rafmacalaba/datause-encoder-data huggingface dataset:
These differences across shocks and regions in our sample relative to the reported incidence in EMDAT may reflect data availability and our restriction to studies in English.

Most studies use data at the micro level— household (35 percent) or individual (21 percent)— while 38 percent use data at the country level and 27 percent at the subnational level (Table 1). These shares are not mutually exclusive since some studies provide estimates using data at different levels. At the estimate level, 54 percent use data at the country level to estimate the poor’s exposure to climate shocks and almost 40 percent use country-level data to estimate the adverse impact of climate shocks on the poor (Table 2). The channels through which the poor are more adversely affected by climate shocks than the non-poor include income reduction (almost a third of estimates), aggregate human capital losses (17 percent of estimates), mortality, poor health, and food insecurity (about 6 percent of estimates each).

# _3.2 Climate shock exposure_

More than two-thirds (68 percent) of estimates found that the poor are statistically significantly more exposed to climate shocks (Table 2). Of the estimates that are not from the World Bank’s CCDRs, 59 percent documented greater exposure of the poor, especially to droughts, extreme heat, and floods (together about three-quarters of estimates). Compared with studies of other natural disasters, studies of heat were 22 percentage points more likely to find that the poor were more exposed than the average household or country. Studies on droughts were 19 percentage points more likely to find that the poor were more exposed and studies on floods were 29 percentage points more likely to find that the poor were more exposed (Table 4, columns 1-2). When the CCDRs are excluded, the results on exposure are qualitatively similar.

The prevalence of studies finding that the poor are more exposed to shocks may reflect the data used in these studies. For example, there is some evidence that the poor are more exposed to floods than other households—but only in urban areas, not in rural areas where the very poorest tend to live (Hallegatte et al. 2020). <mark>There is also evidence that the increase in the poor’s exposure to climate shocks since the 1970s predominantly stems from an increase in their concentration in high-risk zones (</mark> Kim 2012 <mark>). Consequently, individuals living in poverty are nearly twice as</mark> 8
Table 5. Poverty Incidence and Contribution to Total Poor by Main Sector of Occupation, February 1996 and February 1999 (%)

||Febru|ary 1996|Febru|ary 1999|
|---|---|---|---|---|
|Sector|Poverty<br>incidence|Contribution<br>to total poor|Poverty<br>incidence|Contribution<br>to total poor|
|Agriculture|26.29|68.54|39.69|58.38|
|Trade, hotel, and restaurant|7.96|8.10|17.63|11.13|
|Manufacturing industry|10.69|5.71|22.92|7.71|
|Civil, social, and private services|5.73|5.72|13.13|7.36|
|Transport and communication|8.85|3.32|24.02|5.58|
|Construction|14.04|5.42|28.97|5.52|
|Receiving transfer|6.58|1.86|_15.57_|_2.65_|
|Mining and quarrying|15.34|1.01|29.81|1.00|
|Others|13.29|0.10|32.00|0.27|
|Finance, insurance, and leasing|1.24|0.06|5.23|0.23|
|Electricity, gas, and water|6.10|0.16|14.48|0.17|

Note: Sorted by contribution to total poor in February 1999 Table 5 indicates that all sectors uniformly experienced an increase in poverty incidence during the period. This implies that there is no single sector, which was spared from the negative impact of the crisis. In relative terms, the finance, insurance, and leasing sector had the highest increase in poverty incidence, which was more than quadrupled from 1.2 to 5.2 percent. This probably reflects the financial nature of the origin of the crisis, so it is not surprising this sector was the hardest hit. The table also indicates that other modem sectors such as trade, manufacturing, and services were also proportionately hard hit by the crisis.

