rafmacalaba/datause-encoder-data huggingface dataset:
continued increasing. Using the database presented in this paper, Demirgüç-Kunt et al. (2020) show that measures of liquidity support, borrower assistance, and monetary easing moderated the adverse impact of the pandemic on banks’ stock prices. However, the effect is heterogeneous across banks and countries. Banks that were already undercapitalized and/or operated in countries with little fiscal space were adversely affected by borrower assistance and prudential measures. Finally, there is not much evidence on the effect of emergency prudential regulations and banks’ internal policies on banks per se. Bergant and Kockerols (2020) show that for the case of Ireland the most common measure taken by banks, that is an increase in drawdown limits of credit lines, was effective in the short run, but no other measure (e.g., suspension of installment payments, the extension of loan maturity, credit rollover, and decrease in interest rates) significantly reduced the probability of default in the long run. Moreover, forbearance and new lending are interlinked, as new lending is subdued because capital and other resources within the bank are allocated to forborne loans. On the contrary, for a sample of European banks, Altavilla et al. (2020) argue that in the absence of funding and capital relief, banks’ ability to supply credit to the real economy would have been severely affected.

The remainder of the paper is structured as follows. Section 2 introduces the COVID-19 Financial Policy Response Database and the policy classification framework. Section 3 describes the methodological approach to analyze the determinants of the policy response and Section 4 documents the empirical results. Section 5 concludes.

# **2 COVID-19 Financial Policy Response Database**

In this paper, we present a new global database that collects financial sector policy measures that have been publicly announced by the governments, central banks, and financial sector authorities in 154 jurisdictions since the outbreak of the COVID-19 pandemic (see Appendix C for the list of countries). The database offers a regularly updated repository of emergency measures, adopted by domestic authorities with a focus on EMDEs.<sup>3</sup> Policy measures and guidance provided by a selected group of large advanced economies and

> 3 The COVID-19 Financial Policy Response Database is updated regularly and is publicly available at the following link: https://datacatalog.worldbank.org/dataset/covid-19-finance-sector-related-policy-responses.

5
Emigration has long been considered detrimental to origin countries’ human capital due to the loss of skilled workers. However, positive effects are possible either through the brain gain mechanism (Mountford <u>1997) or due to a positive income effect increasing households’ inputs in</u> education.<sup>1</sup> That positive income effect could in theory also increase spending on a particularly corrosive education input - informal payments to teachers. Such payments are common in many developing countries and have also become widespread in post-Soviet countries after the collapse of the USSR as real wages for teachers declined abruptly. This paper shows that the positive income effect can be overcompensated by other effects leading to an overall decrease in informal payments to teachers due to parental migration.

These informal payments are problematic for two main reasons. First, they impose a “tax” on education that may reduce the incentives to human capital accumulation. Second, they distort performance incentives for teachers, parents, and students, for example, by motivating teachers to provide exam results to students instead of teaching them in line with the curriculum. Thus, informal payments are understood to contribute to a less functional and less egalitarian public education system.<sup>2</sup> Often, they are raised by informal parental committees on a per capita basis and tend to be regressive. Parts of the raised funds are spent on maintenance of the school and a large part will supplement wages of teachers. These payments have many organizational similarities to weakly enforced per capita taxes, a fact that can help tailor responses to them. The second and even more problematic form of payments to teachers is competition for higher grades or better treatment of individual students. Here, migrants can be expected to spend more money per child due to an income effect. These bribes are especially common in higher education (ESP/NEPC <u>2010).</u> We study the effect of migration on informal payments and other forms of private educational expenditure and control for self-selection into migration by employing an instrumental variable approach. Our instrument is a network-based pull-effect at the local level, which is constructed using past migrant shares and destination-specific economic growth over time. The identifying assumption is that this network-growth interaction provides exogenous

> 1. For example, <u>McKenzie and</u>

> <u>Rapoport (2010), Antman</u> (2011, 2012), Caleroet al. (2009), Yang (2008), <u>Bansak and Chezum</u> (2009), Cortes (2015).

2. For example, <u>ESP/NEPC (2010), Heyneman et al. (2008), Osipian (2009).</u> 2
―miser index‖, primary school enrollment rate, life expectancy at birth, and the relative price index of investment goods.<sup>30</sup> Table 3 gives the augmented models using both survey means and consumption from national accounts. The table also gives restricted forms that pass comfortably.

