rafmacalaba/datause-encoder-data huggingface dataset:
As noted above, the Federal Reserve capped dividends and banned stock buybacks by large banks from June until the end of the year, which may have helped keep bank capital ratios from falling below healthy levels. Similar to the full sample, the mean liquidity ratio increased after the start of the COVID-19 crisis from 47.71 to 55.13 percent and the minimum rose from 28.03 to 39.14 percent, indicating no liquidity problems.

# **4.1.2. Bank Credit Performance**

We scrutinize several statistics to evaluate bank credit performance during the crisis. We examine credits that are recognized as current losses, represented by charge-offs; nonperforming loans with payment delays that may turn into credit losses, represented by the past due of at least 90 days plus nonaccrual ratio; and whether banks have enough funds set aside for expected losses, measured by loan and lease loss allowances. To the extent that current and impending losses are high and there are insufficient allowances for them, the industry could be in trouble. We again note that charge-offs and nonperforming loans could be biased downward for some institutions during the crisis due to forbearance rules on some bank debt.

Examining the credit performance data for the industry, mean charge-offs remain low throughout the time interval, and are actually lower after the COVID-19 crisis begins than their local peak of 0.14 percent in 2019:Q4. There is a minor upward movement in nonperforming loans from 0.61 percent in 2019:Q4 to 0.65 percent in 2020:Q1, but the ratio fell from there to 0.54 percent in 2020:Q4. We acknowledge that these data means may obscure the view of some banks with more severe credit performance problems – researchers find that nonperforming loans problems are more concentrated where banks more exposed to the pandemic and lockdown policies (e.g., Beck and Keil, 2021). Switching to the more adverse values of these performance issues, however, does not raise alarm bells. The P90 for charge-off rates are lower in every quarter 21
**_Global Value Chains in the Electronics Industry                       Sturgeon and Kawakami_**

Brown, Clair, and Greg Linden. 2009. _Chips and Change: How Crisis Reshapes the_ _Semiconductor Industry_ . Cambridge, MA: MIT Press.

Brülhart, Marius. 2008. “An Account of Inter-industry Trade, 1962-2006.” Research Paper Series in Globalisation, Productivity, and Technology, 2008/08, Leverhulme Centre for Research on Globalisation and Economic Policy, University of Nottingham, UK.

Business Times On-line. 1995. “Precision Engineering Group Uraco Plans Expansion in Jahor.” July 14, p. 3. http://www.businesstimes.com.sg.

- ———. 1996a. “Uraco Aims to Boost Revenue to US$400m.” April 22. http://www.businesstimes.com.sg.

- ———. 1996b. “Uraco Tops Actives with 16.7m Shares Traded.” December 12. http://www.businesstimes.com.sg.

- ———. 1997. “Uraco Managing Director Quits; 3 Executives Replace Him.” January 23. http://www.businesstimes.com.sg.

CADELEC. 2010. “Jaliso High tech Industry: Suppliers Development for the Jalisco Electronics Industry.” Slide presentation. _Cadena Productiva de la Electrónica, A. C._ March. cedelec@cadelec.com.mx.

- Chesbrough, Henry, and Kenichi Kusunoki. 2001. “The Modularity Trap: Innovation, Technology Phase Shifts, and the Resulting Limits of Virtual Organizations.” In _Managing Industrial Knowledge,_ ed. L. Nonaka and D. Teece, 202–30. London: Sage Press.

Dean, J.M., K.C. Fung, and Z. Wang. 2007. “Measuring the Vertical Specialization in Chinese Trade.” U.S. International Trade Commission, Office of Economics Working Paper, No. 2007-01-A.

Dedrick, Jason, and Kenneth Kraemer. 1998. _Asia’s Computer Challenge: Threat or Opportunity for the United States and the World_ . Oxford UK: Oxford University Press.

- Deitrick, Sabina. 1990. _Linkages Between Manufacturing and Services; A Case Study of the Semiconductor Industry In Singapore._ Doctoral dissertation. Department of City and Regional Planning, University of California, Berkeley.

- Escaith, Hubert, Nannette Lindenberg, and Sébastien Miroudot. 2010. “International Supply Chains and Trade Elasticity in Times of Global Crisis.” Working paper, Economic Research and Statistics, World Trade Organization, Geneva.

- Feenstra, Robert, and Gary Hamilton. 2006. _Emerging Economies, Divergent Paths: Business Groups and Economic Organization in South Korea and Taiwan_ . New York: Cambridge University Press.

- Feenstra, Robert. 1998. “Integration of Trade and Disintegration of Production in the Global Economy.” _Journal of Economic Perspectives_ 12 (4 **)** : 31–50.

