rafmacalaba/datause-encoder-data huggingface dataset:
(Polit. Stab.) and Offi cial Development Aid (ODA).

Table 2: Fixed effects (within) regressione with intercept dummies

|Variables|1|2|3|4|5|
|---|---|---|---|---|---|
|I_GDPpc|-0.00003<br>(0.00002)|-0.00004<sup>∗</sup><br>(0.00002)|-0.000035<br>(0.00002)|-0.00004<sup>∗</sup><br>(0.00002)|-0.00004<sup>∗</sup><br>(0.00002)|
|I/GDP|0.120<sup>∗∗∗</sup><br>(0.022)|0.115<sup>∗∗∗</sup><br>(0.023)|0.119<sup>∗∗∗</sup><br>(0.022)|0.114<sup>∗∗∗</sup><br>(0.023)|0.113<sup>∗∗∗</sup><br>(0.023)|
|Tax Rate|-0.072<sup>∗∗∗</sup><br>(0.020)|-0.075<sup>∗∗∗</sup><br>(0.020)|-0.071<sup>∗∗∗</sup><br>(0.020)|-0.074<sup>∗∗∗</sup><br>(0.020)|-0.078<sup>∗∗∗</sup><br>(0.021)|
|Tot. ODA|0.0007<sup>∗∗</sup><br>(0.0003)|0.0007<sup>∗∗</sup><br>(0.0003)|0.0007<sup>∗∗</sup><br>(0.0003)|0.0007<sup>∗∗</sup><br>(0.0003)|0.0007<sup>∗∗</sup><br>(0.0003)|
|Politic. Stab|0.352<sup>∗</sup><br>(0.191)|0.278<br>(0.202|0.356<sup>∗</sup><br>(0.192)|0.282<br>(0.198)|0.256<br>(0.204)|
|Latitude|0.010<br>(0.0077)|0.011<br>(0.0077)|0.010<br>(0.0078)|0.011<br>(0.0078)|0.011<br>(0.0078)|
|Lat. Squ.|0.001<sup>∗∗∗</sup><br>(0.0003)|0.0009<sup>∗∗∗</sup><br>(0.0003)|0.0009<sup>∗∗∗</sup><br>(0.0003)|0.0009<sup>∗∗∗</sup><br>(0.0003)|0.0009<sup>∗∗∗</sup><br>(0.0003)|
|D_Fragile||-0.455<br>(0.404)||||
|D_Africa|||-0.069<br>(0.310)|||
|D_AF_Frag.||||-0.579<br>(0.423)|-0.540<br>(0.431)|
|D_AF_Non.Fr.|||||0.188<br>(0.375)|
|constant|0.366<br>(0.568)|0.685<br>(0.633)|0.413<br>(0.606)|0.727<br>(0.626)|0.695<br>(0.629)|
||N = 245<br>R<sup>2</sup>= 0.262|N = 245<br>R<sup>2</sup>= 0.265|N = 245<br>R<sup>2</sup>= 0.262|N = 245<br>R<sup>2</sup>= 0.266|N = 245<br>R<sup>2</sup>= 0.267|

Polit. Stab. has the predicted signs, but was never significant in any of the OLS specifications, maybe because of reverse causality or unobservable heterogeneity. ODA is statistically significant at 5% in all specifications, except column 5’s specification where the level of significance drops to 10%. Unlike specifications in columns 1-4, specification in column 5 includes dummies for African fragile and non fragile states. In Tables 2 and 3 we use FE regression models to correct for possible unobservable heterogeneity, while Table 4 and 5 use IV-2SLS models to address issues of possible endogeneity.

