rafmacalaba/data-use-mentions-extended huggingface dataset:
A weather product based on normal seasonal rainfall is a special case of a weighted rainfall index, where all the weights are chosen to be the same. In general, a weighted rainfall index may be defined as where denote the weights, denotes realized rainfall and is typically chosen to be the average historical rainfall. Products based on such indices have been sold by ICICI Lombard in Kharif 2003 and AICI and IFFCO Tokio in Kharif 2004 and 2005 (see Table 4).

Weighted rainfall indices offer more flexibility to a designer than indices based on total seasonal rainfall, but suffer from similar limitations. In particular, a weighted rainfall index provided scope for cross-subsidization across different rainfall periods. For example, a large volume of rainfall in a period of low significance (weight) could compensate for poor rainfall in a period of high significance (weight). Application of daily caps (ceilings) for rainfall volume could partly address this constraint at the cost of further complicating the index.

# <u>Multiple phase weather index</u>

In response to the farmer feedback collected after the first pilot, ICICI Lombard developed a rainfall index with multiple phases for the Kharif 2004 season, described in Giné et al (2010). For such an index the growing season is divided into sequential phases of varying duration, typically chosen to correspond to the crop-growth stages as defined by crop calendars and other reference sources for agronomy. For each phase, the schedule of payments is typically piecewise linear in the total rainfall in the phase, reflecting the water requirements of the crop in that phase, and with payments only if the total rainfall in the phase is sufficiently low (or high).

For example, Table 5 provides an example of a product for which payments are triggered if the total rainfall in the phase is below the rainfall trigger , linearly increasing to the maximum payment of for . Such a schedule would lead to a claim payment as follows: This design could be more intuitive to potential policyholders by clearly associating each critical crop-growth phase
.43||2.46|2.35||-0.05|0.08|
|Wage employment|3.53|3.44|**|3.24|3.37||0.29***|0.07*|
|Ganyu employment|2.56|2.47||1.94|2.11|***|0.62***|0.56***|
|**Log annual hours, among employed**|||||||||
|Wage employment|6.82|6.87||6.44|6.75|**|0.38***|0.12**|
|Ganyu employment|5.53|5.72|***|5.15|5.33|***|0.38***|0.39***|
|**Log annual earnings, among employed**|||||||||
|Wage employment|12.63|12.80||12.40|12.53||0.23|0.27***|
|Ganyu employment|10.72|10.90|**|10.02|10.16|*|0.70|0.74***|
|Observations<br>**Notes:**|1,966|8,163||2,411|9,270||||

IHPS = individual interviews; IHS4 = business-as-usual survey approach. Significant differences are indicated with * (p<0.1), ** (p<0.05) and *** (p<0.01). Estimates are weighted with the response weight.

† indicates that means are calculated among those involved in crop agriculture (
the reference evaluation measure, will not necessarily affect the ranking of different types of models within the same context. But it explains much of the wide variation in R<sup>2</sup> observed in Table 1 across different studies, which underscores the benefits of evaluation studies that compare different methods in the same context, using the same reference measure.

Gualavisi and Newhouse (2022) offer another stark example of how sensitive predictive accuracy is to the source of training data. Using a census extract from 10 districts in Malawi, the analysis compared estimates of average village welfare imputed into a household census with estimates derived from combining a survey with publicly available geospatial indicators. However, it also considers a third option, which involves hypothetically supplementing the survey with a partial registry, a “microcensus” that interviews all households in a randomly selected 450 of the 4,500 villages with geolocated data. This involves a two-step approach, where welfare is first predicted into the partial registry and then a geospatial model is trained against the partial registry predictions.

Using a partial registry in this way yields an R<sup>2</sup> of 0.35, as opposed to 0.01 for the geospatial poverty map based on survey data and 0.02 for the wealth estimates from Chi et al. (2021). These R<sup>2</sup> values are much lower than those cited in the previous section. The weak correlation between the Chi et al (2021) estimates and these census-based predictions reflects the challenge of distinguishing between village welfare levels in this context. In particular, the sample consists of 4,500 villages, in 10 poor Malawian districts, for which names could be matched between the census and the Unified Beneficiary Registry data containing household geocoordinates. In this context, the Chi et al. (2021) estimates may perform poorly because they come from a model trained to wealth while the benchmark reference indicator is per capita consumption; these are conceptually different measures of welfare
, average travel time and distance increase by a factor of 1.35 and 1.38 respectively. This is globally aligned with the key findings from Angel and Blei<sup>79</sup> which demonstrate that when US cities double in population, travel time and distance only increase by a factor of 1.07 and 1.13 respectively. The authors explain the less than proportional increase as the result of the effects of spatial adjustments including increasing residential density, locational adjustments of residences and workplaces, and increasing commuting speeds brought about by transit system shifts on the average travel distance and time. This supports our OD sampling approach as a reasonable method to capture representative trips within settlement clusters.