Fixson, Sebastian, and Jin-Kyu Park. 2008. The Power of Integrality: Linkages between Innovation and Industry Structure. _Research Policy_ 37: 1296–1316.

47
in prominence. During 1995-2000, the NE received 0.5 million migrants (including return-migrants), but 1.5 rnillion left the NE (up 8 percent in 10 years) and 71 percent hereof moved into the SE region (census 2000).

**Table 2.2: People Migrating in Past 10 Years, by Source and Destination**

||FROM:|||||||
|---|---|---|---|---|---|---|---|
||**North**|**NE**|**Southeast**|**South**|**Center**|**Foreign**|**Total**|
|TO:||||||||
|North||||||||
|(1)|301,600|237,137|82,424|36,682|156,781|12,748|827,372|
|(2)|2.7%|2.1%|0.7%|0.3%|1.4%|0.1%|7.4%|
|(3)|31.7%|7.2%|2.1%|2.8%|11.4%|3.8%|7.4%|
|**Northeast**||||||||
|(1)|266,150|1,029,772|1,340,810|37,094|230,868|16,381|2,921,075|
|(2)|2.4%|9.2%|12.0%|0.3%|2.1%|0.1%|26.0%|
|(3)|27.9%|31.3%|34.1%|2.8%|16.9%|4.8%|26.1%|
|**Southeast**||||||||
|(1)|124,193|1,622,377|1,588,090|426,396|397,765|137,476|4,296,297|
|(2)|1.1%|14.5%|14.2%|3.8%|3.5%|1.2%|38.3%|
|(3)|13.0%|49.4%|40.4%|32.0%|29.0%|40.5%|38.3%|
|South||||||||
|(1)|52,198|58,736|505,191|683,846|183,571|142,427|1,625,969|
|(2)|0.5%|0.5%|4.5%|6.1%|1.6%|1.3%|14.5%|
|(3)|_5.5%_|1.8%|12.9%|51.3%|13.4%|42.0%|14.5%|
|**Center**||||||||
|(1)|208,350|337,661|410,044|149,213|400,296|30,030|1,535,594|
|(2)|1.9%|3.0%|3.7%|1.3%|3.6%|0.3%|13.7%|
|(3)|21.9%|10.3%|10.4%|11.2%|29.2%|8.9%|13.7%|
|**Total**||||||||
|(1)|952,491|3,285,683|3,926,559|1,333,231|1,369,281|339,062|11,206,307|
|(2)|8.5%|29.3%|35.0%|11.9%|12.2%|3.0%||
|(3)|100%|100%|100%|100%|100%|100%||

_Source:_ Author's own calculations based on PNAD 1999.

_Note: (1)_ total migrants, (2) percentage share of total migrants, (3) percentage share of migrants from total migrants of a state.

SE-NE migration increased over the last 10 years, while NE-SE migration has declined. Over the past 10 years, a substantially higher percentage (34 percent compared to 23 percent) of total migrants. from the SE located in the NE; these migrants also became a larger proportion of total in-migrants into the NE (45 percent compared to 39 percent).

**Table 2.3: Migration Net Flows, by Region and Reference Period**

||EverMigrating|Demographics|
|---|---|---|
|Region:|%ofregional population<br>%oftotalBrazilian<br> <br>|%regional pop./total<br>|
||from net migration<br>populationfromnet<br>migration|pop.ofBrazil|

6
**<u>Table 4: Kitagawa-Oaxaca-Blinder Decomposition of Monthly Wage Earnings</u>**

