rafmacalaba/datause-encoder-data huggingface dataset:
# **The Political, Regulatory and Market Failures That Caused the US Financial Crisis**

_“From the current handwringing, you’d think that the banks came up with the idea of looser underwriting standards on their own, with regulators just asleep on the job. In fact, it was the regulators who relaxed these standards—at the behest of community groups and ‘progressive political forces.’”_ Professor Stan Leibowitz, University of Texas<sup>1</sup>

## **I. Summary and Literature Review**

## **Summary**

In 2010, the US is in the worst financial crisis since the Great Depression. The core of the crisis is that 44 percent of all home mortgages (or 25 million mortgages) are default prone, a figure that is unprecedented in US history.<sup>2</sup> Why did financial institutions and homeowners acquire so many mortgages that are in default or in danger of wider default? I argue that the crisis is a result of regulatory failure, market failure and, most of all, political failure.

First, the seeds of the crisis were sown while Congress was appropriately imposing tougher regulation on banks and savings and loan associations in the early 1990s in response to the Savings and Loan crisis. Congress made a grave error: it agreed to avoid real regulation of the two Government Sponsored Enterprises (GSEs) commonly known as Fannie Mae and

> 1 New York Post, February 5, 2008. See Leibowitz (2008) for an elaboration of his views where he argues  that “in an attempt to increase home ownership, virtually every [relevant] branch of government undertook an attack on underwriting standards.”

> 2 Testimony of Ed Pinto (2008, p. 8), former chief credit officer of Fannie Mae. The principal database of the New York Fed  under reports default prone mortgages. There are 7 million sub-prime loans in the Federal Reserve Bank of New York on-line database. Second, there are about 10 million sub-prime loans classified in the “Loan Performance Prime Database” as prime. (This database is mutually exclusive with the above mentioned New York Fed database.)  Contrary to the name of the index, there were about 10 million sub-prime loans among the 50 million loans in its Prime database (by the conventional definition of a sub-prime loan as a loan with a FICO index of less than 660). Third, there are alt-A loans (such as  “liar loans”) where the borrower had a FICO score above 660, but failed to provide documentation. These were favorite instruments of speculators and have conventionally been classified as prime; but they are defaulting at a rate approaching sub-prime. The New York Fed estimates that there are about 2.67 million alt-A loans, excluding Fannie and Freddie exposure and Pinto reports 2.9 million alt-A loans held by Fannie and Freddie. Finally, there are about 2.5 million other junk loans, such as negatively amortizing option adjustable rate mortgages. See Pinto (2008, Annex I) for further details.

2
Policy Research Working Paper 5763

# **Abstract**

In the past three decades, emerging countries have gone through extensive decentralization reforms. Yet, there are no studies assessing quantitatively the relative importance of various factors known to affect the success of decentralization. This paper builds on a comprehensive dataset the authors constructed for Peru, which merges municipal fiscal accounts with information about municipalities’ characteristics such as population, poverty, education, and local politics. The paper then analyzes the leading factors affecting the ability of municipalities to execute the allocated budget using complementary methodologies, from least squares to quantile regression analyses. According to the existing literature and the Peruvian context, the analysis divides these factors into four categories: the _budget size and allocation process; local capacity; local needs;_ and _political economy constraints._ Although all four factors affect decentralization, the largest determinant of spending ability is the adequacy of the budget with respect to local capacity. The results confirm the need for decentralization to be implemented gradually over time in parallel with strong capacity building efforts.

This paper is a product of the Poverty Reduction and Economic Management Unit, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at jrigolini@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
of Australia’s climate policies. These include: what were the combined and individual effects of the policies during the time of implementation? If emissions were reduced, were the reductions transitory, or did the policies shift emissions onto a long-run path below what would have occurred in the absence of the policy? Did firms sink costly investments that yielded future emissions reductions? Did they wait and see if uncertainty over the tax would be resolved? And finally, did the pricing policy lead to production leakage--in other words, was more Australian coal shipped abroad during and after the tax was in place?

