rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6.7 Health Benefits of Ozone Air Pollution Reduction in the ZMVM**

(in million US$ per year a for income elasticity 1.0, 2010 values in 1999 prices)

|||_Sce_<br>|_nario_<br>||
|---|---|---|---|---|
|_Endpoints_|_10%_|_20%_|_AQS1_|_AQS2_|
|**Morbidityimpacts(Prod.Loss+ COI+ WTP)**|||||
|_Hospitaladmission_|||||
|Respiratory|6.85|13.70|42.35|46.91|
|Cardiocerebrovascular<br>|5.10|10.20|31.53|34.93|
|_Emergencyroomvisits_|||||
|Respiratory|4.35|8.69|26.87|29.76|
|MinorRestrictedactivitydays|||||
|Total(adults)|55.21|104.42|322.83|357.54|
|_Effectsinasthmatics_|||||
|Somerespiratorysymptoms(children)|0.01|0.02|0.08|0.08|
|Lowerrespiratorysymptoms|0.01|0.01|0.04|0.04|
|_Respiratorysymptoms_|||||
|Upperrespiratory symptoms|2.68<br>|5.37<br>|16.60<br>|18.39<br>|
|Lowerrespiratory symptoms|1.56|3.13|9.67|10.71|
|Wheeze|0.82|1.64|5.06|5.60|
|**Morbidityimpacts(Prod.Loss+ COI)**|||||
|_Hospitaladmission_|||||
|Respiratory|6.34|12.69|39.22|43.44|
|Cardiocerebrovascular|4.97|9.94|30.74|34.04|
|_Emergencyroomvisits_|||||
|Respiratory|2.65|5.29|16.37|18.13|
|_Effectsinasthmatics_|||||
|Somerespiratorysymptoms(children)|0.00|0.01|0.02|0.03|
|Lowerrespiratorysymptoms|0.00|0.00|0.01|0.01|
|_Respiratorysymptoms_|||||
|Upperrespiratorysymptoms|1.19|2.37|7.33|8.12|
|Lowerrespiratorysymptoms<br>|0.69<br>|1.38<br>|4.27<br>|4.73<br>|
|Wheeze|0.19|0.39|1.20|1.33|
|**Mortalityimpacts-WTP**|||||
|Mortality-acuteexposure(total)|42.30|84.59|261.53|289.65|
|**Mortalityimpacts-Humancapitallosses**|||||
|Mortality-acuteexposure(total)|1.67|3.34|10.33|11.44|
|Total- Morbidity(Prod.Loss+COI+WTP)and|116|232|717|794|
|WTPformortality|||||
|Total- Morbidity(Prod.Loss+COI+WTP)and|75|151|465|515|
|humancapitallossesmortality|||||
|Total- Morbidity(Prod.Loss+CO)and|18|35|109|121|
|humancapitallossesmortality|||||
|a.Discountrate3%.|||||

_39_
37 Table 6: Trade and Welfare Reduction Indexes, all covered products and all tradables, 19 African focus countries and 16 African focus countries, 1961–64 to 2000–04 (percent)

||1961-64|1965-69|1970-74|1975-79|1980-84|1985-89|1990-94|1995-99|2000-04|
|---|---|---|---|---|---|---|---|---|---|
|**19 Africa focus countries**||||||||||
|**Trade Reduction Indexes**||||||||||
|Covered tradables|49|40|39|39|27|49|29|17|20|
|All covered products|24|22|20|21|15|24|14|9|10|
|**Welfare Reduction Indexes**||||||||||
|Covered tradables|68|61|61|58|54|68|67|45|45|
|All covered products|49|46|45|44|39|45|40|28|27|
|**16 African focus countries**<sup>**a**</sup>||||||||||
|TRI, Covered tradables|21|22|21|26|18|50|18|14|14|
|WRI, Covered tradables|51|51|52|49|50|80|52|37|36|

