rafmacalaba/datause-encoder-data huggingface dataset:
Gawande (2013) describes how Bangladesh attacked the problem. A pilot project team set out to reach 60,000 women in 600 villages: They travelled on foot, pitched camp near each village, fanned out door to door, and stayed until they had talked to women in every hut...Initially, the workers taught up to 20 mothers per day. But monitors visiting the villages a few weeks later found that the quality of teaching suffered on this larger scale, so the workers were restricted to 10 households a day. Then a new salary system was devised to pay each worker according to how many of the messages the mothers retained when the monitor followed up. The quality of teaching improved substantially. The field workers soon realized that having the mothers make the solution themselves was more effective than just showing them. The workers began looking for diarrhea cases when they arrived in a village, and treating them to show how effective and safe the remedy was.

When the Government of Bangladesh took the program nationwide, it hired, trained, and deployed thousands of workers, region by region, who went door-to-door to show 12 million families how to save their children. As Gawande describes it, “The program was stunningly successful...the knowledge became self-propagating. The program had changed the norms.” Surveys showed that nearly 80 percent of recent diarrhea episodes were treated with the solution. Most women in Bangladesh now teach their children about oral rehydration, eliminating the need for repeated campaigns. But in countries that adopted an arm’s-length approach, the information campaign failed almost entirely. Policy makers who are asked to remedy a “scarcity of understanding” about diarrhea treatment might reasonably assume that they need simply to get the right information to the right people; it is not clear that the policy makers would come up with anything close to what happened in Bangladesh.

Our concerns could be dismissed as a quibble over language, but a theme of behavioral economics is that language and labels make a difference for understanding. This is especially true in fields that are closely tied to policy. In an academic setting, common training and frequent interaction among individuals means that a nuanced understanding of ideas can more 27
are in stark contrast to those reported above for NIT, ICRG, CPIA and EIU. Among all of the WEF and BEEPS indicators, the “obstacle” variable is most highly correlated (.83) with the WEF “diversion of public funds.” This result is somewhat surprising, as firms are not well-placed to have first-hand knowledge on diversion of public funds, in contrast to their frontline position with respect to many forms of administrative corruption, state capture and procurement fraud.

The EIU and ICRG indicators are produced by commercial firms specializing in assessing risk to overseas investors. They might therefore focus on assessing corruption conditions faced by foreign-owned companies, which may sometimes differ from those faced by domestically-owned firms. Accordingly, we re-calculated all of the countrylevel BEEPS corruption measures using only the 12% of firms that were majority foreign-owned. On average, the EIU indicator is no more highly correlated with these BEEPS measures than with those calculated using all firms. If conditions facing foreign owned firms are different, the EIU does not appear to measure those differences effectively. Most correlations between BEEPS questions and ICRG, however, are higher (by .05 on average, in absolute value) when BEEPS measures are calculated only for firms that are majority foreign-owned. The correlations with ICRG strengthen the most for BEEPS questions on bribes paid for utility connections, and for environmental, health and safety inspections. Bribe _frequency_ for foreign-owned firms is no different on average than for other firms, but the average bribe _tax_ they report is slightly lower.

The correlations of broadly-defined corruption measures with more specific questions in the BEEPS and WEF firm surveys described above can help reveal what information underlies subjective judgments regarding corruption. Correlations of the 40
# **8 Conclusions**

Severe climate and environmental related natural disasters can have long-lasting negative effects on bank asset quality. Using a large panel dataset and the local projection method, this paper presents a comprehensive analysis on the effects of disaster events on NPLs and other important banking sector variables, and also discusses policies to monitor and mitigate these adverse effects. Severe disaster episodes are estimated to increase system-wide NPLs by between 0.5 and 0.6 percentage points after two to three years. The estimated effects are found for multiple regions across the developing world and for most income groups and four different types of disasters, although cross-country results are less significant for climate related disasters than for environmental ones. To partially address important caveats of the cross-country results, including data quality and granularity, we also look at a country-specific example of the effect of typhoons on NPLs in the Philippines and find the same relationship holds, but the estimated effects are much larger. In addition, we also present tentative evidence that climate change and environmental related disasters could reduce bank intermediation efficiency, lower system-wide liquidity relative to deposits and increase the regulatory capital ratio, although they may not necessarily reduce the overall volume of lending to the private sector. While we do not identify any specific transmission channel, our results point to multiple channels from physical risks to financial stability at work, including through credit risks, operational risks and liquidity risks.