Our synthetic control estimates examine the path of Australia’s aggregate emissions per capita from 1980-2018 in the spirit of Andersson (2019). There are several challenges unique to this context, most importantly the selection of a donor pool of economies to replicate the trajectory of Australia’s emissions during the 2000s. A m o n g o t h e r a d j u s t m e n t s t o e s t i m a t i o n , we limit the donor pool to economies with no national emissions policies during the sample period and focus on other high-emitting economies following the recommendations of Abadie (2021). A battery of robustness and sensitivity checks including the analysis of placebo policies at different times and leave-one-out analyses are carried out to test the validity of the research design and ensure the robustness of the results to changes in the donor pool. Both traditional permutation tests and uncertainty quantification based on prediction intervals are explored. To explain the mechanisms behind the causal effects, we explore patterns in data on emissions by sector and changes in generation capacity by fuel. Finally, a time series analysis of Australian coal production and exports is performed to explore the question of emission leakage.

We estimate that Australia’s increase in renewal subsidies in tandem with its short-lived carbon tax caused a 7% reduction in average emissions per capita from 2009 to 2018. This is in sharp contrast with the general perception in the literature that Australian policies to combat climate change in the 2010s were largely ineffective (e.g. Anderson et al., 2023). The synthetic control analysis shows that Australian emissions per capita declined rapidly while the carbon tax scheme was in place and annual emissions did not rebound to a level consistent with their likely path had the policy changes never been implemented. Moreover, we show that contemporaneous with policy implementation, power sector emissions declined because of declining coal-fired generation, while wind and solar capacity rapidly increased. However, the rapid decline in emissions stalled in 2015, immediately following the repeal of the carbon price in 2014. Through a time-series analysis of coal 4
# **[1] Nearest Neighborhood Matching Method**

Figure A2. Overlap (common support) of propensity scores between the treated and untreated <!-- Start of picture text --> group<br><!-- End of picture text --> <!-- Start of picture text --> (a) Treatment: GVC1, All regions (b) Treatment: GVC4, All regions<br> (c) Treatment: GVC1, MENA region (d) Treatment: GVC4, MENA region<br><!-- End of picture text --> Table A8. Common Support, Outcome: Female Ownership

|**(**<br>Treatment<br>assignment|**a) GVC1, All region**<br>Common<br>support<br>On support|**s**<br>Total|**(b)**<br>Treatment<br>assignment|**GVC4, All region**<br>Common<br>support<br>On support|**s**<br>Total|
|---|---|---|---|---|---|
|Untreated<br>Treated|66,790<br>17,097|66,790<br>17,097|Untreated<br>Treated|75,726<br>1,842|75,726<br>1,842|
|Total|83,887|83,887|Total|77,568|77,568|

## **(c) GVC1, MENA region**

|Treatment<br>assignment|Common<br>support<br>On support|Total|
|---|---|---|
|Untreated|9,235|9,235|
|Treated|2,195|2,195|
|Total|11,430|11,430|

- **(d) GVC4, MENA region**

|Treatment<br>assignment|Common<br>support<br>On support|Total|
|---|---|---|
|Untreated|10,017|10,017|
|Treated|187|187|
|Total|10,204|10,204|

38
- Koppmair, S, M Kassie, and M Qaim. 2017. “The Influence of Farm Input Subsidies on the Adoption of Natural Resource Management Technologies” 61 (4): 556. https://doi.org/10.1111/14678489.12220.

- Mason, NM, and M Smale. 2013. “Impacts of Subsidized Hybrid Seed on Indicators of Economic WellBeing among Smallholder Maize Growers in Zambia” 44 (6): 670. https://doi.org/10.1111/agec.12080.

- Mason, NM, A Wineman, L Kirimi, and D Mather. 2017. “The Effects of Kenya’s Smarter’ Input Subsidy Programme on Smallholder Behaviour and Incomes: Do Different Quasi-Experimental Approaches Lead to the Same Conclusions?” 68 (1): 69. https://doi.org/10.1111/14779552.12159.

