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Author: Dongbei University of Finance and Economics doctoral Zhang Xuemei br /> Communications E-mail: [email protected]
before, causal inference referral team a lot of causal inference aspects of the article, such as dozens of classic article. It not only gives the code of the classic method, but also the related literature. Therefore, scholars can copy the results of an article based on the data and procedures in it.
1. DID uses classic literature, compulsory licensing: evidence from the enemy's trade law
2. Continuous DID classic literature, potatoes have made the old world civilization
3. Cross-sectional data DID tells, cross-section to do the paradigm of double-differential policy evaluation
4. RDD Classical literature, RDD model validity and robustness test
5. Event research method used in DID classic literature "environmental regulation" paper data and procedures
6. Generalized DID method used very classic JHE literature
7. DID classic literature "compulsory license "Thesis data and do program
8. MLM activities affect economic development, AER cross-section data analysis classic text
9. Multi-period DID classic literature big bad banks data and do file
10. Causal inference IV method classic literature, is it a system or Human capital promotes economic development?
11. The establishment of causality on AER, sensitivity testing, heterogeneity analysis and cross-data use of classic articles
12. The second causal inference classic, the impact of work interruption on the subsequent production efficiency of workers?
13. Density Economics: Natural Experiments from the Berlin Wall, Best Econometrica Paper
14. Labor and Health Economics with DID and DDD as Identification Strategies on
AER 15. A policy evaluation method using cross-sectional data can also be issued AER
1. The breakdown of the types of double differential DID, 20 articles that have to be read
2. The endogenous problem operation guide, 22 widely circulated articles
3. The matching method (matching) operation guide, 16 articles worth collecting
4. An encyclopedia-style guide for spatial metrology, this is the only one to master this unique cheat.
5. Panel data model operation guide, 16 articles you have to read
6. Social network analysis latest literature and software learning manual
7. Chinese industrial enterprise database Use problem description
8. The latest research trends and hot topics of JEEM environmental and resource economics
9. Statistics of the review status of English journals in the field of economics, number of review cycles
10. Full statistics of review information of Chinese C journals, number of review cycles , Download as soon as possible
11. Use Stata's 115-page Tips efficiently, the PDF version can be printed and used
1. Anshen + Clark Prize winner's RDD paper, breakpoint regression design
2. What is the breakpoint regression design in policy evaluation, and how to do it?
Volume 3.2 RDD Breakpoint Regression User Manual, including Stata and R software operating procedures
4. Breakpoint regression design RDD is fully explained, and there are many users in the education field
5. Without instrumental variables, breakpoints and random shocks, attribution can also be inferred
6. RDD breakpoint regression, an encyclopedia of empirical complete programs
7. Breakpoint regression design RDD classification and operation case
8. Comparison of four methods of DID, synthesis control, matching, RDD, applicable scope and characteristics
9. RDD classic literature, RDD model validity and robustness test
10.Do you use IV, RDD, DID, PSM more in the education field?
11. RDD slides data public sharing
12. Frontier research status of breakpoint regression design, RDD
13.PSM-DID, DID, RDD, Stata program encyclopedia
14. What should I do if I cannot find IV, RD and DID? There is an alternative method.
Original: THORSTEN BECK, ROSS LEVINE, ALEXEY LEVKOV. Big Bad Banks? The Winners and Losers from Bank Deregulation in the United States[J] The Journal of Finance. 65(5):1637-1667.
The author said: "I voluntarily joined the causal inference group of the econometric circle. I hope to learn more and communicate with the group members. I also hope to join the big family of the econometric circle to grow together."
"Big Bad Banks? The Winners and Losers from Bank Deregulation in the United States" is a classic study of multiple issues of DID, which mainly examines the impact of bank deregulation in the United States on the weakening of income inequality. The econometric tweet once published "Multi-issue DID classic literature big bad banks data and do files" restores the data analysis part of the article. This article aims to show the research ideas of the article by combing the theoretical context of the article.
1 Research background
The gap between the rich and the poor caused by the uneven income distribution is an important social issue. In the 20th century, American research scholars believed that the establishment of large banks would reduce the economic opportunities of the poor, thereby widening the distribution gap. For this reason, they often struggled with the establishment of branches by banks (the more branches established by banks, the greater the size of them. Big, namely big bank) (Southworth, 1928; White, 1982; Kroszner and Strahan, 1999). In the context of the financial crisis, unease about centralized economic power and increasing income inequality have intensified people’s debates on financial regulation. Researchers are committed to assessing the actual impact of financial regulation on income distribution. The author's research evaluates the impact of banking supervision on income distribution from the perspective of econometrics.
2 The study found that
1. Deregulation of banks can significantly suppress the income inequality of low-income households. Eight years after deregulation, the level of inequality has been reduced by about 4% compared to before deregulation.
2. Bank deregulation can increase the income of low-income groups, but it will not cause the income of high-income groups to shrink.
