rafmacalaba/datause-encoder-data huggingface dataset:
Table 8A. 9-sector level factors' remuneration in the 52-sector transformation of the IFPRI SAM by Jensen and Tarr (2010) (in current billions of Shillings and percentages)

||Subsistence|Female|Male|Children|Capital|Total|Subsistence|Female|Male|Children|Capital|Total|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Agriculture/Forestry/Fishing|1388.87|537.038|522.182|21.403|815.323|3284.816|19.10|7.38|7.18|0.29|11.21|45.17|
|Mining & quarrying|0|0.24|1.60|0.09|112.821|114.759|0|0.00|0.02|0.00|1.55|1.58|
|Manufacture|187.38|73.68|179.62|0.55|484.590|925.817|2.58|1.01|2.47|0.01|6.66|12.73|
|Electricity. Gas and water|0|1.15|25.74|0|105.310|132.199|0|0.02|0.35|0|1.45|1.82|
|Construction|0|3.65|228.11|0.66|109.834|342.259|0|0.05|3.14|0.01|1.51|4.71|
|Trade|0|41.58|67.71|0.18|955.078|1064.549|0|0.57|0.93|0.002|13.13|14.64|
|Transport & Communication|0|7.80|48.06|0|200.929|256.786|0|0.11|0.66|0|2.76|3.53|
|Rest of services|373|173|376|0|228|1151|5.13|2.38|5.18|0.00|3.14|15.83|
|Finance. Insurance & Business|0|15.04|46.28|0.18|130.113|191.610|0|0.21|0.64|0.002|1.79|2.63|
|Personal service|372.72|158.16|330.16|0.03|98.325|959.399|5.13|2.17|4.54|0.0005|1.35|13.19|
|ALL SECTORS|1948.96|838.34|1449.47|23.10|3012.322|7272.193|26.80|11.53|19.93|0.32|41.42|100.00|

Source: Jensen and Tarr (2010). Note: The SAM in Jensen and Tarr has 52 sectors which have been aggregated to this 9 sectors to make them comparable with the information available in the ILFS (NBS. 2002). The children considered in this SAM are from 10 and 14 years old.

Table 9A. Mean monthly income of paid employees by occupation and sex

||Se<br>|x<br>|Total|
|---|---|---|---|
||Male|Female||
|Legislators, Administrators & Managers|112551|107473|111705|
|Professionals|148253|94606|134261|
|Technicians & Associate Professionals|83700|72689|79875|
|Clerks|111060|76441|92760|
|Service & Shop Workers|42186|20384|30835|
|Skilled Agriculture & Fisheries Workers|18173|9450|16003|
|Craft & Related Workers|110404|61206|105494|
|Plant & Machine Operators & Assemblers|56908|37461|55939|
|Elementary Occupations|19578|14804|18740|
|Total|54423|38888|49954|

Source: Author's own calculations based on the ILFS (NBS, 2002a). Table 9.2.

40
undermine deposit-insurance design. Cull, Senbet and Sorge (2004) produce evidence that in weak institutional environments deposit insurance reduces financial development. Features of a country’s private and public contracting environments have been shown to be important in deposit-insurance adoption and design (Demirgüç-Kunt and Kane, 2002).

In this paper we test if the impact of deposit insurance is different during normal times and economic downturns. It is possible that the positive stabilization effects of deposit insurance can dominate the negative moral hazard effects during economic downturns. During downturns banks may face tightened funding and limited investment opportunities, leaving little room for excessive risk taking. Deposit insurance can then help enhance depositor confidence and prevent systemic bank runs during the crisis, leading to lower risk and greater systemic stability. During non-stress periods, however, the reverse could be true since there would be plenty of investment opportunities and little need for preventing bank runs. Alternatively, deposit insurance could also be a destabilizing (or stabilizing) influence throughout the whole sample period.

For a sample of 4,109 banks in 96 countries over the time period 2004-2009, we find supportive evidence that the influence of deposit insurance on bank risk is different during normal periods versus global systemic downturns. Specially, we show that existing deposit insurance coverage is associated with lower systemic stability and higher bank risk in the 20042006 pre-crisis period. However, the relationship between deposit insurance and bank risk and systemic stability is reversed in the 2007-2009 crisis period. Nevertheless, the overall effect of deposit insurance over the full sample we study remains negative since the destabilizing effect during normal times is greater in magnitude compared to the stabilizing effect during global turbulence.