- Mel, Suresh de, David McKenzie, and Christopher Woodruff. 2008. “Returns to Capital in

- Microenterprises: Evidence from a Field Experiment.” _The Quarterly Journal of Economics_ 123 (4): 1329–72.

- Osorio, Camilo Gomez, Dwi Endah Abriningrum, Enrique Blanco Armas, and Muhammad Firdaus. 2011. _Who Is Benefiting from Fertilizer Subsidies in Indonesia?_ The World Bank.

- Ragasa, Catherine, and John Mazunda. 2018. “The Impact of Agricultural Extension Services in the Context of a Heavily Subsidized Input System: The Case of Malawi.” _World Development_ 105: 25–47.

- Ricker-Gilbert, J. 2014. “Wage and Employment Effects of Malawi’s Fertilizer Subsidy Program” 45 (3): 353. https://doi.org/10.1111/agec.12069.

- Ricker-Gilbert, Jacob, and Thomas S Jayne. 2017. “Estimating the Enduring Effects of Fertiliser Subsidies on Commercial Fertiliser Demand and Maize Production: Panel Data Evidence from Malawi.” _Journal of Agricultural Economics_ 68 (1): 70–97.

- Ricker-Gilbert, Jacob, Thomas Jayne, and Gerald Shively. 2013. “Addressing the ‘Wicked Problem’ of Input Subsidy Programs in Africa.” _Applied Economic Perspectives and Policy_ 35 (2): 322–40.

- Tanko, M, S Ismaila, and S Abu Sadiq. 2019. “Planting for Food and Jobs (PFJ): A Panacea for Productivity and Welfare of Rice Farmers in Northern Ghana” 7 (1).

https://doi.org/10.1080/23322039.2019.1693121.

- Wossen, Tesfamicheal, Tahirou Abdoulaye, Arega Alene, Shiferaw Feleke, Jacob Ricker-Gilbert, Victor Manyong, and Bola Amoke Awotide. 2017a. “Productivity and Welfare Effects of Nigeria’s e- Voucher-Based Input Subsidy Program.” _World Development_ 97: 251–65.

- ———. 2017b. “Productivity and Welfare Effects of Nigeria’s e-Voucher-Based Input Subsidy Program.” _World Development_ 97: 251–65.

- Yi, FJ, WY Lu, and YH Zhou. 2016. “Cash Transfers and Multiplier Effect: Lessons from the Grain Subsidy Program in China” 8 (1): 99. https://doi.org/10.1108/CAER-07-2015-0078.

- Zaveri, Esha Dilip, Jason Daniel Russ, Sebastien Gael Desbureaux, Richard Damania, Aude-Sophie Rodella, and Giovanna Ribeiro Paiva De Souza. 2020. “The Nitrogen Legacy: The Long-Term Effects of Water Pollution on Human Capital.” _World Bank Policy Research Working Paper_ , no. 9143.

# **Appendix**

# **A1. Key word search**

Database 1: Web of Science 18
defaults may influence use of the transfer through a flypaper effect, with some of the money remaining in the account even though there were very low transaction costs to withdrawing it immediately.<sup>6</sup> Therefore, we examine banking activity on the day of the transfer; within three days of the transfer; within seven days of the transfer; and within 14 days of the transfer. We consider three outcomes: withdrawals, deposits, and net deposits (deposits minus withdrawals).

In the period immediately preceding the intervention, respondents seldom use their accounts. Panel C of Table 1 shows that only 7% of respondents had any bank account transaction in the 7 days prior to round 0 survey collected just prior to respondent’s first bank visit for this experiment. Thirty-three percent of respondents had at least one transaction in the 90 days prior to survey round 0. Deposits over the same period averaged MK 13,045 (about USD 41) and net deposits were slightly negative on average. The standard deviation for deposit and withdrawals values reveals large sample variation, with a relatively small number of very large deposit and withdrawals.

