rafmacalaba/datause-encoder-data huggingface dataset:
need to repatriate capital back to support the existing business rather than to invest in growth opportunities and new ventures<sup>11</sup> .

In such an environment, diligence on the part of supervisors supported by robust regulatory requirements directed at change of control and market exit is critical, as is the monitoring of the extent that shareholder support for local operations can be expected. Improved efforts in liaison between supervisors will be of value.

In addition, although not all markets have seen exposure to the effect of longterm guarantees in contracts, this is a recurring issue in many markets. Such guarantees can take many forms. Examples include the guarantee of a minimum interest rate for investment accrual, the guarantee of a particular level of ultimate benefit (with the implied rate of return associated with such a benefit), the guarantee to convert to an annuity at a defined point of time and at a defined rate, or the guarantee to renew the contract at defined prices or terms. The lesson of the current times is that such guarantees must be regarded with caution.

Many emerging and transition insurance markets face the effects of these challenges either directly or indirectly. These effects can be magnified because of the relative size of the economy or the specific circumstances in the sector. For example, it is common for these markets to have one or more of the following:

- Relatively strong guarantees in product mixes. Where such guarantees are being given, sometimes as a direct result of regulatory requirements that may mandate product features or include procedural limitations that would impede products without such guarantees, the continuation of this course should be reviewed to provide the relevant policy objectives through alternative methods.

- A greater dependence on international reinsurance markets to support local companies.

- Even where local investment markets have been more favorable, the pressures on multinationals has led to exits from local markets.

# **3 International Institutional Developments**

A number of international institutions are pursuing initiatives of unprecedented scope in their potential impact on the insurance sector and its operating conditions.

> 11 See , for example, Industry Outlook Reports from Moody’s Investors Service.

14
consumption and economic activity falling and the economy heading toward a recession, the U.S. Federal Reserve adopted an aggressive monetary policy to mitigate these effects, reducing the funds rate 27 times from 6.5 percent in March 2001 to 1 percent in June 2003 (see Figure 35 for the evolution of the Federal Funds rate).<sup>30</sup> Testifying before Congress on July 15, 2003, Federal Reserve Chairman Alan Greenspan stated that the economy was not ―showing convincing signs of a sustained pickup of growth,‖ such that a further interest rate easing could encourage growth without ―ultimately stoking inflationary pressures‖. Other events in the U.S. also contributed to the Federal Reserve‘s decision to maintain the low interest rate policy. For instance, after the 9/11 terrorist attacks, U.S. stock markets responded with a sharp decline, raising concerns about the health of the U.S. economy after the terrorist attacks. As a result of the active monetary policy, the U.S. economy recovered quickly and the ―dot-com‖ recession was short-lived.<sup>3132</sup>

**Figure 35: Long-term Effective Federal Funds Rate**

<!-- Start of picture text --> 25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Source: Federal Reserve.<br>Jul-54 Jul-56 Jul-58 Jul-60 Jul-62 Jul-64 Jul-66 Jul-68 Jul-70 Jul-72 Jul-74 Jul-76 Jul-78 Jul-80 Jul-82 Jul-84 Jul-86 Jul-88 Jul-90 Jul-92 Jul-94 Jul-96 Jul-98 Jul-00 Jul-02 Jul-04 Jul-06 Jul-08 Jul-10<br><!-- End of picture text --> 56. The Fed‘s monetary policy and low interest rates had a significant impact on the housing market. A 2008 paper by the Federal Reserve Bank of St. Louis finds that ―monetary policy has significant effects on housing investment and house prices and that easy monetary policy designed to stave off the perceived risks of deflation in 2002-2004 has contributed to a boom in

> 30 Rajan (2010).

> 31 According to the National Bureau of Economic Research, the 2001 recession lasted 8 months from March 2001 to November 2001 (shorter than the average duration of a recession in the US in the post-war era).

> 32 The expansionary stance of monetary policy, however, does not explain why real interest rates stayed low after the expansionary monetary policy was scaled back after 2004.  One of the factors explaining the persistence of low interest rates may also be the substantial asset reallocation from equities to fixed income instruments during and after the bursting of the tech bubble. This trend was further enhanced by the decision of monetary authorities to invest in US treasuries (e.g., the People’s Bank of China, Japan and other countries as shown also in Figure 25). Also, global asset scarcity caused by the Asian Financial Crisis of 1997-98 contributed to a fall in US interest rates.  Also, there was a growing consensus that the developed economies had become less risky—as a result of the “Great Moderation”—which also contributed to low interest rates.

