rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 8: Impact of a** **_Unilateral_ Increase in the Tariff Rates to a Uniform Forty Percent, Starting from Initial Tariffs. All results are for the region raising the tariffs***

**<u>Model is the Nine Sector, Three Factor Model with Labor-Leisure Choice and Trade Imbalances</u>**

||<br>**Hicksian E**|<br> **quivalent V**|<br>**ariation**||||||
|---|---|---|---|---|---|---|---|---|
||**as aperc**|**ent of consu**|**mption**|**Terms o**|**f Trade C**|**anges**|**Feenstr**|**a ratios**|
|**Region**|**Armington**|**Krugman**|**Melitz**|**Armington **|**Krugman**|**Melitz**|**Krugman**|**Melitz**|
|Australia-New Zealand|0.34%|1.03%|1.03%|113.4%|114.4%|114.9%|100.2%|99.7%|
|Canada|0.80%|-0.71%|-1.01%|116.5%|114.5%|114.6%|99.4%|98.3%|
|China|0.91%|-4.12%|-4.72%|116.2%|113.5%|113.3%|98.6%|98.3%|
|Japan|0.41%|-1.52%|-1.79%|115.5%|113.6%|113.6%|99.2%|98.9%|
|Mexico-Chile-Peru|1.17%|-0.46%|-0.74%|115.7%|112.5%|112.8%|99.6%|98.2%|
|Low Income NEC|0.16%|-0.21%|-0.33%|111.6%|110.7%|111.0%|99.9%|98.6%|
|Middle Income NEC|0.82%|0.61%|0.55%|114.8%|114.7%|114.9%|99.9%|99.6%|
|OECD NEC|0.66%|-0.79%|-1.03%|117.4%|116.3%|116.3%|99.4%|99.3%|
|Philippines|0.86%|-1.97%|-2.30%|119.6%|114.9%|115.3%|99.0%|97.6%|
|United States|1.88%|0.51%|0.40%|125.8%|124.1%|124.1%|99.4%|99.3%|

*Ten separate simulations for each of the three market structures, with only the results for the region raising the tariffs shown.

Source: Authors’ estimates.

43
objective measure of the food price crisis, district-level wheat price changes between 2008 and 2010, was used. As noted earlier, the wheat price was used because wheat is the most important staple in Pakistan. Wheat consumption accounts for 50 percent of total calorie intake and 11 percent of total household expenditure in an average Pakistani household. The shares of both calories and budget are even higher among poor households. Poor households spend 20 to 30 percent of their budget on wheat products (Figure 1). Although food prices rose more slowly after 2008, PSLM panel households experienced a 33 percent rise in the wheat price from 2008 to 2010, and a 13 percent rise in costs related to education. However, wheat price variations were not the same across the Pakistan, and there is a large price variation within the country. Figure 2 shows the median wheat price, the top 90th percentile, and bottom 10th percentile. Geographic variations in the price of wheat were used to identify the impact of the food price crisis on children’s activity status. Appendix 4 reports the results using the food price index.

The following specification is used: _Yit_ = γ 0 ln( _Wheatprice_ ) _dt_ +γ 1 ln( _Wheatprice_ ) _dt_ × 2 _ndQrth_ +γ 2 ln( _Wheatprice_ ) _dt_ × 3 _rdQrth_ ′ ′ --(E. 2) +γ 3 ln( _Wheatprice_ ) _dt_ × 4 _thQrth_ + _EPdt_ θ+ _X it_ β+ _Yeart_ + α _i_ + ε _it_ , Empirical equation 2 (E.2) is estimated using an individual fixed-effect linear probability model in order to remove the potential bias due to time-invariant individual characteristics correlated with district-level food prices and child outcomes ( ). It estimates how the district-level wheat price change affects child activity status. Since the literature and the theoretical model have shown that the effects tend to be heterogeneous based on welfare status, the interaction between the price and the household asset quartile indicators

> ( 2 _ndQrth_ , 3 _rdQrth_ and 4 _thQrth_ ) were included. Thus, γ 0 captures the impact of the wheat price change on child activity status for households in the first quartile;  γ1, γ2, and γ3 captures the differential impact between households in the first quartile and households in the second, third, and fourth quartiles based on asset index, respectively.