||(1)|(2)|(3)|
|---|---|---|---|
|VARIABLES|Overall|Endow.|Coeff.|
|Male|39,029.396***<br>|||
||(1,582.821)|||
|Female|7,173.695***<br>|||
||(475.450)|||
|Raw Difference|31,855.701***<br>|||
||(1,652.687)|||
|Difference due to|17,597.556***|||
|Endowments|(1,386.519)|||
|Difference due to|14,258.144***|||
|Coefficients|(1,533.097)|||
|Individual Lives in an Urban Area||178.834|-2,129.334*|
|||(541.726)|(1,116.533)|
|Individual Age||-261.938<br>|6,386.937<br>|
|||(2,037.440)|(26,288.082)|
|Individual Age Squared||-687.070|2,807.738|
|||(1,926.942)|(13,785.755)|
|Number of Adults in HH||-259.638***|-4,575.236**|
|||(98.879)|(2,163.628)|
|Number of Children Aged 0-5 in||-185.325|1,246.540|
|HH||(136.780)|(797.302)|
|Individual owns a Financial Account||<br>2,421.506***|<br>557.291|
|||(583.067)|(752.266)|
|Individual is Married||-4,268.291***|9,056.051***|
|||(914.900)|(2,378.079)|
|Individual is Divorced/Widowed/||-248.119**|-44.469|
|Separated||(115.665)|(283.117)|
|Individual Completed Secondary||1,976.755***|-489.806|
|Schooling||(575.774)|(776.826)|
|Individual owns a Vehicle (Bicycle,||-15.031|-293.616|
|Motorcycle, Car)||(107.220)|(1,204.066)|
|Individual Works as Apprentice||-619.218***|-542.991**|
|||(176.251)|(241.723)|
|Individual Works as Manager||8,500.897***|1,520.036*|
|||(845.543)|(815.221)|
|Individual Works as Skilled Worker||<br>2,460.303***|<br>-1,041.654***|
|||(391.392)|(363.568)|
|Individual Works in the Agriculture Sector||<br>9,344.021***|<br>-37,670.460***|
|||(1,393.355)|(5,400.111)|
|Individual Works in the Service Sector||-740.131|-3,799.391***|
|||(866.043)|(1,129.125)|
|Ctt|||43270510***|
|onsan|||,.|
||||(14,906.302)|
|Observations|10,591|10,591|10,591|

The reference dummies are the family worker and the manufacturing sector. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 38
**Table 4:** Econometric results: Debt and education spending

||Dependent variable: log e<br>|ducation expenditure per<br>|
|---|---|---|
||chi<br>|ld<br>|
||Model 1|Model 2|
|Log education expenditure per school-age child (t-1)|0.994***|1.000***|
||(0.025)|(0.025)|
|Log debt to GDP ratio|-0.01||
||(0.023)||
|External debt to reserve ratio (logged)||-0.029**|
|||(0.017)|
|ODA per capita growth (t/t-1)|-0.005|-0.018|
||(0.036)|(0.013)|
|GDP per capita growth (t/t-1)|0.605**|0.625 *|
||(0.297)|(0.331)|
|Constant|0.108|0.006|
||(0.202)|(0.131)|
|Number of observations|361|361|
||F(4, 44) =1066.63|<br>F(4, 44) = 875.95|
||Prob > F = 0.000|Prob > F = 0.000|
||Arellano-Bond test for|Arellano-Bond test for|
||AR(1) Pr > z = 0.001|AR(1) Pr > z = 0.004|
||Arellano-Bond test for|Arellano-Bond test for|
||AR(2) 0.86 Pr > z = 0.391<br>Sargan test of overid.<br>restrictions: chi2(172) =|AR(2) = Pr > z = 0.339<br>Sargan test of overid.<br>restrictions: chi2(172) =|
||171.71|193.86|
||Prob > chi2 = 0.492|Prob > chi2 = 0.121|

The growth of the official development assistance to the education sector seems to not be associated with a significant increase of education expenditure (Tables 3 and 4). A large proportion of education spending is provided to countries through project modalities and may not be counted as part of government education expenditure.<sup>5</sup> In addition, education aid represents a small proportion of education financing. Because of the relatively small contribution of aid to the education budget, an increase of the amount of aid may not be enough to drive a substantial increase of overall government expenditure on education. Tables 3 and 4 also show that GDP growth is associated with higher government expenditure on education. GDP growth drives higher government revenue which in turn contributes to increased education expenditure. This evidence is in support of the Wagner's law on the causal effect of GDP growth on government expenditure (Chandran et al., 2011; Dogan & Tang, 2006; Ebaidalla, 2013; Loizides & Vamvoukas, 2005; Ahuja and Pandit, 2020).

> 5 In 2019, only 3.5% of education ODA was provided through budget support modalities according to the OECD Creditor Reporting System: https://stats.oecd.org/Index.aspx?DataSetCode=crs1.

9
ECA, most Western European countries have a relatively high share of state-owned banks. However, unlike ECA, 64 percent of Western European countries have relatively high banking concentration.

_Other countries_ have a lower credit-to-GDP ratio than the regional average. Furthermore, the top five banks represent a higher share of the total market than the regional average.

Finally, overall results show that the financial systems in countries with interest rate caps have some common characteristics: 58 percent have a relatively low credit-to-GDP ratio; 71 percent have a relatively low share of people receiving loans; only 37 percent of them have a relatively important presence of state-owned banks; and 74 percent score at the regional average or below on the index of the strength of legal rights.

