Kjetil Storesletten教授分享RES（2025）创作心得 Kjetil Storesletten on RES (2025)
本文最初于 2025 年 10 月 11 日 发布于微信公众号 Impactful Research；2026 年 4 月 28 日 同步至本网站。
Originally published on the WeChat official account Impactful Research on 2025-10-11; mirrored to this website on 2026-04-28.
来源：bing图片
这个公众号的第二十六篇文章，我们很荣幸邀请到明尼苏达大学的Kjetil Storesletten教授分享他2025年发表在The Review of Economic Studies 的论文 Barriers to Entry and Regional Economic Growth in China 的创作心得。
本文正文内容约七千字，全文阅读需约12分钟
**Q1：I think your paper is one of the first macro papers to study regional economic differences in China. How did you identify this research question? What motivated you to focus on the regional aspect?
我感觉您的论文是宏观经济学中首批研究中国区域经济增长差异的论文之一。您是如何识别出这一研究问题的？是什么动机促使您关注区域差异？
I am, of course, very fascinated by China and its economic development. When we began this work, a lot of research had already been done trying to understand or describe the economic transformation—but usually, people would either treat China as a whole or focus solely on the coast.
我原本就一直对中国及其经济发展非常着迷。当我们开始这项工作时，已有大量研究试图理解或描述中国的经济转型——但通常，人们要么将中国视为一个整体，要么只关注沿海地区。
I felt that the heterogeneity within China was understudied. That’s something I wanted to understand better. So, one motivation was that it was remarkable how large these regional differences were, yet so few had really examined them. The other motivation came from my perspective that understanding China’s economic transformation—or that of other developing countries—is incredibly important. China stands out especially because it is incredibly big. So, I wanted to understand: what can we learn from this transformation? What factors helped or deterred it?
我认为对于中国内部的区域异质性的研究是不足的 。这正是我想要更深入理解的。因此，第一个动机是，这些区域差异如此巨大，却鲜有人真正去审视它们。另一个动机源于我的一个观点，即理解中国或其他发展中国家的经济转型至关重要。中国的特殊性尤其在于其庞大的规模。因此，我想理解：我们能从这场转型中学到什么？哪些因素促进或阻碍了它？
It occurred to me that China is more like a continent. And when I began working with prefectural-level data with Loren, I was excited because I hadn’t known such detailed information was available—and clearly underutilized. It felt like we suddenly had 350 or 360 natural laboratories to examine—that was essentially the motivation at the start. We study the economic takeoff of each region separately and identify factors correlated with local development. Regional convergence was interesting in itself—and once we dived into the data, we uncovered strikingly rapid convergence, and that naturally became a central theme of the paper.
我意识到中国（的宏观经济发展）更像一个大陆。 当我开始与Loren合作处理地市级数据时，我感到非常兴奋，因为我之前并不知道能够获得如此详细的信息——而且这些数据显然未被充分利用。这感觉就像我们突然拥有了350到360个天然实验室可供研究——这基本上是我们最初的动机。我们分别研究每个区域的经济增长，并识别其影响因素。空间收敛本身就是一个有趣的现象——而一旦我们深入数据，便发现了极其迅速的收敛过程，这自然成为了论文的一个核心主题。
Q2： Let us talk about the concept “entry wedge”—is this a completely new idea that you developed?
我们来谈谈“进入楔子”（entry wedge）这个概念——这是您提出的一个全新概念吗？
The concept of the entry wedge is something we developed. When we began working on this paper, much of the existing literature focused on misallocation-type distortions to explain why firms have a wrong size or an inefficient mix of capital and labor. Two common distortions were overly expensive or cheap capital, and regulatory limits on firm expansion like being taxed or subsidized.
“进入楔子”这一概念是我们提出的。当我们开始撰写这篇论文时，现有文献大多聚焦于资源配置层面的扭曲，以此解释为何存在企业不适当规模的问题或资本劳动配置不当的问题。两种常见的扭曲是资本价格过高或过低，以及对企业扩张的管制限制，如征税或补贴。
As we started to focus specifically on private firms, we quickly observed a clear correlation: in regions with a lot of state-owned enterprises (SOEs), there were very few private firms. We also noted that after the 1998 SOE reforms, which significantly reduced the size of the state sector, the decline was uneven across industries—it was much more concentrated in labor-intensive, light, and non-strategic industries. We considered using this variation as an instrumental variable to predict the decline of SOE firms and understand its impact.
