rafmacalaba/datause-encoder-data huggingface dataset:
In this subsection, we instead examine whether domestic evasion and cross-border evasion are linked to each other. As discussed above, one of the criteria Chinese tax authorities adopt to detect evasion behavior is based on purchasing and selling price differential (PSD). As a result, trading companies that have purchased products from production firms at an underreported price (i.e., domestic evasion) will also have an incentive to underreport export prices (i.e., cross-border evasion), to avoid large PSD and minimize the chance of being caught.

Following the approach proposed by Fisman and Wei (2004), we calculate the discrepancies between China’s reported exports to each importing country in logarithms and partner country reported imports from China in logarithms at HS 6-digit level for year 2005, using the trade data from the UN COMTRADE (i.e., GAP = log(partner reported imports from China) – log(China reported export). When export values are underreported at the Chinese border, GAP tends to be positive. If the evasion through underreporting domestic purchasing price of a trading company is associated with its cross-border evasion through underreporting export prices, we would expect to see stronger evidence of tax evasion through indirect exports for products whose values were underreported at the Chinese border (positive GAP). To test this hypothesis, we carry out a product-country level analysis.

Many characteristics of the importing countries of Chinese products, such as geographic distance and destination market size can also affect firms’ choice of export modes. For example, geographic distance adds to the transportation costs of exporting, so firms tend to avoid exporting directly to countries far away; the probability of indirect exporting to a large foreign market is low because it is worthwhile to invest in the fixed costs of direct exporting when a destination market is large enough. Although these factors are interesting, they are not the focus of this paper and have already been studied by existing papers (e.g., Bernard et al., 2010; Ahn, Khandelwal, and Wei, 2011). Since country fixed effects will be used in our product-country level analysis, all of these country characteristics are absorbed by the fixed effects.

In Table 3, we report the results from regressions at the HS8 product-country level. The dependent variable is the indirect export share calculated at the product-country level. The key explanatory variable is (t-r), which still varies only across products as before because the same rate applies to a product exported to all destination markets. The control 22
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C9.3. SOSD: Social Sustainability Index C9.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 0.9*** 1***<br>H<br>Kendall tau-b 0.89*** 0.78*** 1***<br>Spearman 0.98*** 0.95*** 0.78**<br>A<br>Kendall tau-b 0.94*** 0.89*** 0.67**<br>Spearman 0.98*** 0.92*** 1***<br>M0<br>Kendall tau-b 0.94*** 0.78*** 1***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>1<br>.2<br>.8 .15<br>CCDF .6 .1<br>.4 Difference .05<br>.2 0<br>0 -.05<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C10. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Experience of discrimination indicator removed (weights within the Social Cohesion dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru

# C10.1. FOSD: Counting Vector

<!-- Start of picture text --> C10.2. Dominance Analysis<br><!-- End of picture text --> <!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>1 .05<br>.8<br>0<br>.6<br>CCDF<br>.4 Difference<br>-.05<br>.2<br>0<br>-.1<br><!-- End of picture text --> C10.3. SOSD: Social Sustainability Index C10.4. Rank correlations 58
**Table A2:** The Role of Omitted Variables: Alternative Clustering of Standard Errors