Nevertheless, the agriculture sector consistently had the highest poverty incidence as well as the highest contribution to the total number of poor people during the period. This reflects two things. First, people in the agriculture sector have always been relatively poorer than those in other sectors. Therefore, even 20
57

# **Table 4b Table of Correlation Coefficients: Knowledge Economy Variables**

||**Openness **|**Institutions**|**HC15**<br>**Square**<br>**of HC15 **<sup>**HC25**</sup><br>**Square**<br>**of HC25**|**Log**<br>**(Educational**<br>**Quality)**|**Log**<br>**(Patents)**|**Log**<br>**(Journals)**|**Royalties**<br>**per capita **|<sup>**Phones **</sup>|<sup>**Computers **</sup>|<sup>**Internet**</sup><br>**Users**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Openness**|**1.0000**||||||||||
|**Institutions**|**0.7308**|**1.0000**|||||||||
|**HC15**|**0.6538**|**0.8372**|**1.0000**||||||||
|**Square of HC15**|**0.6315**|**0.8304**|**0.9931**<br>**1.0000**||||||||
|**HC25**|**0.6527**|**0.8450**|**0.9974**<br>**0.9937**<br>**1.0000**||||||||
|**Square of HC25**|**0.6310**|**0.8360**|**0.9875**<br>**0.9974**<br>**0.9931 1.0000**||||||||
|**Log (Educational**<br>**Quality)**|**0.5743**|**0.6663**|**0.6609**<br>**0.6410**<br>**0.6582 0.6346**|**1.0000**|||||||
|**Log (Patents)**|**0.6152**|**0.8468**|**0.8245**<br>**0.8252**<br>**0.8333 0.8311**|**0.5984**|**1.0000**||||||
|**Log (Journals)**|**0.5048**|**0.7948**|**0.7056**<br>**0.7049**<br>**0.7106 0.7084**|**0.4856**|**0.9044**|**1.0000**|||||
|**Royalties**<br>**per capita**|**0.3851**|**0.4940**|**0.4525**<br>**0.4451**<br>**0.4593 0.4476**|**0.3002**|**0.4396**|**0.3750**|**1.0000**||||
|**Phones**|**0.7408**|**0.9099**|**0.8889**<br>**0.8927**<br>**0.9004 0.9011**|**0.6728**|**0.8483**|**0.7391**|**0.4495**|**1.0000**|||
|**Computers**|**0.6452**|**0.8660**|**0.8651**<br>**0.8799**<br>**0.8783 0.8908**|**0.6466**|**0.7648**|**0.6416**|**0.4867**|**0.9219**|**1.0000**||
|**Internet Users**|**0.6638**|**0.8243**|**0.8443**<br>**0.8601**<br>**0.8520 0.8659**|**0.6417**|**0.7410**|**0.6186**|**0.4267**|**0.9087**|**0.9279**|**1.0000**|
LMICs (Mitra, Posarac and Vick (2013) find a significant difference in household per capita expenditures across disability status in only three countries.

This paper’s result needs to be contextualized with the well-established literature on the socioeconomic determinants of health inequalities (Marmot 2005) and with growing evidence of the causal impact of disability on earnings (Mani et al 2018), consumption expenditures and assets (Takasaki 2020).

Several issues should also be noted with regard to using household expenditures as a dimension of economic well-being in the context of this study. First, there is a possibility that the intrahousehold distribution of expenditures is unequal across disability status, which is not captured here.

Second, if poverty is measured through overall or non-health per capita expenditures against a poverty line as done in Table 4.3, the comparison of households with a disability to other households may be biased because households with disabilities may have additional expenditures due to the disability (for example, transportation, personal assistance). Consumption-based poverty indicators used above do not account for extra costs related to disability in order to provide a more accurate assessment of poverty among persons with disabilities (Mont and Nguyen 2017).

More research is needed on the links between disability and monetary poverty, including on extra costs of living with a disability and the dynamics of household expenditures, which may be affected by different patterns of household formation or mortality in relation to disability (Mitra 2018).