# **5. Policy Recommendations**

As this paper has shown, the imposition of caps on interest rates is widely practiced around the world; their primary intention is to protect the consumers who cannot afford the high interest rates offered by formal and informal financial institutions. However, the empirical evidence reveals a misconception about these rate caps, since they do nothing to ameliorate the cause of financial market failures, failures that must be addressed before interest rates can fall over the long term.

In light of those negative findings on interest rate caps, this section outlines some policy changes that may help reduce interest rates on loans in the long run and thus ultimately increase access to finance for underserved segments of the population. We also provide some measures to protect consumers from excessively high interest rates and to increase access to finance.

## **_5.1. Competition_**

Competition policies can not only help reduce interest rates on loans, especially those on microloans, but also help expand access to finance, offer wider choices of products, and encourage better services. In fact, a recent study by Love and Martinez (2012) found that low competition in the banking system has a negative effect on firms’ access to finance. Regarding microcredit, Porteous (2006) describes common practices carried out by three countries to increase competitiveness in the microcredit sector. Some of these initiatives include opening branches in new areas; allowing microfinance institutions to offer products such as savings options to help reduce the cost of lending; making loan terms more flexible; and reducing the time it takes to get a loan. In addition, a recent survey of MFI managers in Latin America shows that competition is one of the main factors determining interest rates (Campion, Ekka, and Wenner 23
iTus, the literature on the macroeconomic impact of aid flows is in want of better models while at the same time the modelling of the impact of reforms inadequately accounts for the role of foreign savings. A marriage of the two is dearly in everyone's mutual interest. This report therefore recommends a research strategy along these lines. [t is also recommended that models incorporate the well-documented links between social sector expenditures and various welfare indicators.

# 7.2 A Research Agenda

Having identified the general strategy that should be followed in analyzing aid's country-wide effects, this report concludes with a list of immediate topics for further research:

- construction of an "aid database" by recipient showing aid flows by functional category.

- modelling and estimation of the interaction of aid inflows with private savings and investment.

- development and empirical analysis of the concept of aid dependence.

- modified control group analysis for social consequences of adjustment policies;

- disaggregation of modified control group results by change in policy, change in policy effectiveness and confidence effect

- development of theoretical and empirical analysis for analysis of aid and the efficiency of investment.

- $ analysis of aid, income and output considering whether economy is supply or demand constrained.

- embedding of categorical fungibility model into economy-wide model.

- proper marriage of macro effects of aid and policy reform in a single (CGE) model.

- incorporation of human-resource/government expenditure interactions into economy-wide

115
The middle class has not only increased in size, but it has also increased the income share appropriated by this group. In Chile, this share increased 13.1 percentage points from 42.6 percent in 1992 to 55.9 percent in 2009; in Mexico it increased 7.8 percentage points from 45.2 to 52.9 percent over 1992-2008, and in Peru it expanded by 13.1 points from 42.7 to 55.7 percent over 1997-2009. These results become clear when looking at the income distribution. Figure 6 shows that the bulge in the middle increased over the analyzed period making the bell taller and confirming the increase of the middle class, partly due to improvements in income distribution (motivated by an expansion and enhancement of social policies) and a reasonable economic performance, which is evident by the movement of the bell to the right.

**Figure 6: Kernel distributions of income in Chile, Mexico and Peru**

<!-- Start of picture text --> 0 2 4 6 8 10<br>Household per capita income at PPP: log scale<br>Chile 1992 Mexico 1992 Peru 1997<br>0 2 4 6 8 10<br>Household per capita income at PPP: log scale<br>Chile 2009 Mexico 2008 Peru 2009<br>.6<br>.4<br>.2<br>0<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text --> _Source_ : Author‘s calculations based on data from CASEN, ENIGH and ENAHO cross-sectional databases. /a The gray line is a lower threshold of $2 a day proposed by B&D and Ravallion; while the yellow lines are the thresholds proposed in this paper.

Overall, the evidence found suggests a higher number of middle class households facing a low probability of falling into poverty in the late-2000s than in the 1990s, which is undoubtedly