We see that the initial poverty rate remains a strong and significant predictor of growth. The Gini index also has a negative effect on the growth rate, but it is only significant (and only at the 10% level) when using consumption from the national accounts. Significant predictors of the growth rate at a given initial mean and poverty rate are the size of the Western middle class (negatively), life expectancy (positively) and the price of investment (negatively). The relative share of the middle quintiles is significant for the growth rates in national accounts consumption (but not the survey means), though with a negative sign; it appears that Easterly‘s (2001) findings on the positive effect on growth of a larger income share held by the middle quintiles vanishes once one introduces the other distributional parameters considered here. The miser index has no significant effect on growth (as also found by Lind and Moene, 2010).

The negative coefficients on both the poverty rate and the share of the Western middle class imply that a higher population share in the developing-world middle class—those living between $2 and $13 per day—is growth enhancing. Thus the data can also be well described by a model relating growth to the population share of the developing world‘s middle class. As one would expect, replacing ln _H it_  and 1  _Fit_  (13) by ln[ _Fit_  (13/) _H t_  ] gave a very similar overall fit, though not quite as good as Table 3. The negative (conditional) effect of the poverty rate may well be transmitted through differences in the size of the middle class.

While the above results appear to be convincing in suggesting that it is high poverty not inequality that retards growth, it is important to recall that the poverty effect only emerges when one controls for the initial mean. The between-country differences in the incidence of poverty at a given mean reflect differences in relative distribution. To the extent that higher overall inequality comes with higher poverty at a given mean it yields lower growth rates.

_6.2 Further tests of robustness_ There are a number of alternative specifications. One might allow for nonlinearities and interaction effects. The log-linear form in equation (9) appears to give the best fit amongst

> 30 This is a common measure of policy distortions, derived from Penn World Tables (following Lopez and Servén, 2009). As noted in section 2, schooling and health attainments can also be interpreted as channels linking initial distribution to growth rather than as independent effects, so the interpretation of the poverty coefficient in these augmented regressions is not strictly the same as for the benchmark regression.

16
Mean con17.5 14.6 tract value 50.9 62.4 6.0 4.6 13.8 _Notes: All values in million US dollars._ The contract values captured by politically connected supervisors vary significantly among sectors (Table 2). Water works exhibit the most contracts (86), followed by the transport and education sectors. In these sectors, 19, 12, and 21 different contractors won at least one contract, of which nine, four, and four are coded as PCF1. In total, supervisors received contracts amounting to $213 million, much of which has been captured by PCF1 consultants. Consultancy contract values in the solid waste and irrigation sectors, for example, have been captured almost entirely by PCF1 supervisors. Such high levels of concentration of contract values contrast with measurements of market competition. The HerfindahlHirschmann Index (HHI), a widely used indicator for industry competitiveness, indicates that the markets for consultancies in the transport, and water works sectors would be competitive, despite that 76% and 73% of projects are captured by PCF1s.<sup>6</sup> PCF2s only play a very minor role in contract allocation.

The allocation of contracts for design consultants exhibits a similar degree of concentration for the solid waste and irrigation sectors. In these sectors, PCF1 designers designed 99% of all contract values. The water works sector, by contrast, has a lower degree of concentration of connected designers.

Table 2: Market competition among sectors

|**Supervision**|**Transport**|**Water works**|**Solid waste**|**Irrigation**|**Education**|**Other**|
|---|---|---|---|---|---|---|
|HHI|2,159|996|4,287|9,171|3,874|895|
|Number of contracts|64|86|8|8|60|71|
|Number of contractors|12|19|6|6|21|28|
|Number of PCF1 firms|4|9|5|5|4|9|
|Number of PCF2 firms|2|4|0|1|2|1|
|PCF1 share in value|76%|73%|99%|99%|88%|66%|
|PCF2 share in value|5.0%|6.0%|0.0%|1.0%|1.0%|2.0%|
|PCF share in value|81%|79%|99%|100%|89%|68%|
|**Design**|||||||
|HHI|1,513|1,396|5,953|9,392|3,624|1,076|
|Number of contracts|41|73|8|8|37|69|
|Number of contractors|12|21|3|6|17|24|
|Number of PCF1 firms|5|8|2|4|4|8|
|Number of PCF2 firms|1|4|0|2|2|1|
|PCF1 share in value|69%|35%|98%|99%|65%|61%|
|PCF2 share in value|8.6%|3.3%|0.0%|0.9%|8.4%|3.5%|
|PCF share in value|78%|39%|98%|100%|74%|64%|

_Notes: Number of contracts based on supervision contracts, rather than infrastructure contracts. Contract numbers can deviate from the above as the same consultancy contract can supervise several infrastructure contracts. HHI for supervisors based on supervision contract values. HHI for designers based on infrastructure contract values._

# **Independent variables: Political connections**

> 6 The HHI index is calculated as the sum of squares of the percentage share of each competing firm competing in a sector, HHI= ∑sn1 n<sup>2</sup> , and ranges between 10,000 for a perfect monopoly and approaches 0 for many firms with equal market shares. An HHI of up to 1,500 is generally considered a competitive market, while scores above 2,500 indicate a highly concentrated market.

