this platform that we looked at on financial is gigantic. It has basically on its platform, 30 million online inventors, and roughly 3 million of them were in kind of this critical zone, which is interesting for us just around the discontinuity.

00:07:28 - to study the effect, and these millions of firmware spread all over China. And so to, provides immense statistical power that the previous literature could not reach, right? If you're a development economist, you deal deal with a sample of maybe a few hundred observations most, and then often the data is very bad because you have to deal with small firms. There's not good data available them. There are many issues with data collection. Here we had millions of observation

Impact of FinTech Credit on Small Business Growth in China

00:07:59 - because they came from the electronic system. So data quality was, by order of magnitude higher than any, any previous study and development economics could reach. So we are actually able then to come up with relatively precise numbers and, relatively exact estimates of the beneficiary effects of red access. So here's what we found. So we looked at a relatively quick response. So one or two months, right?

00:08:30 - the reaction was already quite enormous. We find growth effects of 20%, and which is quite substantial between those firms that were similar except for a minor difference in the discontinuity, right? And that's quite enormous. And over the next month discontinued and over two months we could easily find an average effect of roughly 40% growth. So discredit access in China was a major boost to these online vendors.

00:09:03 - register is the, the growth effects was quite substantial economically, very, very significant. And it's very precisely estimated. Also, we looked at different channels through which they actually pushed their sales, right? There were different things that they did. They used the money to improve their product quality, they improved their website, their improved service quality, they increased their number of products on the platform. So there are a variety of channels, that that

Impact of FinTech Credit on Small Firms in China

00:09:35 - that we can actually also observe from the data that we had supplementary data on their, on their management and their, investment activity. One additional question is then, okay, was it all the same across China across very different firms? And the answer to that question is actually no. It depends on a lot of factors, how big exactly the growth effect was for a particular online vendor or an entrepreneur.

00:10:07 - very different channels that are themselves insightful. So the first channel, going back to the comparative advantage of FinTech credit has to do with information. We know from traditional finance that small firms, the smaller you are, the more information sensitive you are. And in fact, we find that small entrepreneurial firms, meaning less than 10 employees, they were the most sensitive to, to the availability of online credit.

00:10:37 - 40% over two months. So that's, that's a very important variable. And, and obviously those smaller firms needed smaller amounts of credit, and therefore the, the comparative advantage of FinTech credit was bigger, right? So this is kind of the, the information advantage of fintechs of a traditional banks increases with the small size of the, of the firm involved, right? Smaller firms have a relatively bigger, advantage

Advantages of FinTech Credit: Reduced Reliance on Collateral and Lower Costs

00:11:10 - So the other effect is also, also if you think about how banks give credit, well, an important consideration is of course, collateral. In case of default, will you recover your loan, right? That's a prime concern for, for any bank. Now, for FinTech lenders, given that I have better information, you're much less reliant on the collateral channel, meaning, securities don't matter so much if you can correctly predict default profitability based on online

00:11:41 - So, this is the second, channel, which we clearly see firms that have no collateral or very little were relatively, more willing to Take on FinTech credit and boosted their sales even more. So collateral, poor entrepreneurs benefit relatively more from, from this type of, of new, credit, right? It's, we call it the collateral channel. And I think the third going to the issue of distribution cost.

00:12:12 - Traditional banks have high costs, right? You have to pay a credit officer. There's also transactional costs. The entrepreneur has to go to your bank and so on. Online, automated online credit dispenses with all these costs. So the marginal costs essentially zero, right? And the smaller loan is the higher the fixed cost per unit landed, right? And so there's a clear benefit to having small loans, served via, FinTech credit. So the, the effect is bigger for entrepreneurs

Unlocking Capital: The Paradox of High-Cost Online Credit for Entrepreneurs

00:12:45 - So these are three dimensions that support the information advantage to support the collateral, the lower dependence on collateral. And thirdly, also the, just the transaction cost channel is very, very important. All three, kind of mean that those firms that were afflicted by all three problems had the biggest growth effect. That was a thing that also puzzled us a little bit, because the cost of this online credit is relatively high.