Source: Lloyd, Croser and Anderson (2010) and Anderson and Croser (2009) based on NRA and CTE data in Anderson and Valenzuela (2008). a. 1961–64 results are for 1960–64 for 16 African focus countries. The 16 African focus country results are those reported in Lloyd, Croser and Anderson (2010). The 16 countries are those in this study including Egypt and South Africa, and excluding the five cotton countries — Benin, Burkina Faso, Chad, Mali and Togo.
# **<u>Table 1: Summary Statistics</u>**

||Observations|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|Log of sales|16,847|12.92|2.11|4.78|23.35|
|Log of labor (lnL)|16,847|3.14|1.21|0.00|9.41|
|Log of capital (replacement value) (lnK)|16,847|11.31|3.80|-18.42|24.33|
|Log of raw materials (costs) (lnM)|16,847|11.38|3.74|-18.42|22.59|
|Sales (in USD)|16,847|12,100,000|256,000,000|119|13,900,000,000|
|Total number of full time employees, adjusted for<br>temporary workers|16,847|68|278|1|12,150|
|Cost for establishment to re-purchase all its machinery (in<br>USD)|16,847|7,335,669|271,000,000|0|36,900,000,000|
|Cost of raw materials and intermediate goods used in<br>production (in USD)|16,847|3,017,798|75,800,000|0|6,470,000,000|
|No. of water shortages per day in a typical month (all)|16,847|0.02|0.19|0|12|
|Average duration of water shortage|16,847|0.86|4.94|0|300|
|No. of electrical outages per day in a typical month|16,847|0.26|0.79|0|21|
|Average duration of electrical outages (hours)|16,847|2.19|12.62|0|1088|
|Firm owns or shares a generator Y/N|16,847|0.27|0.44|0|1|
|Losses due to breakage/spoilage shipped domestically (%<br>of value of the products)|16,847|0.97|3.77|0|100|
|Log of age of firm|16,847|2.66|0.78|0|5.21|
|Foreign ownership Y/N|16,847|0.11|0.32|0|1|
|Exports 10% or more of sales Y/N|16,847|0.17|0.37|0|1|
|Security costs (% of sales)|16,847|1.26|3.72|0|96.95|
|Establishment competes against unregistered or informal<br>firms Y/N|16,847|0.56|0.50|0|1|
|No. of years that firm was unregistered at start|16,847|0.97|4.33|0|90|
|Formal training Y/N|16,847|0.33|0.47|0|1|
|Expected to give gifts to public officials (to get things||||||
|<br>done) Y/N|15,633|0.22|0.41|0|1|
|Ownership - government/state (%)|16,843|0.32|4.50|0|99|

21
information about the data and the construction of the STRI can be found in Borchert, Gootiiz and Mattoo (2012b).<sup>8</sup> The index number approach adopted here contrasts with methods of econometrically estimating the restrictiveness of policies based on their impact on some outcome variable of interest, controlling for other determinants. A measure of restrictiveness thus derived can obviously not be employed in an analysis of policy impact, for the variation in the outcome variable has already been used to pin down the relative effect of policy measures. Since in this paper we are interested in the relative effect of policy barriers on investment flows in services sectors, our measure of policy restrictiveness needs to be based on exogenous judgment that is not by construction linked to the dependent variable of interest.

The STRI measure is relatively simple and transparent, and the method builds on a long tradition of restrictiveness indices, ranging from the pioneering work in this area of the Australian Productivity Commission to more complex weighted averages (see OECD 2009, 2011). We acknowledge the subjectivity of this approach, but given data constraints as well as the wide range of sectors covered, there is no obviously superior method of quantification. The subjectivity is somewhat mitigated by the extensive consultations conducted with the private sector and regulators on how scores are best assigned. We would argue that on balance the STRI approach is better equipped than any fixed algorithm to turn the rich and difficult-to-quantify aspects of policy information into a broadly plausible restrictiveness score.

# **2.2.3 Patterns of Services Trade Policy**

Based on the approach laid out in the previous section, we begin by mapping out patterns of services trade protection for the sample of countries and sectors for which we are able to match services M&A data.

Figure 3 presents each country’s overall index of services trade restrictions as it relates to

> 8For the present analysis, we assume policy restrictiveness to be exogenous data. In their study of telecommunications and transport sector policies, Borchert et al. (2012) go further and account for the endogeneity of policy choices.

9
PROVISION OF WATER TO THE POOR IN AFRICA The second model, in which the utility directly hires a salaried attendant, is an increasingly uncommon practice still in use in a few countries (3 out of 18 sampled). This model has been rejected in some countries because experience has shown that there is limited incentive for a wageearning employee to ensure cost-recovery. In Zambia, the utility has tried to improve this model by introducing water commissions.