# **Social Protection, Economic Insecurity, Living Conditions and Assets**

Table 4.4 presents several household indicators for households with and without any difficulty. Appendix 8 gives results on the same indicators but at the individual level, i.e. whether persons with and without any difficulty live in households with specific household level outcomes such as receiving social protection or having safely managed drinking water. It should be noted that in Appendix 8 a given household will be counted as many times as it has adult household members with or without functional difficulty. Results are overall consistent with the household level results in Table 4.4 but sometimes vary for some country/indicator pairs.

Results in Table 4.4 in the three panels indicate that households with functional difficulties more often receive social protection benefits in eight out of nine countries, with no statistically significant difference in Nigeria. In the nine countries with social protection information (Figure 4.4 and Appendix C1), the difference across disability status in the receipt of social protection is larger for men in Bangladesh, Liberia, Malawi, Namibia, Nigeria, and South Africa. For instance, in Bangladesh, the share receiving social protection is 12 percentage points higher for males with functional difficulties compared to males without, and nine percentage points higher for females with functional difficulties compared to women without.

30
financial distress in the near future in the U.S. There are plenty of economic dislocations, inflationary fears, and frothy financial markets that resulted from the crisis and some of the policies to deal with it, that have the potential to create near-term banking problems. Nonetheless, the facts that the year 2020 went by without any significant financial distress reflected in the regulatory accounting data in the U.S. banking industry, that the limited information on stock market values suggest that the industry is now more valuable than before the crisis, and that systemic risk is measured as lower than before the pandemic qualifies as a favorable “financial surprise” in our view.

After our presentation of the accounting data in Section 4.1, we provide a few additional statistics that do not depend on accounting data to bolster our claims in Section 4.2. We briefly offer some more sobering thoughts for banking conditions in the remainder of the world in Section 4.3, and provide some suggestions for future research in Section 4.4.

# **4.1 U.S. Banking Data from 2019:Q4 to 2020:Q4**

Table 1 provides quarter-by-quarter snapshots of the U.S. banking system from 2019:Q4 to 2020:Q4 from the regulatory Call Reports, showing several dimensions of bank performance. Thus, we cover the industry from the quarter before COVID-19 struck in the U.S. in 2019:Q4, to the quarter containing the economic peak in 2020:Q1, to the quarter with the recession trough of 2020:Q2, to the two subsequent quarters of recovery, 2020:Q3 and 2020:Q4.

For each quarter, we show summary statistics for eight ratios of various ratios to Gross Total Assets (GTA), expressed in percent terms.<sup>14</sup> These ratios cover dimensions of bank condition (capital and liquidity ratios, based on equity and cash, due from, and securities,

> 14 GTA equals total assets plus the allowance for loan and lease losses and the allocated transfer risk reserve (a reserve for certain foreign loans).  It is a more accurate representation of the size of the balance sheet than total assets because it includes all the dollars of assets that need to be financed with bank equity and liabilities.

18
correlations are strong in the A.an economies but weak in other economies. We suggest that individual case studies should be helpful for us to understand the time path of the development of the stock markets in these economies. NWe also suggest some refinements to the measurement of stock market development. The paper is divided into six sections. Section II describes the data and variables used in the study, Section III reports the results of cross-sectional analysis and Section IV reports the results of time-series analysis. Section V offers some explanations for the observed patterns and Section VI provides some concluding remarks.

II. Data sources and compilation The data sample includes 32 developing economies which are l:sted in the appendix. The sample is confined to the markets which are covered by the IFC Emerging Markets Data Base (EMDB), which includes 20 markets in the IFC's Composite Index and another 12 markets not in the IFC's Composite Index.

"Stock market development" and "financial depth" are multi-faceted concepts and there are a number of indicators which can be used to measure them. In this study, the ratio of market capitalization to GDP (MCY) is used as a measure of the degree of development of the stock market. Annual figures of market capitalization (as at end of year) are available in the EMDB from 1977 for the 20 markets in the IFC's Composite Index, and from 1982 for the other 12 markets not in the IFC's Composite Index. We use three indicators as base measures of the depth of the financial system. The data for the variables were retrieved from IFS data base.<sup>2</sup> The three variables are: M1Y: The ratio of MI to GDP. MI is the sum of currency held outside of banks and demand deposits other than those of the central govemment (IFS line 34).