00:13:18 - or loan charts that would charge 20 or 25%, but it doesn't seem to be a good source of financing since it's so high. Now, one thing to keep in mind is what we, what we discovered by talking more to the entrepreneurs and and looking into this deeper, is that many entrepreneurs actually never took out the loan, which you might ask, how can it be beneficial if I'm not taking out the loan? Well, what you do is you use the loan like a credit line,

00:13:48 - You kind of free up existing capital, which you previously used as kind of rainy day savings. You use this one and you have now the, online credit as a credit line that you can tap in, in case of liquidity problems, right? So it's kind of, the entrepreneurs used them just in second place, and it freed up existing capital then to be used and deployed and invested. So it is actually not, not often that. So, so the, the paradoxical thing is the online credit

Understanding FinTech Credit for Small Firms

00:14:22 - loan was not taken out, right? That's kind of a bit surprising. But if you understand the liquidity problems of small firms, you can understand that this is actually a channel that operates independently from the loan volumes, right? Just the opportunity. I can, I can be sure that I can meet any liquidity needs if it really, if I really need to, and maybe only over short term, right? Many of the, retail activities in China are very cyclical, right?

00:14:53 - Single stay and other events, right? When you have huge capital needs and on these very, very short periods, this credit is actually very, very useful in spite of the high annualized rates, right? So this is kind of a, a bit counterintuitive, but, but this is what we kind of, understood to be the, an important, important aspect of, of FinTech credit. How do, how does traditional banking and traditional credit compete against

00:15:24 - And I think there's gonna be more or less a market segmentation. We have to keep in mind that this FinTech credit mostly involves relatively small loans and also small loans to sectors that are very particular like retail that produces a lot of information without information. There's no FinTech credit, right? Not all kinds of businesses produce a lot of transaction data, in a public space. So it's limited to retail, small firm, a lot of consumer credit also. That's another big thing. 40% is roughly small firm credit.

FinTech's Competitive Edge and the Role of Traditional Banks

00:15:58 - That's where FinTech is, has a clear information advantage. And I, I don't think banks are in a good position to compete on in their turf, right? But then there are large areas of the economy, where I mean, medium-sized large firms where, FinTech has no competitive advantage. So it isn't that, we see all the banks perish because of FinTech credit. FinTech credit is particular in its information advantage.

00:16:33 - it's not gonna abolish banks, right? So, and also banks, pioneer new information sources. They try to compete. you can purchase data. It's a competitive market for financial data. They can get into this. They have other sources. And, so I think the banks, particularly in China and developing countries in general, have to evolve in order to, to on some segment in the middle right, to compete with,

00:17:03 - And, but it, it's, the competition will be limited to a small segment, neither the small, firm credit or consumer credit, nor the large firms. It's sort of in some intermediate segment where we will see, stronger competition. I think regulation plays a very important role in, in actually pioneering FinTech credit. I mean, we have to think of China's credit and banking system very much like the US in the

Transforming China's Credit Market with FinTech

00:17:33 - Where you have very localized segmented markets or local bank controls it. And, there can be big imperfections. The Chinese banking system, at least in the past, was very much like a so economy allocating credit through big banks, and it was a very non-market driven allocation. So you could have a lot of credit in some locations and very little somewhere else. In fact, the poorest places where those,

00:18:04 - because they're often sucked up all the savings, the local savings, and gave it done to so e somewhere else, possibly in some other city or profits. So there, there are very, very large, distortions in China's credit market that creates a very uneven, supply of credit to local entrepreneurs, and therefore is also a, a very big distortion for the development of small firms. So what FinTech credit does, it kind of levels,

00:18:36 - and financial and FinTech lenders were the first that provided basically nationwide similar rates, because they offered it over the online, over the internet, and everyone got the same condition provided on these characteristics, right? There was no local discrimination by credit rich and credit poor cities. So this is actually a very, very big integrating feature. So online credit is a way of unifying the credit market in China and creating much fair on equal credit access conditions

Significance of Accessibility and Benefits

00:19:09 - In that sense, it's, it's very, very beneficial.

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