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23
# WPS3314

## **The Case for Tradable Remedies in WTO Dispute Settlement**

Kyle Bagwell, Columbia University and NBER Petros C. Mavroidis, Columbia Law School, University of Neuchâtel and CEPR Robert W. Staiger, University of Wisconsin and NBER *

### World Bank Policy Research Working Paper 3314, May 2004

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*We have benefited from the comments of an anonymous referee, and from the additional comments of Kerry-Ann Allbeury, Henrik Horn, Jorge Huelta, Alejandro Jara, Matteo Diego-Fernandez, Eduardo Motta, Ben Walther, Jasper-Martijn Wauters and participants in the Bern conference on the reform of the DSU (June 16, 17, 2003). Julia Pain and Rhian-Mary Wood Richards helped us immensely with the selection of documents. Financial assistance by the World Bank is acknowledged.
<!-- Start of picture text --> _ oO<br>=<br>a<br>—Sa<br>a<br>BE<br>a<br>5égZ<br>Z<br>Be<br>t-1 t t+1 t+2 +3. ted t+5 +6 +7 tB to<br>Horizon, h (years)<br><!-- End of picture text --> <!-- Start of picture text --> +<br>a<br>5<br>Ew<br>Ssgy—2<br>=<br>Bes<br>5;a<br>:z2<br>&<br>2.2.Z<br>t-1 t t+1 t+2 +3. ted t+5 +6 +7 tB to<br>Horizon, h (years)<br><!-- End of picture text --> <!-- Start of picture text --> oO<br>=<br>a<br>2g<br>fa<br>eB<br>=3°<br>S<br>&23;<br>2<br>~ a<br>t-1 t t+ t+2 +3. ted t+5 +6 +7 tB to<br>Horizon, h (years)<br><!-- End of picture text --> <!-- Start of picture text --> =<br>5<br>é2<br>veoGi<br>a<br>e<br>a><br>Re<br>Z 2<br>z<br>2Bo2.<br>t-1 t t+ t+2 +3. ted t+5 +6 +7 tB to<br>Horizon, h (years)<br><!-- End of picture text -->
**Table 1: Schooling expansion, 1960–2000**

||1<br>OLS|2<br>FE|3<br>BE|4<br>OLS|5<br>FE|6<br>BE|7<br>OLS|8<br>FE|9<br>BE|
|---|---|---|---|---|---|---|---|---|---|
|_Dependent variable: log year_|_s of schooli_|_ng_||||||||
|Log real GDP per capita|2.185<br>[6.96]**|0.593<br>[1.79]|2.829<br>[3.77]**|1.979<br>[6.44]**|0.301<br>[1.11]|2.443<br>[3.23]**|1.356<br>[3.86]**|0.301<br>[1.11]|1.35<br>[1.52]|
|Sq. log real GDP per capita|–0.105|–0.05|–0.145|–0.111|–0.02|–0.138|–0.082|–0.02|–0.082|
||[5.32]**|[2.54]*|[3.06]**|[5.74]**|[1.26]|[2.96]**|[3.78]**|[1.26]|[1.50]|
|Log real pub. spending|0.041<br>[3.47]**|0.407<br>[15.29]**|0.027<br>[1.09]|0.283<br>[6.83]**|0.102<br>[3.51]**|0.263<br>[2.47]*|0.371<br>[8.81]**|0.102<br>[3.51]**|0.349<br>[3.18]**|
|Log population||||–0.25|0.897|–0.24|–0.349|0.897|–0.34|
|||||[6.08]**|[15.80]**|[2.28]*|[8.02]**|[15.80]**|[3.11]**|
|LAC|||||||0.047||0.019|
||||||||[0.66]||[0.11]|
|ECA|||||||0.319||0.324|
||||||||[3.44]**||[1.91]|
|MENA|||||||–0.432||–0.498|
||||||||[5.39]**||[2.75]**|
|SSA|||||||–0.252||–0.357|
||||||||[2.78]**||[1.79]|
|SAR|||||||–0.284||–0.339|
||||||||[2.72]**||[1.31]|
|EAP|||||||0.186||0.106|
||||||||[2.44]*||[0.60]|
|Constant|–10.044<br>[8.02]**|–8.428<br>[6.60]**|–12.275 <br>[4.16]**|–9.048<br> [7.37]**|–15.995<br> [13.89]**|–10.699 <br> [3.59]**|–6.098<br> [4.33]**|–15.995<br> [13.89]**|–5.705<br> [1.65]|
|Observations|633|633|633|633|633|633|633|633|633|
|R-squared|0.59|0.51|0.61|0.61|0.67|0.63|0.67|0.67|0.72|
|Number of countries||117|117||117|117||117|117|

