rafmacalaba/datause-encoder-data huggingface dataset:
- Patterson, C. J., E. L. Sutfin, and M. Fulcher. (2004). “Division of Labor among Lesbian and Heterosexual Parenting Couples: Correlates of Specialized Versus Shared Patterns.” _Journal of Adult Development_ , 11, pp. 179–189.

- Posel, D. and E. Grapsa. (2017). “Gender Divisions in the Real Time of the Elderly in South Africa.” In: Connelly R., Kongar E. (eds.) _Gender and Time Use in a Global Context_ . Palgrave Macmillan, New York.

- Quisumbing, A., R. J., P. Estudillo, and K. Otsuka. (2004). _Land and Schooling: Transferring Wealth across Generations_ . The Johns Hopkins University Press.

- Ranchhod, V., and M. Wittenberg. (2016). “The Social Pension and Time Allocation in Poor South African Households.” In Timothy Besley (Ed.), _Contemporary Issues in Development Economics_ (pp. 91–111). Hampshire, England: Palgrave Macmillan.

- Rao S. (2017). “Women and the Urban Economy in India: Insights from the Data on Migration.” In: Connelly R., Kongar E. (eds) _Gender and Time Use in a Global Context_ . Palgrave Macmillan, New York.

- Reid, M. G. (1934). _Economics of Household Production_ . New York: John Wiley and Sons, Inc.

- Rogers, Martha (2014). “Forest Access and Human Capital Accumulation.” In _Environment and Development: Essays on the Link between Household Welfare and the Environment in Developing Countries_ , Ph.D. Dissertation, University of Minnesota.

- Rothblum E.D. (2017). “Division of Workforce and Domestic Labor Among Same-Sex Couples.” In: Connelly R., Kongar E. (eds) _Gender and Time Use in a Global Context_ . Palgrave Macmillan, New York.

- Sayer, L. (2016). “Trends in women’s and men’s time use, 1965–2012: Back to the future?” In S. M. McHale, V. King, J. Van Hook, and A. Booth (eds.), _Gender and Couple Relationships_ (pp. 43–77). Geneva, Switzerland: Springer International Publishing.

- Suh, J. (2016). “Measuring the "Sandwich": Care for Children and Adults in the American Time Use Survey 2003-2012.” _Journal of Family and Economic Issues_ , 37: 197–211.

- Suh, J. and N. Folbre. (2016). “Valuing Unpaid Child Care in the U.S.: A Prototype Satellite Account Using the American Time Use Survey.” _Review of Income and Wealth_ , 62(4), pp. 668-684.

- Sullivan, O. (2011). “An end to gender display through the performance of housework? A Review and Reassessment of The Quantitative Literature Using Insights from The Qualitative Literature.” _Journal of Family Theory and Review_ , 3(1), pp. 1–13.

- Vickery, C. (1977). “The Time-Poor: A New Look at Poverty.” _Journal of Human Resources_ , 12(1), pp. 27–48.

26
# **Bank Runs and Moral Hazard:  A Review of Deposit Insurance**

## **Deniz Anginer and Asli Demirguc-Kunt**<sup>**1**</sup>

JEL Classifications: G21, G28 Keywords: deposit insurance, banking crisis, bank runs, moral hazard, deposit insurance design

> 1 Deniz Anginer, World Bank, <u>danginer@worldbank.org.  Asli Demirguc-Kunt, World Bank,</u>

<u>ademirguckunt@worldbank.org. We thank Jinjing Liu for valuable research assistance. All errors are our own.</u> This paper was prepared for Oxford Handbook of Banking, Third Edition, 2020, A.N. Berger, P.Molyneux, and J.O.S. Wilson, editors, Oxford University Press, Oxford.
activity whereas demand-driven oil price increases are associated with rising activity, albeit often with weaker effects (Baumeister and Hamilton 2019; Cashin, Mohaddes, Raissi and Rassi 2014; Peersman and Van Robays 2012; Kilian 2009; Lippi and Nobili 2012; Aastveit, Bjørland, and Thorsrud 2015). This paper tackles a similar question but in a narrative approach which explicitly isolates episodes of large oil price collapses. It distinguishes oil price collapses since 1970 by their main sources and estimates their impact on growth in a local projections model. This approach is much closer to a natural experiment than the identification schemes used by previous studies that often relied on ad hoc assumptions.

