rafmacalaba/datause-encoder-data huggingface dataset:
-2-

- (a) What are tr_ respective profitabilities of the domestic and international markets for Indian industries?;

- (b) Hov much of Indian industry's lack of international competitiveness stems for the extra costs paid for inputs (in the form of duties on imported inputs or high prices for domestic supplies)?;

- xc) What is the impact of India's investment costs, grossed-up by duties and taxes on imported equipment and by higher prices of domestic machinery, on the Value Added and the Effective Protection of Indian industry?;

- (d) What is the minimum level of nominal protection required by Indian industry to compensate for the extra costs paid for its inputs (item b) and for its investments (item c)?; and

- (e) To which extent are the actual nominal and effective protections received by Indian industry in concordance vith the levels of protection stemming from items (c) and (d) above?

5. The results of the analysis presented below should be interpreted with **sowe** qualifications. Firstly, the parameters and data used in DFI project **appraisal** reports tend inherently to err to the favorable side with respect to **comparative** prices (domestic versus CIF) for the projects' outputs. Secondly, the projections implicitly assume that the projects will be operated efficiently to minimize production costs (inputs, labor, capacity operating ratio, ...). In practice, actual production costs are often higher than projected during appraisal, and domestic ex-factory prices are then increased as much as permitted by domestic competition and the degree of protection provided by the Quantitative Restrictions and the protection tariffs levied on competing imports in India. For these reasons, the data are most likely to underestimate the actual domestic profitability, the price uncompetitiveness, and the effective and nominal protections presently experienced by Indian industries. i/ Nevertheless, they provide an indication of the incentives and disincentives steoing from the protection structure as they are perceived ex-ante by the project promoters and financiers. In this sense, they should provide useful indications and benchmarks for a reasonable assessment of the issues raised in para. 3 above.

- 1/ The same favorable bias is a first factor for underestimating the effective protection. Moreover, available data ef appraisal reports could not permit to separate out the excise (or CVD) taxes from the ex-factory or landed prices of inputs to be purchased for the projects' operations. Estimating and separating out such excise or CVD taxes on the basis of the official tax schedules would have been misleading, due to the pervasive exemptions and ad-hoc rates applied in India, and was not done. To this extent, the comparative prices of inputs used in the Effective Protection computations are slightly overestimated, and the resulting Effective Protection rates are further underestimated.
Chetty, R., Friedman, J. N., and Rockoff, J. E. (2014b). Measuring the impacts of teachers II: Teacher value-added and student outcomes in adulthood. _American Economic Review_ , 104(9): 2633-2679.

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<mark>Decristan, J., Klieme, E., Kunter, M., Hochweber, J., Büttner, G., Fauth, B., Hondrich, A. L., Rieser, S., Hertel, S.& Hardy, I. (2015). Embedded formative assessment and classroom process quality: How do they interact in promoting science understanding?.</mark> _<mark>American Educational Research Journal</mark>_ <mark>,</mark> _<mark>52</mark>_ <mark>(6), 1133-1159.</mark> Devine, R. T., White, N., Ensor, R., & Hughes, C. (2016). Theory of mind in middle childhood: Longitudinal associations with executive function and social competence. _Developmental Psychology_ , 52(5), 758.

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Duflo, A., and Kiessel, J. (2017). Evaluating the Teacher Community Assistant Initiative in Ghana. Retrieved on 14 October 2018. https://www.poverty-action.org/study/evaluatingteacher-community-assistant-initiative-ghana.

Dweck, C. S. (1999). Caution: Praise can be dangerous. _American Educator_ , 23(1), 1-5.

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Evans, D. K., and Yuan, F. (2017). Economic returns to interventions that increase learning. Background paper, _World Development Report 2018_ , World Bank, Washington, D.C.

30
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2021). This may explain why a much larger share of households in LICs report to have lost income since the pandemic started. Employment recovery among LICs appears to stall in 2021, with gains driven only by new entrants and, unlike in MICs, the share of adults that had stopped working since the pandemic did not continue to fall between the second half of 2020 and 2021 (Table A6.2 and Table A6.3).

Across all regions, employment fell most in the early phase of the pandemic and was substantially lower in LAC compared to other regions. Employment levels recovered during the second half of 2020, most significantly in Europe and Central Asia (ECA), LAC and SSA, relative to prepandemic levels. The EAP region diverges from these patterns when considering results in a panel of five countries, where there is limited recovery from initial labor market losses throughout 2020 and into 2021. Results for a smaller but stable sample of countries also suggest little recovery in employment levels during 2021 in ECA and SSA (Table A6.1).