||(1)<br>No Tariffs|(2)<br>WITS|(3)<br>New GTD|(4)<br>New GTD|(5)<br>MFN|(6)<br>Glob|(7)<br>Glob-MFN|
|---|---|---|---|---|---|---|---|
|**(A) SE Clustere**|**d by Count**|**ry Pairs**,|FE: i-s-t,j-s-t|, i-j-s||||
|Deep TAs|0.25<sup>_∗∗∗_</sup>|0.21<sup>_∗∗∗_</sup>|0.14<sup>_∗∗∗_</sup>|0.15<sup>_∗∗∗_</sup>|0.07|0.08<sup>_∗_</sup>|0.06|
||(0.05)|(0.04)|(0.05)|(0.05)|(0.05)|(0.05)|(0.05)|
|Shallow TAs|0.02|-0.01|-0.07|-0.06|-0.04|-0.01|-0.01|
||(0.06)|(0.06)|(0.07)|(0.06)|(0.06)|(0.06)|(0.06)|
|Enabl. Clause|0.05|0.04|0.00|0.00|0.03|0.04|0.05|
||(0.07)|(0.07)|(0.08)|(0.08)|(0.07)|(0.07)|(0.07)|
|_ln_(1 +_τ_)||-1.08<sup>_∗∗∗_</sup>|-2.99<sup>_∗∗∗_</sup>|-2.90<sup>_∗∗∗_</sup>|-1.65<sup>_∗∗∗_</sup>|-1.22<sup>_∗∗_</sup>|-0.98<sup>_∗_</sup>|
|||(0.27)|(0.58)|(0.57)|(0.56)|(0.56)|(0.58)|
|MFN Openness|||||0.04<sup>_∗∗∗_</sup>||0.01<sup>_∗_</sup>|
||||||(0.01)||(0.01)|
|N|474,312|360,522|360,522|474,312|474,312|474,312|474,312|
|**(B) SE by Impo**|**rter-Sector, **|**Exporter**|**-Sector, and**|**Year**, FE: i-|s-t,j-s-t, i-|j-s||
|Deep TAs|0.25<sup>_∗∗∗_</sup>|0.21<sup>_∗∗_</sup>|0.14|0.15<sup>_∗_</sup>|0.07|0.08|0.06|
||(0.09)|(0.09)|(0.09)|(0.08)|(0.06)|(0.06)|(0.06)|
|Shallow TAs|0.02|-0.01|-0.07|-0.06|-0.04|-0.01|-0.01|
||(0.10)|(0.11)|(0.10)|(0.09)|(0.09)|(0.08)|(0.07)|
|Enabl. Clause|0.05|0.04|0.00|0.00|0.03|0.04|0.05|
||(0.06)|(0.05)|(0.05)|(0.06)|(0.05)|(0.06)|(0.06)|
|_ln_(1 +_τ_)||-1.08<sup>_∗_</sup>|-2.99<sup>_∗∗∗_</sup>|-2.90<sup>_∗∗∗_</sup>|-1.65<sup>_∗∗∗_</sup>|-1.22<sup>_∗_</sup>|-0.98<sup>_∗_</sup>|
|||(0.59)|(0.78)|(0.82)|(0.58)|(0.69)|(0.58)|
|MFN Openness|||||0.04<sup>_∗∗∗_</sup>||0.01|
||||||(0.01)||(0.01)|
|N|474,312|360,522|360,522|474,312|474,312|474,312|474,312|

**Note:** The information on the depth is provided by Hofmann et al. (2019) (all provisions), definitions of the different types of agreements (enabling clause, shallow TA, and deep TA) can be found in the main text. All columns include importer-sector-time, exporter-sector-time, and importer-exporter-sector fixed effects, columns (5) and (7) include time-varying border controls to account for general trends in globalization. In Panel (A) the data includes three broad sectors (agriculture, manufacturing, and services) for the years 2000 to 2015, Panel (B) has no services-trade but a longer time frame (1989-2015). In Panel (A) the standard errors are clustered by country pairs, in Panel (B) by importer-sector, exporter-sector, and year.<sup>_∗_</sup> for _p <_ 0 _._ 1,<sup>_∗∗_</sup> for _p <_ 0 _._ 05 and<sup>_∗∗∗_</sup> for _p <_ 0 _._ 01.

II
<!-- Start of picture text --> (a) Total public expenditure (b) Public investments<br>35% 5%<br>30% — oooes oe —=<br>a 25%° a,4% a “Wc, reed<br>8 20% B 3%<br>8 15% aS \ 2%<br>10%<br>5% %<br>0% 0%<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>es|||) me UM eee Total eefe || UM! ee eeee Total<br><!-- End of picture text -->
This structure of costs leads to practical trade-offs: Should the institution move up-market to provide larger loans and improve financial performance? Is deposit-taking feasible at such scales? Can socially-minded institutions survive commercial competition and regulation without re-defining their missions?

This paper describes our recent research on important trade-offs that microfinance practitioners, donors, and regulators navigate (Cull et al. 2007, 2009a, 2009b, 2009c). The evidence draws on large, global surveys of microfinance institutions and complements the important body of studies on specific institutions (e.g., Rutherford 2009, Rhyne 2001). The findings establish quantitative benchmarks for conversations among practitioners, experts and regulators about ways to expand financial access.