Pre-paid electronic standpipes have no on-site manager, and are intended to reduce management costs. Lower management costs should reduce costs to the consumer and avoid the politically charged problem of non-payment and consequent

**Table 2.1    Modes of standpipe management**

||||% free<br>of|Man<br>Privat|agement (|% by:)<br>Commu-|
|---|---|---|---|---|---|---|
|Ownership|Country|City|<br>charge|e|Utility|nity *|
||South Africa|Johannesburg|100|0|100|0|
||Namibia|Windhoek|100|0|100|0|
||Lesotho|Maseru|100|0|97|3|
|ili|Zambia|Lusaka|0|5|90|5|
|Utty|Madagascar|Antananarivo|40|60|0|40|
||Nigeria|Kaduna|96|4|96|0|
||Cape Verde|Praia|0|0|100|0|
||Sudan|Great Khartoum|0|0|100|0|
||Benin|Cotonou|0|100|0|0|
||Burkina Faso|Ouagadougou|0|100|0|0|
|Pit|Niger|Niamey|0|100|0|0|
|rvae|Rwanda|Kigali|0|100|0|0|
||Kenya|Nairobi|0|88|0|12|
||Senegal|Dakar|0|85|0|15|
||Mozambique|Maputo|0|44|0|56|
|Community|Ethiopia|Addis Ababa|0|0|0|100|
||Malawi|Blantyre|0|N/V|N/V|70|

* In the community category we merge the delegated management model with direct contracting with a community group and the delegated management model with institution support as discussed later in this section. Source: AICD WSS Survey, 2007.

need to shut standpipes down. A pre-paid system also has the potential to provide more targeted subsidies, as the tokens for pre-payment can be distributed via existing safety net systems. Electronic pre-payment cards and vending machines are currently in use in South Africa and are being introduced in Lesotho and Namibia. In Zambia, as an alternative to vending machines, tokens or monthly cards are used. These systems allow for tariffs to be set at a unit rate that is lower than the smallest coin (Kariuki and others 2003; Brocklehurst 2004) and may allow for more efficient transmission of prices as they eliminate the middleman. In Lesotho, the water utility sells the pre-paid cards, which are also sold at retail outlets. In some instances, however, self-styled “operators” set up at the standpipes and offered higher priced tokens; while this can provide a convenience to some customers, information dissemination on formal prices and formal outlets is important.

17
# Does Africa Need a Rotten Kin Theorem? Experimental Evidence from Village Economies

## Pamela Jakiela and Owen Ozier<sup>_∗_</sup>

May 28, 2012

### **JEL codes:** C91, C93, D81, O12

> _∗_ Jakiela: University of Maryland, pjakiela@arec.umd.edu; Ozier: Development Research Group, The World Bank, oozier@worldbank.org. We are grateful to Felipe Dizon for excellent research assistance, to the staff at IPA-Kenya for their assistance and support in the field, and to the Weidenbaum Center and the Center for Research in Economics and Strategy at Washington University in St. Louis for funding. We also thank Kelly Bishop, Esther Duflo, Garance Genicot, Jess Goldberg, Shachar Kariv, Cynthia Kinnan, Ashley Langer, Karen Levy, Maggie McConnell, Justin McCrary, Sendhil Mullainathan, Alvin Murphy, Carol Nekesa, Silvia Prina, Gil Shapira, Erik Sørensen, Sergio Urz ́ua, Dean Yang, Andrew Zeitlin, and numerous conference and seminar participants for helpful comments. All errors are our own.