To confirm that the randomization produced comparable experimental groups, we present two sets of balancing tests. Table 2 compares the means and standard deviations of baseline variables in the cash transfer treatment and the savings default treatment. The last column reports the p-value for the test that the means are equal, conditional on the village and weekof-survey fixed effects used in the subsequent analysis. We examine a total of 22 variables. There are no statistically significant differences for any of the baseline characteristics. Inspection of the data shows that the economically meaningful differences in asset ownership and in net deposits in the 90 days before survey round 0 are driven by a few outlier observations. The second set of balancing tests reported in Table 3 are comparisons across the three transfer timing conditions. As before, we report the p-value for the test of joint equality in the final column. There are statistically detectable differences in three sub-categories of spending which each account for small percentages of overall spending: transfers and fees, unplanned food, and unplanned non-durables. There are also statistically significant (p=0.044) differences in the percent of each group that recorded any banking activity in the 90 days before the survey. Transactions were somewhat more likely in the 1-day delay group (0.39) than the immediate payment group (0.31) or 8-day delay group (0.26). Overall, we conclude that the randomization produced balanced treatment groups.

> 6Withdrawal fees are flat and do not depend on the amount of the transaction.

8
Ethnic polarization and the duration of civil wars APPENDIX: Definition of civil wars from Uppsala/PRIO.

Uppsala/PRIO25: Uppsala/PRIO definition including armed conflicts that generate more than 25 deaths yearly (minor armed conflicts plus intermediate plus war following Uppsala/PRIO classification). We only consider types 3 and 4 (internal armed conflicts).

Uppsala/PRIOCW: Intermediate and war definition of armed conflict from Uppsala/PRIO. This is a contested incompatibility that concerns government and/or territory where the use of armed force between two parties, of which at least one is the government of a state, results in at least 25 battle-related deaths yearly and a minimum of 1,000 deaths over the course of the civil war. We only consider types 3 and 4 (internal armed conflicts).

18
We describe in the next section a method for gauging comparability between the 2009/10 and the 2011/12 rounds of the NSS.

# **III. Predicted Poverty Trends Using Imputation**

We provide here a brief overview of the survey-to-survey imputation method described in Dang et al. (2014b) before discussing results. Further discussion on technical details and estimation procedures are available in the cited paper.

# **III.1. Overview of the Imputation Method**

Let xj be a vector of characteristics that are commonly observed between the two surveys, where j indicates survey round, j= 1, 2.<sup>10</sup> These characteristics can include household variables such as the household head’s age, sex, education, ethnicity, religion, language, occupation, household assets or incomes, and other community or regional variables. Household consumption (or income) data exist in one survey round but are missing in the other survey round, thus without loss of generality, let (survey) round 1 and round 2 respectively represent the survey round with and without household consumption data, and y1 represent household consumption in round 1. Alternatively, we can also refer to round 1 as the base survey, and round 2 as the target survey.

To further operationalize our estimation, we assume that the linear projection of household consumption on household and other characteristics ( _x_ ) in both survey rounds—if such consumption data were also available in period 2—are given by a cluster random-effects model<sup>11</sup>

> 10 To make notation less cluttered, we suppress the subscript for each household in the following equations.

> 11 This assumption assumes that the returns to the characteristics _xj_ in both periods are captured by equation (1) and precludes the (perhaps exceptionally) rare situations where there could be no correlation between these characteristics and household consumption due to unexpected upheavals in the economy or calamitous disasters. Contexts where there are sudden changes to the economic structures (e.g., overnight regime change) may also introduce noise into the comparability of the estimated parameters.

9
33 working as casual day labourers for other farmers: there has been a decline In th employment of hired hands In agriculture (Gregory,1986). A recent study of an **eldo** In Michoacin demonstrated that only 9 percent of farmers hired in labour: **increased use** of tractors had reduced the need to hire In labour; also, households with insufficient family labour to carry out farm work usually prefetrred to enter Into sharerop arrangaments with "labour-surphks households, rather than eploy wage workors (Mummrt,1987). Comparing this study with a t956 study of the same ejido (Frledrlch,1970). It Is clear that there has been a decline in the number of ejidatarlos mploying temporary wage labourers and a rise In sharecropping (Mummert,1987:536).