Our paper is related to Demirguc-Kunt and Detragiache (2002), which studies the link between deposit insurance and the occurrence of banking crisis. Unlike Demirguc-Kunt and Detragiache (2002), who focus on the likelihood of crisis of a particular country, we study the impact of deposit insurance on bank risk and system stability during a global systemic shock and compare it to normal times. Our paper is also related to Fahlenbrach, Prilmeier, and Stulz (2011) and Beltratti and Stulz (2012), who investigate the determinants of bank performance during the recent crisis. While their focus is on bank characteristics that affect performance, we focus on the effect of deposit insurance on bank risk and stability. We also add to the literature that has shown that features of a country’s private and public contracting environment are important in 4
# Figure 2. Factors lowering the elasticity of global trade with respect to global output

A. World trade, actual and trend <!-- Start of picture text --> Index, 100 = 1970 Actual Historical trend<br>2,000<br>1,600<br>1,200<br>800<br>400<br>0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br><!-- End of picture text -->

- C. Aggregate demand components relative to historical trend, 2019

<!-- Start of picture text --> Index, 100 = trend volume in 2019<br>Fixed investment<br>120 Consumption<br>Government expenditure<br>110<br>100<br>90<br>80<br>Advanced EMDEs World<br>economies<br><!-- End of picture text -->

- E. Share of global value chain-related trade in global trade

<!-- Start of picture text --> Share of global trade<br>60<br>50<br>40<br>30<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> B. Elasticity of global trade with respect to global output <!-- Start of picture text --> Estimates of long-run elasticities<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>1970-2019 1970-90 1990-2011 2011-19<br>D. Import content of components of<br>aggregate demand, 2014<br>Percent<br>40<br>30<br>20<br>10<br>0<br>Investment Exports Private Government<br>consumption expenditure<br><!-- End of picture text -->

- F. New trade measures

<!-- Start of picture text --> Number of new policy measures<br>Trade restrictions<br>2,000<br>Trade restrictions excl. U.S.<br>1,600 Trade liberalization<br>1,200<br>800<br>400<br>0<br>2009 2011 2013 2015 2017 2019 2021<br><!-- End of picture text --> Sources: Auboin and Borino (2018); Constantinescu, Mattoo, and Ruta (2020); CPB Netherlands Bureau for Economic Policy Analysis; Global Trade Alert Database; World Bank.

Note: EMDEs = emerging market and developing economies; GVC = global value chain.

A. World trade refers to average of imports and exports, indexed to 1970 = 100. The historical trend is computed over 1970-2008, smoothed using a Hodrick-Prescott filter.

B. Estimates from an error correction model estimated over the period 1970-2019. The model allows both the long-run elasticity of trade with respect to income (which captures trend, or structural, factors) and the short-run elasticity (which is relevant to short run or cyclical developments). For further details on the model specification, see Constantinescu, Mattoo, and Ruta (2020).