Second, this paper focuses on emerging markets and developing economies (EMDEs) whereas most of the previous literature has restricted itself to advanced economies. Baumeister and Hamilton (2019); Baumeister and Peersman (2013); Kilian (2009); and Kilian and Murphy (2014) estimate the impact of oil demand and supply shocks on U.S. output in vector autoregression approaches. Kanzig (2019) and Lippi and Nobili (2012) extend these exercises to U.S. industrial production and Anzuini, Pagano and Pisani (2015) to a monthly U.S. activity index. Peersman and Van Robays (2012) extend these estimations to broader samples of predominantly advanced economies. Few studies include EMDEs at all and even those that do include only about a dozen large EMDEs (Caldara, Cavallo, and Iacoviello 2019; Mohaddes and Raissi 2019; Aastveit, Bjørland, and Thorsrud 2015; Cashin, Mohaddes, Raissi and Rassi 2014).

Yet, the impact of oil price swings might well be expected to be larger for EMDEs than for advanced economies. EMDEs are almost twice as energy intensive as advanced economies ( Figure 1 ). Almost one-quarter of EMDEs rely heavily on energy exports and another one-fifth of EMDEs rely heavily on agricultural exports, which are themselves highly energy intensive. Hence, this paper relies on data for the largest EMDE sample yet (153 EMDEs).

Third, this paper focuses on the impact of steep oil price collapses. Such oil price collapses allow for asymmetric and nonlinear effects to emerge. This contrasts with previous studies that relied on vector autoregressions without distinguishing between oil price increases and decreases nor between large and small oil price changes. Yet, Hooker (1996), Davis and Haltiwanger (2001), Hamilton (2003), and Jimenez-Rodriguez and Sanchez (2005) show that the impact of oil price changes may not be symmetric: in advanced economies, oil price increases can cause just as much damage to output as oil price collapses. A few studies adopt nonlinear frameworks capable of capturing the asymmetric relationship between oil prices and economic activity which seems evident with large oil price shocks.<sup>3</sup> However, all these studies focus on the U.S. economy.

The paper reports several findings. First, it documents that the oil market disruptions of

> 3 See for example An, Jin, and Ren (2014) and references therein.

3
collects it as part of the National Air Quality Monitoring Program (NAMP). We use hourly pollution data recorded at two monitoring stations in the city, namely at _ITO_ and at _Siri Fort_ .<sup>27</sup> Both are immobile stations that operate on electricity. They provide comparable data as they were bought from the same manufacturer and followed the same monitoring protocol throughout our study period. Hourly data on weather conditions at _Safdarjung,_ Delhi, were obtained from The National Data Center of the India Meteorological Department. Our choice of study period (2004– 2006) was dictated by the overlapping period for which we had both pollution and weather data. The Delhi Metro Rail Corporation (DMRC) provided us with data on metro ridership.

# IV RESULTS

Before presenting the impact estimates, we investigate whether the data validate a sudden increase in metro ridership at the time of each extension.

_Ridership Discontinuities_ For each month, figure 3 shows the percentage change in average daily ridership on the DM over the previous month.<sup>28</sup> The exact magnitudes of change are given in the last column of table 1. Except for the introduction of the Yellow Line and the first extension of the Blue Line, the figure shows a significant rise in average daily ridership for the month (or for the following month)<sup>29</sup> of each extension.

> 27. Under the NAMP there is one other monitoring station located at the Delhi College of Engineering ( _DCE_ ) in north Delhi. We do not use data from this station because our identifying assumption is unlikely to hold at this location. Compared to _ITO_ and _Siri Fort_ there are many more erratic sources of pollution at _DCE_ . This is because, (a) it is surrounded by Badli, a major industrial township; (b) all along its periphery there are other small scale industrial production units; (c) during our study period the college building itself was undergoing repair and renovation; and (d) DCE is in a mainly rural part of Delhi where sporadic burning of biomass and wood is widespread.

> 28. Actual daily ridership, instead of average daily ridership in a month, would have been ideal in order to check the sudden increase in ridership at each extension date. However, this data was not available for our study period. 29. For the second extension of the Red Line and the first extension of the Blue Line, we see the surge in ridership in the month following the one in which the extension took place. This is because these extensions were introduced on the last day of the month, and one would therefore expect average ridership to increase only in the following month.

