rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Regressions of US Hurricane Damages on Intensity and Vulnerability

|Magnitude<br>measure|Constant|Intensity|Income|Population<br>Density|Adj Rsq/<br>F Stat|
|---|---|---|---|---|---|
|Minimum|607.5|-86.3|0.370|0.488|0.501|
|Pressure|(10.39)|(9.96)|(0.45)|(1.53)|35.76|
|Wind Speed|-12.9|4.95|0.903|0.458|0.371|
||(1.42)|(7.83)|(0.96)|(1.28)|22.61|

Note: There were 111 observations in each regression. t statistics are in parenthesis. Source: NOAA 2009.

The vulnerability variables (income and population density) are not significant in Table 1, which poses a dilemma for the analysis. It is difficult to know how large the effect of increasing income and population might be from these regressions alone. But it is not realistic to treat these impacts as negligible just because they do not have statistical significance in these particular regressions. Accordingly, we carry out the analysis using several different possible parameter values. Note that the minimum pressure regression places the income elasticity closer to 0.4 whereas the wind speed model places the income elasticity closer to 1. We pursue both models in the analysis. We also test the importance of these coefficients in the sensitivity analysis.

Figure 2 displays the shape of the Minimum Pressure Damage Function. It is very nonlinear and quickly gets into ranges of damages that are not plausible because they more than destroy everything in their path. Figure 3 displays the results for the wind speed regression. It also is nonlinear but does not increase as sharply as the Minimum Pressure Damage Function.

11
<!-- Start of picture text --> Figure1: Global Prices of Rice and Wheat (2002=100)<br>500<br>450<br>400<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Wheat Rice<br>2002 2003 2004 2005 2006 20072008Jan2008Mar2008April2008May2008June2008july2008Aug<br>Price Index<br><!-- End of picture text --> _Source: IMF International Financial Statistics and World Bank’s Global Economic Monitoring Database_ Global food prices have increased because of a combination of factors including rising population, rapid economic growth in emerging markets, high energy and fertilizer prices, increased use of food crops for bio-fuels, rapid demand increase for some food crops, depreciation of the US dollar, and declining global stocks of food grains due to changes to buffer stock policies in the US and the European Union. Back-to-back droughts in Australia, and growing global demand for grains (excluding for bio-fuel production) have been modest contributors and on their own would not have led to large price increases. Commodity investors and hedge fund activity also seem to have played a minor role. Although empirical evidence is scarce, the prevailing consensus among market analysts is that fundamentals and policy decisions are the key drivers of food price rises, rather than speculative activity (World Bank 2008a).

# **III. Global Supply-Demand Imbalance**

Table 1 shows the world production and consumption trends for wheat, rice and foodgrain over the past 10 years. A number of interesting results emerge. First, between 1999 and 2008 world food consumption has grown faster than production causing a fairly substantial drawdown of stocks (figure 2). Second, wheat production faced difficulties over the 2005-2007, but there has been strong recovery in 2008-2009. Third, in contrast to wheat, rice production increased significantly during 2005-2008 and overall production has basically exceeded consumption.

3
129 Expenditure 7: Interest Payments as Percent of Total Government Expenditure Summary Table

||Number|1975|1980|1985|
|---|---|---|---|---|
|ALL DEVELOPING COUNTRIES|44|4.70|6.85<br>|12.06<br>|
|Low Income|10|6.06|8.70|12.49|
|Middle Income|34|4.30|6.40|11.95|
|AFRICA|9|4.48|7.40|11.24|
|Low Income|6|4.95|8.45|11.66|
|Middle Income|3|3.57|5.30|10.42|
|EAST ASIA|6|5.27|7.40|12.16|
|Low Income|1|1.51|3.70|7.79|
|Middle Income|5|6.02|8.14|13.03|
|SOUTH ASIA|3|9.78|10.88|15.73|
|Low Income|3|9.78|10.88|15.73|
|EMENA|14|2.64|4.14|8.44|
|Middle Income|14|2.64|4.14|8.44|
|EASTERN<br>EUROPE|0|-|-||
|Middle Income|0|-|-||
|LAC|12|5.71|8.24|15.93|
|Middle Income|12|5.71|8.24|15.93|

Note: The summary table includes gnly the countries with complete data for the periods in the table.