C. Trend levels in 2019 are obtained on the basis of the historical average trend growth computed over the period 1995-2008 and rebased to 100. Bars below 100 show deviations of actual 2019 levels from
|**Table**|**3: Regressi**|**on Results fo**|**r Overall Cor**|**ruption**|||
|---|---|---|---|---|---|---|
|Dependent variable: Financially|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Constrained (dummy)|||||||
||**Panel A:**|**Estimated l**|**og-odds ratio**||||
|Overall Corruption (cell average)|0.931***<br>(0.316)|1.027***<br>(0.316)|1.046***<br>(0.316)|1.089***<br>(0.304)|1.125***<br>(0.312)|1.123***<br>(0.310)|
|Annual Sales (2009 USD, logs)||-0.189***|-0.181***|-0.140***|-0.138***|-0.145***|
|||(0.037)|(0.037)|(0.036)|(0.037)|(0.038)|
|Age of Firm (logs)|||0.035|0.038|0.044|0.053|
||||(0.069)|(0.071)|(0.071)|(0.073)|
|Exports (proportion of sales)|||-0.863**|-0.807**|-0.744*|-0.687*|
||||(0.347)|(0.358)|(0.401)|(0.397)|
|Firm Has Female Owners Y:1 N:0||||-0.185|-0.195*|-0.186|
|||||(0.116)|(0.117)|(0.118)|
|Foreign Ownership (proportion)||||0.015|0.009|0.023|
|||||(0.304)|(0.298)|(0.303)|
|Multi-establishment Firm Y:1 N:0||||-0.226|-0.230|-0.220|
|||||(0.181)|(0.189)|(0.194)|
|Firm Purchased Assets Last Fiscal||||-0.576***|-0.560***|-0.567***|
|Year Y:1 N:0||||(0.105)|(0.106)|(0.107)|
|Firm is Audited Y:1 N:0||||-0.214*|-0.225*|-0.189|
|||||(0.121)|(0.123)|(0.127)|
|Hours of Power Outages in a|||||0.000|0.000|
|Month|||||(0.001)|(0.001)|
|Firm Experienced Losses from|||||-0.377*|-0.416**|
|Crime Y:1 N:0|||||(0.199)|(0.201)|
|Firm Inspected/Visited by Tax||||||-0.076|
|Officials Y:1 N:0||||||(0.117)|
|Informal Competition (||||||0.672**|
|average)||||||(0.326)|
|How Much of an Obstacle?: Courts||||||0.296**|
|(average)||||||(0.134)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of observations|19,996|19,996|19,929|19,592|18,765|18,674|
|**Panel**|<br>**B: Margin**|<br>**al effect of th**|<br>**e corruption**|<br>**variable**|||
|Overall Corruption (Cell|0.099***|0.107***|0.109***|0.110***|0.115***|0.112***|
|average)|(0.034)|(0.033)|(0.033)|(0.031)|(0.032)|(0.031)|
|Other controls as above|Yes|Yes|Yes|Yes|Yes|Yes|

All coefficients in Panel A are log odds ratios obtained from logit estimation. Huber-White robust standard errors clustered on country time industry in brackets. *** (1%), ** (5%), * (10%). Sample size varies due to missing data.

38
- Cassar, Alessandra, Andrew Healy and Carl Von Kessler. 2017. “Trust, risk, and time preferences after a natural disaster: experimental evidence from Thailand.” World Development 94:90–105.

- CFR. 2020. “U.S.-Taliban Peace Deal: What to Know.” Council on Forei n Relations . <u>g</u>

- **URL:** _https://www.cfr.org/backgrounder/us-taliban-peace-deal-agreement-afghanistan-war_

- CFR. 2021. “Timeline: The U.S. War in Afghanistan.” Council on Foreign Relations . **URL:** _https://www.cfr.org/timeline/us-war-afghanistan_

- CFR. 2023. “Instability in Afghanistan: Global Conflict Tracker.” Council on Foreign Relations .

**URL:** _https://www.cfr.org/global-conflict-tracker/conflict/war-afghanistan_

- Child, Travers Barclay. 2019. “Conflict and counterinsurgency aid: Drawing sectoral distinctions.” <u>Journal</u> of Development Economics 141:102245.

- Clark, Kate. 2010. “The Layha:The Taleban Codes of Conduct in English.” Afghanistan Anal sis Netwrok . <u>y</u>

- Clark, Kate and Jelena Bjelica. 2018. “One Land, Two Rules: Service delivery in Af hanistan Anal sis Netwrok .

- insurgent-affected areas, an introduction.” <u>g y</u> **URL:** _https://www.afghanistan-analysts.org/en/reports/economy-developmentenvironment/one-land-two-rules-1-service-delivery-in-insurgent-affected-areas-anintroduction/_

- Condra, Luke N., James D. Long, Andrew C. Shaver and Austin L. Wright. 2018. “The Logic of Insurgent Electoral Violence.” American Economic Review 108(11):3199–3231.