Absolute value of t statistics in brackets

* Significant at 5 percent; ** significant at 1 percent.

Columns 7–9 present same results for the full regression specification: where POPjt–population size in country j in period t; LAC, ECA, MENA, SSA, SAR, EAP–regional dummies.<sup>10</sup> Again, all coefficients have anticipated signs. The negative effect of the MENA dummy on schooling now seems to dominate causing the disappearance of income effects; still, education spending remains significantly correlated with schooling.

We have also used total enrollment in primary and secondary education as a measure of educational attainment. The results (see table A2 in the appendix) by and

> 10 Based on the classification used by the World Bank: LAC–Latin America and the Caribbean; ECA–East Europe and Central Asia; MENA–North Africa and Middle East; SSA–Sub-Saharan Africa; SAR–South Asia Region; EAP–East Asia and Pacific.

13
# Figure 10: Quantile Regressions with Full Set of Controls

- (A) Coefficients from QR of Wage on IMR

- (D) Local Mean Effect of IMR on Wages

<!-- Start of picture text --> Local polynomial smooth<br>0 .2 .4 .6 .8 1<br>Percentile 0 .2 .4 .6 .8 1<br>Quantile<br>Coefficient 95% CI<br>kernel = epanechnikov, degree = 0, bandwidth = .02<br>(B) Coefficients from QR of Wage on Sanitation (E) Local Mean Effect of Sanitation on Wages<br>Local polynomial smooth<br>0 .2 .4 .6 .8 1<br>Percentile 0 .2 .4 .6 .8 1<br>Quantile<br>Coefficient 95% CI<br>kernel = epanechnikov, degree = 0, bandwidth = .02<br>(C) Coefficients from QR of Consumption on IMR (F) Local Mean Effect of IMR on Consumption<br>Local polynomial smooth<br>0 .2 .4 .6 .8 1<br>Percentile 0 .2 .4 .6 .8 1<br>Quantile<br>Coefficient 95% CI<br>kernel = epanechnikov, degree = 0, bandwidth = .02<br>.004<br>-.001<br>.002<br>0<br>-.0015<br>-.002<br>-.004 Smoothed Coefficient -.002<br>-.006<br>-.0025<br>.01<br>.004<br>.005<br>.003<br>0<br>.002<br>-.005<br>-.01 Smoothed Coefficient .001<br>-.015 0<br>.001 -.0012<br>0<br>-.0014<br>-.001<br>-.002 -.0016<br>-.003 Smoothed Coefficient<br>-.0018<br>-.004<br>-.002<br><!-- End of picture text -->

- (C) Coefficients from QR of Consumption on IMR

39
This estimator can suffer from bias because of self-selection into the program. In short, the two types of individuals may differ systematically in observable and unobservable characteristics such that a typical control observation is not a good proxy of what would have happened to a treated individual had he not received the treatment. In the case of the concession school, the two groups, for example, are not comparable in terms of income, thus leading to bias. That is, 0 0 _E_ ( _Y_ / _T_ = )1 ≠ _E_ ( _Y_ / _T_ = 0) .

Ideally, a program like concession schools can be evaluated using a simple randomization strategy: given that there exists excess demand for concession schools, the government can perform a lottery to allocate individuals between concessions and other public schools. The randomization would ensure that the group of students that attends a concession school (treatment group) and the one that attends other public schools (control group) are very similar in observables and unobservables characteristics. However, as discussed above, the school placements were not done by a lottery.