In order to further understand the heterogeneity of the impacts, the (E. 2) was estimated stratified by age of child (ages 5–9 and 10–14), child gender, locality (urban and rural residence), and 8
Perkins, Dwight H. and Moshe Syrquin. 1989. "Large Countries: The Influence of Size." Handbook of Development Economics. volume II. Edited by H. Chenery and T.N. Srinivasan. Elsevier Science Publishers.

Phillips, Robert and Oli Havrylyshyn. 1990. "The 'ffect of Trade Liberalization or Productivity: An Aggregate Production Function Analysis". (June).

Pritchett, Lant. 1991. "Measuring OUtward Orientation in Developing Countries: Can it be done?". PRE Working Paper 566. World Bank.

Quah, Danny and James E. Rauch. 1990. "Openness and the Rate of Economic Growth." Preliminary draft (October). Economics Department, Massachusetts Institute of Technology and Economics Department, University of California San Diego, U.S.A.

Ram, Rati. 1985. "Exports and Economic Growth: Some Add.iional Evidence." Economic Develooment and Cultural Change, 33(2):415425.

Summers, Robert and Alan Heston. 1988. "A New Set of International Comparisons of Real Product and Price Levels: Estimates for 130 Countries, 1950-1985." Review of Income and Wealth, March:1-24.

Syrquin, Moshe and Hollis Chenery. 1989. "Three Decades of Industrialization". The World Bank Economic Review 3(2):145-181.

Thomas, Vinod, Nadav Halevi, and Julie Stanton. 1991. "Does Policy Reform Improve Performance?". Background paper for World Development Report 1991.

Thomas, Vinod and John Nash. Forthcoming, 1991. Best Practices: Lessons in Trade Policy Reform. New York, Oxford University Press.

Tybout, J. 1990a. "Researching the Trade/Productivity Link: New Directions." Forthcoming in The World Bank Research Observer.

World Bank. 1991. World Develoyment Report 1991. New York: Oxford University Press.

25
# **China’s Productivity Slowdown and Future Growth Potential**<sup>**1**</sup>

Loren Brandt<sup>2</sup> , John Litwack<sup>3</sup> , Elitza Mileva<sup>3</sup> , Luhang Wang<sup>4</sup> , Yifan Zhang<sup>5</sup> , and Luan Zhao<sup>3</sup>

**JEL Codes:** O47, O14, O23, P31 **Key Words:** China, productivity growth, resource allocation, SOEs, local governments

> 1 Prepared as a background paper for the Development Research Center of the State Council and the World Bank’s (September 2019) flagship report “Innovative China: New Drivers of Growth”. The authors gratefully acknowledge helpful comments from Richard Herd, Bert Hofman, Nicholas Lardy, Hoon Sahib Soh, and Min Zhao, as well as excellent research assistance from Huitian Bai, Yunxia Chao, Shuyu Wu, and Peng Zhou.

> 2 University of Toronto, Canada, loren.brandt@utoronto.ca.

> 3 World Bank, jlitwack@worldbank.org, emileva@worldbank.org, and lzhao1@worldbank.org.

> 4 Xiamen University, China, luhangwang@xmu.edu.cn.

> 5 Chinese University of Hong Kong, Hong Kong, <u>yifan.zhang@cuhk.edu.hk.</u>
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE existing infrastructure budgets to fulfill regional spending requirements. The Democratic Republic of Congo would have to spend an untenable 198 percent of its historic infrastructure budget on regional infrastructure needs. Even the Republic of Congo, Cameroon, and Chad would need to spend 30 to 60 percent of their existing infrastructure spending on regional needs, which is not a very realistic proposition.

**Table 5.1. Regional spending needs by sector**

||Transp<br>|ort<br>|ICT<br>|<br>|Powe<br>|r<br>|Tota<br>|l<br>|Total<br>|
|---|---|---|---|---|---|---|---|---|---|
|($ millions)|Investment|O&M|Investment|O&M|Investment|O&M|Investment|O&M|needs|
|Cameroon|34|27|||268|25|302|52|354|
|Central African<br>Republic|44|18|1|0|||45|18|63|
|Chad|74|22|||||74|22|96|
|Congo, Rep. of|13|13|1|0|15||29|13|140|
|Equatorial Guinea|||0|0|1||1|0|1|
|Gabon|12|8|4|0|2||18|8|26|
|**CEMAC**|**177**|**88**|**6**|**0**|**286**|**25**|**469**|**113**|**680**|
|Angola|34|36|2|0|88||125|36|160|
|Burundi|1|3|0|0|1||2|3|6|
|Congo, Dem. Rep.<br>of|139|69|5|0|748||892|69|961|
|**ECCAS**|**352**|**196**|**13**|**1**|**1,123**|**25**|**1,488**|**222**|**1,808**|

_Source_ : Derived from Carruthers and others 2009, Rosnes and Vennamo 2009 and Mayer and others 2009.

