rafmacalaba/datause-encoder-data huggingface dataset:
keeping all other factors unchanged.

_5.2 Quantitative easing and the effect of sovereign ratings and relative ratings during the postcrisis period_ The results on quantitative easing as an additional control in the baseline specification on country ratings and capital flows are presented in Table 4. Focusing on net capital flows in the post-crisis period, we find that adding an indicator for QE episodes to the baseline specification maintains the results found earlier on the larger effect of relative risk ratings compared to absolute sovereign ratings (columns (1) and (2)) for the post-crisis period. The magnitude of the coefficients of absolute and relative ratings is similar to the baseline model. The QE indicator is statistically significant confirming findings on the positive effect of QE from earlier studies (see Lim and Mohapatra 2016).

**Table 4: Quantitative easing, sovereign ratings, and net capital inflows after the financial crisis**

||**QE in**<br>|**dicator**<br>|**QE co**<br>|**ntinuous**<br>|
|---|---|---|---|---|
|Net Inflows|Absolute rating<br>(1)|Relative rating<br>(2)|Absolute rating<br>(3)|Relative rating<br>(4)|
|L.Net Inflows|0.089**<br>(0.037)|0.088**<br>(0.037)|0.099***<br>(0.038)|0.098***<br>(0.038)|
|S&P Rating|0.181**<br>(0.082)||0.176**<br>(0.083)||
|Relative Rating||0.200**<br>(0.079)||0.202**<br>(0.080)|
|U.S QE indicator|1.814***<br>(0.381)|1.728***<br>(0.383)|||
|U.S Fed Purchases|||0.005**<br>(0.002)|0.004**<br>(0.002)|
|Growth diff.|0.215**|0.214**|0.232***|0.231***|
||(0.087)|(0.087)|(0.087)|(0.088)|
|Interest rate diff.|0.471***|0.487***|0.442***|0.462***|
||(0.116)|(0.117)|(0.116)|(0.117)|
|VIX|0.035<br>(0.039)|0.028<br>(0.039)|0.028<br>(0.039)|0.023<br>(0.039)|
|Country fixed effects|Y|Y|Y|Y|
|No. of obs.|859|859|859|859|
|No. of countries|26|26|26|26|
|Adj. R-squared|0.255|0.257|0.243|0.245|

Notes: The post-crisis sample includes quarterly panel data for 26 emerging and frontier market economies from Q2 2009 until Q4 2017. All regressions have net capital inflows as the dependent variable and are estimated using a dynamic panel data model with a lagged dependent variable. QE refers to the episodes of Quantitative Easing by the US Federal Reserve. Heteroskedasticity-consistent robust standard errors are presented in parenthesis. * indicates significance at 10 percent, ** at 5 percent, and *** at 1 percent.

18
example Mturi and Curtis, 1995; Brockerhoff and Derose, 1996; Lavy et al., 1996; Lalou and LeGrand, 1997). Similarly, maternal health conditions can also be shown to be a key determinant of child survival (for example, as in Brockerhoff and Derose, 1996; Lavy et al., 1996).

In contrast, the evidence on the effect of mother’s education, one of the socio-economic factors of primary interest in this study, is mixed. While quite a few studies only find a rather small and often insignificant effect (for example, Lavy et al., 1996; Brockerhoff and Derose, 1996; Lalou and LeGrand, 1997), others do detect the expected mortality-reducing effect (for example, Derose and Kulkarni, 2005; Ssewanyana and Younger, 2007; Omariba et al., 2007). For Kenya, Derose and Kulkarni (2005) do not only find a direct beneficial effect of mother’s education on child survival, but, on top of this, an indirect community-level effect: The proportion of women in the community completing primary school significantly augments infant survival in addition to the individual-level effect. A positive effect of mother’s education is also identified in Charmarbagwala et al.’s (2005) meta-analysis, supported by joint significance of variables measuring schooling and literacy. However, according to the authors, it is not clear if education by itself has any effect, or if education is only useful if it leads to a higher knowledge about health and nutrition. Finally, we would like to add, such knowledge may only be useful if it can be applied. For example, a mother may not be able to put her knowledge about nutritional requirements into practice, as her and the child’s diet is determined by largely unchangeable agricultural and economic conditions. These two aspects may partly explain the variation in the findings on the effects of mother’s education.