> 2The data for Taiwan are obtained from Statistical Yearbook of the Republic of China 1991, Directorate-General of Budget, Accounting and Statistics, Executive Yuan, Republic of China.

2
TABLE .6 28 SIMULATION EFFECTS IN THE SECOND YEAR OF AN INCREASE IN THE U.S. G'vERNMENT EXPENDITURE OF ONE PERCENT OF GNP Percent Except for Interest Rates in Percentage Points and Current Account in Billions of Dollars

|Fiscal<br>Expansion<br>by Model*|Output|Consumer<br>Price<br>Index|Short-Term<br>Interest<br>Rate|Currency<br>Value|<br>Current<br>Account|
|---|---|---|---|---|---|
|DRI|2.1|0.4|1.6|3.2|-22.0|
|EECa|1.2|0.6|1.5|0.6|-11.6|
|EPAb|1.7|0.9|2.2|1.9|-20.5|
|LINK|1.2|0.5|0.2|-0.1|-6.4|
|LIVERPOOL|0.6|0.2|0.4|1.0|-7.0|
|MCM|1.8|0.4|1.7|2.8|-16.5|
|MINIMOD|1.0|0.3|1.1|1.0|-8.5|
|MSG|0.9|-0.1|0.9|3.2|-21.6|
|OECD|1.1|0.6|1.7|0.4|-14.2|
|TAYLORC|0.6|0.5|0.3|4.0|N/A|
|VARc|0.4|-0.9|0.1|1.2|-0.5|
|WHARTONd|1.4|0.3|1.1|-2.1|-15.4|
|MULTIMODd|1.32|1.04|0.3|2.0|-16.6|

# SOURCE: Frankel (1988), and MULTIMOD.

# N/A Not Available

- The list of these models are explained in APPENDIX D.

- a ROECD short-term interest rate not available; long-term reported instead.

- b ROECD current account is Japan, Germany, the U.K., and Canada.

- c CPI not available; GNP deflator reported instead.

- d Government shock not a permanent shock, sustained for only five years.
The attraction of FYM, from our perspective, is that it is noncontractible. Farmers rarely purchase FYM, but rather typically collect it as a by-product of their own livestock, load it onto carts or donkeys, transport it to the plot and then spread and incorporate it into the soil.<sup>7</sup> This process is extremely labor intensive and thus costly to monitor. The extent and quality of FYM application is also not easily veri fi ed or observed ex-post, since weather and other sources of exogenous uncertainty make it difficult to extract this information from realized output alone.

Direct evidence on the noncontractibility of FYM application can be found in the farm wage employment section of the PRHS, where agricultural jobs held over the past year are enumerated by task. A miniscule 0.08 percent of all agricultural wage labor days fall under the category ”collecting/spreading farmyard manure”.<sup>8</sup> By way of comparison, about 7 percent of labor days are classi fi ed as ”weeding”, another seemingly hard to monitor task, whereas two-thirds of all paid labor days in agriculture are devoted to harvesting various crops. Harvesting work is more readily contractible because productivity per hour is relatively easy to observe.<sup>9</sup> Noncontractibility in itself does not preclude an efficient level of investment. If the landlord is able to commit fully to retaining the tenant for the duration of the investment, a contract that makes the tenant the residual claimant to the returns on his investment would induce optimal investment. The landlord could then simply remove any surplus accruing to the tenant by appropriately increasing his rent. The classic holdup problem arises only when the investment is noncontractible and the landlord is unable to commit. The landlord stands to gain, in this case, by renegotiating the contract terms or contract renewal ex-post. The tenant, aware of this potential for opportunism on the part of the landlord, knows that he is unlikely to recoup all the fruits of his investment. It is thus privately optimal for the tenant to underinvest. The remainder of this section sketches out a simple model to formalize this intuition.