当我们开始特别关注私营企业时，我们很快观察到一个明显的相关性：在国有企业众多的地区，私营企业非常少。 我们还注意到，在1998年国企改革大幅缩减国有部门规模之后，这种缩减在不同行业间并不均衡——它更集中于劳动密集型、轻工业和非战略性行业。我们考虑利用这些行业差异构建关于区域国企退出的工具变量，并理解其影响。
In a simple model, for regions where state employment fell sharply, one would expect massive layoffs and, consequently, a surplus of workers leading to lower wages. You also expect the TFP to fall because of the cheap labor. This is because in a standard model (like Hopenhayn’s), lower wages should enable less productive firms to enter. Surprisingly, we observed the opposite: in these regions, private firms started to pay higher wages and their TFP increased. This suggested we need something else to explain this.
在一个简单的模型中，人们会预期在国有部门从业人数急剧下降的地区出现劳动力过剩和工资下降。同时，由于劳动力廉价，全要素生产率也应下降。这是因为在标准模型中，较低的工资使生产率较低的企业得以进入。但出乎意料的是，我们观察到了相反的情况：在这些地区，私营企业支付更高的工资，并且全要素生产率也提高了。 这表明我们需要引入其他因素来解释这一现象。
Let’s consider a simple story: for some reason, the laid off workers may boost the aggregate TFP. But the problem is that in that case both new and existing firms should increase hiring. However, we found that most of the employment growth occurred in new firms. In regions with faster wage growth, the share of employment by newly created firms increased, and the average age of firms declined.
我们可以设想一个简单的模型：工人下岗通过某种方式提升了整体全要素生产率。但问题在于，如果是这样，新企业和现有企业都应提高雇员人数。然而，我们发现就业增长大部分发生在新成立的企业中。 在工资增长较快的地区，新创企业的就业份额上升，企业的平均年龄下降。
This pattern indicated that TFP growth was driven primarily by new entrants. We concluded that some factor that are related with firm entry—such as lower startup costs or reduced entry barriers—were at play. In China context, getting a business license is hard and how easy to obtain it varies significantly across regions. In business-friendly environments, getting a license is straightforward; in others, it is more difficult. What we call the “entry wedge” captures exactly this mechanism.
上面发现的典型事实说明，全要素生产率的增长主要由新进入者驱动。我们由此得出结论：某些与企业进入相关的因素——例如更低的创业成本或减少的进入壁垒——在发挥作用。 在中国当时的制度背景，获取进入市场的许可是困难的，且其难易程度在不同地区差异显著。在营商环境友好的地区，获取执照相对简单；而在其他地区则更为困难。我们称之为”进入楔子”的概念，正是捕捉了这一机制。
Q3 ：**My next question is about the research process—it seems like quite a complex puzzle. How long did it take from initially observing the correlation to checking all the possibilities and ultimately identifying the story?
我的下一个问题是关于研究的过程——这似乎是一个很复杂的解谜过程。从最初观察到相关性，到检验各种可能性，最终确定核心叙事，这中间花了多长时间？
We started it ten years ago. At first, we were simply playing around with the data and different variables.I think it’s better to start with a simple model. Then, when the data doesn’t align with that simple model, you ask why—what frictions need to be added? That’s how the process unfolds.
我们这项研究始于十年前。最初，我们只是在对数据和各种变量进行初步的探索。我认为，从构建一个简单的模型开始是更好的方式。然后，当数据与这个简单模型不符时，你就会追问原因——需要加入哪些摩擦来解释这种不一致？ 整个研究过程就是这样展开的。
Q4：**I’ve been thinking a lot about how to do research combining empirical analysis and model analysis—particularly in the context of our serial entrepreneur paper. From a reduced-form perspective, the decision to become an entrepreneur the first or second time is endogenous. And in our model, it’s endogenous as well. In this kind of research, I understand that we first find a correlation (like between serial entrepreneurship and productivity), and try to rationalize it through a model. The contribution often lies in proposing a specific mechanism—like a financial friction in our context—to explain the correlation between two endogenous variables. Am I understanding this correctly?