Another socio-economic factor of particular interest in this paper is material well-being, typically measured by consumption, income, and/or wealth. In fact, as almost all the reviewed studies rely on DHS data, researchers are forced to resume to an asset indicator as a proxy for material wealth. This asset indicator is typically computed using principal components analysis on the basis of a list of assets, as suggested by Filmer and Pritchett (2001). When included, wealth, as expected, tends to increase the chances of child survival (for example Hill et al., 2001) or infant survival (for example Ssewanyana and Younger, 2007) – independent of other factors included in the regression. Yet, for infant mortality however, this does not turn to be out a universal finding. The meta-study by Charmarbagwala et al. (2005) suggests that, although there can be little doubt that household income is a crucial factor in determining child health, it appears that income is not a significant determinant of infant mortality in the majority of cases. This can partly be explained by the fact that as mortality falls, the bulk of under-five-mortality is infant rather than child death, and these deaths are 9
25 23.1 hours that teachers ought to be teaching in grades 1-6 in Pernambuco, they were registered to teach only 17 hours. It was not possible to ascertain whether empty registry entries signified failure to document or failure to teach. But some of these teachers may have been scheduled for fewer hours of instruction, as found out by the Pernambuco Superintendency office. This tendency is a pattern also found in other countries, such as Indonesia.<sup>106</sup>

**Figure 3-2. Expected and registered number of hours taught in Pernambuco**

<!-- Start of picture text --> Hours Montly hours by grade<br>35<br>30<br>25<br>20 Expected<br>15 Registered<br>10<br>5<br>0 Grade<br>1 2 3 4 5 6 7 8<br><!-- End of picture text --> Ghana did not provide data on the use of class and school registers. However, the USAID study in 2003 reported that 97-99% of the classes had registers and several were using them effectively.

_Roll call._ In all classes, a significant amount (maybe up to 10 minutes) can be taken up by calling the names of students to check presence. If this is done every day, then up to 8% of class time (about 16 days per year) may be spent in this task.

# **WAS THERE RECUPERATION OF CANCELED CLASSES?**

When asked how often remediation takes place or what activities are done when teachers are absent, responses are contradictory. Teachers in Pernambuco mentioned that in 80% of the days there are substitute activities, but only 60% of the principals mentioned this option. The principal or another teacher may take a class, but substitute teachers in Pernambuco seem to be used only in about 17% of cases. Fewer than 50% of classes lost are recuperated, and the number is uncertain.

In Tunisia findings were similar. Although principals stated that they usually have substitute teachers (Table A-12), teachers stated that students generally go home (Table A-13).

106. also see Millot and Lane 2002
-53<sup>-</sup>

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incomes or consumption. They have to be measured using the same or similar methodology, and need to include as many countries of the world as possible. Perhaps at least 120-130 surveys are needed in order to cover more than 90 percent of the world population and account for 95 or more percent of world income.<sup>3</sup> Ideally of course we would like to have surveys from all the countries in the world. This is a very hard requirement. There are still quite a few countries, mostly in Africa, where household surveys are not regularly conducted and where methodologies change, rather brusquely, from one survey to another, thus rendering comparisons difficult.

Because the calculation of global inequality relies on household surveys, we cannot calculate Inequality 3 with much precision for the period before the mid- or late-1980s. There are simply no household surveys available for too many parts of the world. The first available Chinese household surveys are from 1982, the first usable surveys from the former Soviet Union are from 1988, and for many Sub-Saharan African countries, the earliest household surveys date from the mid-1980s. Thus, for the past, we have to rely on much more tentative data, where countries’ income distributions are only approximated, using various more or less reliable methods. This is particularly so if we wish to study global inequality in the long-run, covering the 19<sup>th</sup> century as well—a topic which I will discuss in Section 3 below.

Figure 2 displays the movements of the three types of inequalities after the Second World War. The Gini coefficient is on the vertical axis. Inequality 1 was stable from 1960 to 1980. This means that there was no systematically faster or slower growth of poor or rich countries. Neither were poor catching on with the rich, nor were the two groups growing further apart. Divergence started only at the beginning of globalization, around 1980, and went on until the turn of the century. These two decades were very bad as far as convergence, or catching up by poor countries, is concerned: rich countries grew, on average, faster than poor countries. However China and India,

> 3 The coverage is always greater for total world income than population because countries that do not have household surveys are generally poor countries whose importance in global output is small.

5
bank risk-taking. Section 3 gives a brief overview of the East Asian financial sectors. Sections 4 and 5 describe the data and methodology that we will use. Section 6 presents our empirical findings. Section 7 explores the power of our measure of risk-taking in predicting actual restructuring of banks in East Asia. Section 8 concludes.