_Note:_ O&M = operations and maintenance; ICT = information and communication technology; CEMAC = Economic and Monetary Community of Central Africa.

**Figure 5.1. Spending on regional infrastructure as a share of GDP**

<!-- Start of picture text --> Congo DR<br>Central African Rep<br>Congo Rep<br>Cameroon<br>Chad<br>Burundi<br>Angola<br>Gabon<br>Equatorial Guinea<br>0% 2% 4% 6% 8% 10% 12% 14% 16%<br>as % of GDP<br>Spending needs for regional infrastructure<br><!-- End of picture text --> _Source:_ Derived from Carruthers and others 2009, Rosnes and Vennamo 2009 and Mayer and others 2009. _Note:_ GDP = gross domestic product.

55
# **Figure 1.1 Output (1989=100)**

<!-- Start of picture text --> 110.0 120<br>100.0<br>100<br>90.0<br>80.0 80 Armenia<br>Azerbaijan<br>70.0 CEE Georgia<br>CIS non CIS-7 60 Kyrgyz Republic<br>60.0 CIS-7 Moldova<br>50.0 40 TajikistanUzbekistan<br>40.0<br>30.0 20<br>20.0<br>0<br>1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text --> Source: Unicef TransMONEE database (2002) As in other CIS countries, _employment_ declined during the 1990s but at a much slower rate than output while employment ratios in the CIS-7 have been kept above those of other CIS economies (figure 1.2). During the 1989-1995 recession, employment fell in all countries with the exception of Uzbekistan where it actually increased by 10%. Georgia, the country that experienced the deepest recession, also experienced the deepest fall in employment by about 30%. Moldova followed with a 20% employment decline while all other countries constrained employment fall within 10% points. It is also evident that the employment ratio for most transitional economies continued to decline throughout the growth period making growth 'jobless'. The employment decline has also been larger for the non-CIS countries that evidently continued to gain productivity margins over the CIS economies.

**Figure 1.2 Employment ratio***

<!-- Start of picture text --> 90.00<br>90.0<br>85.0 85.00<br>80.0 80.00<br>Armenia<br>75.0 CEE 75.00 AzerbaijanGeorgia<br>70.0 CIS non CIS-7 70.00 Kyrgyz Republic<br>CIS-7 Moldova<br>65.0<br>65.00 Tajikistan<br>60.0 Uzbekistan<br>60.00<br>55.0<br>55.00<br>50.0<br>50.00<br>1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001<br><!-- End of picture text --> Source: Unicef TransMONEE database (2002). (*) Employed as % of population in working age 15-59 The combination of the described trends in output and employment has resulted in an e _mployment-output elasticity_ during the recession period lower in the CIS-7 than elsewhere in transitional economies. If we estimate the output-employment gap during the recession period and we compare this gap with other transitional economies, we find that the CIS-7 have experienced by far the largest gap between the decline in output and the decline in the employment ratio (table 1.3).

The output-employment gap evidently turned into _productivity_ losses - the larger the gap, the larger the loss - and these losses eventually turned into _real wages_ declines (table 1.3). In all the CIS-7, the early 1990s recession has been accompanied by a deep fall in real wages, deeper than 9
Industry Restructuring and Regulation - Building a base for sustainable<sup>growth</sup> developing countries. Before considering the way<sup>the state of the labor market impacts the utility</sup> and infrastructure reforms, it is worth considering<sup>evidence from the UK and Argentina.</sup> Figure 3 shows the annual change in employment<sup>for the 12 Regional Electricity Companies</sup> (RECs) that are responsible for the distribution<sup>of electricity in England and Wales -they also</sup> held the monopoly on the sale of electricity<sup>to consumers under IMW from privatization,</sup> although full retail competition was finally introduced<sup>late in 1998. It can be seen from the figure</sup> that immediately after privatization (in late 1990) there<sup>were significant, but not large, reductions</sup> in employment of between 5% and 10% per annum.<sup>The most significant reduction in labor did</sup> not occur until after the first price review. In 1996<sup>there was a reduction of over 20% of the</sup> workforce.