68. In the cash-poor, high-risk environment of most ejidos sharecropping makes better sense for labour-deficit households than hiring In wage workers. Typically, sharecropping entails households that are short of labour handing over land to "labour-surplus' households wlth the latter assuming full responsIbility for cultivatlon. The two households share the harvest (and the risk of crop fallure) equally between them. The household supplying land Is usually responsible for supplying draught anknals, seed and, In some cases, fertilizer. In most cases, the collaborating households belong to the same kinship group (Mummert,1987:529).

69. The labour-surplus, land-deficit households that are actively looking for land to work are not necessarily the poorer or less powerful party to the arrangement. A study of Irrigated ejidos In the Mezqultal Valley (Hidalgo) reveals that these households may be highly entrepreneurial, simultaneously Involved In a number of sharecropping deals. Thus, "sharecropping provides an avenue for economic kIprovement" (Flnkler,1978:105).

70. Sharecropping enhances the labour mobillty of ejidatarlos: It enables them to share In the output of their land while freeing them to pursue off-farm work.
or the poorly entered visible digital lines may be the cause for this. Given that administrative boundaries cannot be changed or modified without consulting with pertinent government agencies, the user should exercise caution when determining the reasons behind cutting buildings and other structures. **Figure 12** , for example, shows a pre-EA output with an outline that cuts buildings. This is due to the administrative boundary of the municipality and thus cannot be modified.

**Figure 12: An example of pre-EA boundary-cutting buildings**

This method has solely utilized publicly accessible natural and man-made features, and settlement boundaries, such as OSM and GHSL, for reproducibility and worldwide application. Nonetheless, several government agencies can provide input datasets such as roads and waterways with higher quality and greater geographic coverage. In addition, future studies could also investigate leveraging the more modern and comprehensive commercial road network (Strano et al., 2017). If inadequate spatial coverage is a major concern, an alternative approach would be to use “mapathons”—a coordinated mapping event—to enhance the current open-source data on roads and rivers before implementing this method.

One of the main challenges for collecting high-quality surveys in Armenia was the lack of boundaries for PSUs. To the best of our knowledge, it is questionable whether there is any digital map of census enumeration areas at the Statistical Committee of the Republic of Armenia (ArmStat).<sup>22</sup> Thus, this paper creates the first

> 22 Although it is hardly accessible, the ArmStat has a physical map of census enumeration areas. It is however uncertain if there is a digital version and what information is embedded in the map.

26
Good _k_ ’s revealed intensity in human capital is similarly calculated as The relationship between the probability of a direct export and the exported product’s factor intensity is shown in Table 12. The dependent variable is, as in Table 11, a dummy variable marking first-export instances where the product (defined at the SITC4 level) is shipped to an OECD country, and we run a probit of that variable on characteristics of the good (revealed factor intensity indices) and the exporting country (level of income).

Table 12 Incidence of « OECD-first » exports and factor intensities: Probit marginal effects

||(1)|(2)|(3)|
|---|---|---|---|
|HK intensity|0.036|0.040|0.037|
||(10.10)***|(11.26)***|(12.50)***|
|Capital intensity|0.000|0.000|0.000|
||(9.51)***|(9.17)***|(8.28)***|
|Land intensity|-0.246|-0.244|-0.171|
||(14.97)***|(14.86)***|(12.63)***|
|Upper middle income||-0.136<br>(5.77)***||
|Lower middle income||0.067<br>(3.49)***||
|Low income||0.056<br>(2.74)***||
|Exporter controls|no|no|yes|
|Observations|19'758|19'758|19'758|
|Pseudo-R2|0.1046|0.1315|0.3313|

Notes: Dependent variable: probability of a direct export to OECD markets; probit marginal effects.

The results are consistent with the vertical-integration conjecture. OECD-first instances are positively correlated with the capital intensity of the product exported, and the correlation is significant at 1%. The positive correlation with the human-capital intensity tells a similar story: in a vertically integrated relationship where the Northern parent company can share training costs (which is plausible) but not labor management, moral hazard will distort, as before, the Southern subsidiary’s hiring decision but not so much the Northern 37