Among the key findings are: (1) Raising interest rates improves profitability for many institutions but, after a point, higher rates are associated with increased loan delinquencies and diminished profits (unless contractual innovations are in place that limit delinquencies). (2) Financial self-sustainability and serving poor households are not, by definition, incompatible. (3) But most institutions serving the poorest customers earn profits too small to attract investors seeking purely commercial returns (rather than a blend of “social” and financial returns). Still, (4), a substantial share of “non-profits” in fact earn profits, even if they are relatively small. (5) Non-profits do not duplicate the work of commercial lenders: non-profits tend to make far smaller loans on average and serve more women as a fraction of customers, relative to commercialized microfinance banks. (6) Rigorous and regular supervision is critical for deposittaking institutions, but it is costly; regulatory supervision thus tends to push institutions to serve relatively better-off customers as a way to maintain profitability. And (7) competition, or potential competition from mainstream, formal-sector banks appears to steer microfinance institutions toward serving poorer customers.

# **2. The** **_MicroBanking Bulletin_ Data**

In each of the four studies described below, we use cross-sectional data compiled by the Microfinance Information eXchange (the MIX). The MIX data are of unusually high quality, and

> of new technologies like mobile banking is to radically slash these kinds of costs, but it is too early to assess the promise and achievement.

3
**FIGURE O.8** From a big role for structural change in Botswana to a drag on growth

<!-- Start of picture text --> Labor productivity growth decomposition, by decade, 1970–2010<br>Within-sector<br>10<br>Structural<br>Total<br>5<br>0<br>1970–1980 1980–1990 1990–2000 2000–2010<br>–5<br>Year<br>Percent<br><!-- End of picture text --> Source: McCaig et al., Chapter 3 in this book. Note: Graph shows decomposition of average annual labor productivity growth (value-added in 2005 output per worker) during each decade.

services. In 2010, diamonds made up only 17.7 percent of value-added and a mere 1.5 percent of total employment. In contrast, the share of services in value-added reached 64.4 percent, while the share of services in employment reached 50.6 percent. Although the share of employment in agriculture remains high at 38.6 percent, its share in value-added has dropped from 27.4 percent to 2.7 percent—an indication of agriculture’s abysmal performance in Botswana.

An interesting feature of both Brazil’s and Botswana’s economies is that trade liberalization in the early 1990s did not have a major impact on the structure of either economy, although it did give a sharp boost to within-sector productivity. This limited impact on the structural front is especially surprising in Brazil, where manufacturing has been central to the economy. Also unlike Brazil, Botswana does not have the same potential in agriculture, as most of the land inhabited by farmers is semi-arid and prone to drought. For both of these countries, growth is more likely to come from improvements in fundamentals that facilitate within-sector productivity growth.

**Typology placement:** The expectation was that Brazil would move from quadrant (1) to quadrant (4); but the country instead ended up in quadrant (3), with much-improved fundamentals, but also sharply reduced growth. Botswana is similarly stuck in quadrant (3), with slow growth and relatively strong fundamentals, although unlike Brazil, it has never industrialized.

27
27

**Table** 14 Proportional-Hazards Nlodel Estimates **of** Unemployment Duration

|**Variable**|**M**|**ales**|**Fe**|**males**|
|---|---|---|---|---|
|Age<br>**Age**<br>2|-.030<br>(-4.654)|-.027<br>(-4.050)|-.086<br>(-1.397)<br>.001|-.067<br>(-1.051)<br>.001|
||||(1.251)|(0.975)|
|Yrs. School<br>School Dummies.|.006<br>(0.476)||_-.005_<br>(-0.189)||
|No Formnal<br>||.095<br>(0.316)||1.894<br>(1.588)|
|Inc. Prirnary||-.104<br>(-0.527)||.229<br>(0.470)|
|Primary||.214<br>(1.202)||.095<br>(0.330)|
|Inc. Secondary||.083<br>(0.395)||.061<br>(0.192)|
|Secondary<br>||.334<br>(1.900)||.347<br>(1.442)|
|High Secondary||.257<br>(1.413)||.033<br>(0.132)|
|University|||||
|Household Head<br>|.414<br>(2.590)|.420<br>(2.587)|.505<br>(1.281)|.425<br>(1.012)|
|WithChildren|.071<br>(2.197)|.076<br>(2.222)|-.024<br>(-0.267)|-.081<br>(-0.844)|
|Chi-Square|24.64|33.35|4.20|8.14|
|Number Obs.|439|439|152|152|