Evidence from Argentina is harder to gather. However,<sup>some evidence for two of the three</sup> distribution companies in Buenos Aires (the three<sup>account for 60% of the electricity distributed in</sup> the country) is available. Table 14 summarizes<sup>this information.</sup>

**Table 14:** Employment changes **in key** Argentinean distribution<sup>businesses</sup>

|**_Employment_**|**_Edesur_**|_Edenor_<br>|
|---|---|---|
|January 1992|7,417|6,368<br>|
|October 1994|4,677|3,759<br>|
|% change|-36.9|-41.0|

_Source:_ Reforning Provincial Utilities: Issues, Challenges<sup>and Best Practice (1996)</sup> <!-- Start of picture text --> j. ....... , . . ,. z... .. . _ .. . .. . . . , ; ,<br>1986 1988 1992 1994 1996<br>5.00%<br>0.00%<br>-5.00% .<br>-10.00% _'5<br>-15.0 00<br>-20.00% g ..........<br>-25.00%<br><!-- End of picture text --> Note: No data is given for 1990, the year of the reforms As can be seen from the table, in the space of less than<sup>two years, there were reductions of around</sup> 40% of the workforce, significantly more than<sup>occurred in the RECs over a comparable time</sup> period. Furthermore, this table understates the<sup>true impact. In Arge,ntina not only was there</sup> significant over-staffing, there was also misuse<sup>of overtime. In 1992 the electricity distribution</sup> workers in the companies in the table worked an average<sup>40 hours of overtime each month. By</sup> 1994, this had been reduced to between three and five hours<sup>per month. Part of this might be</sup> explained by the freeing-up of wages. Overtime may have been used by public sector management as a way of topping off low public sector<sup>wages for their staff. The move to the</sup> 23
**Figure 3: Relationship between governance and entrepreneurship**

<!-- Start of picture text -->
1.0%<br>0.8%<br>0.6%<br>0.4%<br>0.2%<br>0.0%<br>‐2 ‐1.5 ‐1 ‐0.5 Governance 0 0.5 1 1.5 2<br>Entry Density Rate<br><!-- End of picture text -->

Source: 2008 World Bank Group Entrepreneurship Survey and Kauffman and others (2008).

What is more, the majority of the surveyed countries with a negative business growth were those that suffered a war, civil unrest or remarkable political instability during the eight-year period (e.g. Lebanon, Bosnia Herzegovina and Haiti).

# _3.1.2 ‘Red-tape’ reduction_

The indicator used to measure the bureaucratic and legal hurdles an entrepreneur must overcome to incorporate and register a new firm is the “Ease of Starting a Business” indicator from the Doing Business Report. Unsurprisingly, the data shows a strong relation between a cheap and fast incorporation process as measured by the Ease of Starting a Business ranking, and the number of new businesses per capita (Figure 4).<sup>3</sup>

> 3 For additional discussion, see Klapper, Amit, and Guillem, 2008.

9
19 this can be done on the farm through the promotion of irrigation and horticulture enabling multiple crops a year, or through diversification into livestock products such as poultry, eggs or dairy, which are less seasonal.

Where demand for (seasonal) off‐farm labor exists, the focus may be on reducing barriers to mobility, as even small initial travel costs (compared with the potential gains) may compound the effects of inexperience, uncertainty and credit constraints to prevent subsistence farmers from accessing it, as illustrated by Bryan, Chowdhury and Mobarak (2014) in Bangladesh.<sup>16</sup> Or, in the absence of such labor demand, the role for off‐farm employment generation nearby and thus secondary town development, becomes important in accelerating poverty reduction, as discussed by Ingelaere et al. (2017) in Tanzania.

_African households are not unduly tied to agriculture ._ The common view is that families in rural Africa rely more on agriculture compared with other parts of the developing world. Davis, Di Giuseppe, and Zezza (2017) revisit this using comparable employment and income aggregates from 41 national household surveys (14 from SSA of which 6 are LSMS‐ISA) from 22 countries (9 from SSA). They confirm that agriculture remains the mainstay of rural livelihoods in SSA. Virtually all rural households have an on‐farm activity (92 percent on average across countries). But this also holds in other regions (85 percent), with little change observed across countries over time or by GDP.