- 15 -
**Table 13. Heterogeneous effects of preschool availability on female’s work participation by average preschool enrollment in district of residence**

|||DDD|**Work par**|**ticipation**|DDD-FE||
|---|---|---|---|---|---|---|
|Enrollment:|< 20|20-40|> 40|< 20|20-40|> 40|
||percent|percent|percent|percent|percent|percent|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**Panel A: Publi**|**c**||||||
|Preschool *|0.117***|0.056*|-0.064|0.125***|0.015|-0.141|
|Eligible|(0.028)|(0.031)|(0.189)|(0.034)|(0.049)|(0.308)|
|Preschool|-0.031|0.033|0.026|-0.040|0.050|0.049|
|density|(0.028)|(0.043)|(0.089)|(0.036)|(0.053)|(0.120)|
|Eligible child|-0.061***|-0.080***|-0.105***|-0.038***|-0.042*|0.072|
||(0.010)|(0.017)|(0.034)|(0.013)|(0.021)|(0.075)|
|**Panel B: Priva**|**te**||||||
|Preschool *|-0.001|0.004|0.029***|-0.000|0.004|0.038|
|Eligible|(0.003)|(0.004)|(0.008)|(0.006)|(0.007)|(0.023)|
|Preschool|-0.000|0.006|-0.005|-0.000|0.008|-0.010|
|density|(0.004)|(0.005)|(0.011)|(0.005)|(0.007)|(0.013)|
|Eligible child|-0.034**|-0.098***|-0.333***|-0.013|-0.067|-0.245|
||(0.015)|(0.035)|(0.069)|(0.022)|(0.052)|(0.175)|
|Observations|23,076|11,982|2,489|23,076|11,982|2,489|
|Mean|0.583|0.639|0.583|0.583|0.639|0.583|

Note: Sample includes all mothers with and without any preschool-aged eligible child (age 3-6). Comparison group includes non-mothers and mothers of children with the wrong ages. Sample is restricted to PODES years with preschool enrollment data (2003, 2005, 2011, 2014). Preschool enrollment is defined among children aged 3-6. Median preschool enrollment rate at the district-level is 15.6 percent and the 99<sup>th</sup> percentile at 49.8 percent. Median preschool enrollment rate at the individual-level is 17.2 percent. All columns regress work participation on preschool density, preschool eligibility dummy, and their interaction. All regressions include district, year, mother’s age fixed effects and an urban residence dummy. DDD excludes while DDD-FE includes individual fixed effects. Standard errors clustered at the district-level is shown in parentheses; *, **, and *** indicate statistical significance at 10, 5, and 1 percent, respectively. Number of observations and work participation means for each column are indicated at the last two rows.

58
# **Box 2. Trade Facilitation in India**<sup>**15**</sup>

India faces several problems in seamless trade transactions and the costs involved in trading with India are significant<sup>16</sup> . While some impressive gains have been made over the last decade in terms of reducing transaction costs, India is not at par with current global standards. Cargo dwell times at Indian ports and airports is high – with the import dwelling time ranging from 7-14 days compared to the international standards of less than 24 hours (see Table). The trading process is cumbersome and bureaucratic – the number of documents required for export clearance is 29.

## **_Typical Cargo Dwell Time_**

|**Transaction**|**Location**|**Norm**|
|---|---|---|
|Air Freight|Delhi Airport||
|Export|2.5 days|Less than 12 hours|
|Import|15 days|Less than 12 hours|
|Containerized Sea Freight|Mumbai||
|ShipWaitingTime|3-5 days|Less than 6 hours|
|Export Dwell Time|3-5 days|Less than 18 hours|
|Import Dwell Time|7-14 days|Less than 24 hours|

The Government has taken positive steps towards reform to address these issues. EDI for instance, was introduced in 1995 and now covers 90% of total declaration with customs. Recent initiatives have seen a reduction in dwell times, physical inspection and demurrage paid for hold ups. Despite these achievements, dwell time remains relatively high for imports (4-6 days for airports and 6-8 days for seaports) and the paper trial is still a barrier to effective e-interface. The Working Group on Trade Facilitation set up by Government of India in April 2004 found that inefficiency and lack of incentives in proper payment systems for duties and lack of proper interagency co-ordination was the main cause for the high dwell time. They acknowledged the need for further reforms, with an aim at transparent procedures with minimum number of signatures.

Cambodia<sup>17</sup> carried out a Diagnostic Trade Integration Study (DTIS) (part of the Integrated Framework), with the objectives of competing, connecting and conforming to the global market and its requirements. Upgrading supply capacities and infrastructure were identified as key factors for higher competitiveness. Cambodia has adopted an integrated approach to TF encompassing bureaucratic procedures, standards, administrative reforms and interaction with the private sector and the academia. Future strategies are aimed at tackling process inefficiencies by streamlining bureaucracy and business registration as well as adopting a value chain approach to removing

> 15 Please refer to presentation by <u>Jayanta Roy</u> (CII, India)

> 16 India will benefit from TF at the WTO given its high transaction costs and unilateral reform in the same direction.

> 17 Presentation by Sok Siphana on Cambodia’s efforts in setting up a national TF body.

11