rafmacalaba/datause-encoder-data huggingface dataset:
_35_

# Table IEL Bank wise and country-wise break-up of overseas branches of Indian Banks

|Couraiy|Bankot<br>Baroda|Bank of<br>hida|Bhart Oversea<br>BUd Li|s Canara<br>Bank|lndan<br>Bank|hdian Oversea<br> <br>Bank|statse B<br>Of a|Syndicat<br>**B**di<br>a s|e<br>Uoo|Told|
|---|---|---|---|---|---|---|---|---|---|---|
|Bangch|-|-|-|-|-|-|1|-I-|||
|Bdui|1|-|-|-|-|-|1|-|-|2|
|Bohrein|1|-|-|-|-|-|1|-|-|2|
|Begium|1|-|-|-|-|-|1|-|-|2|
|caymn|-|1|-|-|-|-||-|-|2|
|Chmll<br>bds|-|1|-|-|-|-|-|-|-|1|
|FIslans|11|-|-|-|-|-|-|-|-|11|
|France<br>|-|1|-|-|-|-|1|-|-|2|
|-up|1|-|-|-|-|-|-|-|-|1|
|Fedl<br>Rxbc<br>of<br>Gmany|<br>-|-|-|-|-|-|1|-|--|1|
|HmKo|-|2|-|-||6|1|-|2|11|
|JqawM|-|2|-|-|-|-|2|-|-|4|
|Kenya|7|2|-|-|-|-|-|-|-|9|
|Mal<br>hWands|-|-|-|--||-|1|-|-||
|Maurltus|6|-|-|-|-|-|-|-|-|6|
|OnM|3|-|-|-|-|-|-|-|-|3|
|pow.|-|-|-|--||-|1|-|-|1|
|Seychelles|1|-|-|-|-|-<br>|-|-|-|I<br>|
|SouthKam|-|-|-|-|-|1|-|-|-|1|
|SriLanka|-|||-|2|3|1|-|-|6|
|Singapore|-|1|-|-|1|1|1|-|3|7|
|Thaibnd|-|-|||||-|-|-|I|
|Unitd Arab Emlrats|6|-|-|-|-|-|-|-|-|6|
|UnitedKingdom|11|13|-|1|-|-|5|1|2|33|
|UnttedStes<br>of Amerca|1|2|-|-|-|-|4|-|-|7|
|Total|60|25|1|-|3|11|23|1|7|122|

Notes: (1) Includes 8 off-shore banking units in Bahmas, Bahrain, Cayman Islands, Channel Ilands and Singapore. (2) As at end-December 1997.

_Source:_ Reserve Bank of India.
piece rate basis (unless workers are denied access to such jobs) since pay is directly linked to each worker’s productivity. While the cause of the differential is unclear, its large magnitude suggests an area for further investigation.

# **6.  Gender specialization in work tasks**

Both men and women worked in each of the piece rate tasks from which the Jarvis and Vera-Toscano sample was drawn. Workers of both sexes were asked and generally specifically replied that workers of the other sex could do their task equally well. Nonetheless, the workers in each of the tasks were largely of one gender. The tasks could be gender type because productivity in each task is linked to a gender-related attribute. For example, it might be argued that most women excelled at the tasks of cleaning and packing grapes because they possessed greater finger dexterity, paid greater attention to detail, had better eyesight--particularly as related to color discrimination, and perhaps possessed a greater willingness to endure long periods of work in a stationary position, relative to men. Nonetheless, based on anecdotal information received during interviews and observation, Jarvis and Vera-Toscano believed that men and women tended to prefer for social reasons to work with others of their same sex. The sharing of jokes and casual conversation, as well as life experiences and daily concerns was particularly important to the women interviewed.

# **7.  Women’s Knowledge of Work Opportunities and their Work Choices**

Newman and Jarvis (2000) found that women were highly informed about many aspects of the packing shed jobs that they accepted, e.g., shed-related characteristics that affected workers’ productivity, fringe benefits, and the expected duration of the job, and their willingness to accept work at a specific piece rate was strongly influenced by these allowing them to treat this endogeneity.