10
Policy Research Working Paper 9465

# **Abstract**

This paper analyzes how local leaders make targeting decisions in the context of a public workfare program in the Lao People’s Democratic Republic. The study finds that village heads are progressive in their targeting, prioritizing the poorer households in their villages. The study benchmarks this decentralized selection to the common alternative proxy means test method and finds that village heads are at least as progressive as a proxy means test method approach. To illuminate what poverty-related information village heads could plausibly be incorporating into their internal selection decisions, the study designs and administers a set of exercises for village heads to rank villagers on land ownership, access to nutrition, and experience with recent shocks—indicators that are likely to differ in their observability to village heads and could plausibly be associated with need for public support. The study finds that village heads’ perceptions, as revealed through the ranking exercise, differ substantially from actual levels reported in surveys of the villagers themselves. The study then uses a data-driven machine learning approach to identify the predictors of village head selection. It concludes that village heads rely on a combination of easily observable household characteristics, forming a holistic impression of household welfare, rather than specific indicators like actual land ownership, nutrition, or economic shocks.

This paper is a product of the Gender Innovation Lab and the Office of the Chief Economist, East Asia and the Pacific Region. 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 eperova@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
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29
3 International Islamic Rating Agency (IIRA), the Islamic Financial Services Board (IFSB) and the Liquidity Management Center (LMC).<sup>6</sup> While the rapid expansion of Islamic financing activity has created expectations, it has also raised apprehensions on the risks that may be associated with it. Financial innovation often brings with it changes in the perception of risk. Merton (1995) makes the point that “less apparent understanding of the new environment can create a sense of greater risk even if the objective level of risk in the system is unchanged or reduced”. Islamic finance is not immune to such perceptions. The emergence of Islamic finance on the world financial landscape presents challenges similar to that of financial innovation. In line with Merton’s observation, it is generating concerns on the inherent risks it presents and their possible spillover on the rest of the financial system as it is less well understood than conventional finance.

This perception is compounded by a number of factors specific to Islamic finance that may add hurdles to its understanding and uncertainty as to the nature of Islamic financial intermediation. <u>First, there is the divergence between the paradigm of Islamic</u> finance, or its theoretic conception, and the way it is practiced in many instances.<sup>7</sup> <u>Second, actual Islamic financial institutions have to adapt to their environment where</u> they have to compete with conventional financial intermediaries and do not have access to the same money market instruments for liquidity management purposes. <u>Third, each</u> institution’s _Shariah_ board and prevailing local legal tradition and interpretations combine with the market’s competitive pressure to shape the activity of each Islamic financial institution (IFI). <u>Fourth, in most of the jurisdictions, IFI are still required to</u> comply with the regulations governing conventional financing and to use accounting standards that may not be fully adapted to the substance of their business activities. Fifth, there are variations within the paradigm reflecting five schools of thought, each providing

> 6 For a description of the role of each institution, see Ali (2002).

> 7 See for instance Moody’s (2001), which reports that “A survey of published accounts indicates that most Islamic banks do not see their on-balance sheet deposits as being profit-and-loss sharing”, Special Comment, January.
<!-- Start of picture text --> Figure S1.1c: Particulate Matter 2.5, ITO 2004-2006<br>Summer Winter<br>1000 1000<br>800 800<br>600 600<br>400 400<br>200 200<br>CPC B standard CPC B standard<br>0 0<br>Hour of the Day Hour of the Day<br>Source: Based on pollutant data from the Central Pollution Control Board<br>μg/m3 μg/m3<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br><!-- End of picture text --> <!-- Start of picture text --> Figure S1.2a: Residuals with Time Trend, NO2 at ITO<br>Yellow Line (first extension)<br>Source: Based on data described in the text<br>1<br>.5<br>0<br>-.5<br>-1<br>1Jun2005 5Jun2005 9Jun2005 13Jun2005 17Jun2005 21Jun2005 25Jun2005 29Jun2005 3Jul2005 7Jul2005 11Jul2005 15Jul2005 19Jul2005 23Jul2005 27Jul2005 31Jul2005 4Aug2005 8Aug2005<br><!-- End of picture text --> 51