Across all specifications in Tables 2 & 3, I_GDPpc, I/GDP, and Tax rate are statistically significant with the predicted signs. Coeffi cients on ODA are positive and statistically significant 20
TABLE 11: TRAINER CHARACTERISTICS ON STUDENT PSYCHOLOGY (ENTREPRENEURIAL IDENTITY)

|||_y_|_= Entrepreneurial_|_Identity score of st_|_udents after traini_|_ng_|
|---|---|---|---|---|---|---|
|||(1)<br>|(2)<br>|(3)<br>|(4)<br>|(5)<br>|
|||Entrepreneurial<br>Identity<br>(1-5)|Entrepreneurial<br>Identity<br>(1-5)|Entrepreneurial<br>Identity<br>(1-5)|Entrepreneurial<br>Identity<br>(1-5)|Entrepreneurial<br>Identity<br>(1-5)|
|**phics**|Age|-0.00729*<br>(0.00376)|-0.000886<br>(0.00312)|0.00360<br>(0.00550)|0.0132<br>(0.00790)|0.0167<br>(0.0155)|
|**gra**|Female|0.111|0.0522|0.176**|0.112*|0.162|
|**mo**||(0.0720)|(0.0521)|(0.0681)|(0.0622)|(0.238)|
|**De**|Years at school|0.0122<br>|0.0326*<br>|-0.000325<br>|-0.0130<br>|-0.00524<br>|
|||(0.0152)|(0.0168)|(0.0283)|(0.0230)|(0.0287)|
|**preneur**<br>**Model**|Ever been a business||0.325***|0.246***|0.334***|0.303***|
|**Entre**<br>**Role**|owner||(0.0752)|(0.0857)|(0.0738)|(0.106)|
|**e skills**|Digitspan score|||0.0251<br>(0.0234)|0.0571*<br>(0.0291)|0.0196<br>(0.0458)|
|s<br>**itiv**|Raven test score|||-0.0392*|-0.00342|0.00683|
|stic<br>**ogn**||||(0.0204)|(0.0171)|(0.0402)|
|teri<br>**n-c**|Personal intitative|||-0.289*|-0.574***|-0.433*|
|arac<br>**no**||||(0.169)|(0.126)|(0.226)|
|Ch<br>**and**|Error competence|||0.0182|0.0436|0.272|
|iner<br>**ive**||||(0.175)|(0.160)|(0.225)|
|Tra<br>**Cognit**|Prosocial orientation|||0.377***<br>(0.131)|0.392***<br>(0.137)|0.107<br>(0.553)|
||How satisfied are you||||0.0379|0.0657|
|**tion**|with your job?||||(0.0549)|(0.0741)|
|**fac**|Given a choice, would||||-0.128***|-0.137***|
|**atis**|still be a teacher||||(0.0342)|(0.0407)|
|**b s**|Career satisfaction||||0.104***|0.0841*|
|**Jo**|||||(0.0354)|(0.0471)|
|**T**|Years as teacher|||||0.0312<br>00246|
|**TVE**<br>**ge**||||||(.)|
|**at**<br>**lle**|Years at TVET Colleges|||||-0.0199|
|**ars**<br>**co**||||||(0.0289)|
|**Ye**|Perceived|||||0.0609|
||entrepreneurial skills|||||(0.145)|
||Average score for|4.397|4.397|4.397|4.397|4.397|
||students at baseline|(0.0305)|(0.0305)|(0.0305)|(0.0305)|(0.0305)|
||Observations|564|564|564|564|564|

_Notes:_

- significant at 10% level ** significant at 5% level *** significant at 1% level

> (1) OLS regressions sequentially add explanatory variables from columns (1) to (5)
Source: Authors’ calculations based on investment climate survey data

**Table 13: Ranking of Selected Universities in East Asia**

|2008|2007|2008 regional rank|School Name|Economy|
|---|---|---|---|---|
|19|17|1|University of TOKYO|Japan|
|25|25|2|KYOTO University|Japan<br>Hong Kong|
|26|18|3|University of HONG KONG|(China)|
|30|33|4|National University of SINGAPORE(NUS)|Singapore|
|||||Hong Kong|
|39|53|5|HONG KONG University of Science & Techno...|(China)<br>Hong Kong|
|42|38|6|The CHINESE University of Hong Kong|(China)|
|44|46|7|OSAKA University|Japan|
|50|36|8|PEKING University|China|
|50|51|9|SEOUL National University|Korea|
|56|40|10|TSINGHUA University|China|
|166|223|23|CHULALONGKORN University|Thailand|
|230|246|33|Universiti MALAYA (UM)|**Malaysia**|
|250|309|35|Universiti Kebangsaan MALAYSIA (UKM)|**Malaysia**|
|313|307|43|Universiti Sains MALAYSIA (USM)|**Malaysia**|
|320|364|46|Universiti Putra MALAYSIA (UPM)|**Malaysia**|
|356|401-500|51|Universiti Teknologi MALAYSIA(UTM)|**Malaysia**|