42
# **Annex: More Details on Assessment of the Modalities**

# **_1:  Agricultural Market Access_**<sup>**_8_**</sup>

Agricultural market access uses a tiered formula for tariff cutting, which specifies proportional cuts in tariffs which increase when moving between each of four progressively higher bands. The four bands for developed and developing countries shown in Annex Table 1, together with the cuts to be made in bound agricultural tariffs in each band.

**Annex Table 1. Tiered formulae for agricultural tariff cuts, %**

||**Devel**|**oped**|**Devel**|**oping**|
|---|---|---|---|---|
|_Band_|_Range_|_Cut_|_Range_|_Cut_|
|A|<br>0-20|50|<br>0-30|33.3|
|B|20-50|57|30-80|38|
|C|50-75|64|80-130|42.7|
|D|>75|66-73|>130|44-48.6|
|**Average cut**|**Min**|**54%**|**Max**|**36%**|

The Doha tariff-cutting formulae have the economically desirable feature of making larger cuts in the higher—and hence more costly—tariffs. Developing country cuts in each band are two-thirds those of the industrial countries and the bands are wider to allow for the fact that many developing countries would otherwise have more tariffs included in the higher bands.

The average-cut provision in the final row of the table is intended to cross-check the outcome of the formula against the average-cut measure of tariff liberalization used in the Uruguay Round. In the context of the Uruguay Round, this measure notoriously overestimated the extent of liberalization—since countries could choose which tariffs to cut subject to an average-cut constraint of 36% in industrial countries or 24% in developing countries, they had an incentive to make the largest cuts on the lowest tariffs. Under the tiered formula, their largest cuts are on the highest tariffs, making the Doha formula <u>much</u> more ambitious than the Uruguay Round formula. The average-cut provision in the Modalities requires developing countries to increase their tariffs in all bands if they fail to meet a 54% average-cut and allows developing countries to reduce all of their cuts if their average-cut exceeds 36%.

Special provisions apply to two sets of products. Tariff escalation provisions impose higher cuts on processed products. If their tariffs are higher than their raw or intermediate products they are moved into the next higher band. If they are in the highest band, the cut imposed is raised by 6 percentage points. Tropical and diversification products will be subjected to deeper-than-formula cuts. Under one proposal, tariffs below 25% will be reduced to zero, and no sensitive product treatment will be permitted. Under a second option, tariffs below 10% will be reduced to zero, while higher tariffs will be reduced by

> 8 This section and the following section are based on Laborde, Martin and van der Mensbrugghe (2008).

16
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[26] Reinhart, Carmen M., and Kenneth S. Rogoff (2010) “Growth in a Time<br>of Debt,” American Economic Review, 100(2).<br>[27] Romer, Christina D. and David H. Romer (2010). “The Macroeconomic<br>Effects of Tax Changes: Estimates Based on a New Measure of Fiscal<br>Shocks,” American Economic Review, 100(3).<br>[28] Saez, Emmanuel (2001). “Using Elasticities to Derive Optimal Income<br>Tax Rates,” Review of Economic Studies 68(1), pages 205-29.<br>[29] Talvi Ernesto and Carlos A. Vegh (2005). “Tax Base Variability and Pro-<br>cyclical Fiscal Policy in Developing Countries.” Journal of Development<br>Economics 78 (October).<br>[30] Van den Noord, Paul (2000). “The Size and Role of Automatic Fiscal<br>Stabilizers in the 1990s and Beyond” OECD Economics Department<br>Working Papers No. 230<br><!-- End of picture text --> <!-- Start of picture text --> 34<br><!-- End of picture text -->
# **References:**

Antràs, P., 2019. Conceptual Aspects of Global Value Chains (No. w26539). National Bureau of Economic Research, Cambridge, MA. https://doi.org/10.3386/w26539 Antràs, P., 2021. De-Globalisation? Global Value Chains in the Post-COVID-19 Age. 2021 ECB Forum: "Central Banks in a Shifting World" Conference Proceedings.

Antràs, Pol and Davin Chor, “Organizing the Global Value Chain,” Econometrica, November 2013, 81 (6), 2127–2204.

Automotive News, 2011. Opel, Renault production hit by shortage of Japanese parts.

