rafmacalaba/datause-encoder-data huggingface dataset:
continuing with our example, if the EC is permitted to bid to retire the right of retaliation against its feta cheese exporters in the auction run by Honduras, the EC will place a bid that “beats” the highest possible bid that either Canada or the U.S. could “conceivably” make, and the right of retaliation will be retired.<sup>12</sup> How does the extended auction perform with respect to the three dimensions of benefits described in section 3.1? Here we discuss the ability of the extended auction to deliver on each of these dimensions relative to no auction. In the next subsection, we will compare the relative merits of the basic and the extended auctions.

Consider first the incentives for compliance. Interestingly, while we have just observed that there will be no retaliation against the infringing government when it is permitted to bid to retire the right of retaliation against it, it is nevertheless the case that incentives for compliance will be created by the extended auction: they simply take the form of _cash payments_ made by the infringing government to the auctioneer. Hence, with regard to incentives for compliance, relative to no auction the extended auction accomplishes two things: it increases the cost of non-compliance faced by an infringing government; and it changes the form of payments normally associated with WTO “compensation,” from retaliatory tariffs to cash. In effect, then, the extended auction provides a way to _induce_ infringing governments to pay cash compensation to successful claimants in a WTO dispute.

Consider next the readjustment of concessions. Of course, the flip side of the compliance effect of this cash payment is the readjustment of concessions for the

> 12There are a number of more intricate technical issues that arise in constructing the equilibria of the extended auction, and we refer the interested reader to Bagwell, Mavroidis and Staiger (2003) for details.

26
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WPS3945 the empirical work). Replacing Mj in 1 with ABMij and differencing the equation across children within a household, yields where ∆ is the difference operator and ∆ABMj = ABMij. The OLS estimates of β1,β2 and β3 from this regression will be consistent provided that E [ABMij∆εj] = 0.<sup>18</sup> Note that while we have differenced out any time invariant household unobservables, it is conceivable that a time varying unobservable, such as an idiosyncratic shock, may have infl uenced the timing of migration as well as the educational outcomes of group I siblings in the household. Since we are interested in precisely this aspect of migration, i.e., its capacity to release credit constraints, this presents no problem.

# 4 Main Results

Tables 1-5 present the estimation results for the three measures of school attainment and the two measures of child labor, respectively. Both measures of labor market activity use the more restrictive de fi nition of work. The fi rst speci fi cation in all cases (column 1) presents the OLS estimates of the migration coefficient and its interaction with child gender under the assumption that the migration decision is uncorrelated with unobserved village and household attributes. The full set of controls (see Appendix Table A3) are included in this speci fi cation.

In speci fi cation 2 we relax the assumption that the migration decision is uncorrelated with unobserved household characteristics which could in fl uence schooling choices. Since the endogeneity bias in the migration coefficient could work in either direction, we have no priors on the direction of the bias. The instrument in this speci fi cation is the village migration network and its interaction with child gender. The explanatory power of the instruments, conditional on the included household and child characteristics, is extremely high in all cases. The coefficient estimates for the full set of controls for this speci fi cation is presented in Appendix Table A3.

In speci fi cation 3, we add the number of adult males to the set of controls. It fails to attract a signi fi cant coefficient and the strength of the instruments is unaffected. This con fi rms that the number of adult males exercises no residual in fl uence on schooling or

> 18 Note that since ηjv differences out of this equation, we do not need to be concerned about the possibility that migrant households constitute a selective sample (i.e, that E �ηjv|Migrant� = 0).

15
This paper addresses the question whether the reforms were successful in raising the share of the world market price received by growers and improving transmission of price signals from the world to domestic markets in the case of coffee. An error-correction model is specified to account for the dynamic nature of price adjustment. Short-run price transmission, the speed of adjustment and the equilibrium producer price share are estimated prior and after the reforms. Asymmetric price transmission is tested in both periods to check whether price increases are passed through to producers as fast as price decreases and whether the nature of the asymmetry has changed after the reforms.