Future extensions using more granular data would be promising. To arrive at a generic estimate using the largest possible sample, we have chosen to work with data at the country-year level. This has very likely biased us against finding stronger results, as disasters tend to affect a specific geographical area within a country disproportionately, and financial institutions serving specific areas. In general, stronger results are likely found at the more granular geographical level. While we try to mitigate this concern with the case study of the Philippines, a global sample using balance-sheet-level loan data and geographical NPL data would potentially improve the results while allowing us to explore in more detail the particular transmission channel at work. Promising work in this direction has already been undertaken for the LAC region in Calice and Miguel Liriano (2021).

17
|**Table 3: Correlates of Changing Mi**|**gration Inte**<br>Chang<br>|**ntions due**<br>e in Europe<br>|**to COVID**<br>Migration In<br>|**-19**<br>tention<br>|Chang<br>|e in Senegal<br>|Migration I<br>|ntention<br>|
|---|---|---|---|---|---|---|---|---|
||Less|Likely|More|Likely|Less|Likely|More|Likely|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Age|0.005**|0.005**|-0.000|-0.000|0.002|0.002|-0.000|-0.000|
||(0.002)|(0.002)|(0.001)|(0.001)|(0.002)|(0.002)|(0.001)|(0.001)|
|Speaks English|-0.024|-0.020|-0.018|-0.017|-0.023|-0.019|-0.002|-0.002|
||(0.030)|(0.030)|(0.011)|(0.011)|(0.030)|(0.030)|(0.009)|(0.009)|
|Schooling Grade|0.004|0.004|0.002|0.002|0.006*|0.006*|-0.001|-0.001|
||(0.003)|(0.003)|(0.001)|(0.001)|(0.003)|(0.003)|(0.001)|(0.001)|
|Skipped a Meal at Baseline|0.077***|0.078***|0.005|0.005|0.040|0.039|0.010|0.011|
||(0.027)|(0.027)|(0.011)|(0.011)|(0.028)|(0.028)|(0.008)|(0.008)|
|Asset Index|-0.008*|-0.009*|-0.002|-0.001|-0.012**|-0.012**|0.004***|0.004***|
||(0.005)|(0.005)|(0.002)|(0.002)|(0.005)|(0.005)|(0.002)|(0.001)|
|Baseline: received remittances|0.013||0.021***||0.018||-0.008||
||(0.021)||(0.008)||(0.021)||(0.006)||
|Baseline: worked for pay|-0.030||0.002||-0.019||-0.002||
||(0.022)||(0.008)||(0.023)||(0.007)||
|Baseline: income earned|0.000||0.000||0.000||0.000||
||(0.000)||(0.000)||(0.000)||(0.000)||
|Intended to Migrate pre-COVID-19|0.064***|0.063***|0.031***|0.031***|0.178***|0.178***|0.015**|0.015**|
||(0.020)|(0.020)|(0.008)|(0.008)|(0.021)|(0.021)|(0.006)|(0.006)|
|Maybe would migrate pre-COVID-19|0.099***|0.098***|-0.018|-0.019|0.193***|0.192***|0.003|0.003|
||(0.023)|(0.023)|(0.012)|(0.012)|(0.024)|(0.024)|(0.008)|(0.008)|
|Stopped receiving remittances||0.061**||0.027***||0.049*||-0.014|
|||(0.024)||(0.009)||(0.025)||(0.009)|
|Started receiving remittances||0.000||-0.000||-0.033||-0.031**|
|||(0.030)||(0.012)||(0.030)||(0.015)|
|Stopped working for pay||-0.004||0.011||0.019||0.000|
|||(0.022)||(0.008)||(0.022)||(0.006)|
|Started working for pay||-0.015||0.001||-0.006||-0.022|
|||(0.031)||(0.012)||(0.031)||(0.015)|
|Sample Size|2661|2661|2661|2661|2627|2627|2627|2627|
|Dependent Variable Mean<br>Notes: Coefficients shown are marginal<br>*, **, *** denoting significance at the 1<br>Odd-numbered columns only use pre-d<br>between 2019 and 2020.|0.31<br>coefficients f<br>0, 5, and 1 p<br>etermined va|0.31<br>rom probit e<br>ercent level<br>riables pre-|0.04<br> <br>s respective<br>COVID-19;|0.04<br>ly.<br>even-colum|0.34<br>ns add cha|0.34<br>nges in some|0.02<br>variables|0.02|

26
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model illustrates that there is no significant impact of public debt on long-term government bond yields. This implies that domestic investors are insensitive to expected public debt. In other words, domestic investors may not require a risk premium against large debt when there is strong home bias without any other assets hedging fiscal risk, which is line with Japan’s debt literature (Sakuragawa and Sakuragawa (2016)).

