rafmacalaba/data-use-mentions-extended huggingface dataset:
The rationale for using this proxy is that, although the share of informed voters is not observed, we can observe the share of media users. Because both move together, it is sufficient to examine the levels and changes in the share of media users to test the effect of media bias (see Strömberg 2004b). Moreover, in our specific context, another justification for the use of these indicators is derived from Strömberg’s (2004a, p. 266) argument that “the emergence of broadcast media increased the proportion of rural and low-education media consumers as it became less expensive to distribute radio waves than newspapers to remote areas, and as these groups preferred audible and visual entertainment to reading. As politicians could reach rural and low-education voters more efficiently, the model predicts an expansion in programs that benefits these voters.”

# **_Income and group size variables_**

The hypotheses advanced in section II imply that the relationship between the _media_ variable and _RRA_ ( _NRA_ ) is conditional on the level of development (income), partially due to group size effects. As an indicator of development (income), we use real per capita GDP in purchasing power parity ( _gdppc_ ) taken from the Penn World Tables.<sup>6</sup> The most direct indicator of (relative) group size is the share of agricultural employment, _emps_ , based on FAO data. However, as is well known, both are strongly correlated because the agricultural employment share decreases with economic development. Therefore, _gdppc_ is itself an indicator of relative group size.

radios. Our identification strategy exploits the within-country variation in the data (see section 5). As long as the number of licenses and of TVs/radios display similar growth paths, this limitation of the data should not pose a major problem.

> 6 Specifically, we use the variable _rgdpch_ from the Penn World Tables, version 6.3.

14
<sup>4</sup> While the analysis presented here predates the earthquake, the conclusions and corresponding policy implications remain relevant, particularly considering the significant donor resources expected in the coming years for the education sector in Haiti, and the paucity of data to guide those investments.

# **2. The Data**

We draw mainly on the Demographic and Health Surveys (DHS) and the 2003 School Census. The DHS was carried out in Haiti in 1994-95, 2000, and 2005 and is representative at

> 3 World Bank (2006), based on analysis of the 2001 _Enquête sur les Conditions de Vie en Haiti_ . 4 See Government of Haiti (2010) and UNICEF (2010)

3
# **6 The impact of typhoons on the stability of the Philippine banking system**

There are important caveats to our main empirical results from a large cross-country sample, and hence we complement the main results with a country-specific example using the same methodology and more granular data. Using cross-country data on banking sector indicators ensures comparability, consistency and maximal sample coverage, but it limits our data at the country level. Identifying a disaster in the particular geographical area where it occurred would permit more precise identification. Further, we use official declaration and multiple types as proxies for identifying the severity of the disasters, as the available cross-country damage figures are limited and unreliable, which is open to criticism that these disasters may not be that severe. Therefore, we present empirical results from a country-specific example from the Philippines where we focus on a severe disaster, typhoon, alone and use geographical NPL data. This exercise was conducted between 2020 and 2021 as part of the joint World Bank-IMF Financial Sector Assessment Program (FSAP) for the Philippines (see the World Bank FSAP technical note (Regelink, 2021) and joint WB-IMF research paper (Hallgatte et al., 2022)).

The Philippines is among the most disaster-prone countries in the world. At least 60 percent of its total land area and close to 74 percent of its population are exposed to multiple natural hazards, many exacerbated by climate change, including typhoons, floods, storm surges, tsunamis, and landslides.6 In the past thirty years, 33,000 people have died and 120 million people have been adversely affected by disasters.7 For instance, the 2013 Super Typhoon Yolanda (Haiyan)—the strongest typhoon to ever make landfall in the world— was a tragic reminder of the devastating impact natural disasters can have in the Philippines. Over 6,000 people lost their lives and more than 16 million were affected, with 2.3 million people falling below the poverty line. Over 1.1 million houses were damaged or destroyed. The typhoon also
( _−β/η_ ) _._ A similar transition equation applies to the bilateral export shares model of equation (8).

In addition to allow for non-stationarity, this specication is also less likely to be aected by the reverse causality problem discussed above, since it controls for initial size dierences across sectors and convergence eects. Finding similar evidence on growth rates would strongly support the view that it is the natural hedge oered by the correlation that favors certain sectors.