Baldwin, R., 2020. Thinking Ahead on COVID-19 and GVCs. World Bank Lecture, 22 April 2020. Available at: https://www.worldbank.org/en/news/video/2020/04/22/trade-and-global-value-chains-in-the-ageof-covid-19 Boehm, C.E., Flaaen, A., Pandalai-Nayar, N., 2019. Input Linkages and the Transmission of Shocks: FirmLevel Evidence from the 2011 Tōhoku Earthquake. The Review of Economics and Statistics 101, 60–75.

Bündnis Entwicklung Hilft, 2011. World Risk Report 2011. Berlin: Bündnis Entwicklung Hilft.

Canis, B., 2011. The Motor Vehicle Supply Chain: Effects of the Japanese Earthquake and Tsunami. Congressional Research Service.

Carvalho, V.M., Nirei, M., Saito, Y.U., Tahbaz-Salehi, A., 2016. Supply Chain Disruptions: Evidence from the Great East Japan Earthquake (No. ron287), Discussion papers, Discussion papers. Policy Research Institute, Ministry of Finance Japan.

Constantinescu, C., A. Mattoo, M. Ruta, 2020. The Global Trade Slowdown: Cyclical or Structural?, The World Bank Economic Review, Volume 34, Issue 1, February 2020, Pages 121–142, https://doi.org/10.1093/wber/lhx027 Egger, P., Larch, M., 2008. Interdependent preferential trade agreement memberships: An empirical analysis. Journal of International Economics 76, 384–399. https://doi.org/10.1016/j.jinteco.2008.08.003 21
This paper has sought to identify the impacts of WTO reforms on farm households in rich and poor countries. It has done so via innovative use of newly available household survey data that identify the income sources and degree of earnings specialization of households. This proves to be a critical factor in assessing the household welfare impacts of trade reforms. In the rich countries, we focus our attention on the United States, where survey data permit us to assess the impacts of trade reforms by wealth decile and commodity specialization. In the poor countries, we focus our attention on the poverty headcount – among both farm and non-farm households.

Our findings highlight the fact that wealthy farmers are the main beneficiaries of current trade policies aimed at protecting agriculture in the rich countries. Furthermore, these benefits tend to be concentrated in a few products that receive very high levels of support presently. In the United States, rice stands out – followed by cotton, sugar and dairy. When we look at aggregate farm household welfare in the United States, it is little affected by agricultural trade policy reforms. This is because many of the farm products receive little or no support and improved market access in other countries benefits exportoriented producers. Indeed, this is why the average farm household in Australia, Canada and New Zealand is expected to gain from rich country agricultural trade reforms. A second reason why the average farm household in the US is not more severely affected by trade policy reform stems from the degree of earnings diversification in that country. On average, only 8% of farm household income in the US is derived from farming. This income diversification is also critical in Japan where just 12% of farm income is obtained from on-farm earnings. As a consequence, while Doha trade reforms cause on-farm incomes to drop by 16% in Japan, the average farm household impact is just 1.4%.

38
indigenous students. An additional IDB-sponsored evaluation found that _telesecundaria_ schools were effective in increasing math and language test scores. The report found that _telesecundarias_ entirely eliminated the math performance gap between _telesecundaria_ and non- _telesecundaria_ students, though _telesecundaria_ education was not as effective in Spanish instruction (Castro, Wolff and García 2001).

An additional evaluation by Paqueo and Lopez-Acevedo (2003) studied the effect of CONAFE support on rural and indigenous children using a production function, measuring performance by sixth grade Spanish test scores. The study found that CONAFE improved learning outcomes for poor students, but that it appeared to have no distinct effect for indigenous poor students.

# **Modeling Background and Achievement**

Prior evaluations imperfectly measure CONAFE’s effect because student socio-economic backgrounds differ markedly between CONAFE and non-CONAFE schools. For example, practically all indigenous students attend CONAFE schools, so one cannot determine the effect of CONAFE on indigenous students by looking at only indigenous students—one would have no appropriate group against which to compare those students. Construction of a control group of non-indigenous students, however, is difficult—indigenous students are more disadvantaged than their non-indigenous peers, and indigenous students may lack Spanish as a mother tongue. So we seek to develop a model that can use available data on student test scores, CONAFE support, and student background to distinguish the effect of CONAFE on student achievement from the effect of student background on student achievement.