It should be noted that the existence of price intervention does not _per se_ imply imperfect price transmission. A constant export tax is perfectly compatible with full transmission. Other policies, such as export quotas, may impede transmission. At the limit, fixed producer prices imply zero transmission of world market signals. However, administered prices tend to change over time in response to world market prices, so positive transmission is usually found when time-series data are used. Applying an error-correction model allows evaluation of how the transmission works under different policies.

Several previous papers investigate the responsiveness of domestic prices in developing countries to fluctuations on the world commodity markets. The evidence of the relationship between world market prices and domestic prices has been mixed. The estimates of the elasticity of transmission from border to domestic markets seem to be highly sensitive to the methodology applied.

Hazell _et. al._ (1990) examine whether the volatility in the world market prices has been passed through to producer prices in developing countries. The authors use data from 22 countries during 1961 to 1987 to test whether price instability has increased over time and whether fluctuations in domestic markets followed the variability of the world prices. The methodology involves obtaining the residuals from a trending price regression and regressing their absolute values on the time component to test whether variability has increased over time. The authors find that world market prices indeed grew more volatile over time, but that price variation was explained more by declining average prices than by absolute variability. The fluctuations in world market prices were in general transmitted to countries’ export unit values, but not to producer prices, since the real exchange rates and government intervention in agriculture played a buffering role. In the case of coffee high correlation between producer prices and export prices is found, but as with other commodities there is no evidence of greater integration between domestic and export prices.

4
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Nathan, M. J., Kim, S., & Grant, T. S. (2009). Instituting change in classroom discourse structure: Human and computer based motif analysis. WCER Working Paper No. 2009-1, Madison, WI: University of Wisconsin-Madison, Wisconsin Center for Education Research.

National Academies of Sciences, Engineering, and Medicine. 2018. _How people learn II: Learners, contexts, and cultures._ Washington, DC: The National Academies Press.

55
Iviii INTRODUCTnON Table 15 Long-Run and Stationary Population, Momentum, Doubling Year, and Year When Net Reproduction Rate Reaches I by World Bank Region and Country Department