A second approach<sup>5</sup> would be to take as a control group students who applied to a concession school but were assigned to another public school. Still, evaluations based on the comparison of these two groups may be prone to bias, mainly because the selection of students into concession and other public schools is not random.<sup>6</sup> In contrast, this paper uses the whole sample of individuals who attend public schools and uses matching estimators as the strategy to “reduce” selection bias. In short, the estimator will try to match each treated individual with an individual who does not attend a concession school, based on the observable characteristics of the individuals (a vector _X_ ). In this way, the estimator can be ~ modified by _IP_ = _E_ ( _Y_ 1 / _X_ , _T_ = )1 − _E_ ( _Y_ 0 / _X_ , _T_ = 0) .

Moreover, instead of using the vector of characteristics _x_ , it is possible to determine the probability of participation in the program (where _T_ is equal to 1 or 0), such that

> 5 This approach was suggested by an anonymous referee.

> 6 Moreover, the information on who applies to a concession school is not available to the public.

9
# Volume in Monitored Outlets: Cabbage (01/21192--08/25/92)

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64
<!-- Start of picture text --> 30)<br>i. ee| oe ee, anes<br>2= Dorf ° .<br>as Pay<br>=°¢<br>Yua15 I p<0<br>= ° WEN * o * NIC<br>Oo ° _ PAR °<br>thFi 10 EEG --;o memo meneoosone AB RB grPs) enn<br>on o & @<br>i oF a 2 CHI<br>a THA ,<br>an oeCHL?ae Se ICEHc oo UMS<br>0 UK Fea JAP<br>0 2 | & 8 10<br>Public spending average growth rate<br><!-- End of picture text -->
TAKLE t0 3AZIL: MEVUU **IF Lt 1WXIAL AS£1**

||Jaaur3|,1986|Octobe|r,<br>1986|June,|I7|Otobe|r, 198|
|---|---|---|---|---|---|---|---|---|
||RealValu<br>(|eXShar<br>1; k^}|eReal Value<br>~~~i3|I Share<br>(4;|Real Valu<br>(5)|e S Shire<br>to)|Real Valv<br>!?;|e_X S;are_<br>iSi|
|1.**be3**(!|tlU|11.2|316|27.3|<br>123|13.5|73|8.3|
|2. FinaKial Assets|1N|E8.7|IF|72.7|<br>to|86.5|78|91.7|
|Savings Dqosits|to|29.2|96|21.4|<br>107|3.7|113|33.5|
|**Tin**beosits|IN|18.6|14|21.0|<br>93|16.9|68|12.8|
|Exche lills|1I|4.0|<br>1t2|3.1|31|1.2|13|.5|
|Governmnt<br>Seciritie|s 1t|36.9|<br>76|27.2|16|37.6|Il|44.9|
|3. TotalAssets|10|IN.0|131|100.|<br>102|I1.I|99|1N.0|

Surce: Cntral lnk hbiletin.

- 33 -
but is not affected by the corporate tax increase as much as is the third quintal. The negative impact of the corporate tax upon the lowest quintal is explained by the fact that the end of period sectoral capital stocks are considerably lower with the corporate income tax than with either of the other simulations (Appendix Tables 1-4), so that the marginal product of labor is also lower. This reduced marginal product, and hence wage, tends to impact the poorest households most severely since most of their income comes from labor. Thus, there seems to be strong support for the conclusion that the sales tax increase is welfare superior to the equal tax yield corporate tax increase, at least from the point of view of impact upon consumption.

Finally, it should be noted that the tariff increase leads to nearly as large an overall reduction in consumption as does the sales tax increase. This outcome occurs because although the sales tax impacts a broader set of consumption goods than does the import tariff, the tariff also directly affects the cost of production for many sectors of the economy. The resulting price increases are passed on to consumption.

# **Poverty Rates**

Over the past 15 years, Pakistan has made important progress in reducing poverty. According to the latest available household survey data, the incidence of poverty decreased from 64.3 percent in 2001 to 29.5 percent in 2013 (See Figure 1). Despite a temporary setback between 2010 and 2011, poverty resumed its rapid decline thereafter. Poverty has declined in both urban and rural areas, although the headcount rate remains twice as high in the latter as in the former (36.9 percent vs. 18.2 percent).<sup>11</sup>

> 11 Our estimates follow closely the official methodology, even though slight discrepancies may arise due to differences in data cleaning protocols.