Initial aggregated summaries of mean travel time at the country level reveal a discrepancy between developed and developing regions: countries in less developed (Tanzania, Mozambique, Zambia, Niger, Zimbabwe) and emerging economies (China, India, Philippines, Cambodia, Indonesia) have longer mean travel time compared to those in developed regions (United States, Canada, Sweden, Finland) (Fig. 5d). The standard deviation of mean travel time summarized at the country level shows a higher dispersion of average travel time values in developing countries, especially in East Asia, Southeast Asia, and Africa (Fig. 5e). Due to the discrepancies in the total number of settlement clusters in different countries (Fig. 4), country-level result summaries are more robust in countries with more clusters and less robust in countries with few clusters (e.g., African countries).

> 10 Results from sensitivity tests on show that the threshold value of 0.1 s or below yields the most consistent (variance of average travel time between 100 runs < 1s) across 100 sampled clusters. The chosen value of 0.01 s in relatively conservative. εε

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strategy might be to explore how movements in high-frequency variables, such as spreads on the interbank market or on commercial paper issued by banks, stock market valuation of banks, and corporate vulnerability, move before the onset of crises. Significant data collection efforts are needed to make this type of exercise feasible for a large sample of countries, however.

# **5. Studies of the determinants of banking crises**

Following the early studies by Kaminsky and Reinhart (1999) and Demirgüç-Kunt and Detragiache (1998), work on the determinants of bank fragility has proceeded on several fronts. Most of the studies use the multivariate limited dependent model, while the signals approach has remained more popular in applications aimed at constructing early warning systems. In this section we summarise some of this work, organising the material based on the category of explanatory variables investigated.

# _Individual bank measures of fragility and systemic crises_

The literature on early warnings of individual bank failure is well established, with empirical studies dating back to the early 1970s. This literature uses bank balance sheet and market 10 information to explain and forecast the failure of individual institutions. A few studies have adapted this approach to study systemic banking crises. For instance, González-Hermosillo (1999) uses bank-specific as well as macroeconomic data to investigate episodes of banking distress in different regions of the US and in two countries, Mexico and Colombia. She finds that non-performing loans and capital asset ratios often deteriorate rapidly before bank failure. This study also explicitly investigates how individual bank failure can be affected by overall fragility in the banking sector, and finds little evidence of such contagion.

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Meehl et al., 2007) and decrease the occurrence of dry years in Tanzania (Table 7). These results suggest that Tanzania could experience decreased agricultural stress from precipitation deficits in the future.

However, dry years do persist in Tanzania through the 21<sup>st</sup> century (Table 7). Quantifying the cooccurrence of these dry years with non-dry years in Tanzania’s key trading partners indicates the likelihood that Tanzania’s trading partners will experience non-adverse climate conditions in the same year that Tanzania experiences adverse conditions, therefore offering the potential for Tanzania to ameliorate adverse conditions in Tanzania through increased imports. Analysis of the CMIP3 archive of global climate model simulations indicates that those dry years that do occur in Tanzania in the 21<sup>st</sup> century will often coincide with non-dry years in Tanzania’s key trading partners both within and outside of Africa (Table 8). These results suggest that importing grains from other trading partners could help to alleviate negative effects of severe dry conditions going forward in the 21<sup>st</sup> century, particularly for a mix of trading partners that can help to hedge against the coincidence of severe dry years both within and outside of Africa. In particular, our analysis identifies Sudan as the trading partner within Africa that is most likely to consistently experience non-dry conditions in the years in which Tanzania experiences dry conditions, and Kenya, Mozambique and Zambia as the least likely. Likewise, our analysis identifies Canada, China, the Russian Federation, and the USA as the trading partners outside of Africa that are most likely to consistently experience non-dry conditions in the years in which Tanzania experiences dry conditions, and Mexico and Australia as the least likely.