|||Populat|ion<br>(miLl|ions)|Station|-|Year||
|---|---|---|---|---|---|---|---|---|
||||||ary over|Popu-|<br>when||
|Region and<br>country<br>department|1985|<br>2050|<br>2100|Statio<br> <br>ary|1985<br>n- popula<br>tion|lation<br>- mo-<br>mentum|popula<br>tion<br>double|- Year<br>when<br>s NRR=1|
|World|4,844|10,035|11,33|0 11,514|<br>2.4|<br>1.4|<br>2044|<br>2060|
|Borrowers andnon-borrowers|||||||||
|Sub-Saharan Africa|457|1,986|2,634|<br>2,716|5.9|1.6|2008|2060|
|Asia and Oceania|2,574|4,715|5,001|<br>5,058|2.0|1.5|(a)|<br>2055|
|Europe,<br>Middle<br>East,<br>and North Africa|1,147|2.187|2,502|2,539|<br>2.2|<br>1.3|<br>2059|<br>2055|
|America|667|<br>1,146|1,192|1,201|<br>1.8|<br>1.4|<br>(a)|<br>2030|
|Borrowers<br>only|||||||||
|Africa<br>Region|415|<br>1,858|2,480|2,559|<br>6.2|<br>1.6|<br>2008|<br>2060|
|1 Occidental<br>and Central|40|<br>185|<br>250|<br>258|<br>6.5|<br>1.5|<br>2008|<br>2050|
|2 Eastern|106|<br>486|<br>654|<br>674|<br>6.4|<br>1.6|<br>2008|<br>2050|
|3South-Central and|||||||||
|Indian<br>Ocean|52|<br>245|<br>333|<br>343|<br>6.5|<br>1.5|<br>2008|<br>2055|
|4 Western|119|<br>472|<br>592|<br>608|<br>5.1|<br>1.6|<br>2009|<br>2050|
|5 Sahelian|36|<br>175|<br>252|<br>263|<br>7.3|<br>1.5|<br>2009|<br>2060|
|6Southern|S2|<br>295|400|<br>412|6.7|1.6|2007|2055|
|Asia Region|2,384|4,483|4,771|<br>4,827|2.0|1.5|2099|2040|
|1 Other South|134|351|401|<br>412|3.1|1.6|2021|2030|
|2Southeast|274|568|609|<br>616|2.2|1.6|2042|2030|
|3China|1,040|1,626|1,672|<br>1,681|1.6|1.5|(a)|2000|
|4 India|765|<br>1,602|1,732|1,759|<br>2.3|<br>1.4|<br>2041|<br>2015|
|5 Indonesia<br>and Pacific|170|<br>335|<br>356|<br>360|<br>2.1|<br>1.5|<br>2055|<br>2040|
|EMN Region|474|1,451|1,766|<br>1,800|3.8|1.5|2017|2055|
|1 Pakistan<br>and Turkey|146|<br>502|<br>607|<br>618|<br>4.2|<br>1.6|<br>2012|<br>2040|
|2 North Africa|55|170|197|<br>199|<br>3.6|1.6|2014|2050|
|3Middle<br>East|168|<br>657|<br>839|<br>859|<br>5.1|<br>1.6|<br>2010|<br>2055|
|4 Europe|105|<br>121|<br>123|<br>124|<br>1.2|<br>1.2|<br>(a)|<br>2030|
|LAC Region|387|<br>800|<br>850|<br>857|<br>2.2|<br>1.6|<br>2043|<br>2030|
|1 Brazil|136|<br>265|<br>278|<br>280|<br>2.1|<br>1.6|<br>2061|<br>2005|
|2 Central<br>America|104|<br>237|<br>254|<br>256|<br>2.5|<br>1.7|<br>2029|<br>2030|
|3Caribbean<br>and Other|70|<br>150|<br>162|<br>164|<br>2.3|<br>1.6|<br>2037|<br>2030|
|4 Temperate<br>and Tropical|78|<br>148|<br>156|<br>157|<br>2.0|<br>1.5|<br>2101|<br>2025|

Note: World Bank regions and country departments are defined in Exhibit 2. a. Populatlon will not double with projected rates.
# **1. INTRODUCTION**

Temporary migration has come to be regarded by many as a way of squaring the policy circle around international migration.<sup>1</sup> It apparently offers many of the benefits – such as relieving labor shortages in developed countries and aiding development in developing countries - with few of the perceived costs - such as permanent losses of talent in developing countries, and social stresses, fiscal costs and irreversibility in developed countries. Much ink has been spilt making this case and debating how best to design such systems. A notable feature of this debate, however, is that it has been almost wholly based on a priori reasoning or casual empiricism. There is almost no formal empirical evidence about what works in temporary migration schemes or why it works. This paper and its companions seek to fill this lacuna, by conducting a formal evaluation of New Zealand’s newly created Recognized Seasonal Employers scheme, which aims to accept up to 5,000 unskilled horticultural workers per year, the majority from the Pacific Forum States. This paper studies Vanuatu, the largest supplier of labor in the first year, to explore the developmental impacts which the scheme may have.

The RSE scheme has been based on previous experience and analysis, and contains many of the features that constitute current ideas of best practice in seasonal worker schemes. But that does not remove the need for an in-depth evaluation, which will both aid future fine-tuning (and possibly extension) in New Zealand and help other countries contemplating the introduction of similar schemes. Australia, under new Prime Minister Rudd, has just announced its own similar pilot scheme, which will also take workers from Vanuatu.

> 1 See, for example, Winters (2003), GCIM(2005), World Bank (2006a, b), Pritchett (2006).