Rural households also derive about two‐thirds of their income from on‐farm agriculture, compared with one‐third (on average) in other developing countries. But when differences in the level of development are taken into account (as reflected in GDP per capita), Africa is not on a different structural trajectory from the other developing regions. There are nonetheless some important differences.

Rural households in Africa are less engaged in wage employment, both on and off the farm. With the exception of Malawi, where it contributes 15 percent of income, the share of agricultural wage income is only 5 percent on average. This suggests that the second‐round effects from a food price increase through the agricultural wage labor market (Ivanic and Martin, 2008) are likely limited in SSA, unlike in India (Jacoby, 2016) or Bangladesh (Ravallion, 1990) where about a third of rural households are involved in agricultural wage labor, contributing 15‐20 percent of income (Davis, Di Giuseppe, and Zezza, 2017, Table A2‐3). Far fewer households are also involved in nonfarm wage employment, even after controlling for the level of development, resulting in a small share of nonfarm wage income in total income (8 percent compared with 21 percent in the rest of the world). Most off‐farm income in Africa is derived from informal self‐ employment.

> 16 They show how a US$ 8.5 grant or credit incentive (conditional on seasonal migration), equivalent to the round trip fare, induced 22 percent of households to send a seasonal migrant, increasing consumption at origin by 30‐50 percent and re‐migration 1‐3 years after the program by 8‐10 percentage points.
Table <u>5: Impacts of hosting refugees on the components of household consumption expenditure per capita</u>

|||Food<br>||<br> <br>|Education<br>Households<br>|<br>|O<br>|ther non-foo<br>|d<br>|
|---|---|---|---|---|---|---|---|---|---|
||All|Rural|Urban|All|Rural|Urban|All|Rural|Urban|
|Refugee Intensity|-0.163**|-0.220**|0.126|0.365|0.584|-0.562|-0.207|-0.238|-0.007|
||(0.082)|(0.087)|(0.162)|(0.453)|(0.523)|(1.000)|(0.146)|(0.174)|(0.176)|
|Constant|9.366***|9.651***|7.779***|-5.706*|-7.819**|4.157|7.559***|7.263***|8.250***|
||(0.488)|(0.529)|(0.881)|(3.010)|(3.570)|(5.120)|(0.928)|(1.109)|(1.237)|
|Round FEs|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Fixed Effects (FEs)|HH|HH|HH|HH|HH|HH|HH|HH|HH|
|Controls|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|R-squared|0.067|0.078|0.048|0.066|0.075|0.056|0.029|0.029|0.069|
|# of EAs|433|290|143|433|290|143|433|290|143|
|N|12937|9337|3600|12937|9337|3600|12937|9337|3600|

_Notes:_ This table provides estimates of the impact of logarithm of refugee intensity on logarithm (first three columns) or “started log” (last six columns) of the components of total household consumption expenditure per capita. Household sample weights from the ESS2 applied. The first, middle, and last three columns report results for food, education, and non-food consumption expenditure per capita, respectively. Columns labeled (1), (2), and (3) report results for all, rural, and urban households, respectively. All columns control for household (HH) FEs, round FEs, and 12 time-varying characteristics. The coefficient estimates for these 12 control variables are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

43
9 20<sup>9</sup> . The minimum amount of 425 CHF applies to total wealth strictly below 300,000 CHF, increasing by 101 CHF for each additional 50,000 CHF until 1,750,000, and by 151.5 CHF for each additional 50,000 CHF. The maximum contribution amounts to 10,100 CHF.

Table 2 offers an overview over the contribution rates and the benefits of the AHV/AVS since its onset in 1948. The benefit structure will be explained below.