8
**Figure 3: Provincial government expenditure on education in relation to local GDP, 2000‐2008**

<!-- Start of picture text --> 2000<br>Provincial government<br>1800 spending on education<br>per capita (RMB)<br>1600<br>1400<br>1200<br>2008<br>1000<br>800<br>600<br>400<br>200<br>2000<br>0<br>1500 15000<br>Provincial per capita GDP (RMB, log scale)<br><!-- End of picture text --> Source: China Statistical Yearbooks 15
PROVISION OF WATER TO THE POOR IN AFRICA

**Figure 1.5   Price per cubic meter by source in peri-urban Maputo, 2001 and 2006**

<!-- Start of picture text --> 25.0<br>22.3<br>20.6<br>19.6<br>20.0<br>16.6 16.5<br>15.0<br>11.3<br>10.1<br>10.0 8.7 9.2<br>5.0 3.3<br>0.0<br>AdeM SSIP Neighbours tap Public SP Private w ell/bh<br>connections connections<br>BA 2001 BA 2006<br>Source: Thompson, SAL Consultants 2007.<br>Figure 1.6 Percent price increase by source in peri-urban Maputo, 2001 and 2006<br>400<br>340<br>350<br>300<br>257<br>250 223<br>200<br>164<br>150 119<br>100<br>50<br>0<br>AdeM SSIP Neighbours tap Public SP Private w ell/bh<br>connections connections<br>Price per cubic metre in MT<br>% price increase 2001 - 2006<br><!-- End of picture text --> Source: Thompson, SAL Consultants 2007.

**Figure 1.6   Percent price increase by source in peri-urban Maputo, 2001 and 2006**

Source: Thompson, SAL Consultants 2007.

Finally, by dissagregating the different types of peri-urban neighborhoods by social, water, and density characteristics, one can see that in the outer peri-urban areas where the utility’s piped network did not reach, SSIPs quickly filled the gap (table 1.5), while in inner peri-urban (slum) areas, purchases from those with house connections were more important than public standpipes.

14
_Figure 4: Mean change in disposable income and decomposition of change_ <!-- Start of picture text --> MT IE IT EL CY SK ES PT EE FR NL PL SI DE LV HU SE BG RO FI LT BE DK LU AT CZ HR<br>Market incomes Automatic stabilisers New work compensation schemes - compensation part Work compensation scheme - earnings part Other COVID policies Total<br>16<br>12<br>8<br>4<br>0<br>-4<br>-8<br>-12<br>-16<br>-20<br>-24<br><!-- End of picture text --> Note: See Figure A4 in the Appendix for results by income groups. Mean change in disposable income refers to the difference between Scenario A vs. D. The decomposition itself is based on all four scenarios. Source: Own calculations based on EUROMOD I4.0+.

The increasing pattern of automatic stabilizers is most likely the effect of the contribution of taxes and social insurance contributions to changes in mean disposable income. To dig deeper into the latter, Figure 5 and Figure A5 in the appendix break down the effect of automatic stabilizers into the contribution of specific tax-benefit instruments: (i) social insurance contributions, (ii) taxes, (iii) unemployment benefits, (iv) social assistance benefits, and (v) other benefits. Figure 7 shows that in all countries, except Bulgaria, Denmark, Estonia, Italy and Sweden, the contribution of taxes and social insurance contribution exceeds that of benefits. In 13 countries, taxes contribute the most to automatic stabilization. In Bulgaria, Denmark, Estonia, Sweden and particularly in Italy, unemployment benefits pay the most important role in cushioning the impact of the economic crisis. Social assistance and other benefits play only a minor role, namely because in the short-term, unemployment benefits play the main role in terms of benefits. Figure A5 in the appendix confirms that the effect of tax increases along the income distribution due to the progressivity of personal income tax in most EU countries. The effect of social insurance contributions in uniform or slightly increasing in most countries. In Belgium and Luxembourg, we observe a negative impact of taxes and SIC on changes in mean disposable income. In countries where unemployment benefits cushion household incomes, to some extent (more than 0.5% contribution in at least one quintile), three patterns are observed across the income distribution. In Portugal, Slovenia, Spain and Hungary, unemployment benefits provide more protection at the bottom of the distribution. In Italy, Finland, Latvia, Estonia, Belgium, Bulgaria and Ireland, the largest contribution of unemployment benefits is observed in the middle quintiles. In Lithuania, 15
MULTILINGUAL CHILD ASSESSMENT Table 3. The association between baseline receptive vocabulary and follow-up English receptive vocabulary