23
<!-- Start of picture text --> 0.72<br>°<br>*.<br>. °<br>.<br>=— 068 ° ° “.° Ps . ‘<br>rd2 ..° ° © PyHoee yeoe oe .@ °<br>£ oor? .<br>ir ow* « oe < «<br>E<br>iz 0.64 7 tateeeoe o % =—*<br>* % ove °<br>. od<br>* wore<br>° war<br>0.60 =<br>-10 0 10 20 30 40<br>Uncertainty in the Time to Import (Days)<br><!-- End of picture text --> <!-- Start of picture text --> ° ry ¢<br>0.60 o ey<br>? o o° . .<br>os o - . o.ooBo oe, * ¢<br>= o 4<br>=® 056 . . weeee 8 = *<br>x ° ° , e<br>wx ofot ., °4% °<br>=£ te .0% an4 rsee .<br>0.52 *% °<br>. ° &<br>ry oo.<br>. ° .<br>0.48<br>-10 0 10 20 30 40<br>Uncertainty in the Time to Import (Days)<br><!-- End of picture text --> <!-- Start of picture text --> 0.40<br>a<br>=<br>@a0.36 . a<br>©oO %e¢<br>2<br>®2 0.32 °re hae e . 2 ry<br>> oo Fat ¢<br>a bd oo 6<br>2 *. 24 . bd<br>ira wm ¢ .<br>e 0.28 *’ + ee .<<br>o°°svae°<br>©%oe<br>°<br>0.24 « = oe °<br>-10 0 10 20 30 40<br>Uncertainty in the Time to Import (Days)<br><!-- End of picture text -->
of change fairly well. Furthermore, the average prediction error is – 37 kWh/month, which is approximately one-fifth of the sample variance of the consumption. The root mean square error (RMSE) for the predicted data is 0.548, which is lower than the standard deviation of the consumption sample. Overall, the model appears to deliver reasonable forecasting performance.

# **VI. Conclusions**

This paper analyzes the distributional impact of the 2008 energy pricing reform in Turkey. To assess the consumer welfare change, I estimate a short-run demand specification that allows income-based heterogeneity in household price sensitivities. The model is also useful for understanding the effects of alternative tariff design on energy efficiency, in predicting future revenue and energy demand, as well as in understanding the potential impact of a carbon tax.

The results confirm that the price elasticity of demand differs systematically along the income distribution. The price elasticity of the bottom quintile is -0.18, about a third of the price elasticity of the top income quintile at -0.54. The results indicate that high-income households, with decreasing marginal utility of electricity, are more sensitive to energy price changes than low-income households.

Using consumer surplus change to approximate the welfare impact, I found that the price spikes in 2008 caused a welfare loss of 164 TL for an average bottom income quintile household, or 2.16 percent of household disposable income, compared with 330 TL, or 0.75 percent of disposable income for the top quintile. The results demonstrate that while poor households are less flexible to adapt to price increases, they also experience a proportionately higher amount of welfare loss.

The results imply that when electricity price elasticity varies across household groups, a uniform increase in the price of electricity can be quite regressive. Furthermore, from the point of view of energy efficiency policy, energy savings from removing subsidies to poor households will be less effective than imposing a tax on high-income households because the latter is more sensitive to price changes. Finally, given the relatively high affordability ratios for the poorest households, there is a strong case for carefully crafting social protection policies to ensure that stringent energy pricing policies do not impose undue hardship on poorest households.

Last, the work presented in this paper illustrates the advantages of using a rich micro dataset to analyze the energy demand of household sector. There are other developing countries experiencing similar energy price reform. From a methodological point of view, the availability of household survey data in other developing countries suggests that similar analyses could be carried out in settings where differentiated price elasticity estimates are needed to evaluate the distributional impacts of price increase on households.

15
Curry, L.A., Nembhard, I.M. and Bradley, E.H., 2009. Qualitative and mixed methods provide unique contributions to outcomes research. _Circulation_ , 119(10), pp.14421452.