T-Statistics in parentheses. See variable definitions in Table 8. Household head = dummy variable which takes or. the value I if individual is household head. With children = similar dummy variable for having children. School dummies include no formal education, incomplete primary (1-5 years school), primary (6 years school), incomplete secondary (7-8 years school), secondary (9 years school), higher secondary (10-12 years school) and university (13 or more years of school).
explanatory variables, appeared in the top 25 percent list for all seven indicators. In Table 4B the corresponding figure is just 1 out of 45 variables, or only 2.5 percent of explanatory variables. And in Table 4C just one out of 303, or 0.3 percent of variables, falls in the top 25 percent list for all seven outcome indicators. We also find that far fewer variables also consistently do _not_ appear in the top 25 percent of explanatory variables. In Table 4A for example 33 percent of variables (4 out of 12) appeared in the top 25 list for none of the outcome variables. In Table 4B and 4C the corresponding rates are 18 percent and 14 percent, respectively. More formally, the p‐value for the chi‐squared test of the null hypothesis that inclusion in the top 25 percent of variables by PIP is independent across outcome variables is 0.12 in Table 4B, and 0.99 in Table 4C, and so we do not reject the null hypothesis of independence at conventional levels in both cases.

Since we can disaggregate the GII data at three levels, we can also look at the extent to which variables that appear important at one level of disaggregation are also found to be important at other levels. To take a specific example, consider the DRI outcome variable. At the most aggregated GII‐12 level in Table 4A, we saw that PQElections had by far the strongest explanatory power, with a PIP of 0.90. However, the three subcomponents of PQElections in Table 4A have considerably lower explanatory power for the DRI measure of corruption (ranking 8<sup>th</sup> , 9<sup>th</sup> , and 35<sup>th</sup> out of 45 variables). By contrast, by far the most significant variable for DRI at the GII‐45 level of disaggregation is LQ3.4BudgetProcess, with a PIP of 0.9. However, the corresponding aggregate at the GII‐12 level of aggregation to which this variable belongs, LQ3GovtAccountability, has only the third‐highest PIP of 0.14. We can see a similar pattern of instability at lower levels of aggregation. Continuing with DRI as an example, when we move to the GII‐303 level of disaggregation, we find that just 5 of the 23 individual variables making up PQElections fall in the top 25 percent of variables at this highest level of disaggregation.

More systematically, we perform the following simple test to document the degree of stability across levels of aggregation in terms of which variables are identified as important by the BMA procedure: we ask whether the probability that a variable is in the top 25 percent of all 303 fully disaggregated GII measures is significantly higher conditional on it belonging to a cluster of variables that was identified in the top 25 percent at a lower level of disaggregation. We perform this test using simple contingency tables that document whether a given variable is in the top 25 percent at both levels of disaggregation, neither level, or the one level but not the other. Across all 7 outcome variables, and 22
Table 6. Average annual (1995-98) and potential exports

|_Trading_<br>_partners_|_E_|_xports (USD_|_million)_|_Partner's_|_share (sampl_|_e = 100), %_|
|---|---|---|---|---|---|---|
||_Actual_|_Potential_|_Potential!_<br>_Actual ratio_|_Actual_|_Potential_|_Potential-_<br>_Actual_|
|Armenia's|||||||
|exports to:|||||||
|Azerbaijan|--|3,276|--|--|0.6|0.6|
|Georgia|7,478|22,214|3.0|3.3|4.0|0.7|
|Turkey|4,692|35,641|7.6|2.0|6.4|4.4|
|EU|63,223|230,768|3.7|27.6|41.6|14.0|
|Russia|72,457|79,530|1.1|31.6|14.3|-17.3|
|USA|5,916|101,948|17.2|2.6|18.4|15.8|
|Iran|38,233|44,313|1.2|16.7|8.0|-8.7|
|Other CIS|37,304|37,396|2.4|16.3|6.7|-9.5|
|Subtotal|229,303|555,087|2.4|100|100|--|
|Azerbaijan's|||||||
|exports to:|||||||
|Arnenia|--|3,234|--|--|0.3|0.3|
|Georgia|86,813|14,575|0.2|14.1|_1.5_|-12.5|
|Turkey|60,654|35,609|0.6|9.8|3.8|-6.1|
|<br>EU|92,900|384,419|4.1|15.1|40.6|25.6|
|Russia|124,475|205,833|1.7|20.2|21.8|1.6|
|USA|4,808|171,376|35.6|0.8|18.1|17.3|
|Iran|161,604|62,798|0.4|26.2|6.6|-19.6|
|Other CIS|85,038|68,172|0.8|13.8|7.2|-6.6|
|Subtotal|616,292|946,018|_1.5_|100.0|100.0|0.0|
|Georgia's|||||||
|exports to:|||||||
|Arnenia|18,869|21,534|1.1|10.9|1.8|-9.1|
|Azerbaijan|20,148|14,312|0.7|11.6|1.2|-10.4|
|Turkey|29,927|73,905|2.5|17.2|6.2|-11.1|
|<br>EU|20,255|495,236|16.5|11.7|41.3|29.6|
|Russia|56,892|238,154|11.8|32.7|19.8|-12.9|
|USA|4,322|211,939|3.7|2.5|17.7|15.2|
|Iran|2,328|63,346|14.7|1.3|5.3|3.9|
|Other CIS|21,100|81,761|35.1|12.1|6.8|-5.3|
|Subtotal|<br>173,841|<br>1,200,186|<br>6.9|100.0|100.0|0.0|