- Crowne, D. P., Marlowe D. 1964. “The approval motive: Studies in evaluative dependence.” New York: Wile . . <u>y</u>

28
**Table 1: FDI Spillover Potential, FDI Spillovers, All Domestic Firms**

|Dependent variabl|e: ln_lpirst_<br>|Ordinary<br>|Least Squares<br>|||Instrume<br>|ntal Variables<br>|Two-Stage Lea<br>|st Squares<br>||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|_FDI_<sup>_Y_</sup>_sct_|-0.3300|-0.3243**|-0.0341|0.1777|-0.5360|-0.6277|-1.4594|-0.7505|-2.2952|0.2180|
||(0.134)|(0.012)|(0.790)|(0.360)|(0.680)|(0.201)|(0.112)|(0.129)|(0.187)|(0.643)|
|_FDI_<sup>_Y_</sup>_sct*ownsct_|0.3158<br>(0.228)||||0.5961<br>(0.680)||||||
|_FDI_<sup>_Y_</sup>_sct*marketsct_||0.3979**||||0.8586|2.1830||||
|||(0.028)||||(0.141)|(0.123)||||
|_FDI_<sup>_Y_</sup>_sct*inpsct_|||-0.0844<br>(0.700)|||||1.1346<br>(0.114)|4.0729<br>(0.198)||
|_FDI_<sup>_Y_</sup>_sct*techsct_||||-0.1050<br>(0.147)||||||-0.1177<br>(0.479)|
|ln_capintirst_|0.2726***|0.2727***|0.2734***|0.2728***|0.2664***|0.2661***|0.2665***|0.2660***|0.2643***|0.2665***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Observations|15,137|15,137|15,106|15,137|5,193|5,193|5,152|5,193|5,162|5,193|
|R-squared|0.62|0.62|0.62|0.62|0.14|0.14|0.13|0.13|0.11|0.14|
|Endog. var.|||||1|1|2|1|2|1|
|Instruments|||||3|3|4|3|4|3|
|F-stat. FDI|||||2.81|4.81|19.62|5.35|17.28|4.99|
|F-stat. market_int|||||||13.89||||
|F-stat. inp_int|||||||||10.82||
|Hansenp-val.|||||0.1133|0.0805|0.0967|0.3629|0.8662|0.1458|

Note: p*<0.1, p**<0.05, p***<0.01 (p-values in parentheses). All regressions include sector, subnational region, and year fixed effects. Standard errors are clustered at the country-sector level.

22
# **CGE Analysis of the Impact of Foreign Direct Investment and Tariff Reform on Female and Male Wages**

María C. Latorre (Universidad Complutense de Madrid) Keywords: gender, FDI in services, trade, ad valorem equivalents, endogenous productivity effects.

JEL codes: C68, F21, F23, F17, F16, J16 Acknowledgements: The author wishes to thank David Tarr and Jesper Jensen for very helpful comments and suggestions. The paper was financed under BNPP Growth and Equity Trust Fund number TF012466: “Reducing Trade Costs in East Africa: Analytical Development and Capacity Building.”
<!-- Start of picture text --> NATURAL RESOURCES<br>Mining<br>Sites Potential<br>WO Ge PreciousMetals PreciousMetalsMetals « Diamonos<br>WWD orethertotalsMetal orethertotalsMetalthertotalsMetaltotalsMetalMetal<br>& oirieis<br>Cropland Extent Extent<br>< 10% 10% but High Suitability<br>e 10%=Sov=SovSov<br>@ > 50%<br><!-- End of picture text --> <!-- Start of picture text --> ee CONGO NATURAL RESOURCES<br>SKS<br>SSSA, Mining<br>KORE OPS, Sites Potential<br>Seessee 1. WO Ge PreciousMetals PreciousMetalsMetals « Diamonos<br>KRterete a DEM REP OF CONGO WWD orethertotalsMetal orethertotalsMetalthertotalsMetaltotalsMetalMetal<br>RK0.9. ROKK¢,ws - r . TM & oirieis<br>SKKRRKCES 7 Ww Cropland Extent Extent<br>ee etetetete Zaire { < 10% 10% but High Suitability<br>“CRY BESreticalSOK] FF.Vige a=~ e 10%=Sov=SovSov<br>SRRKS pet) ‘ r @ > 50%<br>ee RK ~~ | =e ne,<br>BhOvo. r YaiI ES \ fis none)/=) = I .<br>Reese = Cuanza Norte = 1 Vea of i<br>LXer’PRCHIO we sek, re { i) ones* + -<br>QS,Sx \ i= é 7 /f Lunda sit }<br>Oees , » = x 4 > i,i 4<br>; \<br>Eee anza Sul- | : J ‘<br>4 . it )<br>\. Benguela Huagpo- | Bie f 7 ~ , . «<br>} \ \, Moxico i<br>>4<br>j \<br>t} \ \ ~<br>@ Huila We= \ ; oe ZAMBIA<br>Namibe * . f5 . ~<br>| } : Cuando Cubango :<br>‘ }o~—— \<br>; ~T"" 8 ‘Cunene \ . \ \ s,<br>med N . —— ><br>NAMIBIA m a, - ANA<br><!-- End of picture text -->