The CMIP3 suite of global climate model projections also suggests that further global warming is likely to increase the occurrence of dry conditions in many of Tanzania’s trading partners, and to alter the co-occurrence of dry years in Tanzania’s trading partners with non-dry years in Tanzania as Tanzania
One paper that stands out in the recent literature and is closely linked to our paper is Jotikasthira et al. (2012). Their paper shows that movements in investor flows force significant reallocations in equity fund portfolios related to emerging markets, which in turn affect equity returns, correlations among emerging markets, and the developed market betas of emerging markets. These effects are particularly acute during periods of financial distress. Their paper provides important evidence on the role that mutual funds play in emerging markets and how they transmit shocks across international markets through their impact on returns. Two other earlier exceptions that are also good complements to our paper are Kaminsky et al. (2004) and Hau and Rey (2008). Kaminsky et al. (2004) study momentum trading by investors and managers. Hau and Rey (2008) use data on equity funds to analyze whether foreign exchange and equity risk measures trigger rebalancing behavior at the fund and stock level.<sup>5</sup> In this paper, we use a micro-level dataset on international mutual funds to shed new light on how investors and managers react to shocks and crises and how they impact capital flows through their investment reallocations across developed and emerging countries. International mutual funds are especially useful because they enable us to analyze separately: (i) injections/redemptions driven by the underlying investors; (ii) actual portfolios across countries that are allocated and rebalanced at the sole discretion of managers (and do not need to be inferred from other data); (iii) their interactions (how investors monitor managers); and (iv) the relative contribution of investors and managers to capital flows.<sup>6</sup> The main data consist of portfolio weights and assets invested in each country around the world for 1,076 equity and bond mutual funds on a monthly basis during 15 years, from January 1996 to November 2010. The data cover portfolio allocations to 124 developed and emerging countries and cash, plus fund returns that allow us to obtain
auxiliary data drawn from the population, such as census or administrative data. Furthermore, this source of model-based bias disappears when considering design-model bias, taking the expected value of the predictions prior to drawing the sample. Omitted variable bias is therefore not a relevant concern when selecting between different types of models.

Corral et al. (2022) nonetheless recommend the use of an area-level model rather than a householdlevel model when the predictors are available at the sub-area level, largely on the basis of results from a particular model-based simulation. This model-based simulation takes a simple random sample of households from all PSUs in the population. When the model-based simulation is altered to use a more realistic two-stage sample design in which a subset of PSUs are selected, the household model with subarea means generates more accurate predictions than the area-level model.<sup>3</sup> Using a two-stage sample effectively increases sampling error in the direct estimates, which in turn increases the benefit of using more geographically disaggregated auxiliary data to fill in the geographic gaps of the sample. Using a one-stage sample, on the other hand, makes the direct survey estimates more accurate, which favors area-level models in this case. This partly illustrates why it is important to be careful before inferring general results from particular model-based simulations (Tzavidis et al, 2018).

Relative to the area-level model, the household model benefits from using auxiliary data at the sub-area level. The greater variation of more spatially disaggregated data is particularly important when using algorithmic variable selection methods such as LASSO or stepwise regression to select models, which is increasingly common among practitioners. The availability of sub-area variation also becomes more

> 3 Code is available upon request from the author.

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As unit of observation, we use what the UNHCR refers to as the “case”. A case is a group of individuals who register at the UNHCR together with a principal applicant (PA) who takes responsibility for the group. This group may be a family, a household or an extended household. For simplicity and practical purposes, we will consider a case and the PA as a household and its head respectively. The poverty line used is 50 JD/month/person, which is what the UNHCR used in 2014 to select beneficiaries of the cash assistance program. In 2014, this poverty line was higher than the international poverty line and lower than the poverty line used for the Jordanian population. In our case, this poverty line is more relevant than either the national or international poverty line, as it corresponds to what the UNHCR—the UN agency specialized on refugees— considers a sufficient amount to meet basic needs. As for the welfare aggregate, we use the same aggregate used by Verme _et al._ (2016) and Verme and Gigliarano (2019).<sup>8</sup>

# **III.2. Estimation Results**

For the purpose of this paper, the HV data are considered the “survey” data containing information on consumption and the PG registration data are our “census” data containing predictors of consumption but no consumption data. The primary objective of the exercise is, therefore, to test how accurate the estimated poverty figures are using the PG data alone (as both the base and the target survey).

As a first step, we generated two samples by extracting 50% of observations from the HV sample randomly (Sample 1) and using the remaining observations as second sample (Sample 2). We then impute from Sample 1 to Sample 2 to obtain the imputation-based poverty rate in Sample 2, and we compare this imputed poverty rate with the true poverty rate that can be directly calculated from Sample 2 for robustness checks. We also
calculate seed values. To account for the highly noisy nature of the price estimates, median EA-level input and output prices are calculated within each category (e.g. median EA-level maize price per kg, median hourly wage of male agricultural workers). If the number price observations are below a threshold of 10, median prices are calculated across the next larger administrative unit. If the number of observations in that larger administrative unit is also below 10, the next larger unit is chosen, and this process is repeated up to the national level. The prices are then multiplied by the output and input quantities recorded on the plot, which have themselves been converted to standardized quantity units such as kilograms. The resulting variables are in local currency units (LCUs). We provide another set of variables which are converted into USD using an exchange rate at the year of the survey and deflated to 2020 dollars values. T</mark> he CPI and exchange rate data are drawn from <mark>World Bank collections of development indicators and consist of yearly time series, made available through the World Bank Open Data Initiative.</mark>

- “Main crop” types are defined to facilitate inter-country comparisons with the plot-level dataset. Each crop is classified into the following categories: barley, wheat, rice, sorghum, maize, millet, perennials (including fruit and tree crops), legumes, root crops, nuts and a catch-all “other” category. In case users want to define their own categories, the exact names of the crops (i.e., as they appear in the raw data) are maintained in the plot-crop dataset.