Source: IMF Government Financial Statistics: Consolidated Central Government Expenditure.
In our opinion, these differences can be explained the by more global concept captured by our indicator, which aims at reflecting the FDI that cannot be explained by the size of the local market and the availability of natural resources. As mentioned earlier, it reflects not only the policy and political environment in a host country but also a series of factors such as the geographical location, infrastructure and the stock of human capital. The ICRG and II indexes capture only two of these multiple elements: the political and financial risks in each country. Another major difference is that these indexes are built with investors' surveys, mainly intemational banks, and thus are more subjective and forward-looking than our indicator that is constructed by using actual FDI flows and economic data. These differences can be illustrated by the cases of Zimbabwe and South Africa. Although Zimbabwe appears to be a country with low political (fourth out of 24 countries) and financial (third) risks, the fact of the matter is that most foreign investors have been reluctant to invest there. Their prudence may be explained by the weak growth performance over the past few years and numerous barriers against FDI, especially when Zimbabwe is compared to market-oriented neighbours such as Zambia, Uganda and Mozambique. Those obstacles are not captured by the ICRG or II index. The South African economy has benefited from large inflows of FDI in the recent years, but they have been mainly due to the privatization process, the return of companies based in neighbouring countries during the apartheid period and the interest of investors in the large domestic market (about three times greater than the second largest African country, i.e. Nigeria). Those factors are not related directly to the business climate, which remains quite problematic. The trade liberalization process remains timid with the exclusion of some important industries and relatively long transition periods. The economic growth performance in recent years has proved to be too modest to convince foreign investors, which is reflected in our indicator but not clearly in the ICRG or II index.

It might be useful to examine the variations in the business climate, as a source of attraction for FDI, for the group of 29 African countries over the past decade (table 3). At the end of the 1 980s, the most attractive countries were Zambia, Mauritius, Chad and Benin. Then, in the early 1 990s, Benin, Namibia, Chad, Zambia and Mozambique were ranked as the most performing countries. In the last few years, Namibia, Mali and 8
<mark>are included in market indexes are bought and sold more frequently and are held by a wide range of investors, which means that holding a bond that is included in the index enhances its liquidity. Bonds that are included in the index collectively define the benchmark of market performance, which means that holding those bonds limits an institutional investor’s risk of underperforming the market benchmark.</mark> <mark>Two of the most relevant benchmark indexes for emerging market bonds are the J.P. Morgan EMBI Global Diversified Index (which focuses on sovereign bonds) and the J.P. Morgan CEMBI Narrow Diversified Index (which focuses on corporate bonds).</mark><sup>3</sup> <mark>Both indexes include bonds based on certain security attributes, notably the amount of outstanding debt. Thus, only debt issues with face value equal to or greater than $500 (US$500) million are included in these indexes. A broader index (the CEMBI Broad) also exists, which includes corporate debt with face value equal to or greater than $300 million.</mark> <mark>Because of their advantages, some institutional investors that expand their holdings of emerging market corporate debt purchase bonds that are included in the major market indexes. This means purchasing large bonds. One would expect that this preference would increase bond prices through an inclusion premium and reduce bond yields, an effect that we label the “size yield discount.” Also, one would expect that this preference would increase the likelihood of issuing large bonds, as firms participating in international bond markets take advantage of cheaper financing costs.</mark> <mark>In this paper, we analyze how the change in global market conditions after 2008 interacted with market structure to affect the size and pricing of U.S. dollar-denominated bonds issued by emerging market corporations. Specifically, we analyze a period when the low interest rate environment created by developed countries’ monetary policies after the GFC interacted with preferences of international investors that follow rules governing the</mark>

> 3 EMBI stands for Emerging Market Bond Index and CEMBI stands for Corporate Emerging Market Bond Index.