|Outcome: Follow-up English receptive<br>|||||||
|---|---|---|---|---|---|---|
|vocabulary|e1|e2|e3|e4|e5|e6|
||b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|
|Receptive vocabulary in English (_z-_score)|0.3554****|||0.2811****|0.3035****|0.2593****|
||0.0478|||0.0477|0.0487|0.0485|
||<0.001|||<0.001|<0.001|<0.001|
|Receptive vocabulary in Swahili (_z-_score)||0.2669****|||0.1618****|0.1034**|
|||0.0472|||0.0469|0.048|
|||<0.001|||0.0006|0.0318|
|Receptive vocabulary in Luo (_z-_score)|||0.3157****|0.2139****||0.1812****|
||||0.0465|0.0451||0.0463|
||||<0.001|<0.001||0.0001|
|Constant|–0.0119|–0.0025|–0.003|–0.0104|–0.0107|–0.0099|
||0.0469|0.0483|0.0474|0.0454|0.0458|0.045|
||0.7998|0.9585|0.95|0.8187|0.8158|0.8267|
|_R_-squared|0.1332|0.0696|0.1009|0.1737|0.1560|0.1821|
|N. of cases|442|442|442|442|442|442|

Notes. English, Swahili, and Luo receptive vocabulary are measured using three assessments based on the British Picture Vocabulary Scale (BPVS). Vocabulary scores are age-adjusted _z-_ scores for children ages 2 to 6 years. Baseline and follow-up were conducted approximately 5 weeks apart. * _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01, **** _p_ < 0.001.

37
its project implementation capacity. The Local Government Engineering Department (LGED) has performed excellently in the past and should continue to be the principal agency for development of small water schemes in collaboration with local users.

The other important organizations are the research institutions, like the River Research Institute (RRI), the Surface Water Monitoring Cell (SWMC) and the Geographical Information System (GIS) unit. SWMC and GIS have developed as excellent centers for water modeling and mapping under the Flood Action program (FAP). They should be maintained, at least in their present form, for future assistance in water planning including development of the national water plan, either with WARPO or as independent units.

The overall management of the water resources in Bangladesh would therefore be shared between state water agencies, users of water including the public, NGOs, and other government agencies in agriculture, industry, commerce, water and sewage, public health, municipalities, inland water transport, roads and highway, fisheries, forestry, environment, etc.
The first step of pre-listing was intended to mitigate undercoverage that had plagued previous data collection efforts in 2005 and 2008 and to ensure that the spatial distribution of households surveyed would follow more closely the spatial distribution of poverty. A poverty mapping exercise was conducted using the Elbers et al. (2003) methodology and the 2010 Population Census to estimate household welfare (proxied by per capita consumption) for the entire population. Although the use of highly localized poverty mapping approaches for community or individual targeting is subject to important caveats given the potential for prediction error (see Elbers et al, 2007), this initial exercise was widely viewed by Indonesian policymakers as a means of limiting the scope for local leaders and enumerators to systematically distort benefits away from intended groups. Government planners estimated enumeration quotas separately for each district using consumptionbased poverty lines from the July 2010 National Socioeconomic Survey (known as _Susenas_ ).<sup>10</sup> All households in each village with a predicted per capita consumption level below the enumeration quota cutoff were included on a pre-listing to be surveyed for inclusion in the UDB. Based on this exercise, about 27 million households, or 43 percent of the population were pre-listed to be surveyed for registration in the UDB.

The second step of the registration process aimed to incorporate community-led modifications of the pre-listings in the field. Nationwide, about 10.3 million households were removed from the prelistings. The vast majority of these households, about 7.7 million, was removed based on community suggestions, as they were considered non-poor, while the remaining could not be found (e.g., due to relocation). In addition, about 8.4 million households that were initially not on the pre-listings were registered based on community suggestions. As a result, in total, about 25.2 million households, twothirds of whom were on the enumeration pre-listings, were registered in the UDB nationally, with varying coverage across districts (TNP2K, forthcoming).

Conducted in July-August 2011, the UDB registration survey comprised household-level information such as demographics, housing characteristics, sanitation, access to basic domestic energy services,

> 10 Administered to a sample of households this is representative at the district level, _Susenas_ includes a detailed consumption module which is used to estimate poverty lines.

9