21
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Table 3: Quarterly Drivers of Total Debt Inflows by Sector, 2002–2014)

|||Panel A|: All Coun|tries|||
|---|---|---|---|---|---|---|
||(1)<br>Total|(2)<br>Public|(3)<br>Banks|(4)<br>Corp.|(5)<br>Total<br>w/DI Debt|(6)<br>Corp.<br>w/DI Debt|
|log(VIX_t−_1)<br>GDP Growth_it−_1|-4.974<sup>_∗∗∗_</sup><br>(1.260)<br>0.232<sup>_∗∗∗_</sup><br>(0.0650)|0.960<br>(0.667)<br>-0.00864<br>(0.0146)|-4.362<sup>_∗∗∗_</sup><br>(0.989)<br>0.190<sup>_∗∗∗_</sup><br>(0.0490)|-1.572<sup>_∗∗∗_</sup><br>(0.419)<br>0.0501<sup>_∗∗∗_</sup><br>(0.0156)|-5.744<sup>_∗∗∗_</sup><br>(1.516)<br>0.239<sup>_∗∗∗_</sup><br>(0.0541)|-2.003<sup>_∗∗∗_</sup><br>(0.696)<br>0.0730<sup>_∗∗∗_</sup><br>(0.0164)|
|Observations<br>|2695|2695|2695|2695|2615|2615|
|_R_<sup>2</sup>|0.041|0.002|0.045|0.028|0.044|0.028|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|
|||Panel B: Ad|vanced Ec|onomies|||
||(1)<br>Total|(2)<br>Public|(3)<br>Banks|(4)<br>Corp.|(5)<br>Total<br>w/DI Debt|(6)<br>Corp.<br>w/DI Debt|
|log(VIX_t−_1)|-9.101<sup>_∗∗∗_</sup><br>(2.676)|0.813<br>(1.400)|-7.630<sup>_∗∗∗_</sup><br>(2.068)|-2.284<sup>_∗∗_</sup><br>(0.962)|-10.57<sup>_∗∗∗_</sup><br>(3.132)|-3.196<br>(1.563)|
|GDP Growth_it−_1|0.506<sup>_∗∗∗_</sup><br>(0.179)|0.0616<br>(0.0340)|0.363<sup>_∗∗_</sup><br>(0.131)|0.0819<br>(0.0466)|0.480<sup>_∗∗∗_</sup><br>(0.141)|0.101<sup>_∗∗_</sup><br>(0.0420)|
|Observations|1127|1127|1127|1127|1109|1109|
|_R_<sup>2</sup>|0.065|0.002|0.056|0.026|0.065|0.027|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|
|||Pa|nel C: EM||||
||(1)<br>Total|(2)<br>Public|(3)<br>Banks|(4)<br>Corp.|(5)<br>Total<br>w/DI Debt|(6)<br>Corp.<br>w/DI Debt|
|log(VIX_t−_1)|-2.261<sup>_∗∗_</sup><br>(0.829)|1.077<br>(0.652)|-2.265<sup>_∗∗∗_</sup><br>(0.706)|-1.073<sup>_∗∗∗_</sup><br>(0.253)|-2.336<sup>_∗∗_</sup><br>(0.922)|-1.117<sup>_∗∗∗_</sup><br>(0.374)|
|GDP Growth_it−_1|0.116<sup>_∗∗∗_</sup><br>(0.0347)|-0.0394<sup>_∗∗∗_</sup><br>(0.0123)|0.118<sup>_∗∗∗_</sup><br>(0.0346)|0.0381<sup>_∗∗∗_</sup><br>(0.00928)|0.142<sup>_∗∗∗_</sup><br>(0.0416)|0.0635<sup>_∗∗∗_</sup><br>(0.0161)|
|Observations|1372|1372|1372|1372|1310|1310|
|_R_<sup>2</sup>|0.071|0.021|0.116|0.075|0.073|0.053|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|