2
<!-- Start of picture text --> 70% 25%<br>60%<br>20%<br>50%<br>15%<br>40%<br>310%<br>10%<br>20%<br>5%<br>10%<br>0% 0%<br>1994 1996 1998 2000 2002 2004 2006 2008 2010 2012<br>G@shaecfsevitizediims = s-_serice intensity<br><!-- End of picture text -->
Most of the work improving measurement of the top tail of the income or wealth distributions uses administrative data. But there is very little work on how one might improve estimates of incomes or wealth (and income from that wealth) that are outside the tax system. This is very important in LMICs where the informal sector is a large share of economic activity (Ravallion, 2022). Ravallion (2022) notes that if economic growth improves state capacity and this shifts more activity into the formal sector then this could generate rising measured top income shares, even when there is no actual change. This does not seem to have been investigated in the research discussed in this paper. Ravallion (2022) also highlights that there is little evidence on how large the informal or hidden sector share of top incomes or wealth is in any LMICs. This also seems an important area for further work. Alstadsæter et al. (2022) used leaked data on Dubai real estate ownership to show that the value of property ownership in Dubai by LMIC residents is a large share of GDP for several of these LMICs. Such data would be useful to improve the measurement of the top of the wealth distribution in these countries and serve as estimates for how much administrative tax data misses important sources of wealth at the top of the distribution.

# Conclusion

There has been a resurgence in research on the measurement of the top tail of the income and wealth distribution in the last 20 years. Kuznets and Jenks (1953) were the key authors that began this research agenda, but it declined in importance as household surveys became more widely available and researchers turned their attention to microdata. Piketty (2001, 2003) put the measurement of the top tail back onto the research agenda with his long run measurement of top incomes in France, and subsequently this area of research has exploded.

In this paper we have surveyed this literature, focusing on the challenges that have been identified when trying to measure the top of the income and wealth distributions, as well as the solutions that have been used to overcome these challenges. Our focus has been low- and middle-income countries, although we have discussed research from high-income countries, particularly when no research on a specific topic has been undertaken in any LMIC.

There are multiple challenges that researchers measuring the top end of the wealth and income distributions face. We focused first on the challenges when using household survey data, given that this is the most common form of data on incomes and wealth available in LMICs and that other forms of data (like administrative tax microdata or even tax tabulations) are uncommon. These include missing high income or wealth individuals due to non-random non-response (item or unit) or due to sampling error and measurement error (either from respondents themselves, proxy respondents or 30
It is also found that the bundling strategy would likely reduce bidder participation. The coefficient of ln _x_ 1<sup>ln</sup> _x_ 2 is estimated at –0.005, which is negative and significant. Therefore, the expected number of participants will decline as the sizes of involved treatment plant and network works increase simultaneously. It means that the number of firms that can implement both activities seems to be limited. This is important evidence to design the procurement auctions in this sector. The extent to which potential bidders are already occupied with other development projects may have a negative impact on bidder entry, but the coefficients are largely insignificant.