Source: Times Higher Education Supplement (http://www.topuniversities.com/university_rankings/results/2008/overall_rankings/fullrankings/) How frequently and how closely universities and firms might eventually interact with each other will also depend upon the future composition of Penang’s industry. This is because scientific content differs among industrial subsectors, as noted earlier. Using Japanese patent data, F. Kodama and Suzuki (2007) find that biotechnology has the highest scientific content (measured by citation to academic papers rather than patents), followed by nano-technology, IT, and environment related technologies. Patents in these technology areas contain more citations to academic papers and more frequent incidence of citing academic papers. Universities in the United States also tend to patent more in biotechnology related fields (see Table 14). Similar to the difference in R&D intensity by industrial subsector (van Pottelsberghe de la Potterie 2008), the propensity to cite (or utilize) research findings from universities and research institutes differs among industrial subsectors. If this is the case, then the degree of linkages between universities and firms are dependent on the industrial composition. Given the current concentration of 32
as the total volume of fresh water used to produce a unit of the biofuel (Gerbens-Leenes, Hoekstra, and van der Meer 2009). The most water intensive stage in the biofuels production process is the cultivation of feedstocks (in particular dedicated energy crops).

The estimated water footprints for the production of several biofuels, using various feedstocks are presented in Table 20. Compared to other feedstocks, sugarcane has the lowest water footprint. Bioethanol production based on corn and sugarbeets have a relatively higher water demand. Biodiesel produced from rapeseed and soybean oils has higher water footprint than biomass-based ethanol.

**Table 20: Average water footprint of biofuel produced with different feedstocks**

|**Biofuel type**|**Feedstock**|**Water footprint of biofuel (m**<sup>**3**</sup>**/L)**|
|---|---|---|
|Bioethanol|Maize|2.01|
|Bioethanol|Cassava|2.64|
|Bioethanol|Sugarcane|1.47|
|Bioethanol|Sugarbeets|2.24|
|Bioethanol|Sweet potato|1.83|
|Biodiesel|Rapeseeds|5.82|
|Biodiesel|Soybean|15.63|

Source: Yang, Zhou, and Liu (2009) The water footprints of biofuels vary significantly over (1) the type of feedstock applied; (2) the climate conditions during the agricultural production period; and (3) crop yields and related agricultural practices. Compared to some food crops including barley, rice and wheat, oilseeds, such as rapeseed and jatropha, have low water efficiency. On the other hand, sugarcane, corn, and sugarbeets are the most efficient crops in terms of freshwater demand. A low water efficiency of jatropha when compared to other feedstock sources was also reported by Rajagopal and Zilberman (2007). However, and as of 2007, little was known about water use and efficiency of jatropha, as (differently from other crops) no study was conducted to evaluate actual water usage in crop production (Jongschaap et al. 2007).

# **4.4    Biodiversity impact**

Intensification of feedstock production for second-generation biofuels can have both positive and negative impact on biodiversity (Anderson, Haskins, and Nelson 2004).