**我一直在自己思考如何将实证分析与模型分析相结合来进行研究——特别是在我们关于连续创业者的研究过程中(Serial Entrepreneurship in China)。从简化型（reduced-form）的视角看，首次或再次创业的决定是内生的。而在我们的模型中，它也是内生的。在这类实证与模型结合的研究中，我的理解是，我们首先发现一种相关性（例如连续创业与生产率之间的相关性），然后试图通过一个模型来将其合理化。其贡献往往在于提出一个具体的机制——比如在我们的情境中是一种金融摩擦——来解释两个内生变量之间的相关性。我的理解正确吗？
Ideally, if we could run real-world policy experiments, that would be fantastic for learning about how the economy works. But usually, we don’t have experimental evidence —for many important questions, clean causal evidence isn’t available.That’s where models become very useful.**Just calculating correlations does not work.If you have a model that gives meaning to a correlation, and that model makes sense, then it becomes meaningful.**
理想情况下，如果能够在现实世界中进行政策实验，那对于理解经济如何运行将是极好的。但通常，我们并没有实验证据 ——对于许多重要问题，干净的因果证据是无法获得的。这正是模型变得非常有用的地方。 当然，仅仅发现两个变量之间的相关性意义不大。如果有一个模型能为相关性赋予意义，并且这个模型本身是合理的，那么这种相关性的分析就变得有意义了。
Q5：**For example, in the serial entrepreneur paper, what part is meaningful? Some people might find it interesting to see the performance premium of serial entrepreneurs, while others might appreciate learning a model that captures the step-by-step decision process of entrepreneurs who start multiple businesses. I’m very curious how you view this?
例如，在关于连续创业者的论文中，您认为哪部分贡献最有意义？有些人可能对观察到的连续创业者的TFP更高这一现象本身感兴趣，而另一些人则可能更欣赏能够分析创业者逐步决策过程的理论模型。我很好奇您如何看待这篇文章的贡献？
For some questions, you can go quite far away with a purely empirical approach—though even then, you always have some models in mind. I still find it useful to use a model as a benchmark. A model gives you testable predictions, and you can check whether the data align with some of those predictions.
对于某些问题，纯粹依靠实证方法也可以进行很深入的分析——尽管即便如此，你脑海中始终会存在某种理论模型。我仍然认为，使用一个模型作为基准是有益的。模型能提供可检验的预测，你可以去验证数据是否与其中的一些预测相符。
Take our serial entrepreneur paper, for example. We found that serial entrepreneurs are more productive than first-time entrepreneurs. However, that’s a prediction that can emerge from many models. But then we also observed that the advantage of serial entrepreneurs is much larger in terms of equity and capital than TFP. This could be due to measurement error in TFP. However, when we broke the sample into “stayers” (those who remain in the same industry) and “switchers” (those who switch industries), we found something revealing: stayers showed a super high TFP advantage—even larger than their advantage in capital and equity. In contrast, switchers had more capital and equity than stayers but lower TFP than non-serial entrepreneurs. This pattern suggests that the simple model isn’t enough—something is missing. It let us extend the model by incorporating additional elements, such as heterogeneity in costs of capital, which helped explain these empirical patterns. It’s not saying that other explanations aren’t possible, but this offers one way to interpret the data—and one that aligns well with what others observe as relevant dynamics in China’s context.
以我们的连续创业者论文为例。我们发现连续创业者比首次创业者有着更高的生产率。然而，这个结论可能是许多模型都能推导出的预测。但随后我们还观察到，连续创业者的优势在资本方面的优势远大于在TFP的优势。这当然可能是由于TFP的测量误差所致。然而，当我们将样本划分为”坚守者”（留在同一行业的人）和”转换者”（跨行业的人）时，我们发现了一个揭示性的现象：坚守者表现出超高的TFP优势——甚至比他们在资本方面的优势还要大。相比之下，转换者比坚守者拥有更多的资本和股权，但其TFP却甚至低于非连续创业者。这种现象表明，简单的模型不足以解释全部——遗漏了某些因素。这促使我们扩展模型，纳入新的要素，例如资本成本的异质性，这有助于解释这些实证发现。 这并不是说其他解释不可能成立，但我们提供了一种解读实证发现的机制——而且是一种与中国情境下其他人观察到的相关动态十分吻合的机制。
Q6：Okay, so I understand that one advantage of combining models with empirical work is that while many models or mechanisms can explain simple empirical findings, when you have a set of related and more complex empirical results, this approach can help narrow down the specific mechanism or type of model that fits best. That makes the research more interesting—is that right?
**
我明白了。所以，将模型与实证工作相结合的一个优势在于：虽然许多模型或机制都能解释实证发现，但当您面对一组相互关联且更为复杂的实证结果时，模型与实证相结合的分析方法有助于筛选出最契合的特定机制或模型类型。这使得研究更加深入和有力——我这样理解对吗？**
Absolutely.
没错。
Q7：**When you were writing and revising the paper—the entry barrier paper—what do you think was the greatest challenge throughout the whole process?