The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent.
10-A Figure 2.a <!-- Start of picture text --> Internatinal Reserves and Premium<br>Tanzania: 1970-88<br>PREMIUM<br>9 -600<br>8<br>500<br>7<br>6 -400<br>300<br>4<br>3 -200<br>2<br>100<br>0~~~~~~~~~~~~~~~~~~~<br>1970 1972O1974 1976 1978 1980 1982 1984 1986 198<br>1,970 1972 1974 1976 1978 1980 1962 1984 1986 1988<br>sInternational Reserves/GDP(%|<br>|Ea- Parallel Premium (%N<br><!-- End of picture text -->
## **Table 3: Regressions Predicting Entry Rates and Density, Panel of 76 Countries, 2003-2005**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||ENTRY<br>[GLS]|ENTRY<br>[GEE]|ENTRY<br>PER<br>CAPITA<br>[GLS]|ENTRY<br>PER<br>CAPITA<br>[GEE]|DENSITY<br>[GLS]|DENSITY<br>[GEE]|
|PROCEDURES|-0.0080|-0.0044|-5.0875|-8.6206|-1.6417|-1.2862|
||[-0.96]|[-0.53]|[-1.84]*|[-1.62]|[-3.06]***|[-1.94]*|
|RIGIDITY EMPLOYMENT|-0.0003|-0.0002|-0.1003|-0.0577|-0.0125|-0.0232|
||[-1.11]|[-0.83]|[-0.81]|[-0.34]|[-0.64]|[-1.11]|
|GOVERNANCE|0.0125|0.0178|-1.9257|3.8461|0.6388|1.3942|
||[1.85]*|[2.56]**|[-0.62]|[0.78]|[1.21]|[2.31]**|
|DOMESTIC CREDIT|-0.0001|-0.0001|0.0665|0.0278|0.0008|-0.0045|
||[-1.49]|[-1.61]|[2.15]**|[0.50]|[0.14]|[-0.65]|
|GDP PER CAPITA|0.0058|0.0028|16.4294|10.9955|1.1195|0.6106|
||[1.00]|[0.48]|[5.10]***|[2.51]**|[2.24]**|[1.15]|
|YEAR||0.0047||1.0996||0.2736|
|||[3.94]***||[1.62]||[3.17]***|
|Constant|0.0682|-9.4265|-101.469|-2,250.14|-3.0723|-547.17|
||[1.31]|[3.91]***|[-3.45]***|[-1.66]*|[-0.68]|[-3.17]***|
|Observations|197|197|197|197|197|197|
|R-squared|0.19||0.32||0.34||
|Wald Chi-squared||38.89***||56.42***||63.67***|

Note: Variables are defined in Table 1. z-scores are shown in brackets beneath regression coefficient. Asterisks, *, **, and ***, indicate significance at 10%, 5%, and 1%, respectively.

Source: World Bank Group Entrepreneurship Database and World Bank (2007).

# **7.  Business Registries and Electronic Business Registration (EBR)**

Many governments have taken action to make it easier for entrepreneurs to start a new firm, such as deregulating and automating the registration process, which can reduce time and cost for entrepreneurs.<sup>23</sup> A larger number of formally registered firms is associated with a smaller informal sector, which is associated with slower growth and employment and lower tax

> 23 Cross-country data on the cost, time, and number of procedures required to register a business is available in the Doing Business Report: www.doingbusiness.org.

26
**Can Mothers Afford to Work in Poland? Labor Supply Incentives of Social Benefits and Childcare Costs**<sup>1</sup> Ali Bargu* and Matteo Morgandi**

* Oxford University

** World Bank, Social Protection and Jobs GP

JEL: I38, J13, J22.

Keywords: Female Labor Force Participation; Child Care; Social Assistance; Fertility

> 1 This study was prepared as part of the Poland Labor Market and Social Policy Technical Assistance, with cofinancing of the World Bank Gender Umbrella Trust Fund. We thank for the valuable comments and feedback provided at different stages of this research to the staff in the Ministry of Family, Labor and Social Policy of Poland, peer reviewers Iga Magda (Institute for Structural Research, Warsaw) and Paulo Bastos (World Bank, Development Research Group), as well as Michael Myck (CNEA), Manuel Salazar (World Bank), Karolina Goraus (Warsaw University), Gabriela Inchauste (World Bank) and Wojciech Paczyński (European Commission). We are thankful to Herwig Himmervoll and the team in charge of Tax and Benefit Models at the OECD for sharing the program file of the 2014 Tax and Benefit Model for Poland, which this research built on.
<!-- Start of picture text --> cE sr = : aR<br>oh by<br>Fat<br>ingpur HOLD SURVEY REGIONS<br>otoé glhakurgoon Dilphemen ‘ee\ ASP‘G,. ? 7( “<br>on © DIMSION CAPITALS<br>RANGA, ls g o _ DISTRICT CapITals<br>“. 0 Te Vane<br>9 Dinajpur Rangpur he of sees DIVISION BOUNDARIES<br>“Oe —=_ INTERNATIONAL BOUNDARIES<br>) BP ff } @ NATIONAL CAPITAL<br>> Gabendhag bie<br>yore ban “~<br><!-- End of picture text --> <!-- Start of picture text --> 25° ane i ‘) Se et<br>+ { RAJSH<br>ne _ obogra | |\VS, aa oNetrokona * = » L *<br> ar ; ye : ort os<br>Cone Rajshahi oNetore Serajganj , \ Une i: 4 SY LHET :<br>o i M\ clonal =) J] A KOA —_ ; gebiges| 57 y<br>aut<br>{' aLePabna y X\ cain, co ¥).me$ <7fo.Ja :}<br>SMeberpee ss a i —_ ~Y 3 : ay i gorbar INDIA 2m.<br><!-- End of picture text -->