- 2 -
|Table 4: The Determinants<br>of Child Labor De|cision**by**Gender in<br>G|Peru, 199<br>irls<br>(1)|4 and**1997**<br>**8**<br>|<br><br>Boys<br>(2)|
|---|---|---|---|---|
||Coeff|t-ratio|Coeff|t-ratio|
|Age|0.373|3.04|0.498|<br>4.13|
|Square<br>of age|-0.013|- -2.47|-0.01|6<br>-3.387|
|Female-headed<br>household|0.180|0.67|-0.18|1<br>-0.65|
|Rural<br>xfemale-headed<br>household|-0.510|<br>-0.95|-0.85|0<br>-1.68|
|Native|0.292|0.71|0.397|<br>1.00|
|Rural xnative|0.203|0.45|0.451|<br>1.06|
|Wealth<br>per capita<br>(log)|0.057|0.80|-0.00|6<br>-0.09|
|Rural xwealth<br>per capita|-0.208|<br>-2.18|0.117|<br>1.26|
|# of children<br>under<br>4years|0.183|1.57|0.166|<br>1.52|
|# of adult females<br>between<br>15 & 60|0.024|0.27|0.025|<br>0.27|
|# of eldedy<br>females,<br>over 60|0.171|0.72|-0.18|3<br>-0.73|
|Household<br>size|-0.040|<br>-0.65|-0.11|6<br>-1 .97|
|Adult wage (cluster<br>median;<br>log)|-0.204|<br>-1.34|-0.01|1<br>-0.08|
|Female<br>unemployment<br>rate|0.470|0.56|1.18|1<br>1.37|
|Rural<br>xfemale<br>unemployment<br>rate|-0.206|<br>-0.16|-0.02|5<br>-0.02|
|Household<br>energy:<br>fuelwood/coal|-0.180|<br>-0.70|-0.40|9<br>-1.41|
|Rural x household<br>energy:<br>fuelwood/coal|0.214|0.64|0.45|9<br>1.31|
|Water<br>source:<br>outside.|0.585|1.78|0.58|4<br>1.92|
|Rural<br>xwater<br>source<br>outside|-0.633|<br>-1.65|-0.54|3<br>-1.52|
|School<br>expenses<br>(duster<br>average)<br>)x1O-<br>3|-0.188|<br>-0.44|0.07|5<br>0.19|
|Time to school<br>(cluster<br>average)|-0.001|<br>-0.09|-0.02|2<br>-1.23|
|Rural<br>xtime to school|-0.017|<br>-1.46|0.02|2<br>1.11|
|Educational<br>of oldest<br>prme age female|-0.018|<br>-0.74|-0.05|7<br>-2.29|
|Rural x highest<br>education<br>in household|-0.063|<br>-1.63|0.044|<br>1.19|
|Birth order (=1 for youngest)|0.140|1.54|0.165|<br>1.80|
|Rural xbirth order|-0.108|<br>-1.09|-0.12|3<br>-1.33|
|Rural|2.669|1.80|-0.19|5<br>-0.13|
|Child sick<br>b|-0.108|-0.31|-0.02|3<br>-0.06|
|Rural<br>xchild sick|-1.233|<br>-3.08|0.076|<br>0.19|
|Adult sickb|0.511|1.21|0.24|1<br>0.62|
|Rural xadult sick|0.534|2.13|-0.06|6<br>-0.27|
|_N_|||||
|_Wald_<br>_statistic_<br>_(e with 24 df)_|||||
|p (0c4au+aj)|||||
|<br>R<sup>2</sup>_(within)_<br>|||||
|_R_<sup>_2_</sup>_(between)_|||||
|_R_<sup>_2 _</sup>_(overall)_|||||

- **a** Random-effects prob **i** t egression. Dependent variable is=1 if child worked inincome generating activities and =0 otherwise. The sample consists ofchildren aged 6-18 in panel households only. The regressional **s** o included an intercept, dummies for regions (Uma, Costa, Selva) and a time dummy.