在您撰写和修改这篇entry barrier论文时，您认为整个过程中最大的挑战是什么？
I think one of the biggest challenges was figuring out how to find a standard or widely recognized model. It might have been easier if we had written it more in the style of a full Hopenhayn model—that could have made our approach clearer to readers. Instead, we approached it more as a measurement paper—we were charting new territory, studying something people hadn’t really looked at before. There wasn’t a natural or obvious modeling framework to adopt.
我认为最大的挑战之一在于如何找到一个标准或广受认可的基础模型。 如果我们当初采用更完整的Hopenhayn模型为基础来构造模型，或许会更容易些——那样可能让我们的方法对读者而言更清晰。但事实上，我们更多是从一篇测量型论文的角度来写作——我们是在开拓新领域，研究的是前人未曾深入关注的问题。当时并没有一个现成或显而易见的基础模型可供采用。
These days, spatial models and related frameworks have become more common. Perhaps if we were writing it today, we might place it more clearly within that context. But back then, the real challenge was deciding on the right theoretical framing. The model we used was simpler than a full spatial model—much simpler, actually. We approached it more as an accounting framework.
当前，空间模型及相关框架已变得更为普遍。如果现在重写这篇论文，我们或许能更清晰地将它置于空间模型的框架下。但在当时，真正的挑战在于确定合适的理论框架。我们采用的模型比完整的空间模型更简化——实际上简化得多。我们更多是将其作为一个核算框架来使用。
Ideally, it’s very convenient if you can take an existing model that people already know and introduce just one or two changes to make your point. That makes it much easier for readers to understand your contribution. Like my “Growing Like China” paper, it’s almost a neoclassical model.
理想情况下，如果能采用一个学界已知的基准模型，仅通过一两个改动来阐明的观点，会非常便利。这能让读者更容易理解你的贡献。 就像我的《Growing Like China》那篇论文，它基本上是一个典型的新古典模型。
Q8 ：What do you think are the key factors that made your paper impactful?**
您认为这篇论文变得具有高影响力的关键因素是什么？**
I think it’s very important to present your work widely —travel to conferences, seminars, and discuss it within relevant research communities. That’s essential. Looking back on my career, I’ve spent much of it in Europe, and I think one mistake some European researchers make is that they don’t travel enough to present their work. In contrast, researchers based in the U.S. often present their paper again and again at different venues. In economics, that is perhaps the most important things you can do to increase a paper’s impact. You want to show people what you’re doing, hear their comments, and refine your work based on their input.
我认为非常重要的一点是广泛地展示你的工作 ——参加各种会议、研讨会，并在相关研究群体中进行交流。这至关重要。回顾我的职业生涯，其中很大一部分时间我在欧洲度过，我认为一些欧洲研究人员的一个失误就是他们不够积极地外出交流展示自己的成果。相比之下，美国的研究人员通常会在不同场合反复宣讲他们的论文。在经济学领域，这或许是提升论文影响力所能做的最重要的事情。你需要向人们展示你的工作，听取他们的评论，并根据反馈来完善你的研究。
Q9 ：**Do you have any golden principles? For example, how many times should a paper be presented before submission?
您有什么黄金法则吗？比如，一篇论文在提交前应该宣讲多少次？
I think you should always focus on presenting your best paper. Suppose you have three papers—one is really strong, and the other two are just okay. I would recommend presenting the very best one many many times and dedicating your efforts to improving it further. The returns from improving your best paper are surprisingly high.
我认为你应该始终展示你最好的论文。 假设你有三篇论文——一篇非常出色，另外两篇只是尚可。我会建议你将那篇最优秀的论文反复宣讲多次，并投入精力进一步打磨它。因为优化你最好的论文，其回报会高得惊人。
This is especially true for young scholars. When we say impactful, you should talk with people and should talk about your best work. For instance, if you were to sit next to Pete Klenow at a conference, you’d want to discuss your best paper. If you were colleague with him over a longer period—say, several months—then you could also bring up some of your other projects.
这对年轻学者来说尤其如此。当我们谈论影响力时，你需要与人交流，并且应该谈论你最好的工作。例如，如果你在会议上碰巧坐在Pete Klenow旁边，你应该向他介绍你最好的论文。但如果你能与他有更长时间的共事机会——比如几个月——那么你才可以再提及其他一些研究项目。
Q10 ：**I’d like to ask for your general advice for young scholars interested in China’s growth—especially given your influential work on structural transformation and the China growth. More specifically, I’m curious about how to properly incorporate the issue of misallocation into studies of China’s growth. Do you see this as a field where there’s still a lot to be done? Or is it already mature, with established models ready to be applied?