# **4.2 We’re all in this together? Employment gains across different population groups**

One of the main findings in studies of initial COVID-19 impacts described in Section 2 related to the uneven impacts of the pandemic on different population groups—those with lower levels of education, informal employment, and women have been impacted to a greater degree initially by job losses.

Table 3 reports the percentage change in employment across different population groups in 2020 and 2021, relative to each group’s prepandemic employment level (based on recall), both for the full set of countries and for the country panel. Our main interest is in comparing the magnitudes of relative employment changes across different population groups over time. To make comparisons across countries in terms of education level, we define low education as primary or less in LICs and LMICs, and secondary or less in UMICs and HICs.

The estimates from the expanded HFPS sample confirm that the initial impacts of the pandemic on employment were more pronounced for women, urban workers, and those with lower levels of education. For instance, there was proportionally greater employment loss among women relative to prepandemic levels, and this gap persists into 2021 (Table 3). Work – – stoppages the main component of net employment changes decreased over time, but the rate of decrease tended to be lower among women, who report a higher incidence of work stoppages in the later part of the pandemic (Table A6.2). For a number of countries, particularly in the LAC region, women experienced larger proportionate declines in employment 15
**Table A 1: Summary statistics**

|**V**|**ariables used in**<br>|**estimation of w**<br>|**age equations**<br>||
|---|---|---|---|---|
||**Mean**|**Min.**|**Max.**|**Std. deviation**|
|_w_<sup>_U_</sup> (yuan)|12121.0|5873.1|33593.0|3548.2|
|_w_<sup>_R_</sup> (yuan)|4455.2|1232.0|21349.0|2052.8|
|_Y_(100 m. yuan)|838.7|10.1|12189.0|1243.6|
|_Human_(years)|7.48|2.13|9.99|1.01|
|_I pw (urban)_(yuan)|51217.0|9435.2|298030.0|29154.0|
|_Ipw (rural) _(yuan)|4736.9|17.4|98372|8184.6|
|**Additionalp**|**arameters requ**|**ired for numer**|**ical solution of**|**model**|
||**Mean**|**Min.**|**Max.**|**Std. deviation**|
||0.0030|0.00|0.03|0.0037|
||0.0030|0.00|0.01|0.0022|
|**Extra v**|**ariables used in**|**assessing fit on**<br>|**"after" solutio**|**n**<br>|
||**Mean**|**Min.**|**Max.**|**Std. deviation**|
|_GDP pw_(yuan)|36857.6|6133.0|226829.0|27599.7|
|_GDPpc_ (yuan)|20214.4|3405.0|98398.0|15476.1|

# **A.2. Travel time estimates**

We employ a spatially explicit model of transport infrastructure by using geographic information systems (GIS) data on the Chinese road network. Geo-referenced road information for China is from the _Australian Consortium for the Asian Spatial Information and Analysis Network_ (ACASIAN; <u>www.asian.gu.edu.au). The "before" network, which is</u> used to construct the travel time matrix _TIME_<sup>_Before_</sup> described in section 3.2, consists of 20,899 line segments with attribute information indicating the type of road represented by each link. The "after" network, which includes the NEN and is used to construct _TIME_<sup>_After_</sup> , has 31,538 segments. Maps of the before and after networks are shown in Figures A1 and A2 respectively (see also World Bank, 2007).

Standard GIS techniques allow us to determine the most likely routes through both the before and after networks that will connect each prefecture with each of the remaining 330 prefectures. As discussed in section 3.3, transport costs are modelled as a function of network travel time for both the urban and rural sectors. While road upgrading might not significantly change the distance by road between two locations, it typically reduces travel times. For each road type, we determined a suitable feasible travel speed (design speed) ranging from 10 km/h for unpaved city streets to 75 km/h for expressways (see Table A2). These speed estimates were arrived at following consultation with World Bank transport specialists working in the East Asia region. Given _dk_ , the GIS calculated length, in km, of the _k_ th segment of road, we compute the time required to traverse this segment as _tk_ = _dk/sk_ where _sk_ is the travel speed along segment _k_ . The estimated travel time between prefectures _i_ and _j_ is then given by: 37
# **6. Concluding remarks**

This paper analyzes how blanket guarantees, liquidity support, recapitalizations, and nationalizations during crises affect banking competition. We also study the effects of these interventions for consumer welfare in terms of loan and deposit pricing and access to finance. Our work builds on a growing literature that examines the intended and unintended consequences of government aid for the banking sector. Unlike these studies which focus on risk-taking on the micro level following bailouts using single-country datasets, we test aggregate effects using a sample of 124 countries.