Figure 8 shows the range of the time-varying impacts of expected public debt-to-GDP ratio on the interest rate during 2006H2-2018H2. Given that the share of foreign private investors in the small open economy is easily affected by the capital flows, sovereign risk can be volatile over the short-term. On the other hand, the corresponding impact would be small and stable in large economies or countries with capital control or higher home bias because the share of foreign private investors is low. According to the upper panel, a problem with the IT model is that some estimated impacts are negative in countries where the share of private foreign investors is low. This unreasonable result suggests that a highly non-linear model is appropriate. The middle and upper panels show there are no negative estimates from the PSTR and GPSTR.

# **3.1.2 Marginal impact of the share of foreign private investors**

Another parameter of interest here is the coefficient on the lagged share of foreign private investors; _훿_ . According to Table 2 and consistent with the literature (Arslanalp and Poghosyan (2016) and Ebeke and Lu (2015)), an increase in foreign private holdings of government debt is associated with a reduction in the interest rate as downward pressure on long-term interest rates results from expansion of market liquidity.

Figure 7 shows the marginal impact of the share of foreign investors on long-term interest rates (<sup>_휕_</sup> _휕푓푝_<sup>E</sup><sup>_푡퐿_</sup> _푖,푡_<sup>_푖,푡_</sup> −<sup>+</sup> 1<sup>_푛_) .</sup> The left panel of Figure 7 from the result of the IT model shows that this impact varies, depending on the public debt to GDP ratio. The expected public debt-to-GDP ratio E _푡 퐷푒푏푡푖,푡_ + _푛_ is estimated to be about 120 percent of GDP when the marginal impact of the share of foreign private investors on the interest rate is zero.<sup>19</sup> That is, although an increase in foreign private holding of government debt is associated with a reduction in interest rates, this downward effect would be reversed if the expected public debt-to-GDP ratio exceeds this threshold.

The right panel of Figure 7 from the GPSTR model shows that the marginal impact of the share of foreign investors on long-term interest rates also depends on the composition of debt, as well as the expected public debt. Expected public debt E _푡 퐷푒푏푡푖,푡_ + _푛_ triggers a surge in government bond yields through an increase in the 19The threshold can be calculated from E _푡 퐷푒푏푡푖,푡_ + _푛_ = − _훽_ 1 _<u>훿</u>_<sup>in Table 1.</sup> 14
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30
The second set of decompositions is along geographical lines. A guiding principle of most public education systems is that of standardization and equity. Indeed, in Pakistan, as in most other countries including India, _inputs_ into the educational system are driven by this principle—salaries for teachers for example are dictated almost entirely by experience rather than ability or motivation. The principle of “equality” may also imply that _outcomes_ , and in this case, a child’s learning be the same, regardless of the village she is born in or the public school she attends. Thus, in addition to child characteristics, we also look at differences across villages and across schools within the same village. Given the focus of the LEAPS project in Pakistan, we also present the gap between private and government schools. In India, the data do not allow us to look either at the difference across private and government schools or across schools in the same village (there is currently _no_ dataset that allows us to replicate the Pakistani analysis). A useful characterization though, is the difference across districts; despite the lack of comparability with the Pakistani geographical decomposition the results have independent implications for the design of future monitoring systems.

The technical issue is what test scores to use in the subgroup decompositions. One problem with the most natural candidate—the percentage of questions correctly answered in the test—is that the measured gap depends on the structure of the test. An example illustrates. Consider a test where there are ten questions on single-digit addition, five questions on double-digit addition and five questions on fractions. A child who knows single-digit addition will score 50 percent on the test, one who knows double-digit addition scores 75 percent and one who knows fractions 100 percent. Using the percentage of correct questions leads us to conclude, somewhat erroneously, that the gap between the first and the second child (25 percent) is identical to that between the second and the third child (also 25 percent), whereas its likely that the knowledge-gap between the second and third child (who can correctly answer questions on fractions) is a lot more than between the first and the second. Studies of learning achievement in low-income countries typically report results using a “standardized” version of the percentage correct (i.e., subtracting the mean and dividing by the standard deviation), but this approach suffers from the same problems.