<!-- Start of picture text --> .<br>JESSORE\ 3 athe COMILLA a \<br>er x, etre PO nit i Chendp L<br>A " xe (\ ¢<br>5<br>( 4 cPopagan| wt 7 20¢) ta \ ‘ 5 Khagrachhari 4<br>, ars<br>5 o ira f canis + Bari Ae h, oNeokhali 9 TITTA ¢ Dy te \<br>o zr. okt p = =e<br>\ } Y \<br>b LADS SL O% 1 PED \) Gracey ,<br><!-- End of picture text -->

<!-- Start of picture text --> { ¥ aN Yeap 3)% faS ff LA “ie<br>I 1<br>/ ( ‘ iS ®) A) IN A yr ct Uy % Bandarben<br>|Lage VJ aS CMP (<br>yrSF ner<br>KILOMETERS , Mh<br>’ MYANMAR<br>? Pie 2 Bay 0% 9 7% 10 ox's Bazar f* \<br>\ ; - / of<br>eae ria) Bengal Lert armas ccae eameld plant<br>me ee bait \<br>wo +i be = \<br><!-- End of picture text -->
### Table 3: Summary statistics (Continued)

- (1) Most recent as of 2000

- (2) O¢ cal amount charged as of term 1, 2000

- (3) Terms 2-3, 1999 and term 1, 2000

- (4) Ethnic diversity is an index of ethno-linguistic fractionalization in all schools within 5 km of the school to whose baseline the child belongs. Following Miguel and Gugerty(2002), it is de...ned as 1-Spi2 where pi is the proportion of ethno-linguistic group i.

- (5) Assessment by ICS ...eld sta¤ during a 1999 visit, scale: 1= good, 2=average, 3=poor

- (6) Standard errors are clustered at the school level

- (7) Non-salary expenditures incl. construction, desks, blackboard, books, etc.

2000. To the extent that the dummy for treatment that is used in the analysis does not capture the variation in serving size, the estimates will su¤er from attenuation bias.

# 4 Identi...cation strategy

The program’s randomized design is the most important source of identi...cation. In the absence of transfers between schools, a pupil’s treatment status is not correlated with her observed or unobserved characteristics. However, the project might have attracted pupils who otherwise would have attended another school. Counting those pupils as new participants would bias the estimates upwards. To avoid this bias, we de...ne a set of pupils whose composition is exogenous to the feeding program.

## 4.1 Baseline Survey

Randomized evaluations of programs in primary schools have commonly used a set pupils enrolled prior to the program as their sample (e.g. Miguel and Kremer (2001)). For several reasons, this approach is not feasible in preschools. First, the vast majority of preschool age children in Kenya never attend preschool, therefore we would pick up the e¤ects of the program on a very select set of the population of preschool age children. Second, while we would pick up e¤ects of the program on outcomes conditional on school enrollment, we would not capture any e¤ects of the program on the decision to enroll in school.

The ideal sample for the evaluation of the preschool feeding program consists of all targeted children rather than of children who were enrolled before the start of the program. The targeted population of the intervention is the set of children between 4 and 6 who live within walking 12
# **Table 6: Electricity Sector Outcome Variables by Income Group**