Sample is from 2002q4-2014q4, countries as listed in Appendix A.4. Total Debt is the sum of Portfolio Debt and Other Investment Debt inflow data, constructed by AHKS as described in Section 2. Dependent variables are expressed as a percentage of GDP. VIX is the implied volatility of S&P 500 index options. GDP growth is calculated as a year-on-year percentage growth. Errors are clustered at the country level. ** p _<_ 0.05, *** p _<_ 0.01 30
# Robustness of Shared Prosperity Estimates: How Different Methodological Choices Matter

Aziz Atamanov, Christina Wieser, Hiroki Uematsu, Nobuo Yoshida, Minh Cong Nguyen, Joao Pedro Wagner De Azevedo and Reno Dewina<sup>1</sup> JEL codes: I32, D31, D63 Key words: shared prosperity; robustness tests

> 1 All authors are with the World Bank, Washington DC. Contact email: aatamanov@worldbank.org.
|||||**Contact**|
|---|---|---|---|---|
|WPS41|8**World**Bank**Efforts at**Poverty<br>**Measurement**<br>in the Third World: Th<br>Living Standards Measurement<br>Stu|Paul Glbwwe<br>e<br>dy|||
|WPS41|7 Modeling the Macroeconomic<br>Requirements<br>of Policy Reform|William Easterly<br>E. C. Hwa<br>Piyabha Kongsamut<br>Jan Zizek|May 1990|R. Luz<br>34303|
|WPS41|8 Does Devaluation<br>Hurt Private<br>Investment? The Indonesian<br>Case|Ajay Chhibber<br>Nemat Shafik|May 1990|W.Colinet<br>33490|
|WPS41<br>9|<br> The**Design**and Sequencing<br>of<br>Trade and Investment<br>Policy Refor<br>An Institutional Analysis|Brian Levy<br>m:|May 1990|B. Levy<br>37488|
|WPS42|0 Making Bank Irrigation Investment<br>More Sustainable|s Gerald T. OMara|May 1990|C. Spooner<br>30464|
|WPS42|1 Taxation of Financial Intermediatio<br>Measurement<br>Principles and<br>Application<br>to Five African Countrie|n: Christophe Chamley<br>Patrick Honohan<br>s|May 1990|W. Pitayato-<br>nakam<br>37666|
|WPS42|2 Civil Service Reform and the World<br>Bank|Barbara Nunberg<br>John Nellis|May 1990|R. Malcolm<br>37495|
|WPS42|3 Relative Price Changes and the<br>Growth of the Public Sector|M. Shahbaz Khan|May 1990|WDR Office<br>31393|
|WPS424|Mexico's External Debt<br>Restructuring<br>in1989-1990: An<br>Economic<br>Analysis|Sweder van Wijnbergen|||
|WPS425|The Earmarking<br>of Govemment<br>Revenues in Colombia|William A. McCleary<br>Evamaria<br>Uribe Tobon|||
|WPS426|Growth-Oriented<br>Adjustment<br>Programs: A Statistical Analysis|Riccardo Faini<br>Jaime de Melo<br>Abdel Senhadji-Semial<br>Julie Stanton|i||
|WPS427|Exchange<br>Reform, Parallel Markets<br>and Inflation in Africa: The Sase<br>of Ghana|Ajay Chhibber<br>Nemat Shafik|||
|WPS428|Perestroyk**a**nd Its Implications<br>for European Socialist Countries|Bela Balassa|May 1990|N. Campbell<br>33769|