- 17 -
What then are the implications of these findings for developing countries? It is interesting to start this discussion by noting that the empirical testing of the altemative models of investment in developing countries have been confined to accelerator, neoclassical, and the cash flow models. The Q theory of investment has not been in this mix given the rather exacting data requirements for the computation of the Q ratio. Overall, the evidence for the developing countries have favored the cash flow theory of investment. 2 While there exists no current research that directly distinguishes between managerial and information-theoretic approaches that underpin the cash flow theory of investment for developing countries, there are some interesting pointers. For instance, based on a detailed comparison of U. S. and Indian firms, Samuel (1996) has shown that apriori, information and agency problems are likely to be less severe for Indian firms compared to U. S. firms, given that the Indian financial system is predominantly a bank-oriented one compared to the U. S. stock marketoriented system. Further, the analysis of the financing of project costs for existing and new Indian firms suggested that internal finance and debentures played a much greater role in project financing for existing firms, while external equity and loans from All-India Financial Institutions' played a much greater role for new companies. Since agency costs are likely to 23 See Athey and Laumas (1994) for India, Harris et al. (1994) for Indonesia, Jaramillo et al. (1993a, 1993b) for Ecuador, Nabi (1989) for Pakistan, and Tybout (1983) for Colombia. Also, Bilsborrow (1977) found support for accelerator and cash flow theories using panel data for manufacturing firms in Colombia.

24 There are three All-India Development Banks: Industrial Development Bank of India (IDBI), Industrial Finance Corporation of India (IFCI), and Industrial Credit and Investment Corporation of India (ICICI). In addition to IFCI, ICICI, and IDBI, Industrial Reconstruction Bank of India (IRBI) also provides long-term finance to Indian corporations. Unit trust of India (UTI), Life Insurance Corporation of India (LIC), and General Insurance Corporation of India (GIC) also provide financial assistance and take equity positions in Indian companies. In addition, there are state-level financial institutions (SFCs, SIDCs) that provide long-term finance to Indian companies.

23
**Appendix Table 15: Treatment heterogeneity of primary and selected secondary outcomes.**

||(1)<br>Total cons.|(2)<br>Total cons.<br>per capita|(3)<br>FCS|(4)<br>rCSI|(5)<br>LCSI|(6)<br>Prop.<br>children<br>in school|(7)<br>All<br>children in<br>school|(8)<br>No children<br>in school|
|---|---|---|---|---|---|---|---|---|
|6 months|||||||||
|Treatment|79.01***|-15.90***|0.21***|0.20***|0.20***|0.01|-0.01|-0.03|
||(24.26)|(5.14)|(0.04)|(0.04)|(0.04)|(0.02)|(0.02)|(0.02)|
|Propensity score|129.80*|-52.24***|0.10|-0.11|-0.09|0.04|-0.06|-0.10|
||(69.99)|(13.85)|(0.13)|(0.16)|(0.12)|(0.05)|(0.07)|(0.06)|
|Treat * Propensity score|97.35|43.51**|-0.08|0.07|-0.03|0.17***|0.21***|-0.16**|
||(87.98)|(18.24)|(0.16)|(0.17)|(0.15)|(0.06)|(0.08)|(0.08)|
|12 months|||||||||
|Treatment|100.78***|-13.99***|0.20***|0.08*|-0.04|0.02|-0.02|-0.06***|
||(24.65)|(4.66)|(0.04)|(0.04)|(0.04)|(0.02)|(0.02)|(0.02)|
|Propensity score|135.73**|-55.85***|0.04|-0.11|-0.24*|-0.01|-0.09*|-0.03|
||(55.30)|(10.34)|(0.12)|(0.12)|(0.12)|(0.04)|(0.05)|(0.06)|
|Treat * Propensity score|84.23|32.36**|0.02|0.13|0.00|0.17***|0.18**|-0.16**|
||(90.35)|(15.38)|(0.16)|(0.16)|(0.16)|(0.06)|(0.07)|(0.08)|
|18 months|||||||||
|Treatment|34.09|-33.25***|0.14***|-0.18***|-0.04|-0.00|-0.03|-0.03|
||(26.76)|(6.33)|(0.05)|(0.05)|(0.05)|(0.02)|(0.03)|(0.02)|
|Propensity score|202.07***|3.09|0.05|-0.08|-0.47***|-0.02|-0.15**|-0.04|
||(71.03)|(19.67)|(0.13)|(0.14)|(0.15)|(0.05)|(0.07)|(0.06)|
|Treat * Propensity score|78.98|-8.34|-0.04|-0.30*|0.04|0.19**|0.21**|-0.15*|
||(93.29)|(24.90)|(0.16)|(0.17)|(0.20)|(0.08)|(0.10)|(0.08)|