# **1. Literature**

The literature distinguishes two types of bank efficiency. The first is operational efficiency as introduced by Farrell (1957) to _measure_ efficiency and the second is X-efficiency as introduced by Leibenstein (1966) to _explain_ differences in efficiency between banks. The concept of operational efficiency is a purely technical and can be defined as the product of technical efficiency, which tells us "how far" the bank is from the isoquant, and allocative efficiency, which captures inefficiencies due to the fact that the bank picked a sub-optimal input combination given input prices. Under X-efficiency the basic problem is viewed as one that is intrinsic to the nature of human organisation. X-inefficiency may arise for reasons outside the knowledge or capability of management, including corporate governance problems and the difficulties of principal-agent relationships within organisations.

There are two widely used approaches to examine a bank's input-output process. In the first, the so-called production approach, banks are treated as firms which employ capital and labour to produce different types of deposit and loan accounts. Outputs are measured by the number of deposit and loan accounts or number of transactions performed on each type of product, while total costs are the operating costs used to produce these products. The bank is viewed as a producer of two types of services: an acquirer of funds and user of funds. In the second, the so-called intermediation approach, banks are viewed as intermediators of financial services rather than producers of loan and deposit account services. Banks produce intermediation services through the collection of deposits and other liabilities and the transfer of these funds to interest-earning assets. Deposits are included as a third input along with capital and labour. As a result, operating costs, as well as interest cost, are taken into account in the production process. In this study we will use the intermediation approach. However, as Mester (1996) points out, both approaches have the disadvantage that they do not take into account risk factors. Also, both concepts assume that all banks produce the same output quality. It is likely, however, that there are unmeasured differences in quality, because banking data do not fully capture the heterogeneity in bank output. Berger and Mester (1997) try to alleviate this problem 3
# **APPENDIX B:  DEPRIVATION PROPORTIONS OF VENN DIAGRAM REGIONS IN THE OVERLAP ANALYSES**<sup>**11**</sup>

**<u>Table B1. Deprivation proportions by Venn diagram region in Figure 2: urban and rural populations</u>**

|||Urban<br>|||Rural<br>|<br>|
|---|---|---|---|---|---|---|
||2000|2011|Change|2000|2011|Change|
|money poor|0.41|0.31|-0.09***|0.50|0.34|-0.16***|
|education deprived|0.26|0.16|-0.10***|0.83|0.58|-0.25***|
|sanitation deprived|0.51|0.47|-0.04|0.93|0.42|-0.50***|
|_Distribution of the population_|||||||
|not deprived|0.28|0.33|0.05***|0.01|0.18|0.16***|
|only money poor|0.11|0.12|0.01|0.01|0.08|0.07***|
|only education deprived|0.07|0.05|-0.01|0.03|0.21|0.18***|
|only sanitation deprived|0.19|0.25|0.07***|0.08|0.12|0.03***|
|money poor, education deprived|0.04|0.03|-0.01*|0.02|0.11|0.09***|
|education, sanitation deprived|0.06|0.05|-0.02|0.37|0.16|-0.21***|
|sanitation deprived, money poor|0.17|0.14|-0.03*|0.06|0.05|-0.02*|
|allthree deprivations|0.09|0.03|-0.06***|0.41|0.10|-0.31***|

**Table B2. Deprivation proportions by Venn diagram region in Figure 3: rural population**

|||Rur<br>|al<br>|
|---|---|---|---|
||2000|2011|Change|
|money poor|0.50|0.34|-0.16***|
|health access deprived|0.61|0.32|-0.30***|
|education deprived|0.83|0.58|-0.25***|
|_Distribution of the population_||||
|not deprived|0.04|0.21|0.17***|
|only money poor|0.04|0.09|0.05***|
|only health access deprived|0.05|0.08|0.03**|
|only education deprived|0.14|0.25|0.10***|
|money poor, health access deprived|0.04|0.04|0.00|
|health access, education deprived|0.26|0.12|-0.14***|
|education deprived, money poor|0.16|0.13|-0.03|
|allthree deprivations|0.27|0.08|-0.19***|

11 Note: For all result tables in Appendix B, the “Change” column shows the coefficient estimate for the difference in proportions from 2000 (or 2005) to 2011. The asterisks indicate the significance level: *** p<0.01, ** p<0.05, * p<0.1 32
4.9 The literature on fiscal federalism has touched upon the assignrment of natural resource tax revenues. Generally speaking, arguments in favor of assigning revenues to the federal government include: (1) regional revenue stability; (2) allocative efficiency; (3) horizontal equity; and (4) the communal property of the natural resource heritage. On the other hand, arguments in favor of assignment to sub-national governments include: (1) compensating for the negative externalities suffered locally due to resource exploitation; (2) the local heritage of natural resources; and (3) the prevention of secession from the Federation because of fiscal abuse (McLure 1'994; McLure _et al._ 1996; USGTAT 1998b).