14
|**Box 1: Sidr Damage to the Economy**|
|---|
|Total damage including indirect loss: $1.67 billion<br>Of which<br>,|
|• Housing damage & loss: $839 million (50%)|
|• Agricultural damage & loss: $438 million (26%)<br>• Transport‐related damage & loss: $141 million (8.4%)<br>• Water Resource Control‐ related damage & loss: $71 million<br>(4.3%)|
|• Education infrastructure‐related damage & loss: $69 million<br>(4%)<br>• Other sectors (industry, electricity, water and sanitation,<br>urban and municipal, health, nutrition, commerce, tourism,<br>environment, etc.): 7.3%|
|Source: GoB (2008)|
# **1. Introduction**

Using a popular web mapping and transportation service, we generate information for more than 22 million counterfactual trip instances in 154 large Indian cities.<sup>1</sup> We then use this information to estimate a number of indices of mobility (speed) of motorized vehicle travel in these cities. We first assess the robustness of our indices to a wide variety of methodological choices. Second, we decompose overall mobility into uncongested mobility and the congestion delays caused by traffic. Third, we examine how indicators of urban economic development and other city characteristics correlate with mobility, uncongested mobility, and congestion delays. Finally, we provide additional mobility indices for walking and transit trips.

To the best of our knowledge, our paper provides the first systematic empirical investigation of mobility and congestion across cities in a developing country.<sup>2</sup> Our main substantive findings are the following. First, there are large differences in mobility across Indian cities. A factor of nearly two separates the fastest and slowest cities. Second, this variation is driven primarily by uncongested mobility, not congestion. An index of uncongested mobility explains 70% of the variance in overall mobility across cities. Traffic is generally slow in many Indian cities, even outside peak hours.<sup>3</sup> In the slowest decile, we find both small cities, which are slow even without congestion, and large congested cities. Congestion only really matters close to the center of the largest cities. Finally, we find that denser, more populated cities are slower, that there is a hill-shaped relationship between city per capita income and mobility, and that a city’s mobility is related to characteristics of its road network.

This investigation is important for four reasons. First, there is an extreme paucity of useful knowledge about urban transportation, especially in developing countries. As a first building block towards a more serious knowledge base on urban transportation, some stylized facts

> 1By counterfactual, we mean trip instances that have not been actually taken by a household. As we show below, these trips were selected to mimic some characteristics of trips that are taken by households in other contexts.

> 2Two new studies focusing on a single developing city complement our cross-city investigation: Kreindler (2018) studies the welfare impact of congestion pricing in Bangalore, and Akbar and Duranton (2018) measure the cost of congestion in Bogotá.

3We take a broad definition of ‘congestion’ and measure it as difference between travel time at a given time relative to travel time in the absence of traffic. Alternative natural measures of congestion with our data include, for instance, the ratio of the fastest to the slowest instance of trips.

1
earnings (wage rate times employment) does not change above the BIG. This will hold if the wage elasticity of labor demand is -1. This seems to be a reasonable assumption for the average wage elasticity based on past research on labor demand in the U.S.<sup>12</sup> However, there is undoubtedly variation around this mean elasticity, so we can expect (positive and negative) effects on earnings for many “non-BIG” workers not captured by our analysis.

It is of obvious interest to see how the size of this market compares with the current count of undocumented migrants. However, this count is uncertain, with current estimates in the (wide) range 11-22 million. The lower bound is derived from the Census Bureau’s survey-based estimates of the total migrant population (foreign-born people who have not become naturalized citizens) after deducting the count of legal migrants based on administrative data.<sup>13</sup> There are naturally concerns that illegal migrants will be reticent to reveal this fact, and may instead report that they are citizens. (Some illegal migrants may also be outside the sample frame of the household surveys.) The upper bound of 22 million is based on <u><mark>Fazel-Zarandi et al. (2018)</mark></u> <mark>who used a time-series accounting of flows with demographic modelling. There is a continuing debate on the size of the undocumented migrant population in the U.S.</mark><sup>14</sup>

# **4. Results**

When the option of renting out one’s WP is confined to those currently in the U.S. workforce, we obtain the results in <u>Table 1, for various combinations of parameter values. (We</u> have chosen the succession in pairs of scenarios to help assess the partial effect of the parameters and policy choices.) Column 1 is for no costs of moving (though official taxes on earnings in the U.S. remain). This is obviously implausible, and we only provide it as a benchmark. We then consider a range of values to reflect the potential frictions discussed above. Our parameter combinations in columns 4-6 that give a total cost of 20-30% of U.S. earnings (extra living costs in the U.S. plus the remittance levy) provide a fairly close match to the total migration costs implied by the estimated earnings differentials between the U.S. mainland and Puerto Rico and

> 12 See, for example, Beaudry et al. (2018), who find a wage elasticity of labor demand of -1 based on the variations across cities and industries over 40 years.