36
<!-- Start of picture text --> Sius ee ee -<br>Gasoline ant ig Li MA “/ 4<br>0-72Consumption (1000 bbi/d)Consumption (1000 bbi/d) (1000 bbi/d) bbi/d) ty) B? oN ) 7<br>72 " ers ae 1 ¥Q) Ay ,<br>258- 258- 258 258 AMAIA S MW Ae 7A ,<br>lm 462- 462- 462 462 iy: au Y XY -é rn .<br>Mm 2038- 2033- 2033 2033 ay) bs NG. k= a><br>- 9031 9031 CY ee AAT. hy<br>Consumption(1000 b/d) 2005-2016 , eya) gf7 . nea<br>2,033 J<br>462 ,<br>258<br>=: ° | id a<br>[ tae0 .a ; * _¢ 4IBRD 45215°| AUGUST2020<br><!-- End of picture text -->

<!-- Start of picture text --> 0-72Consumption (1000 bbi/d)Consumption (1000 bbi/d) (1000 bbi/d) bbi/d)<br>72<br>258- 258- 258 258<br>lm 462- 462- 462 462<br>Mm 2038- 2033- 2033 2033<br>- 9031 9031<br><!-- End of picture text --> <!-- Start of picture text --> Pe 4 ee -<br>_ : ogee > > . - u . ;<br>j Distillate Fuel Consumption, 2016 (Mb/d) y a<br>0 OT<br>a 7 a.<br>; _* IBRD 45216] AUGUST 2020<br><!-- End of picture text -->
i

# TABLE OF CONTENTS

||Page No.|
|---|---|
|EXECUTIVE SUMMARY|iii|
|CHAPTER I.<br>INTRODUClION: PURPOSE AND SCOPE OF REVIEW|1|
|CHAPTER II. FY90 OPERATIONS: LENDING AND SECTOR WORK|2|
|OVERVIEW OF LENDING|2|
|Lending Statistics|2|
|PHN Projects|2|
|<br>Social Development Projects|4|
|SECTOR WORK STATISTICS|6|
|CHAPTER III. A PERSPECTIVE ON PHN WORK<br>||
|ACCOMPLISHED IN FY90: SPECIAL FEATURES,||
|ACCOMPLISHMENTS, TRENDS, AND ISSUES|8|
|POPULATION, HEALTH AND NUTRITION|8|
|Policy/Program Approacit|8|
|<br>Management/Institutional Development|9|
|PHN Financing|10|
|<br>Roles and Responsibilities in the Management of Social||
|<br>Development Projects|11|
|Non-governmental Organizations|11|
|<br>Methods of Carrying Out Project Design and Implementation||
|<br>and Sector Work|13|
|Co-financing and Aid Coordination|13|
|POPULATION|15|
|HEALTH|18|
|NUTIRITION|20|
- Akehurst, G., Simarro, E. and Mas-Tur, A. (2012). Women entrepreneurship in small service firms: motivations, barriers and performance. _The Service Industries Journal_ , 32(15), 2489-2505.

- Amankwah, A., Castaing, P., Owoo, NS.,  Palacios-Lopez, A. (2024). Labor Market Participation and Employment Choice in Ghana: Do Individual Personality Traits and Gender Role Attitudes Matter?. Policy Research Working Papers, 10664, World Bank.

AHIES (2022) Ghana Annual Household Income and Expenditure Survey- Quarterly Labor Force report. Ghana Statistical Service (2022) <u>https://www.statsghana.gov.gh/gssmain/fileUpload/pressrelease/AHIES%202022%20Q1 %20and%20Q2%20Labor%20Force%20Report.pdf</u>

- Amoakohene MI. (2004). Violence against women in Ghana: a look at women's perceptions and review of policy and social responses. _Social Science and Medicine_ , 59(11), 2373–85.

- Amu, JN (2000). The role of women in the Ghanaian Economy, Accra, Ghana. [library.fes.de/pdffiles/bueros/ghana/02990.pdf].

- Anambane, G., and Adom, K. (2019). Assessing the role of culture in female entrepreneurship in contemporary Sub-Saharan society: insights from Nabadam district of Ghana. _Journal of Developmental Entrepreneurship_ , 23(3). https://doi. org/10.1142/S1084946718500176.

- Aterido, R. and Hallward-Driemeier, M. (2011). Whose Business is it Anyway? _Small Business Economics_ , 37(4), 443-464.

- Aterido, R., Beck, T. and Iacovone, L. (2011) Gender and Finance in Sub-Saharan Africa: are Women Disadvantaged? Policy Research Working Paper Series 5571, World Bank.