|Variable Name|Sample|Obs.|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
||**Entire Sample**|**129**|**13**|**11**|**2**|**83**|
|Electric power<br>|Low Income|13|20|15|2|58|
|transmission and<br>distribution losses<br>|Lower Middle<br>Income|33|18|14|6|83|
|(% of output)|Upper Middle<br>Income|39|15|8|6|56|
||High Income|44|7|3|3|17|
||**Entire Sample**|**125**|**5**|**5**|**0**|**27**|
|Value lost due to|Low Income|28|8|6|0|27|
|electrical outages<br>(% of sales)|Lower Middle<br>Income|44|5|4|0|18|
||Upper Middle<br>Income|44|3|3|0|14|
||High Income|9|1|1|0|2|
||**Entire Sample**|**84**|**67**|**33**|**9**|**100**|
||Low Income|18|22|12|9|44|
|Electrification rate<br>(% of population)|Lower Middle<br>Income|30|63|25|16|100|
||Upper Middle<br>Income|25|89|20|34|100|
||High Income|11|99|1|98|100|
||**Entire Sample**|**139**|**5**|**2**|**1**|**7**|
|GCI Quality of<br>Electricity Score|Low Income|21|3|1|1|4|
|<br>(1="insufficient and<br>suffers frequent|Lower Middle<br>Income|34|3|1|1|5|
|interruptions" to 7=<br>"sufficient and reliable")|Upper Middle<br>Income|39|5|1|1|6|
||High Income|45|6|1|5|7|
||**Entire Sample**|**139**|**5**|**1**|**3**|**7**|
|GCI Bribes in Electricity|Low Income|21|4|1|3|6|
|<br>Score (1= "very<br>common" to 7 = "never|Lower Middle<br>Income|34|4|1|3|6|
|occurs")|Upper Middle<br>Income|39|5|1|3|6|
||High Income|45|6|1|4|7|

37
**Figure 5. Inward and outward oriented policies**

## **(a) Inward-oriented policies**

## **(b) Outward oriented policies**

<!-- Start of picture text --> 2.9<br>3.0 3.0<br>2.5 2.1 2.2 2.2 2.4 2.5 2.0 2.5<br>2.0 1.8 1.8 1.8 2.0<br>1.5 1.5<br>1.4 1.4<br>1.5 1.3 1.5 1.1 1.1<br>1.0 1.0 0.9<br>0.5 0.5<br>0.0 0.0<br>Slovak Republic OECD EU15Romania Bulgaria MICsCzech Rep. Hungary Poland EU15Czech Rep. OECDRomania BulgariaSlovak Republic Hungary MICs Poland<br><!-- End of picture text --> _Source:_ see Figure 3. _Note_ : Other MICs are Brazil, Mexico, and Turkey. Values refer to 2006 for Romania and Bulgaria, 2004 for Brazil, and 2003 for all other countries. Romania’s 2002 score was calculated using a different methodology so is not strictly comparable. For full data set see appendix I.

32. The fact that Romania positions itself around the EU average for inward oriented policies suggests that it has already met most of the requirements of the _acquis communautaire._ More generally, the great progress observed since 2002 in inwardoriented policies can be attributed to both the implementation of the _acquis communautaire_ (e.g. competition policy), and to implementation of less restrictive policies in areas that are subject to domestic discretion. This indicates that the government has been diligent in complying with international commitments in domains that are often controversial from the standpoint of gathering sufficient domestic consensus.

33. As for outward-oriented policies, the data, hence the ranking and score, reflect policies in place as of the spring of 2006. A number of these have changed by the mere fact of Romania joining the EU and therefore becoming governed by EU trade policies and agreements. Inward and outward oriented policies and the underlying indicators are discussed in details below.

# **Inward Oriented Policies**

34. In order to identify the drivers of Romania’s progress since 2002, inward-oriented policies can be decomposed into two broad categories: indicators of state control and barriers to entrepreneurship (Figure 6).

12
25 exogenous. Second, all trade data, but especially those of developing countries, are subject to recording error.

- 6/ The IV technique was programmed in GAUSS and was based on Aigner, Hsiao, Kapteyn and Wansbeek (1984). It presumes that the instrumental variables (aggregate industrial production, prices and exchange rates in the markets and in Korea) are correlated with the true values of the variables in equation (5) but not their errors of observation. If that is true, our estimates are consistent and asymptotically efficient. Further details on the estimation technique are provided in the appendix.

- 7/ The peak of the footwear industry occurred in 1978, one year after the signing of the OMA agreement. Although this peak is later than predicted by our model, it is not out of line with the detailed account of the CMA given by Yoffie (1983). He remarks that Korean producers went to considerable lengths to negotiate the OKA in a fashion that allowed extended periods of adjustment. Thus it is quite conceivable that output and employment remained high into 1978.

- 8/ Our estimates of the price elasticity of export demand are consistent with the range of 0.5 to 1.0 reported in Goldstein and Khan (1985).

- 9/ The size of the industry in the market for Z and eC aLe not independent. For example, for an industry like footwear e5 is likely to be in the range of 2 to 4 and the size of the market, L, for Z in relation to the initial allocation of Z in footwear is likely to be 5 or more whereas in textiles, the corresponding pair would be es in the range of 0.5 to 2.0 and L in the range of 1 to 5.