Table 4. Truncated Negative Binomial Regression on Number of Bidders

|Dependent variable|_n_|_n_|
|---|---|---|
|_D _<sup>_WATER_</sup>|–0.266|0.217|
||(0.261)|(0.284)|
|_D _<sup>_TE_</sup>|0.033|0.201|
||(0.255)|(0.264)|
|ln_x_1|–0.009|0.284|
||(0.021)|(0.181)|
|ln_x_2|–0.020|–0.356 <sup>***</sup>|
||(0.014)|(0.092)|
|(1/2)ln_x_1*ln_x_1||0.037 <sup>**</sup>|
|||(0.019)|
|ln_x_1*ln_x_2||–0.005 <sup>**</sup>|
|||(0.002)|
|(1/2)ln_x_2*ln_x_2||–0.045 <sup>***</sup>|
|||(0.012)|
|ln_MONTH_|–0.298 <sup>*</sup>|–0.029|
||(0.180)|(0.175)|
|ln_WAID_|–0.329|0.489|
||(0.761)|(0.653)|
|ln_CAID_|–0.053|–0.098|
||(0.068)|(0.067)|
|Constant|5.205|–1.693|
||(6.926)|(5.780)|
|Obs.|69|69|
|Wald chi-squared|14.80 <sup>**</sup>|36.27 <sup>***</sup>|
|Chi-squared test statistics:|||
|Ho: All ln_x_1, ln_x_2 = 0|2.44|27.59 <sup>***</sup>|
|Implied elasticity:|||
|(_∂n_/_∂x_1)(_n_/_x_1)|–0.009|–0.093 <sup>***</sup>|
||(0.021)|(0.032)|
|(_∂n_/_∂x_2)(_n_/_x_2)|–0.020|–0.121 <sup>***</sup>|
||(0.014)|(0.029)|

Note that the robust standard errors are shown in parentheses. *, **, and *** indicate the 10%, 5% and 1% significance levels, respectively.

Source: Author’s calculation.
Two types of pension surplus or deficit are used in the report. In Appendix Table A.5, the last column subtracts pension spending from the revenues which derive from employer and employee "contributions" or payroll taxes and divides this difference by total pension spending. When there is a surplus of contribution revenues over benefit expenditures, the number in this column will be positive. Note that in table A.5, the data for pension spending do not necessarily correspond in terms of year or source to the data presented in the last column. The second type of surplus/deficit concept, which includes investment income as a revenue source, is used in Figure 4.2. Neither concept includes explicit government transfers or subsidies as a revenue source; these are treated as part of the deficit.

The first measure -- payroll tax revenues minus pension benefits - may be more interesting from the fiscal policy perspective since the income from pension reserves typically represents an interest payment from the general budget to the pension system which itself is paid for with tax revenues or new borrowing. On the other hand, if pension reserves were invested in such a way as to increase economic growth and tax revenues this measure may overstate the fiscal burden imposed by the pension system. In this case, the figure which included investment returns would be a better indicator of the fiscal impact of pensions.

Sources, Much of the previous empirical work on international patterns of pension financing has employed the concept of "social security" spending and revenues rather than separating out pension spending and revenues. Data on separate items falling under the heading "social security" have not been readily available. The IMF's "Yearbook of Government Finance Statistics" does not provide the disaggregation necessary to differentiate between family allowances or unemployment expenditures on the one hand and old age pension benefits on the other. As a result most studies have been forced to use "social security" as a proxy for pension spending (see Palacios 1996).

The International Labour Office (ILO) is the only institution which systematically collects data on pension expenditures and revenues for a wide range of developing countries. These data are available in disaggregated form in the ILO publication, "The Costs of Social Security: Basic Tables" which presents data based on the results of surveys sent to the major institutions in each country responsible for the various programs which are covered under the umbrella term "social security". In many cases, however, survey 4
The recommendations in each row were counted and their total standardized in either of two ways:

- One approach, emphasizing the _overall focus_ (OF) of the FSAP, divided up the row totals by the overall number of recommendations.

- The other approach emphasized _relative focus_ (RF), i.e., the relative importance of any theme or sector within a given area; the row totals were divided by the number of recommendations in the tier immediately above. For example, “Contractual” was divided by “Infrastructure”, “Specialized courts” by “Judiciary”, etc.

Simple tabulations were first produced, breaking down the data in various ways. Vertically, two alternative breakdowns of the oversight component of the grid were used, one by themes and the other by sectors. Horizontally, the data was broken down by FSAP date (FSAPs are distributed in three even periods over the life of the program) and country income level. While the former gives a glimpse on FSAPs’ main focus and whether it has changed over time, the latter provides a window on how FSAPs’ content has adapted to reflect economic and financial development.