The key result suggests that reservations by policymakers that interventions undermine banking competition can be dismissed: Liquidity support, recapitalizations, and nationalizations reduce Lerner indices and net interest margins. These effects are amplified in more concentrated and less contestable banking systems, and in the absence of deposit insurance. However, transparency mitigates the effects. While our regressions leave open the possibility that other policy responses that coincide with the interventions could drive these inferences, a battery of checks that confront alternative explanations and account for possible non-random assignment of interventions suggest the effects are causal in nature. Consistent with evidence from the bank level by Gropp, Hakenes, and Schnabel (2011), and Duchin and Sosyura (2012a), our findings underscore that banks’ conduct following government aid is conditional on the government measures. The evidence from the studies on the micro level in combination with our findings indicates that the mechanism by which government aid increases risktaking runs via competition.

Our analysis of consumer welfare effects points towards disparities. Reductions in interest margins are primarily driven by increased credit market competition, suggesting that borrowers benefit from liquidity support, recapitalizations, and nationalizations, whereas deposit rates are reduced. To illustrate the net impact for consumer welfare, we estimate the upper bound (based on nationalizations) for the net welfare gain for Indonesia, a country that experienced government interventions in 1997, to be 798.41mn USD. The lower bound (based on recapitalizations) is 605.15mn USD. These welfare effects are substantial in magnitude. With a GDP of 238.408bn USD in 1997, these effects are equivalent to 0.33 and 0.25% of the Indonesian GDP, respectively.

24
Policy Research Working Paper 6593

# **Abstract**

Using household surveys and bank penetration data at the district-level in 2006 and 2009, this paper examines the impact of Equity Bank—a leading private commercial bank focusing on microfinance—on access to banking in Kenya. Unlike other commercial banks in Kenya, Equity Bank pursues distinct branching strategies that target underserved areas and less-privileged households. Equity Bank presence has a positive and significant impact on households’ use of bank accounts and bank credit, especially for Kenyans with low income, no salaried job, and less education and those who do not own their own home. The findings are robust to using the districtlevel proportion of people speaking a minority language as an instrument for Equity Bank presence. It appears that Equity Bank’s business model—providing financial services to population segments typically ignored by traditional commercial banks and generating sustainable profits in the process—can be a solution to the financial access problem that has hindered the development of inclusive financial sectors in many African countries.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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 authors may be contacted at rcull@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
size) are observed in quintile three, four and five, and the Slovak Republic where the first quintile experiences the smallest drop and the fourth quintile, the largest.

The pattern along the pre-COVID-19 income distribution is reversed when mean disposable income is considered. In all countries, except Bulgaria, the largest drop in disposable income is observed at the top of the distribution. In Bulgaria, the drop in disposable income is broadly similar across quintiles with a slightly larger drop in the second quintile. Additionally, in 16 countries’ mean disposable income increased in the bottom quintile. In Austria, Croatia, Czechia and Luxembourg, mean disposable income increase in all quintiles except the top one.

Figure 2 shows the share of the population that is severely affected by the shock in market incomes and the share that was less severely affected. Severely affected is defined based on the median change in market incomes across the EU. Losses higher than 11 percent are defined as severe, losses of 11 or less as less severe. The difference between 100 percent and the sum of the two shows the share of people not affected by the adverse effects.

Countries with higher mean changes in market incomes usually also present a larger group of severely affected individuals. 40 percent of the Maltese population and close to 30 percent of the Irish and Slovak population experienced market income losses of more than 11 percent compared to less than 5 percent in the Netherlands and Finland. The share of individuals not affected by changes in market incomes ranges between 90 percent in the Netherlands and 43 percent in Cyprus.

<!-- Start of picture text --> Figure 2: Share of population affected by changes in market incomes by level of severity<br>NL FI SE LV BG DK PL LT AT RO LU HU PT DE ES CZ EL BE EE SI IE SK HR FR IT MT CY<br>Severely affected Less severely affected<br>60<br>55<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br><!-- End of picture text --> Note: See Figure A2 in the Appendix for results by income groups. Severely refers to losses in market income of more than 11 percent (Scenario A vs. D).

Source: Own calculations based on EUROMOD I4.0+.

Looking at the prevalence of the severely affected population across the pre-COVID-19 income distribution (Figure A2 in the Appendix), we can distinguish four groups of countries. In Belgium, Germany, Estonia, Greece, Ireland, Croatia, the Slovak Republic, Malta and Poland, the share of severely affected population increases with income. In contrast, the share of the severely affected decreases with income in Austria, Denmark, Spain and Luxembourg. In some countries, the share of the severely affected is broadly similar across income quintiles. This is the case of Bulgaria, Czechia, Cyprus, Finland, France, the Netherlands and Sweden. A third group is made of countries where the severely affected population is concentrated in the middle of the distribution: Latvia, 10
3 variables, but Eichengreen's sensitivity analysis to different weighting patterns indicate there should be no major changes expected.