3. Deregulation of banks is the effect of bank performance on income distribution. Therefore, the more severely regulated regions, the more obvious the improvement in income distribution inequality after deregulation. Further analysis shows that areas with severe control, areas with strong competitiveness of small banks, areas with many small companies, and areas with more dispersed populations, after deregulation, the unequal income distribution has improved significantly.
4. The mechanism test found that deregulation of banks can increase education loans and corporate loans for low-income groups, and increase the demand for labor, thereby increasing the income of low-income groups and reducing income inequality.
5. These findings support the fact that restricting the establishment of branches by banks protects the local banking monopoly and is not conducive to providing economic opportunities for the relatively poor.
3 Research Contributions and Limitations
Empirically analyzes the research on the relationship between financial market policy formulation and income gap, and studies three possible influence mechanisms, providing new ideas for the research between bank performance and individual income. However, the conclusions of the study are relatively indirect. Although the mechanism test found that bank deregulation can increase education loans and corporate loans, there is no evidence that income-increasing groups increase their income by starting businesses or raising their educational level. The impact of deregulation on low-income groups is mainly to increase the wages of low-skilled workers, increase working hours, and mainly support the mechanism that deregulation is conducive to increasing labor demand.
4 Sample Screening and Data Sources
In the early 1970s, the US government began to loosen restrictions on bank holding companies with low shareholding ratios to integrate subsidiaries and allow them to begin state-wide expansion. With the passage of the Riegle-Neal Act and the Branching Efficiency Act in 1994, bank controls were gradually relaxed. Consistent with Jayaratne and Strahan (1996), the author’s research period began in 1976 and ended in 2006, with a total sample period of 31 years. Contains data for 49 states in 48 states and the District of Columbia. The information on income distribution comes from the "Current Population Survey" (March Supplement). Mainly examine the income of young adults aged 25-54, and exclude the following samples: 1. Samples with missing key variables (education, demographic structure, etc.); 2. Total personal income is below the 1st percentile or exceeds the 99th percentile The sample of 3 lives in (group quarters); 4, the income is zero or negative income; 5. CPS assigns a sample of zero weight or missing value. A total of 1,859,411 annual-state-individual samples and 1519 annual-state samples were obtained
5 Empirical design
5.1 Variables measure
income distribution using Gini coefficient, Theil index, the natural logarithm of the difference between the upper and lower decile (log(90/10)), and the upper and lower decile difference of income (log(90/10)). The natural logarithm (log(75/25)) of the difference between the 25th and the lower 25th.
5.2 The model design
uses a double difference model to measure the impact of deregulation on the income distribution gap. Such as model (1)
Among them, As and Bt are state dummy variables and time dummy variables, respectively, which represent individual state differences and time differences. Xst represents the state-level control variable that changes over time. Dst represents the deregulation of banks by state s, and the value is 1 after the deregulation year, otherwise it is 0. It is equivalent to the treat post in the double difference model , where the dummy variable Dst is used. It can be seen from this that the design method of the multi-period double scoring model is similar to the double difference, including treat post, treat, post and control variables. The difference is that the double scoring treat and post in multiple periods are not fixed, and over time Change, Dst can capture this process.
5.3 The
regression results of the research results are not difficult to see that, regardless of whether other variables are controlled, bank deregulation can curb regional income distribution inequality.
The variable bank deregulation is the Dst in the model.
In order to examine the beneficiary groups, the author divides individual income into 19 equal parts, aiming to reveal whether deregulation of banking makes the rich become poorer or the poor become richer. Figure 2 shows this result. It can be seen from Figure 2 that the deregulation of branches in the state has tightened the income distribution gap by disproportionately increasing the income in the income distribution, rather than by reducing the income in the income distribution. .
Further, the author uses a dynamic model to analyze the impact of time changes on the income gap. It can be seen from Figure 3 that after the implementation of the policy, the income gap is basically distributed below 0, indicating that the income gap has decreased. About the node of deregulation for 8 years, the income gap has fallen the most.
5.4 Further analysis
Since the main idea of the study is that deregulation can affect income disparity, the more severe the bank regulation, the more obvious the effect of deregulation. Based on this idea, the author separately investigated four situations where bank regulation is severe (regions with severe regulation). , Areas with strong competitiveness of small banks, areas with many small companies, and areas with more dispersed populations) After deregulation, the unequal distribution of income has improved significantly.
The author also examines the mechanism of influence. Since deregulation mainly increases the income of low-income groups to reduce the income gap, how can deregulation of banks increase the income of low-income groups? The author examines three possible impact mechanisms, namely, increasing education loans, increasing corporate loans, and lowering capital costs, resulting in increased labor demand.
At this point, the highlight of the article has come to an end. The summary is as follows: The article is designed rigorously, examining the impact of bank deregulation on income inequality, analyzing the different effects of low-income groups and high-income groups, and finding that low-income groups benefit. The benefit is that deregulation of banks has increased financial opportunities for low-income groups, that is, increased education loans, corporate loans, and labor demand. In addition, focusing on the situation of severe bank supervision is to focus on the role of deregulation, and found that the more severe the bank regulation, the more obvious the effect of deregulation on suppressing income inequality.
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