Tabulations have the advantage over formal statistical tests that they can adapt better to the data. In particular, they can reveal nonlinearities that may not come through in simple linear regression exercises. However, simple tabulations may also be misleading in that averages can be driven by a few outliers, or because they fail to integrate multivariate linkages. For example, apparent changes in focus over time could simply reflect a change in the relative participation of low or middle income countries over time relative to high income countries, rather than changes in focus for a particular type of country.

To address these issues, we back up our conclusions with OLS estimates whenever possible. Row-by-row OLS estimates were conducted, up to the fourth tier, according to both the OF and RF methods. Recommendation percentages are explained as a function of:

- A standard set of country structural variables that includes (the logs of) GDP per capita (a proxy for economic development), population (a proxy for country size), and population density (denser countries tend to have more developed financial systems).

- A set of FSAP-specific variables, including the year of the FSAP and whether it was an initial FSAP or an update (a dummy variable).

- Regional dummies, using the World Bank classification system (to better isolate these regional effects, regional dummies were introduced one at a time).

Two more statistical tests were conducted to help identify the breadth of the FSAPs and their focus (broad-based or mostly concentrated on a few areas or issues). We regressed the total number of recommendations, as well as their standard deviation across the upper on collateral), it belongs to the section on pledge registries. At the same time, however, the recommendation may also be aiming at improving the contractual rules supporting the use of collateral, justifying its inclusion under the creditor rights section.

8
significantly associated with both productivity and firm growth. A one-standard deviation increase of the Enterprise Survey’s Web indicator is associated with a 0.4 percentage point increase in employment growth and a 14 (9) log point increase in labor productivity (TFP). Moreover, the Global Competitiveness Broadband indicator is also positively and significantly associated with the productivity level. Increasing it by one standard deviation is associated with around a 10-log point increase of labor productivity and TFP. The general consistency between Enterprise Survey and Global Competitiveness on the importance of broadband renders further support to the findings of the previous literature on internet as a general-purpose technology (Clarke, Qiang, and Xu 2015).

_Human Capital._ The _Enterprise Survey_ and the World Bank HLO indicators yield robust patterns in terms of the associations between firm performance and human capital. A onestandard deviation decrease in the Enterprise Survey’s Skill Shortage Obstacle indicator is associated with an increase of TFP of 6 percentage points and a decrease in firm growth of 0.4 percentage points. The same conclusion is observed based on the World Bank’s HLO measure: a one-standard deviation increase in the HLO measure is associated with an increase in productivity of 16 log points, and a decrease in firm growth by 1.0 percentage points. In addition, the Global Competitiveness indicator of Quality of Education System is significantly associated with TFP. A one-standard deviation increase of the Education System indicator is associated with a 9-percentage point increase in TFP. The consistent findings of the productivity-boosting effect of human capital by all three sources underscores the critical importance of human capital. The negative association between our human capital measures and firm growth is suggestive of the trade-off between the quantity and quality of jobs.

_Access to Finance._ Access to finance tends to be positively associated with both productivity and firm growth. The Enterprise Survey’s Overdraft indicator is positively associated with both the productivity level and firm growth (it is significant when regressed alone and insignificant when all three groups of BE indicators are included). A one-standard deviation increase of the Overdraft measure is associated with an increase in labor productivity and TFP by 14 and 8 percentage points, respectively. The Global Competitiveness Financial Development indicator is positively associated with firm growth, and positively and significantly associated with TFP (but not log labor productivity, LP). Increasing it by one standard deviation is associated with a nearly 1-percentage point increase of employment growth and 13- percentage point increase of TFP. In contrast, _Doing Business’_ Getting Credit indicator is not significantly associated with firm growth, and is negatively associated with 16