||**Table 2 A**<br>|**HV: Con**<br>|**tribution **<br>|**Rates and **<br>|**Benefits si**<br>|**nce 1948**<br>||
|---|---|---|---|---|---|---|---|
|Coming<br>into force|Sing<br>(current|le Benefits<br>prices (cp))|Old-age<br>benefits<br>(cp)<br>average|Sing<br>(corrected|le Benefits<br>by NLI<sup>10</sup>)|Contrib<br>(as|ution Rates<br>% of wage)|
||Minimum|Maximum|(women &<br>men)|Minimum|Maximum|Employees|Self-emp.|
|1948|40|125||407|1,271|4.0%|4.0%|
|1951|40|125||381|1,191|4.0%|4.0%|
|1954|60|142||540|1,279|4.0%|4.0%|
|1956|60|142||505|1,196|4.0%|4.0%|
|1957|75|155||603|1,247|4.0%|4.0%|
|1961|90|200||611|1,358|4.0%|4.0%|
|1964|125|267||685|1,464|4.0%|4.0%|
|1967|138|294||617|1,315|4.0%|4.0%|
|1969|200|400||805|1,609|5.2%|4.6%|
|1971|220|440||719|1,437|5.2%|4.6%|
|1973|400|800||1,051|2,102|7.8%|6.8%|
|1975|500|1,000|735|1,089|2,179|8.4%|7.3%|
|1977|525|1,050||1,094|2,188|8.4%|7.3%|
|1979|525|1,050||1,026|2,053|8.4%|7.8%|
|1980|550|1,100|845|1,020|2,041|8.4%|7.8%|
|1982|620|1,240|966|1,012|2,023|8.4%|7.8%|
|1984|690|1,380|1,085|1,056|2,111|8.4%|7.8%|
|1986|720|1,440|1,142|1,032|2,063|8.4%|7.8%|
|1988|750|1,500|1,201|1,014|2,028|8.4%|7.8%|
|1990|800|1,600|1,292|985|1,969|8.4%|7.8%|
|1992|900|1,800|1,465|988|1,976|8.4%|7.8%|
|1993|940|1,880|1,568|1,005|2,010|8.4%|7.8%|
|1995|970|1,940|1,632|1,009|2,018|8.4%|7.8%|
|1997|995|1,990|1,670|1,018|2,036|8.4%|7.8%|
|1999|1,005|2,010|1,663|1,018|2,036|8.4%|7.8%|
|2001|1,030|2,060|1,626|1,005|2,011|8.4%|7.8%|
|2003|1,055|2,110|1,673|998|1,995|8.4%|7.8%|
|2005|1,075|2,150|1,704|1,007|2,015|8.4%|7.8%|

Source: Bundesamt für Sozialversicherungen (2005), T4.2 on p.99

# **_2.1.3 Benefits in the First Pillar_**

The first pillar aims at providing a basic subsistence level of income to all retired residents with a full contribution period of 45 years for men and 44 years for women. Thus, there is no specified target replacement rate. In reality, the average replacement rate with respect to pre-retirement income is approximately 35 percent. It is much higher for lowincome individuals with a full contribution period, and very low for high income people.

> 9 This second component of the contribution defining total wealth can be seen as an approximation of the present value of additional income.

> 10 NLI = Nominal Wage Index, 100 = year 2000.
- 2 -

# **ABBREVIATIONS AND ACRONYMS**

|BLP|Barbados Light & Power Co., Ltd.|
|---|---|
|CARICOM|Caribbean Community|
|CARILEC|Caribbean Electric Utilities Service Corporation|
|CAT|Catastrophe|
|CCR|Caisse Centrale de Reassurance|
|CEA|California Earthquake Authority<br>|
|CHA|Caribbean Hotel Association|
|CUBIC|Caribbean Uniform Building Code|
|EC|Eastern Caribbean|
|ECCB|Eastern Caribbean Central Bank|
|ESC|East Caribbean (OECS, Barbados, Trinidad & Tobago)|
|EPL|Estimated Expected Loss|
|FM|Factory Mutual|
|FAIR|Fair Access to Insurance Requirements|
|FJUA<br>|Florida Joint Underwriting Association<br>|
|GAAP|Generally Accepted Accounting Principles|
|GDP|Gross Domestic Product|
|IBRD|International Bank for Reconstruction and Development|
|IFC|International Finance Corporation|
|IRI|Industrial Risks Insurers|
|HHRF|Hawaii Hurricane Relief Fund|
|JER|Japanese Earthquake Reinsurance Co.|
|JUA|Joint Underwriting Association|
|LIBOR|London Interbank Offer Rate|
|MAP|Market Assistance Plan|
|MPH|Miles Per Hour|
|OAS|Organization of American States|
|OECS|Organization of Eastern Caribbean States|
|PCS|Property Claim Service|
|P+I|Principal and Interest|
|PML|Probable Maximum Loss|
|ROL|Rate on Line (reinsurance price)|
|SPV|<br>Special Purpose Vehicle|
|T&D|Transmission and Distribution|
|VR|Vulnerability Reduction|
|WTO|World Trade Organization|
|XL|Excess of Loss (reinsurance contract/cover)|