# 3 Data

Data on export composition come from Feenstra et al. (2005), NBER-UN World Trade Flows database, which reports bilateral trade ows (imports and exports) for a large number of countries at detailed 4-digit SITC (Revision 2) level during the period 1962-2000. This database has been broadly used in the empirical trade literature, specially in the estimation of gravity models of disaggregated trade (Chaney (2008), Colacelli (2008), Do and Levchenko (2007)). Because of restrictions to the availability of other variables, the analysis focuses on the 1984-2000 period. Our nal sample includes 12
**Table 1. Cost of a basic-needs food basket (current Nu/person/month)**

||2017<br>prices|2022<br>prices|
|---|---|---|
|Basket defined on 2017 consumption pattern|1,431|2,349|
|Basket defined on 2022 consumptionpattern|2,035|2,455|

Notes: The reference group for both baskets is households in the first decile of per capita household expenditures; we select items that make 80 percent of total food expenditure (this amounts to 45 different items for 2017 and 53 items for 2022); average quantities are rescaled so that each basket yields 2,200 kcal/person/day.

f f The index 2017 can be used to project food poverty lines back in time, from 2022 to 2017, and therefore to identify the poverty trend. To preserve consistency with the 2022 CCFFCC official poverty estimates, we use F as an anchor for the projections. Accordingly, the 2017 food poverty line becomes: FF<sup>2022</sup> F =<sup>F</sup> 2017 f f<sup>FF2022</sup> FF<sup>2017</sup> CCFFCC The upper-bound CBN poverty line (UBPL) is defined as: U F = F Following the same procedure, we can estimate<sup>2022</sup> FF<sup>2022</sup> U+ NNFFNNF<sup>20172022</sup> .

Table 2 shows the estimates for both food (FPL) and total (UBPL) poverty line values.

**Table 2. Comparable poverty lines (current Nu/person/month)**

||**2017**|**2022**|
|---|---|---|
|FPL|1,737|2,852|
|UBPL|3,448|5,786|
September 2006 and September 2020.

# **3.3 Mapping Household Data with Remote Sensing Data**

We create a 2-km buffer around each household location to extract data for weather indicators around the buffer area. For the weather indicators, we combine descriptive statistics (mean, minimum, maximum, standard deviation) for every month between September 2006 and September 2020 in one image from the data sources described above. Then, these descriptive statistics were extracted into a single month-year image for each household location (and its buffer). Since a cluster is the smallest administrative unit (from which 10 households were sampled in the IE-LFS 2019-20), each household within a cluster is expected to experience similar weather conditions. Thus, weather data is extracted at the cluster-level. For each cluster, weather data (averaged for the month) is

> 12A higher resolution can be obtained through the use of Landsat data, although this product is more raw, and the researcher needs to calculate the relevant vegetation indexes manually.

8
**Figure 2. Correlation between local residuals and distance between localities**

<!-- Start of picture text -->
1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>0-25 Km 20-25 Km 25-40 Km 40-65 Km 65 + Km<br>Linear model Quadratic model<br><!-- End of picture text -->

- _Note:_ The legend indicates the specification chosen for the neighborhood effect. Mean values are computed at the VDC level.

# **8. Microsimulation results**

The microsimulations performed are based on an iterative process, using the preferred estimation of the hedonic price function at the household level and measures of actual destruction at the local level. The process involves first evaluating the physical loss of assets each household might have experienced due to the disaster. Then, the monetary damage from this loss is evaluated as if asset prices had not changed, which results in new market values for all dwellings in each locality. A mean of these new market values allows assessing the change in neighborhood effects due to the disaster. Plugging this change in the hedonic price function leads to a second-round impact of the disaster, hence to new market values for dwellings and new neighborhood effects. The computation is repeated until changes become marginal.

More specifically, the first step in this process is to adjust the market value of each dwelling in a way that reflects the physical destruction it might have experienced due to the disaster. In terms of equation (6), the damage term 0h 1h 0 is assessed by allocating the estimated local destruction rates 0 1 ⁄ 0 across the households covered by the 2011 NLSS III. Four ii �KK −KK �pp alternative allocation rules are used to this effect: ii ii ii �KK −KK �KK

- _(i) Proportional._ The market value of all dwellings in a locality is reduced by a fraction
<!-- Start of picture text --> Figure 3<br>Earnings Profile for teachers and other comparable groups<br><!-- End of picture text --> <!-- Start of picture text --> 5.000<br>4.500<br>4.000<br>3.500<br>3.000<br>2.500<br>2.000<br>1.500<br>1.000<br>1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41<br>Years of experience<br>Teacher in basic public schools Teacher in basic private schools Other government workers Private sector workers<br>Log Hourly Earnings<br><!-- End of picture text --> Source: Authors’ estimates based on ENIGH 1996 Teachers in basic private schools face the most uncertainty about lifetime salary and job tenure. Moreover, between eleven and thirty years of experience they earn more than public school teachers and the other groups. Teachers in basic public schools and comparable groups deal with a significantly lower risk from their salaries than do basic private school teachers. Teachers labor earnings in basic public school profiles are slightly flatter than the income profile for the private sector workers. At their initial stage of their professional life, teachers are paid about 79% more per hour than the private sector workers, and about 77% more than the other government workers. However, as can be seen in Figure 3, the public teacher's earnings grow at a slower rate than the comparable occupations. Note that other government workers’ wages grow at a significantly higher rate than do public teacher's salaries. Other government workers face significant risk through their professional life, possibly due to the uncertainty of obtaining retirement benefits and the lack of a civil service career in the public sector. But the public teachers ́ union has been effective
Policy Research Working Paper 10273