> 7 Recommended quantities of FYM vary greatly by crop—at the high end, 10-15 cartloads (8000-12000 kg) for an acre of sugarcane, and even more for vegetables (Government of Pakistan, 1997).

> 8 We do not have information from the survey that would allow us to compute the proportion of family labor used for this task. However, data from northeast China, which has an agricultural technology not disimilar from that of rural Pakistan, indicates that 8 percent of annual family farm labor goes into FYM application (Jacoby, Li, and Rozelle, 2002).

> 9 Indeed, according to the data, harvesting jobs are nearly twice as likely as weeding jobs to pay piece rates, which rely on directly observing output or effort.

5
data. They can open researchers to what they should be monitoring in terms of indicators and help in the observation or recognition of adverse effects of the intervention.

## _C ONSTRAINTS_

Ethnographic evidence needs to be considered within the context of both observer bias, where the researcher’s priors shape the understanding or collection of data, as well as observer expectancy effects, where participants modulate their behaviors because they are being observed. Moreover, these types of studies tend to require significant time and resources, making them difficult to maintain and replicate at scale.

## _M APPING OF CONSTITUENCIES / ACTORS_

The visual mapping of key constituencies, actors and formal and informal institutions is meant to capture the overlapping sets of institutions and actors that drive attitudes and behaviors related to cooperation and collective actions, and that enforce norms.

## _A DVANTAGES_

Mapping exercises are particularly useful to understand drivers of local grievances and patterns of competition between actors. For practitioners, they can be visually helpful in identifying and taking stock of context at present and over time, more than as a measurement tool for diagnosis, monitoring, or evaluation. Mapping can be done through key informant interviews or focus groups and other available information.

## _C ONSTRAINTS_

There is a tradeoff between capturing complexity and maintaining visual and user focused simplification. This can be addressed by being clear about the audience and purpose of the map, and curating information accordingly.

# 2.2. IDENTIFYING PRIORITIES ALONG THE MEASUREMENT CONTINUUM

As the above section shows, there are a broad range of approaches to understanding social cohesion. In identifying an appropriate measurement strategy, it is thus useful to begin with a clear definition of the main purpose of data collection along the measurement continuum: will data be used to inform project and intervention _design_ , to monitor _implementation_ of an existing project, provide _early warning_ tools, or to _evaluate_ impact? Being clear about the objective helps to identify specific data needs, including indicators and collection frequency. Mapping this against an assessment of what data is already available begins to frame the needs for new data collection, which in turn can be assessed against feasibility and advantages and disadvantages described above to settle on the most appropriate data collection tools.

## _D IAGNOSTICS_

A strategy for social cohesion diagnostics relying on multiple sources of data can help ensure accurate contextualization, particularly in conflict-affected environments settings. Qualitative tools in particular, such as 24 | P a g e
adjustments are considerable; the y typically increase standard errors by a factor 1.2 to 1.7.

Our results show that higher levels of education enhance growth. Controlling for the level of educational attainment, larger variation in education is good for growth. Our results also indicate that income inequality does not affect growth.

# **7. References**

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Benabou, R. (1996), Heterogeneity, stratification, and growth: Macroeconomic implications of community structure and school finance, _American Economic Review,_ **86** (3) **,** 584-609.

Birdsall, N. and Londono, J. L. (1997), Asset inequality matters: An assessment of the world bank's approach to poverty reduction, _American Economic Review, Papers and Proceedings,_ **87** (2) **,** 32-37.

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Brock, W. A. and Durlauf, S. N. (2001), Growth empirics and reality, _World Bank Economic Review,_ **15** (2) **,** 229-272.

22
- Beaman, L., R. Chattopadhyay, E. Duflo, R. Pande, and P. Topalova (2009): “Powerful women: does exposure reduce bias?” _The Quarterly journal of economics_ , 124, 1497–1540.

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16