- Bardasi, E., Sabarwal, S. and Terrell, K. (2011). How do Female Entrepreneurs Perform? Evidence from Three Developing Regions. _Small Business Economics_ , 37(4), 417-441.

- Benzing, C., and Chu, HM. (2009). A comparison of the motivations of small business owners in Africa. _Journal of Small Business and Enterprise Development_ , 16(1), 60–77.

- Bird, B. and Brush, C. (2002). A gendered perspective on organizational creation. _Entrepreneurship Theory and Practice_ , 26(3), 41-65.

- Brewster, K. L., and Padavic, I. (2000). Change in gender-ideology, 1977–1996: the contributions of intracohort change and population turnover. Journal of Marriage and Family, 62, 477–487. doi: 10.1111/j.1741-3737.2000.00477.x

- Bruni, A., Gherardi, S. and Poggio, B. (2004), “Entrepreneur-Mentality, gender and the study of women entrepreneurs”, _Journal of Organizational Change Management_ , 17(3), 256268.

- Brush, C., Edelman, L. F., Manolova, T., and Welter, F. (2018). A gendered look at entrepreneurship ecosystems. _Small Business Economics_ , 52(3), 393–408. https://doi. org/10.1007/s11187-018-9992-9.

- Brush, C. G., Edelman, L. F., and Manolova, T. (2002). The impact of resources on small firm internationalization. Journal of Small Business Strategy, 13(1), 1–17.

- Bullough, A., Guelich U., Manolova, T. S. and Schjoedt, L. (2022). Women’s entrepreneurship and culture: gender role expectations and identities, societal culture, and the entrepreneurial environment. _Small Business Economics_ , 58, 985-996.

- Burger, J. M. (2010). Personality. Wadsworth Publishing: Belmont, CA

21
<!-- Start of picture text --> .020 15<br>015<br>10<br>.010<br>-.005<br>5<br>-.010<br>-10<br>-.015<br>-.020 -15<br>2005 2006 2007 2008 2009 2010 2011 2012<br>----- Export Lao PDR - Vietnam —— Lao harvest (relative difference)<br><!-- End of picture text -->
||Cross-Nationa|Table 2<br>l<br>Patterns of Inflation|
|---|---|---|
|Mean Annual|Inflation|Inflation Episodes|
|1960-1980|1980-1986|Year of peak inflation,<br>inflation rate|

1.<sup>7</sup> 1<sup>ow Inflation Countries</sup>

|Malaysia|4.9|1.4|
|---|---|---|
|Taiwan|8.3|5.8|
|Thailand|6.8|3.0|
|Venezuela|8.7|8.7|

1974 (16.1) 1960 (18.4); 1974 (47.5); 1980 (19.0) 1974 (24.3); 1980 (19.7) 1980 (21.5); 1987 (28.1)

# 2. Countries with Mixed Records

|Colombia|17.4|22.6|
|---|---|---|
|Costa Rica|11.3|32.3|
|Jamaica|12.8|19.8|
|Mexico|13.1|63.7|
|Peru|20.5|100.1|
|Indonesia|34.3|8.9|
|Korea|18.8|5.4|
|Philippines|11.7|18.2|
|Turkey<br>3. High Infla|20.7<br>tion Count|37.3<br>ries|
|Argentina|78.3|326.2|
|Brazil|31.3|157.1|
|Chile|129.9|20.2|
|Uruguay|57.8|50.4|