# **Abstract**

Government intervention to bail out troubled banks can produce a sizable fiscal contingent liability. Drawing on a rich history of various forms of staggered bailouts, this paper studies the link between bank bailouts and fiscal contingent liabilities using bank-level data for Kazakhstan—an upper-middle-income country in Central Asia. The paper first estimates the probability that a bank in distress is bailed out, conditioning on bank characteristics and financial soundness. Second, it estimates the magnitude of bailout costs depending on the size of banks, their ownership type, financial soundness, and the type of bailout instrument used by the government. The latter aims to contrast the fiscal costs when the government uses bailout instruments without recourse on bank future profits—such as government purchases of bad loans above market value—versus instruments that require repayment of the public support with adequate compensation for the government’s alternative costs—such as properly governed equity injections. Third, the paper illustrates how the estimations could be used for projecting the expected contingent liabilities from bank bailouts.

This paper is a product of the Finance, Competitiveness and Innovation 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 dmare@worldbank.org, mmelecky@worldbank.org, and hannamurina@hotmail.com.

_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
*|**Total**|
||Poorest tercile|19.9|14.7|8.5|43.1|
|||_(0.8)_|_(0.7)_|_(0.6)_|_(1.0)_|
||Middle tercile|8.0|13.0|11.3|32.3|
|**1994**||_(0.6)_|_(0.7)_|_(0.6)_|_(0.9)_|
||Richest tercile|5.2|7.6|11.7|24.5|
|||_(0.5)_|_(0.5)_|_(0.6)_|_(0.9)_|
||**Total**|33.7|36.8|29.5|100|
|||_(1.0)_|_(1.0)_|_(1.0)_||

**Note** : Estimation results are obtained based on total household income per capita. Linearized standard errors of cell percentages are in parentheses. The terciles are defined using the cross-sectional sample for each year. All numbers are deflated with December to December regional CPIs and weighted with population weights, where the first survey round in each period is used as the base year. Estimation sample size is 4941 panel individuals from the 5th and 13th round of the RLMS, 3719 panel individuals from the 13th and 24th round of the RLMS and 2478 panel individuals from the 5th and 24th round of the RLMS

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Finally, the mechanisms we have proposed so far abstract from any within-firm adjustment across products. As NTMs impose fixed costs per product and firm, in countries with high NTMs multi-product firms may focus on their core product, i.e. the product in which they have the largest market share; this implies that our results could be due to an extensive margin adjustment within firms, across products rather than within sectors, across firms. In Section C.16 of the online appendix we show that our results are not purely driven by multi-product firms’ ability to adjust their export portfolio. First, we show that controlling for the number of products exported by the firm leaves our results unchanged. Second, we show that our results still hold when focusing on a sample of single-product firms where such a channel of adjustment is _de facto_ absent.

# **6.2 Role of import shares**

An alternative way to test the mechanisms underlying our theoretical predictions is to look at market shares. Since we do not have market-share data (which is why we identify market power indirectly through pricing behavior in the first place), we attempt to proxy, albeit very imperfectly, the size of firms through their share of aggregate imports. Import shares are computed over destination _d_ ’s total imports of product _p_ , obtained from the BACI database, with products defined at the 4-digit level, and are evaluated at the beginning of the period.<sup>33</sup> Import shares are poor proxies for true market shares given that variations in the sales of domestic firms, which are key for the measurement of rent-shifting effects, are not observed. We try to go around the problem by instrumenting import shares with tariffs and NTMs. That is, our postulate is that the variation of import shares explained by the variation of tariffs and NTMs has something to do with the effect of trade policy on market structure.

This exercise has its limitations. First, we cannot directly instrument a firm’s import share with trade-policy
Two prevailing theories evaluate the link between political trust and citizen participation, but their perspectives differ greatly. On the one hand, the stealth democracy theory posits that trust in government is inversely related to citizen participation. Put another way, citizens only participate when they feel compelled to prevent corrupt politicians from embezzling the public. Individuals’ lack of trust in the government may push them to speak out in order to change the behavior of the authorities or replace them. Conversely, people who have confidence in the government see no reason to engage, as they trust those in power to do what is best for society. In sum, trust in government discourages citizen involvement (Hibbing and Theiss-Morse, 2002; Theiss-Morse and Hibbing, 2005; Lee and Schachter, 2019; Lavezzolo and Ramiro, 2018; Muhlberger, 2018). Deliberative theorists, on the other hand, believe that a trustworthy political system encourages citizens to participate in government processes. If people feel the political system is rigged or uncaring, they are less inclined to engage with it. Apathy stems from a lack of trust, while a responsive government prompts citizen participation (Lee and Schachter, 2019; Bächtiger et al., 2018; Neblo et al., 2010). Assessing which (if any) of the two theories most applies to Latin America and the Caribbean is not only an academic exercise but of policy relevance, given that most countries throughout the region display low levels of citizen participation.