4.10 Table 19 shows how the revenues collected from some natural resource and environmental taxes are supposed to be shared among the various levels of government.

Table 19. Statutory Assignment of Selected Natural Resource Revenues in Russia

|(inper|centage)|||
|---|---|---|---|
|Userfeetype|Federal<br>share|Regional<br>share|Localshare|
|Subsoiluserfees(oilandgas)|40|30|30|
|Subsoiluserfees(otherthanoil andgas)|25|25|50|
|Subsoilwateruserfees|40|60|0|
|Oilexcises|100|0|0|
|Gasexcises|100|0|0|
|Oilreplacementfeea|20(100)|20(0)|0|
|Forestuserfeesb|40|60|0(100)|
|Surfacewateruserfees|40|60|0|
|Aquaticbiologicalresourceuserfees**c**|100|0|0|
|Pollutionchargesd|19|27|54|
|Water supply,sewageandtreatmente|0|0|100|
|Landtaxesand rentals|30|20|50|

- a) 20% to the federal fund for mineral base replacement and 20% to the regional fund for mineral base replacement. The remaining 60% stays in the company conducting exploration activities, depending on the clauses of the _ad hoc_ agreement with the Ministry of Natural Resources. If the company does not conduct such activities itself, 100% flow to the federal fund for mineral base replacement.

- b) The breakdown corresponds to the revenues from minimal stumpage fees. Excess stumnpage fees accrue to the local _leskhozy._

- c) The receipts are sent to the federal fund for the management, research, protection and reproduction of biological resources.

- d) Divided into general budget revenues and earmarked budgetary environmental funds: the federal share breaks down into 10% to the federal budget and 9% to the federal environmental fund; the regional and local shares accrue to the regional and local environmental funds, respectively.

- e) All proceeds go to municipal water utilities for cost recovery.

Sources: East West Institute (1999); GOR (1996); IFEI (1998); Roskoshnaya (2000); Summers (2000); Titova (2000); USGTAT (1998a); Ezhov (2000).

41
Policy Research Working Paper 9401

# **Abstract**

This paper examines the influence of sovereign credit ratings and relative risk ratings on private capital flows to 26 emerging and frontier market economies, using quarterly data for 1998–2017. A dynamic panel regression model is used to estimate the relationship between ratings and capital flows after controlling for other factors that can influence capital flows, such as growth and interest rate differentials and global risk conditions. The analysis finds that while absolute ratings were an important determinant of net capital inflows prior to the global financial crisis in 2008, the influence of relative risk ratings increased in the post-crisis period, which was characterized by easy monetary policies and global liquidity, on the one hand, and greater caution and discretion on the part of investors on the other. The post-crisis effect of relative ratings appears to be driven mostly by portfolio flows. These findings imply that emerging and frontier markets need to pay greater attention to their relative economic performance and not just their sovereign ratings. Tracking changes in relative ratings could help predict macroeconomic disturbances resulting from volatile portfolio capital movements.

This paper is a product of the Migration and Remittances Team, Social Protection and Jobs Global Practice. 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://www.worldbank.org/ prwp. The authors may be contacted at sde@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
_Corruption_ equals 0.203 and the standard deviation is 0.208. The corresponding values for the _Depth of Petty Corruption_ are 0.165 and 0.184.

The ES also provides information on a firm’s estimate about bribes that firms like itself pay to public officials to “get things done”. The motivation for this question is that firms are likely to report their own experience with bribery in answering the question. This measure of overall corruption differs from the petty corruption measures above in two important ways. First, unlike the petty corruption measures that are based on a firm’s actual experience with bribery, the overall corruption measure is based on a much bigger cognitive effort by the firms that involves first thinking of all interactions with the government and then estimating which interactions involved a bribe payment and how much. Second, the overall corruption measure is based on an open question about what is included in “get things done”. Thus, part of the difference in reported bribes may have to do with how firms interpret the question. For these reasons, we focus on the incidence and depth of petty corruption as our main corruption variables. For robustness, however, we show that our main result holds if we use the incidence of overall corruption. That is, we use cell averages of the dummy variable equal to 1 if the firm reports paying (strictly positive) bribes to public officials to “get things done” and 0 otherwise ( _Overall Corruption_ ). In our baseline sample, the mean value of _Overall Corruption_ equals 0.184 and the standard deviation is 0.220.

# 2.5 C _ontrol variables_

Reverse causality from a firm’s financial condition to corruption experienced by other firms in the cell is highly unlikely, although it cannot be completely ruled out. The problem of omitted variable bias is also mitigated by use of cell averages, although to a lesser extent than reverse causality. For instance, if firms within a cell share common 12