- The plot-level dataset in Uganda is effectively at the _parcel_ level, as GPS measurements were obtained at this level of granularity. This also allows users to merge across seasons, since parcels

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Figure 2: Zero-transport expenditures and GPD per capita <!-- Start of picture text --> Sierra Leone<br>Albania<br>Malawi<br>Ethiopia<br>Niger<br>Uganda<br>Mali<br>Iraq<br>Nigeria<br>Afghanistan Nicaragua<br>Burkina Faso<br>Nepal Tanzania Mongolia<br>Cameroon<br>Pakistan<br>Kyrgyz Republic<br>South Africa<br>6 7 8 9<br>Log of 2015 gdp per capita<br>80<br>60<br>40<br>20<br>Percentage of household with zero expenditure on transport overall 0<br><!-- End of picture text --> Note: Authors’ calculations based on LSMS surveys.

Figure 3 uses LSMS household data to plot the share of households with null transport expenditures against household expenditures per capita within country. The dashed lines show the density of households by expenditure level. As households get richer, they increasingly start spending on transport and the share of households with null expenditure decreases. Differences across countries depend on income levels but also exogenous factors such as the weather and geography, the distribution of population and economic activities, and the supply of transport goods and services. The LSMS sample covers countries with different levels of GDP per capita. As a result, for some countries including Malawi, Niger, Sierra Leone or Uganda, a substantial share of households has yet to move through the income levels associated with non-zero transport consumption.

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_28_ <!-- Start of picture text --> Unfunded Govemrnment Benefit Obligations<br>under the Civil Service Retirement Plan<br>(% of concurrent GDP)<br>40<br>30.1<br>20 19.5<br>10.6<br>1 D 0<br>0<br>PLnsion Post Total<br>Retirenrent<br>Health Care<br><!-- End of picture text --> Note: Government pension liabilities are 77.5 percent of total estimated obligations. Sources: Data provided by Taspen and staff calculations.

_Obligations to be Paid out of_ **_the Pension_** _Assets_ In principle, workers' contributions, and the attendant investment income, are used to pay for all of the THT benefits and 22.5 percent of the pension. The pension assets are managed by Pt. Taspen. In practice, however, these assets fall considerably short of the corresponding actuarial liabilities. The shortfall may be attributable to:

# (a) incompatibility of contributions and benefits;

# (b) low returns on investment; and high administrative costs.

Under the THT program, the assets available were about Rph. 2.7 trillion as of year-end 1994, while the accrued liabilities are estimated to be Rph. 6.2 trillion, implying an unfunded amount of Rph. 3.5 trillion.

Under the pension plan, the assets amounted to Rph. 6.3 trillion, while the accrued liabilities were Rph. 21.4 trillion (equal to 22.5 percent of total pension obligations), leaving unfunded liabilities of Rph. 15.1 trillion, as shown in the chart below.
index measures rules and practices affecting the coverage,<br>scope and accessibility of credit information available through either a public credit registry<br>or a private credit bureau. The public credit registry coverage indicator reports the number<br>of individuals and firms listed in a public credit registry with information on their<br>borrowing history from the past 5 years.|
|`o`<br>The private credit bureau coverage indicator reports the number of individuals and firms<br>listed by a private credit bureau with information on their borrowing history from the past 5<br>years.|
|<br>Enforcing contracts: the efficiency of the judicial system in resolving a commercial dispute.<br>`o`<br>The list of procedural steps compiled for each economy traces the chronology of a<br>commercial dispute before the relevant court.|
|`o`<br>Time is recorded in calendar days, counted from the moment the plaintiff decides to file the<br>lawsuit in court until payment.|
|`o`<br>Cost is recorded as a percentage of the claim, assumed to be equivalent to 200% of income<br>per capita.|
|Source: The World Bank, DoingBusiness|

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,969 million in 2004, and, after Vietnam’s accession to the WTO in January 2007, increased at an accelerated pace to $80,714 million in 2008. Since United States goods enjoyed MFN tariff status in

> 12 The implicit assumption of this exercise is that export products are typical of their industries. However, Figures 4.1-4.3 may be understating the labor content of exports if the exports were relatively more labor-intensive than other non-exported products within the same industries. On the other hand, the figures may be overestimating the labor content of exports as exporting firms tend to be more productive and thus use less labor to produce a given value of output than non-exporters.

> 13 Some household enterprises sold directly to the international market. Out of 4,326 households who reported nonfarm household activities in the VHLSS 2004, 50 households (about 1.2 percent) responded affirmatively to the question asking whether they “have sold goods/services on international market”.

8