1963 (32.0); 1966 (19.1); 1977 (33.1) 1985 (24.0) 1974 (30.1); 1982 (90.1) 1974 (27.2); 1978 (34.9); 1984 (27.8) 1977 (29.0); 1987 (131.8) 1968 (19.0); 1975 (23.6); 1981 (75.4) 1985 (163.4) 1966 (1044.8); 1973 (40.6); 1978 (18.2) 1964 (29.5); 1970 (16.1); 1975 (25.3); 1980 (28.7) 1971 (21.4); 1974 (34.2); 1980 (18.2); 1984 (50.3) 1975 (19.2); 1980 (110.2) 1952 (38.1); 1959 (113.9); 1966 (31.7); 1971 (61.2); 1976 (443.2); 1985 (672.1); 1987 (229.7) 1956 (21.8); 1959 (51.9); 1964 (91.4) 1979 (52.7); 1987 (227.1) 1955 (84.0): 1959 (33.3); 1963 (45.3); 1970 (34 9); 1974 (504.7) 1959 (39.5); 1968 (125.3); 1973 (97.0); 1986 (76.4) Note: Inflation is measured by change in the consumer price index. Inflation ,episodes' are periods when the inflation rate crosses one of three thresholds: 'moderate, inflations exceed 15 percent for a calendar year, 'high' inflations exceed 30 percent, and "very high' inflations 90 percent. In the discussion, an 'episode' includes all those years when the rate of inflation increased without interruption, based on the highest annual inflation rate reached; this peak inflation rate is reported here in parentheses.

16
# **Table A3. Changes in GDP Growth Across the Business Cycle on Goods and Services: Elasticities from Fixed Effects and IV Regressions for Emerging and Advanced Economies**

|VARIABLES|(1)<br>Emerging<br>economies -<br>FE|(2)<br>Advanced<br>economies -<br>FE|(3)<br>Emerging<br>economies -<br>IV|(4)<br>Advanced<br>economies -<br>IV|
|---|---|---|---|---|
|Real GDP Growth * Exp|1.60<br>[0.218]***|0.48<br>[0.183]**|1.58<br>[0.526]***|0.63<br>[0.139]***|
|Real GDP Growth * Rec|0.38<br>[0.366]|-0.36<br>[0.214]|0.29<br>[0.399]|-0.45<br>[0.166]***|
|Lagged Goods and Services Growth|-0.22<br>[0.095]**|0.12<br>[0.092]|-0.22<br>[0.070]***|0.07<br>[0.060]|
|Lagged General Government Expense Growth|0.02|0.13|0.01|0.19|
||[0.058]|[0.079]|[0.087]|[0.086]**|
|Constant|0.00<br>[0.009]|0.01<br>[0.004]***|||
|Observations|817|432|810|418|
|R-squared|0.089|0.078|||
|Number of Countries|40|18|40|18|
|Country Fixed Effect|Yes|Yes|Yes|Yes|
|Adjusted R-squared|||0.036|0.052|
|**First Stage IV Estimates**|||First Stage|First Stage|
|Trade Weighted Growth * Exp|||1.18<br>[.03]***|1.27<br>[.02]***|
|Trade Weighted Growth * Rec|||1.35<br>[.03]***|1.43<br>[.04]***|
|**Underidentification - Weak Identification - Weak**|**Instruments Tes**|**ts**|||
|LM test statistic for underidentification|||92.65|35.91|
|p-value of underidentification LM statistic (Anderso|n or Kleibergen-P|aap)|0|2.07e-09|
|F statistic for weak identification (Cragg-Donald or|Kleibergen-Paap)||1008|856.6|
|Anderson-Rubin chi-sqared test of significance of e|ndogenous regress|ors|15.59|28.69|
|p-value of Anderson-Rubin chi-sqared test of endog|enous regressors||0.000411|5.89e-07|

Source: Authors’ calculations based on World Economic Outlook database 2019.

Robust standard errors in brackets.*** p<0.01, ** p<0.05, * p<0.1.Underidentification tests. Ho: matrix of reduced form coefficients has rank=K1-1 -underidentified. Ha: matrix has rank=K1-identified. Weak identification test. Ho: equation is weakly identified. Weak-instrument-robust inference. Tests of joint significance of endogenous regressors B1 in main equation. Ho: B1=0 and overidentifying restrictions are valid.
Krauth, B (2011), Bounding a linear causal effect using relative correlation restrictions, Working Paper, Simon Fraser U.

Lechner, M., Miquel, R. and C. Wunsch (2011): Long-Run Effects of Public Sector Sponsored Training in West Germany, The Journal of the European Economic Association 9, 742-784.