> 13 The methods and results using this method are described in <u>Passel (2019).</u>

> 14 <u><mark>Capps et al. (2018) question the Fazel-Zarandi et al. (2018) estimates. Rodilitz and Kaplan (2021)</mark></u> <mark>find similar results to Fazel-Zarandi et al. using data from Mexican surveys of prior illegal migrants to the U.S.</mark>

10
**Table 2. 3  Costs of checking account services by income quintiles**

||Avg.<br>Cost|% half<br>min.wage|% min.<br>wage|% Quintile<br>1|% Quintile<br>2|% Quintile<br>3|% Quintile<br>4|% Quintile<br>5|
|---|---|---|---|---|---|---|---|---|
|Average monthly|||358,000|379,241|727,087|846,080|917,584|2,321,841|
|income (in COP)|||||||||
|**Checking** **account**|||||||||
|Min. balance|300,000|167.6%|83.8%|79.1%|41.3%|35.5%|32.7%|12.9%|
|Check value|2,057|1.1%|0.6%|0.5%|0.3%|0.2%|0.2%|0.1%|
|Monthly admin. Fee|3,200|1.8%|0.9%|0.8%|0.4%|0.4%|0.3%|0.1%|
|Monthly handling fee|7,050|3.9%|2.0%|1.9%|1.0%|0.8%|0.8%|0.3%|
|Monthly fee other|5,250|2.9%|1.5%|1.4%|0.7%|0.6%|0.6%|0.2%|
|**TOTAL**|**317,557**|177.4%|**88.7%**|**83.7%**|**43.7%**|**37.5%**|**34.6%**|**13.7%**|
|**Excl. min. balance**|17,557|9.8%|4.9%|4.6%|2.4%|2.1%|1.9%|0.8%|
|**ATM transactions**|||||||||
|Cost of statement|1,594||0.4%|0.4%|0.2%|0.2%|0.2%|0.1%|
|(own bank)||0.9%|||||||
|Cost of statement<br>(other bank)|2,888|1.6%|0.8%|0.8%|0.4%|0.3%|0.3%|0.1%|
|Cost of withdrawal<br>(own bank)|1,532|0.9%|0.4%|0.4%|0.2%|0.2%|0.2%|0.1%|
|Cost of withdrawal<br>(other bank)|2,918|1.6%|0.8%|0.8%|0.4%|0.3%|0.3%|0.1%|

_Source: Superintendencia Bancaria; Encuesta de Calidad de Vida 2003_ 15. Interestingly, credit cards are available in Colombia even for people without bank accounts, as outstanding balances can be settled by cash or by check. However, in the practice, only high income banked clients use credit cards. Processing and monthly fees for credit cards are relatively high, corresponding to 4-5% of a minimum monthly salary. In addition, eligibility criteria de facto preclude low income clients from access to credit cards. For example, banks such as Bancolombia require a minimum salary of at least 2.5 times the regulatory minimum salary for credit card issuance.

**Table 2. 4  Costs of domestic credit card services by income quintiles**

|||% half<br>min. wage|% min.<br>wage|% Quintile<br>1|% Quintile<br>2|% Quintile<br>3|% Quintile<br>4|% Quintile<br>5|
|---|---|---|---|---|---|---|---|---|
|Average monthly<br>income (in COP)||179,000|358,000|379,241|727,087|846,080|917,584|2,321,841|
|**Domestic credit**<br>**cards**|||||||||
|Processing costs|5,250|2.9%|1.5%|1.4%|0.7%|0.6%|0.6%|0.2%|
|Monthly fee|11,925|6.7%|3.3%|3.1%|1.6%|1.4%|1.3%|0.5%|
|Other monthly fees|1,137|0.6%|0.3%|0.3%|0.2%|0.1%|0.1%|0.0%|
|**TOTAL**|**18,312**|**10.2%**|**5.1%**|**4.8%**|**2.5%**|**2.2%**|**2.0%**|**0.8%**|

_Source: Superintendencia Bancaria; Encuesta Calidad de Vida 2003_ 16. **More bank services are available for higher income clients.** Less than 10 out of the 26 banks surveyed by the Superintendencia Bancaria provide savings services for the lowest population quintile, by income level. In addition, none of the banks offer credit card services to this segment. This supply side finding confirms the demand side finding that income is a key factor in determining access to financial services (see Chapter 3).