A crisis is defined as a period in which ISP t > _u_ + 1.5a (where u is the sample mean and cr the standard deviation of the ISP). Table 1 summarizes the dates when the ISP surpasses the threshold. For countries experiencing hyperinflation (Argentina, Brazil and Peru), different thresholds were calculated both for these episodes and for more stable periods<sup>6</sup> . A total of 64 crises result and will be used in the analysis.

|Perio|Table 1<br>ds of Excessive Market Volatility in Several LAC Countries<br>1980:01-1998:04|
|---|---|
|Argentina|83:12; 89:04-05; 89:12; 90:02; 91:12; 92:11; 95:03|
|Brazil|82:09; 87:01-02; 89:06; 89:11-12:90:01-02; 90:11;91:09; 94:04-06; 95:03;<br>97:10|
|Chile|82:06-11; 83:03; 84:10-11; 85:07; 89:04-06; 92:04; 98:01|
|Colombia|84:01-04; 85:01-05; 86:09; 90:10: 92:08; 95:08; 97:09|
|Ecuador|81:12; 82:05; 83:03; 84:01; 85:12; 86:08; 88:07-08; 89:07; 90:01; 91:01; 92:05;<br>93:06; 95:02|
|Mexico|82:02-06; 82:12; 85:07; 87:12; 90:03; 94:04; 94:12; 95:01; 95:03; 95:10|
|Peru|81:01; 83:07; 87:10-12; 88:03-09; 90:03;92:04-10; 93:01|
|Venezuela|84:02; 86:12; 89:02-03; 90:07; 94:05; 95:12; 96:04; 98:01|

# III. Leading Indicators of Crisis and Signal-Generating Mechanisms

There is a wide set of options regarding which variables to use and how to use them. Concerning the variables to use, the IMF (WEO, ch. IV) narrowed the list to 3: M2/Reserves, real domestic credit growth, and the real exchange rate. We will also include, alternatively, the inflation rate due to the consistency of this variable in papers by A. Demirguc-Kunt and E. Detragiache<sup>7</sup> on determinants of banking crises.

Following the IMF procedure, we construct an index of macroeconomic vulnerability (IMV) with these variables, standardized to have mean zero and unit variance, circumventing the issue of weighting the individual indicators differently.

IMV= REER + DCG + M2/R + ,z REER Real effective exchange rate DCG Domestic credit Growth in real terms M2/R = M2/International reserves ;T = Inflation

> 6 For Argentina 2 sub-samples were used: 1980:01-91:06 and 91:07-98:03; for Brazil, two sub-samples 80:01-94:06 and 94:07-98:03. And for Peru, 3 sub-samples were used 1980:01-1988:07, 88:08-91:08, 91:09-98:04.

7 Asli Demirguc-Kunt and Enrica Detragiache (1997) "Banking Crises around the World: Are there Common Threads?"
41 Africans. For example, the average compensation of non-African top managers rose by 65 percent, whereas that of Seneplese top managers rose by 59 percent, over the 1980-1985 penod. This differential growth rate increased the already sizable ratio of the average compensation of non-African to Senegalese top managers from 2.5 to 2.6. While comparable data for earlier periods are not readily available, it should be noted that the above evolution in relative managerial compensation is probably quite favorable for tae Senegalese by historical standards.

TABLE 4.13 Sengal: Avcnge Annual Comnston Per Employee by National and bi Groups

|A. 1980|SEN ALESE|QOTHER<br>AMR|KN--AM|
|---|---|---|---|
|Top Technicians|5,013|10,210|12,S95|
|Top Tochnicians and<br>Mfiddle Management|<br>2,767|2,268|9,167|
|TechniCians and<br>Skillcd Workers|1,43S|1,232|5,949|
|Workers and<br>Apprentices|581<br>|656<br>|774|
|...<br>....<br>....<br>.......<br>B. 1984|..<br>. ..................<br>SENEGALESE|............................<br>THER AP|!<br>_NON-AFIL_|
|Top Management|7,751|6,774|19,199|
|Top Technicians and<br>Middle Management|<br>4,125|4,707|12,943|
|Technicians and<br>Skilled Workers|1,680|1,665|9,062|
|Workers and<br>Apprentices|_855_|833|655|

(Continued)