While a growing body of research links citizen participation and accountability, few comparable empirical studies have investigated the relationship between citizen participation and trust. In one recent paper, Lee and Schachter (2019) investigate the relationship between trust in government and citizen participation in the United States using data from the World Values Survey. However, we did not find similar studies across developing countries in general and the LAC region in particular.<sup>2</sup> Hence, it is not possible to draw any conclusions about whether the above theories are borne out in the
2015 2018 2021<br><!-- End of picture text --> F. Persistence in potential growth estimates, 2000-19 <!-- Start of picture text --> Percent<br>1.2 PF MVF UVF Forecasts<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>World Advanced economies EMDEs<br><!-- End of picture text --> Source: World Bank.

Note: “PF” stands for production function approach, “MVF” for multivariate filter, “UVF” for univariate filter, and “Forecasts” for five-year-ahead growth forecasts from the IMF World Economic Outlook. “EMDE” = emerging market and developing economies. Aggregates refer to weighted averages ( constant real GDP weights at average 2010-19 prices and exchange rates).

A.B. Blue bars denote production function-based estimates. Orange whiskers indicate the range of eight estimates.

C. Graph shows the share of country year pairs during each period in which each methodology generates the highest or the lowest estimate of potential growth. Only country-year pairs are considered for which at least two methodologies are available. “UVF” stands for any of four univariate filters (Christiano-Fitzgerald filter, Baxter-King, Hodrick-Prescott, or Butterworth). Unbalanced sample of 30 advanced economies and 25 EMDEs for 1998-21.

20
survey approximately 18 months after the first participants were enrolled in the study and received an account. The endline survey was completed by 2,376 of the 3,136 respondents of the baseline survey.<sup>29</sup> Of the participants in this sample, 1,109 completed the full panel of baseline, follow-up, and endline surveys.<sup>30</sup> Table 1 reports summary statistics for our sample, using data from the baseline survey. The average respondent in our sample is female, has been employed at the firm for three years, and earns a monthly base salary of BDT 6,779 (US$80). On average, workers in our sample have completed only elementary school, 57% are not literate, and many workers in our sample also struggle with numeracy, as reflected in comparatively low average scores on a digit span test. While most respondents in our sample state that they plan to save, many are unable to meet their savings goals: 22% of our respondents would not be able to save BDT 5,000 for emergency expenses, 47% needed to borrow at the end of the month, and 65% report having difficulty sticking to financial plans.

Workers in our sample have very little experience using formal financial services and low trust in formal accounts. While a high share of workers stated that they planned to save, only 25% report had savings in any formal account at baseline. The median worker sends monthly remittances worth approximately 22% of her monthly base salary, indicating high demand for remittance services. While seventy-five percent had used a mobile payments platform to send money, very few used their own account – less than 1% had a balance in a mobile account. The use of informal financial services, on the other hand, is widespread: 33% of workers had informal savings, such as keeping cash at home or with local savings groups, while approximately half of all participants had informal loans outstanding, typically at
# 5. Results

We start this section with the description of the main outcomes of interest, including households’ vulnerability to poverty and simple comparisons by displacement status and access to PDS. Then we measure the impacts of the PDS program on household welfare by limiting the analytical sample to displaced households. Regression-based associations between access to PDS and the measures of welfare among displaced households using specification 2 are presented next. The probable causal impacts of the PDS on household subjective well-being, food security, poverty, vulnerability, and calorie intake using the PSM technique are presented at the end of the section.

## 5.1 Variable definitions and descriptive findings

Our analysis takes advantage of both the short-form and the long-form questionnaires of the SWIFT survey. The analysis focuses on household consumption, vulnerability to poverty, food security and selfassessment of current and future economic status. While the food security/household hunger and subjective well-being questions were answered by all the households (short-form), information on food consumption and expenditure was collected only on the long-form subsample.

Variables on subjective well-being are presented in Table 3 (Panel A). The survey collected information on what a respondent thinks of her household’s current economic condition, how is it compared to the past condition, and her perceived outlook for the next two years. We combine all three binary variables into one by simply adding them up and call it the subjective well-being index (row A4). Not surprisingly, the displaced paint a gloomier picture about their current economic situations compared to the non-displaced Iraqis (rows A1 and A2). However, they are more optimistic about their future economic prospects (row A3). Responses are more homogeneous among the displaced households regardless of their access to the PDS program.

The SWIFT survey asked four questions regarding household food deprivation in the last 30 days, namely: if any member in a household had to eat fewer meals, if the household ever had no food of any kind to eat, if member(s) went to bed hungry,