38
||**202**<br>**0**|**2021**|**2022**|**2023**|**2024**|**2025**|**2026**|**2027**|**2028**|**2029**|**2030**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Educatio**<br>**n**|0.00|0.00|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|
|**Health**|0.00|0.00|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|
|**Other**<br>**Services**|0.00|0.01|0.03|0.04|0.04|0.04|0.04|0.04|0.04|0.04|0.04|

_Table B. 2. Annual spending associated with investments and households’ consumption under the ISUI1 Measure relative to the Reference Scenario by sector of economic activity for the period 2020-2030 (in million euros)_

||**2020**|**2021**|**2022**|**2023**|**2024**|**2025**|**2026**|**2027**|**2028**|**2029**|**2030**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Agricul**<br>**ture**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Forestr**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**y**||||||||||||
|**Mining**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Food**<br>**Manuf**<br>**acturin**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**g**||||||||||||
|**Textile**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Wood**<br>**and**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Paper**||||||||||||
|**Chemi**<br>**cal and**<br>**Plastic**<br>**Produc**|0.00|3.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**ts**||||||||||||
|**Metal**<br>**Produc**|0.00|1.50|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**ts**||||||||||||
|**Machi**<br>**nery**<br>**and**<br>**Equip**<br>**ment**|0.00|1.50|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Energy**|0.00|-0.07|-0.34|-0.69|-1.05|-1.05|-1.07|-1.08|-1.09|-1.11|-1.12|
|**Constr**<br>**uction**|0.00|15.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Trade**|0.00|0.92|0.12|0.25|0.38|0.38|0.39|0.39|0.40|0.40|0.41|
|**Accom**<br>**modat**|0.00|0.01|0.04|0.08|0.12|0.12|0.12|0.13|0.13|0.13|0.13|
|**ion**<br>**and**<br>**Food**<br>**Servic**||||||||||||
|**es**||||||||||||
|**Transp**<br>**ortatio**<br>**n**|0.00|0.01|0.05|0.11|0.16|0.16|0.17|0.17|0.17|0.17|0.17|

35
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Table 6: Individual Probability of In-Migration, 1995, 2000, 2005, 2009

|In-Migration Indicator:|(1)<br>1989 to_t_|(2)<br>_t-5_to_t_|(3)<br>_t-1_to_t_|
|---|---|---|---|
|Panel A: 1995 sample||||
|Regional tariff change|0.633|0.317**|0.476|
||(0.393)|(0.157)|(0.335)|
|Observations|529,940|532,384|613,067|
|R-squared|0.098|0.094|0.047|
|Panel B: 2000 Sample||||
|Regional tariff change|0.637***|0.364**|0.150**|
||(0.179)|(0.176)|(0.075)|
|Observations|399,197|437,615|517,185|
|R-squared|0.126|0.106|0.050|
|Panel C: 2005 Sample||||
|Regional tariff change|0.997***|0.488***|0.170***|
||(0.171)|(0.149)|(0.039)|
|Observations|351,495|387,428|478,112|
|R-squared|0.128|0.092|0.049|
|Panel D: 2009 Sample||||
|Regional tariff change|1.082***|0.268**|0.124***|
||(0.158)|(0.104)|(0.034)|
|Observations|320,840|387,620|460,446|
|R-squared|0.125|0.090|0.054|

Individual-level panel data from RAIS. Each panel represents a separate sample of individuals present in RAIS in the year listed. The dependent variable varies by column. In column (1) it is an indicator for having lived in a different region in 1989 than in the reference year listed in the panel title. In columns (2) and (3) is is an indicator for having lived in a different region five years or one year before the reference year, respectively. Positive coefficient estimates for the regional tariff change imply larger declines in in-migration to regions facing larger tariff declines. All specifications include controls for 1986-89 in-migration probability premia after controlling for individual characteristics. Standard errors adjusted for 112 mesoregion clusters. *** Significant at the 1 percent, ** 5 percent, * 10 percent level.

68