- b Both child and adult sickness are consider **e** ndogenous. d See the appendix for details. Statistically significanat he 5% level. Statistically **t** significant at the 10% level.

36
# **Table A8.1 The Effect of PMT on Height-for-Age of Children 6 to 12 Months: Using Exposed Share of Eligible Life to Measure Program Exposure**

||Poole|d OLS|Com<br>Fixed|munity<br>-Effects|
|---|---|---|---|---|
|Year (2000=1)|0.021|0.087|-0.047|-0.064|
||[0.170]|[0.170]|[0.341]|[0.356]|
|_Exposed Share_*Year|-0.044|-0.122|0.220|0.241|
||[0.187]|[0.190]|[0.476]|[0.500]|
|Gender (boy=1)|0.110|0.122|-0.007|-0.005|
||[0.124]|[0.124]|[0.197]|[0.207]|
|Age|-0.128***<br>|-0.128***<br>|-0.172***<br>|-0.171***<br>|
||[0.036]|[0.035]|[0.057]|[0.058]|
|Mother education (years)|0.003|0.004|-0.024|-0.023|
||[0.015]|[0.015]|[0.022]|[0.024]|
|Father education (years)|0.007|0.009|0.013|0.011|
||[0.015]|[0.015]|[0.022]|[0.022]|
|Mother height (cm)|0.063***|0.062***|0.060***|0.058***|
||[0.012]|[0.012]|[0.018]|[0.019]|
|Father height (cm)|0.047***|0.049***|0.070***|0.070***|
||[0.012]|[0.012]|[0.019]|[0.019]|
|Head of HH (male=1)|-0.025|-0.051|0.282|0.278|
||[0.221]|[0.220]|[0.442]|[0.460]|
|Number of children 5-14 yo in HH|0.018|0.014|0.018|0.027|
||[0.061]|[0.061]|[0.088]|[0.090]|
|HH owns private toilet (Yes=1)|0.124|0.077|0.184|0.195|
||[0.135]|[0.136]|[0.219]|[0.226]|
|HH has sanitation (Yes=1)|0.063|0.001|0.245|0.306|
||[0.171]|[0.194]|[0.396]|[0.453]|
|Type of community (urban=1), C|-|0.363**|-|0.090|
|||[0.170]||[0.460]|
|Distance to district capital (km), C|-|0.001|-|-0.001|
|||[0.003]||[0.006]|
|Distance to bus station (km), C|-|<br>0.028***|-|<br>0.007|
|||[0.011]||[0.021]|
|Land w/ technical irrigation (%), C|-|-0.026|-|0.214|
|||[0.335]||[0.839]|
|Comm. has asphalt road (Yes=1), C|-|-0.255|-|-0.017|
|||[0.164]||[0.383]|
|C h Y=1 C||0065||0272|
|omm. as sewerage system (es),|-|.|-|-.|
|||[0.140]||[0.337]|
|Comm. has piped water (Yes=1), C|-|<br>-0.019|-|<br>-0.172|
|||[0.152]||[0.358]|
|Constant|-17.085***|-17.223***|-20.175***|-19.528***|
||[2.457]|[2.458]|[3.797]|[3.934]|
|R-squared|0.14|0.16|0.57|0.58|

Notes: _Exposed Share_ is the share of a child’s eligible life exposed to the program and is calculated as described on page 11. n is 525. (C) indicates community-level variables. Robust standard errors are in parentheses. (*), (**) and (***) indicate significance respectively at 10%, 5%; and 1%.