The established norm in all test-score reporting in the United States or in international tests such as the TIMSS is to _weight_ questions by their difficulty. Continuing with the example above, if the single-digit questions are weighted as “very easy”, the double-digit as “easy” and the fractions as “very hard”, the weighted averages scores the child who completes the fractions much higher compared to one who completes the double-digit addition. The process of assigning these weights

- 14 -
A limitation with using partial equilibrium analysis to assess the impact of price rises is that it does not take into account the economy-wide impacts of changes in relative prices. There are different ways to look at the issue, starting from simple simulations using Social Accounting Matrices (SAMs) to more complex Computable General Equilibrium (CGE) models. SAMs have for example been used to trace the multiplier effects of the recent price shocks in Africa (e.g. Nganou et al. 2008; Parra and Wodon 2008), but before reaching any firmer conclusions it would be better to have the results of more complex CGE models that can accommodate induced impacts such as changes in labor markets, the knock-on impacts on other industries from changes in wages in selected sectors, and the effects on outputs and prices of these industries. It is because only limited work on the impact of the recent food price crisis has been done to date with CGEs in sub-Saharan Africa that we focus the following discussion of the African experience on simple partial equilibrium analysis.

By contrast, much more CGE work is available for Asian countries. For example, a CGE model was used in Indonesia to assess the impact of changing rice tariffs (SMERU 2003). The results suggested that the overall welfare impact from the proposed change in rice tariffs was small in relation to average household income, but that this effect varied significantly across household groups. Specifically the increase in real wages for unskilled workers was insufficient to make up for the increased living costs for the poor. Imports of rice were actually banned in Indonesia after 2004. Warr (2005) found that the ban raised the price of domestically produced rice which led to an increase in poverty (see also Timmer and Dawe 2007 on the Asian experience with rice policies). Another paper on Indonesia by Sumarto et al. (2005) suggests rice subsidies helped to reduce household vulnerability (see also previous research on Indonesia by Ravallion and van de Walle 1991). In Vietnam, Niimi et al. (2004) and Minot and Goletti (2000) found that liberalizing rice exports helped reduce poverty due to higher production and despite an increase in domestic prices.

# **_2.2. Global Estimates_**

Clearly, an increase in food prices will have uneven impacts across countries and population groups. At the macroeconomic level, countries that are net food exporters will experience improved terms of trade, while net food importers will face increased costs in meeting domestic demand. There are about four times more net cereal-importing countries in the world than net exporters (IFPRI 2007). Almost all countries in sub-Saharan Africa are net importers of cereals, and therefore likely to be affected negatively. The vulnerability of poor households is also related to the extent that locally consumed staples are traded on international markets. This varies from 24% in Ethiopia to 64% in Vietnam (World Bank 2007).

Price rises hurt net consumers of food and the 2007 World Development Report provides estimates on the share of households who are net sellers or buyers of staple food. The data suggest that in four out of
- ➢ FDI in sector _u_ could generate indirect effects on sector _j_ through forward linkages. Javorcik (2004) and Newman et al., (2015) show that a foreign firm in the upstream sector _u_ can also transfer improved technologies to the domestic affiliate in the downstream sector _j_ . In our conceptual framework, there is no reason to believe that country border matters. Therefore, FDI in the sector _u_ in a foreign country _f_ might also have effects on sector _j_ in the country _i_ .

- ➢ FDI spillovers could also take place through backward linkages. FDI in sector _d_ could affect sector _j_ . Javorcik (2004) provides an anecdotal case of the Czech producer of aluminum alloy castings and its multinational customer. In this case, the staff from the multinational firm would visit the domestic suppliers two days in each month, and the domestic suppliers applied these improvements to its other production lines. FDI in the foreign downstream sector _d_ in country _f_ might also generate spillovers to sector _j_ in country _i_ .

In the next section we want to test the strength and causal relations of the arrows depicted in Figure 5 in the last decade.

# 4 Data and Metrics

## 4.1 Data

To implement the empirical analysis, we link data on FDI flows across countries available since 2003 with data on input and output linkages. The main sources are fDi Markets data monitored by the Financial Times and the MRIOT compiled by the Asian Development Bank.

- ➢ The fDi Markets is the most comprehensive online database of cross-border greenfield investments; the database covers all countries and sectors. For a specific FDI project, it documents the source and destination country, the destination sector, the number of jobs, the amount of investment, and the industry classification.

- ➢ For the measure of the GVC participation of a sector in a country, we need a data set on international input-output tables (IOTs). The Asian Development Bank’s Multi-regional input-output tables (MRIOT) version published in 2019 covers 61 individual economies-and the rest of world as one group for a total of 62--and 35 sectors. It is the most appropriate dataset because it has the broadest coverage of countries (especially for developing Asia)