Datta, U., 2015. Socio-economic impacts of JEEViKA: A large-scale self-help group project in Bihar, India. _World Development_ , 68, pp.1-18.

Deininger, K. and Liu, Y., 2013. Economic and social impacts of an innovative selfhelp group model in India. _World Development_ , 43, pp.149-163.

Doepke, M. and Tertilt, M., 2014. Does female empowerment promote economic development? (No. w19888). National Bureau of Economic Research.

Duckworth, Angela L., Christopher Peterson, Michael D. Matthews, and Dennis R. Kelly. "Grit: perseverance and passion for long-term goals." _Journal of Personality and Social Psychology_ 92, no. 6 (2007): 1087.

Fafchamps, M. and La Ferrara, E., 2012. Self-help groups and mutual assistance: evidence from urban Kenya. _Economic Development and Cultural Change_ , 60(4), pp.707-733.

Field, E., Jayachandran, S. and Pande, R., 2010. Do traditional institutions constrain female entrepreneurship? A field experiment on business training in India. _American Economic Review_ , _100_ (2), pp.125-29.

26
8

# A Simulation Model for Household Saving

Households are the behavioral units of the model here. Therefore, first step is to construct a picture over time of the demographic composition of the typical household in each cohort (those started in a 5 year period, or pentad). The Population, Health, and Nutrition Division (PHRHN) has detailed demograpnic projections from which one can infer what typical households will look like for the next couple of centuries, given the PHRHN assumptions out the trajectory of fertility and mortality. We assume that the number or households in each cohort is the number of women in that cohort who have left their parents' houw hold. There is thus one adult woman per household, and a cohort of households lasts as iong as the life expectancy of the women. There are as many men per household of a cohort as there are men per women. Children enter the household at birth and phase out as young adults. We used fertility rates for different cohorts of women to allocate children of each cohort to the proper cohorts of households (of women).

Households in the model receive labor incomie and interest income from any accumulated saving. The base-line interest rate is 3 percent (real). People enter the work force starting at age 15, and they leave at 65. Participation rates are 0.8 for ages 15-19 (for the 30 percent of young persons that have separated so early from their parents), 0.7 for 20-60, 0.6 for 55-69 and 0.4 for 60-64. Productivity and wages of the standard worker grow each year, 3 percent per year in +-,e base-line case. Young workers earn less, 50 percent of the standard for ages 15-19, 60 percent for ages 20-24, and 80 percent for ages 25-29. Teenagers at home were assumed to make no income contribution to the household in the base case. After the age of 30, all workers earn the same amount in any given year, although a worker will
indicators of the household's potential to generate income through own account activities.<sup>3</sup> In the urban areas regression, the ownership of farm land was included as a dummy variable (due to the lack of variation in farm size) but for rural areas the number of hectares of farm land was used as regressor. Farm equipment was entered both as a count of the number of pieces of equipment and as a value (the correlation between those is actually quite low). For non-farm enterprises, the number of such enterprises in the household and their gross revenue were used as variables. Gross revenue is used as an indicator of business volume and a proxy for equipment value.

In addition to these productive assets, we included whether the household owns a house, and the number and value of durable goods. While these assets do not directly contribute to the generation of income, they may do so indirectly, both through their function of shelter and as collateral for borrowing. Consumer durables are also often sold when the household faces economic duress perceived as temporary, and re-bought in better times. They thus serve a function of income smoothing over time.

Labor markets in CMte d'Ivoire do not function perfectly and past analysis (e.g., Grootaert, 1987, 1990) has indicated that gender and nationality are potential sources of market segmentation and discrimination. We included these two characteristics of the head of household in the regression to control for such possible segmentation.

The region and socio-economic status of the household are included as sets of categorical variables. The panel analysis referred to earlier found that poverty trends were different according to these criteria and they must thus serve as control variables in the regressions.

Income composition variables were included as proxies for household's ability 31 A similar argument can be made for these assets as for household size regarding their exogeneity. In the long run, physical capital assets are endogenous because they reflect successive choices of saving and accumulation by the household. For short-term analysis though, the amount of such assets is largely given.