_Note: Significance level p< 0.01 - ***, 0.05 - **, 0.1 - *; Robust standard errors in parentheses; Each coefficient in this table is obtained from a different regression of the outcome on specification (1), which includes a binary treatment indicator fully interacted with the outcome at baseline and the demeaned propensity score, the model also includes strata fixed effects (n=5); all consumption values are deflated across regions and months to average 2017 TL; the regressions on school attendance are estimated on the subsample of households with at least one child._ 58
Table 1: Main Table of Results

|||_Dependen_|_t variable:_||
|---|---|---|---|---|
|||ln(WSF inc|rement + 1)||
||(1)|(2)|(3)|(4)|
|Drought|1.53<sup>_∗∗∗_</sup>|0.40<sup>_∗∗∗_</sup>|0.24<sup>_∗∗∗_</sup>|0.12<sup>_∗∗∗_</sup>|
||(0.03)|(0.03)|(0.02)|(0.03)|
|Controls|✓|✓|✓|✓|
|Year FE||✓|✓||
|Grid cell FE|||✓||
|Country_×_ year FE||||✓|
|<br>N|1,456,033|1,456,033|1,456,033|1,453,503|

_Notes_ : “Drought” represents our spatially and temporally lagged measure of drought, as discussed in section 3.3 of the paper. Constant for model (1) was estimated but is not shown. Controls include distance to international border, average annual temperature, the area of each grid cell covered by land, the baseline number of urban pixels in each grid cell in 1985, a non-lagged measure of drought, as well as drought that is only lagged temporally or spatially. Standard errors are clustered at the grid cell level. FE = fixed effects. Statistical significance:<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0.01.

14
and their standard deviations increase for all maturities. It is important to note that the increase in excess returns cannot be accounted for by the increase in volatility. The reason is that the Sharpe ratio (i.e. the ratio of excess returns over their standard deviation) increases substantially during crisis times. The average Sharpe ratio across maturities is 0.006 during non-crisis periods and larger than 0.06 during crisis periods. Interestingly, the Sharpe ratio is higher for long-term bonds than for short-term bonds during crises.

In sum, the results above are consistent with the model presented in Section 2, which shows that crises can be due to shocks to the country’s repayment capacity or to shocks to investors’ wealth (or other determinants of risk appetite). In both cases, the model predicts that the term premium should increase. In the case of debtor side shocks, or demand shocks, the country demands more long-term funds, driving up the cost of long-term borrowing. In the case of investor side shocks, or supply shocks, bondholders become more sensitive to the price risk of long-term bonds and demand a higher term premium. To complement the price evidence presented so far, the next section studies bond issuance at different maturities, during crisis and tranquil times. This will allow us to shed light on the relative role of debtor and investor side shocks during crises. But before doing so, we discuss three points related to the results presented in this section.

# 4.3 Interpreting the evidence

We end this section with a discussion of three issues that might help clarify the results related to the risk premium and the term premium. First, can our results be due to a peso problem? Second, what is the relation between our results and the literature on the term structure of interest rates in developed countries? Third, does the fact that the risk premium is close to zero during tranquil times imply that our explanation of why countries borrow short term is crisis contingent?

## 4.3.1 Peso problem

It is important to point out that it is very unlikely that our results on the risk premium and, even more, on the term premium are the consequence of defaults being underrepresented in our sample, a phenomenon also known as the peso problem.<sup>32</sup> First of all, our sample does include a number

> 32 To be precise, the name comes from the study of devaluations (of the Mexican peso) rather than defaults, but the same argument applies.

23
16 _Computational Statistics & Data Analysis_ , 55(7), pp. 2372–2387. doi:10.1016/j.csda.2011.02.004.