<!-- Start of picture text --> POWER<br>Power Plants Plants | Type and Capacity (MW) and Capacity (MW) Capacity (MW) (MW)<br>HYDROee THERMAL@@ OTHER@@ <01010010100<br>-<br>> 100<br>Power Lines Lines (KV)<br>== Medium lim High<br><!-- End of picture text -->

<!-- Start of picture text --> rr<br>RKO CONGO a POWER<br>RRRSO KS"4entce Power Plants Plants | Type and Capacity (MW) and Capacity (MW) Capacity (MW) (MW)<br>NeneteteterSOSNotetetene” %BS! HYDROee THERMAL@@ OTHER@@ <01010010100<br>ataretatete = as DEM REP OF"°GONGO -<br>SSLSOO i ott e > 100<br>SSSx?RK” SOKO s Zaire. Ww ° Power Lines Lines (KV)<br>RESORY SOSBEEoerates 5 | Uige i == Medium lim High<br>sees ye ° 4 ~ A,<br>eaSee<>S aow r Pen.J 1 My ae|t of *. aks. Nortej=4 fm) ‘ =.a<br>weeds = Cuanza Norte = ] Ve ak = A,<br>een ah ro abs 8<br>MsNatate be ekgpic a<br>BRIS — © 1. be) fag<br>XSsete.Soe©> ‘ < an. =: = é {FFTr. ; unda’ sit<br>oxy geephianza Sul a28 a)<br>o fee YA a’ a an 7 :<br>°;. \e ,° 7Z<br>> 4 ( . y<br>\ Benguela. <n ~~ ) / \ \, Moxico tn<br>4<br>4 . } \ -<br>y _—-& Ww ane ZAMBIA<br> Namibe : e } ~ .<br>‘ H y Cuando Cubango ‘<br>}\~—.<br>, ee "" 48 Gunene \ \ \ ‘\ \ \<br>a ANAMIBIA \ . ‘\ ‘ a eTHS , x “4<br><!-- End of picture text -->
Using historical data from Saudi Aramco’s contract price on butane and propane, World Bank energy specialists estimated the ‘efficient’ LPG price to be about USD 1,428 per ton. This implies JD 15.3 per cylinder to be the final LPG unit price without any subsidy (Masami Kojima<sup>6</sup> , personal communication August 28, 2014).

Two scenarios are used for simulation. In the first scenario, we simulate the full removal of subsidies without any compensating measures by the government. In the second scenario, subsidies reform is combined with the actual cash transfer program that accompanied the petroleum price increases. The cash transfer targets resident Jordanian households (with the households being the unit of reference) with yearly incomes not exceeding JD10,000. The transfer amounts to JD70 per person per year, for up to a maximum of 6 individuals per household (Araar et al., 2013).

**<u>Table 6. Pre and post reform prices of petroleum products</u>**

||Pre-reformprices<sup>1</sup>|Unit subsidy<br>New|prices<sup>2</sup>after removalofsubsidies|Increase, %|
|---|---|---|---|---|
|Gasoline (Octane-90)|0.7|0.1|0.8|14|
|Kerosene|0.52|0.170|0.685|33|
|Diesel|0.52|0.170|0.685|33|
|LPG|6.5|8.8|15.3|135|

Source: Araar et al. (2013). <u>Notes:</u><sup>1</sup> As <u>of October 2012;</u><sup>2</sup> As <u>of November 2012</u> _<u>except</u>_ LPG.