Lefgren, L, Matthew J. Lindquist and David Sims, 2012. Rich Dad, Smart Dad: Decomposing the Intergenerational Transmission of Income, Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 120(2), pages 268 - 303.

Lewbel, A., “Using Heteroskedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models, Journal of Business and Economic Statistics, Vol. 30, pp. 67-80, 2012.

Li, Shi, Luo Chuliang, Wei Zhong, and Yue Ximing. 2008. The 1995 and 2002 Household Surveys: Sampling Methods and Data Description. In the book Inequality and Public Policy in China, edited by Bjorn Gustafsson, Li Shi, and Terry Sicular, Cambridge University Press, New York, NY, USA, page 337 Lillard, Lee and Robert Willis. 1995. Intergenerational Educational Mobility, Effects of Family and state in Malaysia. The Journal of Human resources, Vol. (29), pp 1126-1166.

Lam, David, and Robert F. Schoeni. 1993. Effects of Family Background on Earnings and Returns to Schooling: Evidence from Brazil. Journal of Political Economy 101(4):710 40.

Lanjouw, Peter, and Gershon Feder. 2001. Rural Nonfarm Activities and Rural Development. Rural Strategy Background Paper # 4, World Bank.

Ma, Guiping and Mingqiang Ding. 2008. The Evolution of Basic Education Policy in Rural China after the Reform (in Chinese). Journal of Suihua University, Vol. 28 (Aug), 45
**_Index_**

v

|_WPS #_|<br>_Author_|_Working_<br>_Paper Title_|_Page_|
|---|---|---|---|
|136|Raghuran Rajan|Pricing Commodity Bonds Using Binomial Option Pricing|48|
|148|Takamasa Akiyama<br>and Panayotis N. Varangis|Impact of the International Coffee Agreement's Export<br>Quota System on the World's Coffee Market|51|
|154|Jock R. Anderson|Forecasting, Uncertainty, and Public Project Appraisal|53|
|161|Timothy Besley and Andrew Powell|Commodity-Indexed Debt in International Lending|56|
|175|Robert D. Weaver|An Integrated Model of Perennial and Annual Crop<br>Production for Sub-Saharan Countries|61|
|180|Boum-Jong Choe|Structural Changes in Metals Consumption|63|
|**Intern**|**ational Economic Analysis and Prospect**|**s**||
|187|Menahem Prywes|Linking Wages to Changing Output Prices: An Empirical<br>Study of 13 Industrial Countries|66|
|**Intern**|**ational Trade**|||
|16|Paterick Messerlin|Antidumping Laws and Developing Countries|8|
|63|Kenji Takeuchi|Does Japan Import Less Than It Should?|21|
|78|J. Michael Finger|Economists, Institutions, and Trade Restrictions:<br>A Review Article|27|
|95|Junichi Goto|International Trade and Imperfect Competition Theory<br>and Application to the Automobile Trade|32|
|99|Sam Laird and Julio Nogues|Trade Policies and the Debt Crisis|33|
|102|Junichi Goto|Effects of the Multifibre Arrangement on Developing<br>Countries-<br>A Survey|37|
|137|Sam Laird and Alexander Yeats|Trends in Nontariff Barriers of Developed Countries:<br>1966-1986|48|
|165|Alexander J. Yeats|Shifting Patterns of Comparative Advantage:<br>Manufactured Exports of Developing Countries|58|
|182|Julio Nogues|Latin America's Experience with Export Subsidies|63|
|186|Patrick A. Messerlin|The Uruguay Negotiations on Subsidies and<br>Countervailing Measures: Past and Future Constraints|65|
|**Macro**|**economic Adjustment and Growth**|||
|91|Nissan Liviatan|Israel's Stabilization Program|31|
|101|Luis A. Riveros and Ricardo Paredes|Measuring the Impact of Minimum Wage Policies on<br>the Economy|37|
|138|William R. Easterly|Fiscal Adjustment and Deficit Financing during the<br>Debt Crisis|48|