Masaki, T. _et al._ (2020) _Small Area Estimation of Non-Monetary Poverty with Geospatial Data_ . World Bank, Washington, DC. doi:10.1596/1813-9450-9383.

Meda, M. _et al._ (2020) ‘Unintended consequences of long-sleeved gowns in a critical care setting during the COVID-19 pandemic’, _Journal of Hospital Infection_ , 106(3), pp. 605–609. doi:10.1016/j.jhin.2020.07.036.

Mocumbi, A.O. _et al._ (2019) ‘Cardiovascular Effects of Indoor Air Pollution from Solid Fuel: Relevance to Sub-Saharan Africa’, _Current Environmental Health Reports_ , 6(3), pp. 116–126. doi:10.1007/s40572-019-00234-8.

Mu, X., Yeh, A.G.-O. and Zhang, X. (2021) ‘The interplay of spatial spread of COVID-19 and human mobility in the urban system of China during the Chinese New Year’, _Environment and Planning B: Urban Analytics and City Science_ , 48(7), pp. 1955–1971. doi:10.1177/2399808320954211.

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37 indices into an overall index of UHC attainment, we also allow for a nonlinear tradeoff – this time between the two dimensions of UHC.

Our operationalization of the index is perhaps best seen as illustrative, since we have emphasized inter-country and temporal comparability, and because of this we are inevitably constrained by what data have been collected through household surveys on a regular basis. Arguably it is on the treatment domain where the available data are weakest: we have only two indicators that capture need for treatment, and both relate to outpatient treatment of illness among young children; our indicator capturing treatment in hospital is a broad-brush indicator and we are forced to rely on WHO inpatient admission rate norms to give it a need-based benchmark. We express all indicators such that higher scores are better and run from 0 to 100.

We find a cluster of countries with UHC scores of between 79 and 84: Brazil, Colombia, Costa Rica, Mexico, and South Africa. At the other extreme, we find a cluster of countries in the range 35-57, namely Ethiopia, Guatemala, India, Indonesia, and Vietnam. Countries achieve their UHC scores in different ways: Colombia outperforms Costa Rica on service coverage but falls behind on financial protection; Vietnam outperforms Ethiopia on service coverage but falls behind on financial protection, in part probably because Vietnam has high rates of service coverage especially on the treatment indicators but reimburses them at low rates. We find that countries have, on average, improved their UHC scores between the earliest and latest years for which we have data – by about 5 points. However, the 37
# 6.2.1. Discontinuity of the Running Variable

In this test, we examine whether the number of observations just above the cutoff is roughly similar to the number of observations below the cutoff. The idea underlying this test is that if individuals do not have precise control over the value of the income score they receive, they should be as likely to receive a score marginally above the cutoff as they are to receive a score marginally below the cutoff. Thus, lumping in the density graph of the running variable, especially near the threshold, may be indicative of manipulation by households. In particular, if we observe lumping just _below_ the cutoff, which would yield beneficiary-status, we could suspect some manipulation by households to get selected into the cash transfer program.

At the same time, there is a growing body of work, which argues that continuity of the running variable’s density function is neither necessary nor sufficient for regression discontinuity identification (McCrary 2008, Sekhon and Titiunik 2016, Dong 2018, and Frölich and Sperlich 2019, Choi and Lee 2020). “The score density continuity should not give a false sense of security when not rejected, and lack of it should not be taken as a reason to give upon the RD study hastily” (p.1. Choi and Lee 2020). In this light, we attempt to check the identifying assumptions of our empirical strategy through multiple tests.

As a first step, abiding by the conventional wisdom, we check the density of the running variable, i.e., the PMT score (see Figure 4). We find higher density just above the cutoff, i.e., lumping among ineligible households. This is intriguing and unlikely to be explained by intentional manipulation on behalf of 4P households - lest we would have observed lumping just below the cutoff which would have yielded beneficiary status. Still, this may indicate manipulation on behalf of program implementers. However, the income data used to determine eligibility of households was collected – as part of the _Listahanan_ – much before the 4P program was conceived and implemented. Further, the sampling for this study was done _after_ the scores had been determined and as described in Section 4, our sample was randomly drawn from the sampling frame used in Orbeta et al. 2014. In light of these considerations, it is unlikely that implementers could have exercised _precise_ control over the estimated income, and we suspect that the observed discontinuity could instead be an artifact of the targeting formula (weights used to develop the poverty score could cause jumps in the distribution) or an upshot of a non-standard sampling procedure (Ambler and Brauw 2019).