Scenario 1: Subsidy cuts without cash transfers The simulation reveals that the full removal of subsidies on petroleum products would on average lead to an estimated 2.9 percent drop in consumption per capita of households (table 7). For the poorest quintiles, the drop will be higher (3.8 percent). The adverse impact on the poor results mainly from increased LPG prices. The increases in gasoline and kerosene prices have tiny impacts while the increase in diesel price has no impact on consumption. When all households are considered, LPG and gasoline are the two main products to affect household consumption.

**-** **<u>Table 7. Jordan: The impact on the per capita well being of removing petroleum subsidies</u>**

|Quintiles, consumption per<br>capita|Pre-reform,<br>JD<br>Total<br>expenditure<br>s percapita|Post-refor<br>kerosene|m impac<br>LPG|t onpercap<br>gasoline|ita wellbei<br>diesel|ng, JD<br>Total|Change in<br>per capita<br>consumptio<br>n, %|
|---|---|---|---|---|---|---|---|
|Quintile 1|843|-2|-28|-2|0|-32|-3.8|
|Quintile 2|1,240|-2|-34|-6|0|-42|-3.4|
|Quintile 3|1,624|-3|-39|-10|0|-52|-3.2|
|Quintile 4|2,198|-3|-47|-15|0|-65|-3.0|
|Quintile 5|4,336|-4|-65|-27|-9|-104|-2.5|
|Total|2,048|-3|-42|-12|-2|-59|-2.9|

Source: Authors’ calculations based on extrapolated HEIS 2010 data. <u>Notes:</u> Population quintiles <u>based on spatially adjusted consumption per capita before the reform.</u>

> 6 Masami Kojima is a lead energy specialist at the World Bank.

10
The remainder of the paper is organized as follows. Section II identifies four alternative channels through which output shocks can have nonlinear effects on poverty--in the sense of generating not only asymmetric responses but also regime switches and jumps between equilibria. These channels are expec:.ations and confidence factors; credit rationing at the level of firms, bor-owing constraints at the household level; and the "labor hoarding" hypothesis. 'Section IlIl proposes a simple vector autoregression technique (involving the detrended components of real output, real wages, the unemployment rate, and the 1-overty rate) to test for the existence of the two types of asymmetric effects described earlier. This technique is then applied in Section IV to Brazil, using annual data covering the period 1981-99. Finally, Section V summarizes the main results of the analysis, draws some policy implications, and identifies possible directions for future research.

# 11. Sources of Asymmetric Effects: Theory

This section discusses several channels through which, and conditions under which, movements in output (associated, again, with either "rormal" cyclical movements or full-blown crises) can have an asymmetric eff*ect on poverty. Four main classes of explanations are distinguished. The first is based on asymmetric changes in expectations and confidence factors; the second on a "credit crunch" at the firm level, with rationing induced by either adverse selection problems or negative shocks to net worth; the third emphasizes the impact of borrowing constraints on household consumption behavior; and the fourth dwells on "labor hoarding" by firms facing high turnover costs. Whereas credit and borrowing constraints are, in a sense, "induced" by the initial adverse sl-ock to output, the labor hoarding hypothesis can be viewed as capturing a "pure" asymmetric effect.

_5_
Effects of Fiscal Consolidatioi **n** ihe OECD impact on aggregate demand. In the long run output is above baseline due to higher aggregate saving. Although the intertemporal consequences working through expectations are important for this result, the key effect is on the consequence of lower interest rates on the return to investment but also on the cash flow of firms which stimulates private investment. A number of different scenarios for deficit reduction including spending cuts, and corporate or household income tax increases are presented.In addition the important difference between temporary, permanent and anticipated policies is illustrated. From these results it is difficult to see how simple reduced form econometric studies of the relationship between deficit reduction and interest rates could provide robust results. Indeed the empirical literature on the key parts of the transmission mechanism is mixed.

The consequence of fiscal consolidation in the United States and Europe on other OECD economies and on developing countries are quite different. For countries with open capital markets, the transmission of the OECD fial contractions are positive as long as the United States and Europe are not large trading partners for these economies. For other countries that are debt constrained, the significant fall in real interest rates is likely to raise growth in these economics either direcdy through private capital inflows or indirectly by relaxing the balance of payments constraint, allowing more resources to be channeled to domestic investment needs.

27