43
- 11 -

# Table 2 (Cnntinued)

||Seor and Size o|f Enterprim|. Offered in th|e Firas Wavo (1|991)||
|---|---|---|---|---|---|---|
|Subecor|CSF R<br> <br>Number of<br>Enwrprlae|<br>Avewage<br>Boo<br>Value<br>Millio Kca|CZEC<br>k<br>Numbar of<br>Enterprlc|H REPUBLI<br>Averag Book<br>Value<br>(Milion KlC)|SLOVA<br> <br>Number of<br>Enterprieu|K REPUBLIC<br>Average Book<br> <br>Valuo<br>(Milion K¢J)|
|S;curAiy and nationd dfence||18.3|4|15.4|1|<br>32.1|
|Suyply ndeas of goods|4|274.6|1|614.4|3|<br>161.3|
|_Suplyof_agriculturalg od|43|146.2|14|167.0|29|<br>136.2|
|Teotleinduatry|42|293.1|36|272.2|6|<br>418.1|
|Tranport|39|2S2.1|33|288.5|6|<br>52.0|
|Watsraupy|6|83S.9|6|83S.9|-|-|
|WoodworIn4g ladunry|S1|<br>233.2|34|190.9|17|<br>317.8|
|Total|84182|<br>381.3|57595|<br>433.9|33S12|<br>277.5|

Table 3 Chracteuatl of Enterprise Offered in te Firt 'Wave (1991) (Total)

||Number of<br>Enteprls.|Average<br>Book Value<br>(Mlion K¢s)|Average<br>Equity/<br>Out|Avorugo<br>Equity/<br>Emnploymn|Average<br>Profit/<br>Ouput (S)|Average<br>_PtofW_<br>Equity(%)|Averago<br>Employmm±<br>Output (%)|Averge<br>/<br>Debt/<br>Equiy (%)|
|---|---|---|---|---|---|---|---|---|
|CSFR|1491|<br>381.3|1.22|<br>519.43|6.29|17.59|<br>0.41|<br>140.-|
|Czech Republc|990|433.9|1.24|<br>632.27|7.96|21.49|<br>0.37|<br>139.7|
|SlovakRepubl|ic<br>S01|<br>277.5|1.18|<br>296.44|2.99|9.87|<br>0.51|<br>141.4,|
|**Crisis-affectedCountry**|**ProgressattheNationalLevelinFinancialSector**<br>**Restructuring**<sup>**12 8**</sup>|
|---|---|
||restructuredloans.<br>*<br>Favorableloanloss provisioningtreatmentforrestructuring<br>NPLs.<br>*<br>Nosecondary marketforbank NPLs.|
|Thailand|*<br>Financial RestructuringAuthority (FRA)andAsset<br>Management Corporation (AMC)establishedin1997for<br>resolutionof56closedfmcosandsaleoftheirassets.<br>_*_<br>57outof91fmcos,1outof15banksliquidated.<br>*<br>TB300billiontorecapitalizeviablebanks.<br>*<br>Atleast8banks haveforeignequity;3banks have issued<br>Tier-Icapitalthroughadomesticinnovativecapital<br>instrument(aboutTB80billion).<br>*<br>RecognizedfullextentofNPLswithimplementationofnew<br>loan classification rulesin1998.<br>*<br>Market-based guidelinesandincentivesputinplacefor<br>restructuringofviableNPLs.<br>*<br>Newbankruptcyandforeclosurelawpassedin 1999.<br>Utilizationlow.<br>*<br>AuctionprocessforUS$20billionofshutdownfincosloans.<br>Nocentralizedmechanism forbanksNPLs(onlyprivate<br>sector-based).|

> Source: Compiled from Kawai (2000, Table A13.1)

> While it is necessary that certain reforms be carried out at the national level, it is often the case that when individual countries pursue strategies and formulate policy based on their own self-interest, the benefits derived are often less than optimal for the country concerned and may even be detrimental to its neighbors (so-called "beggar-thy-neighbor" policies). On the other hand, benefits can often accrue to most countries in a region when mutual interests are taken into account and policy is set on a cooperative basis.

It is also true that a country may have little difficulty carrying out reforms in one area, but often policy changes or reforms in related areas are necessary to achieve the desired overall effect or to prevent undesirable consequences. A case in point is Chile's capital controls in the 1990s. Eichengreen (1999) points out in his discussion of Chile's capital controls that taxes on capital inflows are effective only when supplemented by 52