我想请教您对于研究中国增长的年轻学者有什么总体建议——特别是考虑到您在中国结构转型和增长方面的诸多开创性研究。更具体地说，我很好奇如何将资源错配的问题恰当地纳入中国增长的研究中。您认为这仍然是一个大有可为的研究领域，还是已经成熟、只需应用现有模型即可？
If you look back a hundred years from now, the economic transformation of China will stand as a profoundly important event—far more significant than, say, the Great Recession or the Great Inflation, which, while heavily studied, were relatively small bumps in the (economic) history of the world. The fact that China went from one of the poorest countries to a middle-income economy in just a few decades is truly monumental.
回望百年后的今天，中国的经济转型必将成为影响深远的重大历史事件，其意义远非大衰退或大通胀可比。后者虽然被广泛研究，但在世界经济史中只是相对较小的波动。中国在短短几十年内从最贫困的国家之一发展成为中等收入经济体，这一成就确实具有里程碑意义。
There is so much we still need to understand about China—and about emerging economies in general—and still relatively few economists working deeply on these questions. So, I certainly wouldn’t call it a mature field.
关于中国——以及广义的新兴经济体——我们仍有大量问题需要理解，而深入钻研这些问题的经济学家相对仍属少数。因此，我绝不会称其为一个成熟的领域。
Understanding and measuring misallocation remains a crucial challenge—one that will engage researchers for generations to come. Now, we have a lot of micro-level data, and there’s been a boom in applied economics. But there were always interest in the big questions, and understanding growth is a big question.
理解和测度资源错配依然是一个核心挑战——这将吸引未来几代研究者的持续探索。当前，伴随大量微观数据的出现，实证研究在蓬勃发展。但人们对重大问题的兴趣始终存在，而理解增长正是一个重大问题。
There is a view that Charles Jones may state most clearly. Endogenous growth theory is problematic when treating each country as an observation. It doesn’t make any sense that one country can grow faster alone than the rest of the world forever.Instead, the best way to think about growth for an individual country is to use a semi-endogenous growth model. You can influence the growth for a while, but in the long run, the development is cointegrated with the rest of the world. That’s what misallocation is all about. Misallocation can explain the level difference and growth for a while. For example, in the Chinese context, the removal of frictions led to a reduction in misallocation. The reduction of misallocation is one way to understand the growth. This is the semi-endogenous growth view.
有一种观点，或许Charles Jones阐述得最为清晰。内生增长理论将每个国家视为一个独立观测点的处理方式是有问题的。一个经济体是不可能永远独自以高于世界其他地区的速度增长。相反，思考单个国家增长的最佳方式是采用半内生增长模型。你可以在一段时间内影响其增长速度，但长期来看，其发展会与世界其他地区协同整合。这正是资源错配研究的核心所在。 资源错配可以解释水平差异和一段时间内的增长。例如，在中国的背景下，摩擦的消除带来了资源错配程度的下降。而资源错配程度的下降带来的水平变化可以理解为短期的经济增长。这就是半内生增长理论的视角。
学者简介：
Kjetil Storesletten 是美国明尼苏达大学的理查德和贝弗利·芬克经济学教授，同时也是世界计量经济学会会士（Econometric Society Fellow）。他曾在奥斯陆大学、明尼阿波利斯联邦储备银行和国际经济研究所任职。Kjetil 曾担任经济学顶级期刊Review of Economic Studies的执行编辑（2006-2010），以及该期刊的主编（2013-2017）。他还曾担任挪威货币政策执行委员会成员（2014-2019），并于2019年担任欧洲经济学会主席。Storesletten教授于1995年获得卡内基梅隆大学的经济学博士学位。他是一位宏观经济学家，专注于不平等、税收和发展经济学。其研究成果已发表在包括Quarterly Journal of Economics、Journal of Political Economy、American Economic Review、Review of Economics Studies和Econometrica在内的顶级经济学期刊。Storesletten教授是国外研究中国宏观经济学的顶级专家之一，2012年因发表在American Economic Review上的论文“Growing Like China”获得中国经济学最高奖“孙冶方奖”。
参考文献：
[1]Brandt L, Kambourov G, Storesletten K. Barriers to Entry and Regional Economic Growth in China. The Review of Economic Studies, 2025: rdaf029.
[2]Brandt L, Dai R, Kambourov G, Storesletten K, Zhang X. Serial Entrepreneurship in China, CEPR Discussion Paper No. 17131, 2022.
[3]Song Z, Storesletten K, Zilibotti F. Growing like China. The American Economic Review, 2011, 101(1): 196-233.
| 责任编辑 | 戴若尘 |
| 整理翻译 | 张诗怡 |
| 校对 | Kjetil Storesletten |