18
# Table 5

# Comparison of Norms and Compliance for Drugs Across Selected Diagnoses (DR$)

|Diagnoses|Number<br>of Drugs<br>Indicated<br>in Norms|Expected<br>Cost of<br>Meeting<br>Norms|Actual<br>Expenditure<br>on Drugs<br>Specified<br>in Norms|Total<br>Expenditure on<br>Norm-Specified<br>& Unspecified<br>(Other)<br>Drugs|
|---|---|---|---|---|
|Hernia|4a|$148.80|$79.46|$212.28|
|Acute Tonsillitisb|<br>2|4,387.44|200.59|1,250.24|
|Appendicitis|6<sup>a</sup>|160<sup>.</sup>76<sup>c</sup>|0.12|5.77|
|Cataract Surgeryc|4<sup>a</sup>|56.01|36.57|109.13|
|Hypertensiond|4|752.76|227.26|1,085.89|
|Diarrhea|1<sup>-</sup>3<sup>e</sup>|679.77|39.78|145.54|
|Total|3.7|6,185.54|583.78|2,808.85|

# Source: The Urban Institute

- a. Drugs required for treatment and for surgery.

- b. Data only indicates the number of individual drug orders made were consistent with the norms. It does not indicate that the necessary quantity (e.g., 10 day therapy) was provided in each case.

- c. Applies only to inpatients.

- d. There is very wide variation in both the numbers and costs for hypertensive patients. For example, in emergency, one drug is ordered 58.3% of the time. but no other drug is ordered.

- e. Number of drugs required varies by severity of the diagnoses. The figures reported here are for moderate cases and 2 drugs.
**Plc,Planning, and Research**

**Debt and International Finance**

The recent rise in _domestic_ public nonmonetary debt and in domestic bond yields is imposing a heavier burden on governments in countries like Brazil and Mexico than foreign debt does. This is a relatively new experience for developing countries but not for OECD countries.

of GDP, both explained largely by depressed savings and investment.

* Massive devaluation of the real exchange nrte and big swings in value among key currencies.

Reisen's discussion of rising government indebtedness, therefore, includes the experiences of four developing (Brazil, Mexico, Korea, and Indonesia) and three highly indebted OECD countries (Belgium, Ireland, and Italy). (Neither are the former four, especially Korea and Indonesia).

Why, Reisen asks, has government debt been rising since 1984 despite rationed foreign lending and efforts at fiscal consolidation? He finds the major determinants of debt to be: X Extemal transfers, which imply an internal transfer of resources from the private to the public sector.

* Fiscal rigidities, because of failure to broaden tax bases and cut government consumption.

* High interest rates coupled with low growth

How can the rise in government debt be stopped in the long run? _Not_ through a burst of inflation, even when i; -s largely unanticipated - because **tiie** demand for base money is now too small relative to public domestic debt. _Nor_ through domestic and foreign default - unless the government runs a substantial primary surplus (which is mostly not the case) and can credibly commit to not defaulting again (which is unlikely).

Possible remedies, Reisen sugge ., are growth-oriented fiscal adjustment, improved debt management, and voluntary debt reduction. He calculates the noninterest surplus govemments would have to run to stabilize (and then rcduce) public debt ratios and make their budgets consistent with other macroeconomic targets. He also discusses how fiscal adjustment can foster growth at the same time that it minimizes real depreciation of the exchange rate and reduces the cost of domestic public debt.

This paper, prepared for the conference "Dealing with the Debt Crisis," is a product of the Debt and International Finance Division, Intemational Economics Department Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Sheilah King-Watson, room S8-029, extension 33730 (34 pages with tables).

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy. Planning, and Research Complex. An objective of **the** scries is to get thcsc findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not nccessarily represent official policy of the Bank.

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