rafmacalaba/datause-encoder-data huggingface dataset:
**Table S1.2: Enumerator effect: R**<sup>**2**</sup> **and confidence intervals**

|Respondent<br>characteristic|R-squared<br>Coefficient|Bootstrapped<br>Standard Error|z|p-value|Norma<br>[95% Con|l-based<br>f. Interval]|
|---|---|---|---|---|---|---|
|(1)|(2)|(3)|(4)|(5)|<br>(|<br>6)|
|gender (male)|||||||
|team 1|0.007|0.005|1.32|0.187|-0.003|0.018|
|team 2|0.010|0.006|1.56|0.120|-0.002|0.022|
|team 3|0.017|0.008|2.11|0.035|0.001|0.033|
|team 4<br>age|0.011|0.008|1.43|0.153|-0.004|0.026|
|<br>team 1|0.011|0.006|1.86|0.062|-0.001|0.022|
|team 2|0.011|0.007|1.62|0.105|-0.002|0.023|
|team 3|0.012|0.007|1.79|0.074|-0.001|0.025|
|team 4|0.023|0.010|2.23|0.026|0.003|0.043|
|marital status (single)|||||||
|<br>team 1|0.008|0.005|1.51|0.132|-0.002|0.018|
|team 2|-0.002|0.004|-0.43|0.670|-0.009|0.006|
|team 3|0.010|0.008|1.32|0.186|-0.005|0.025|
|team 4<br>household size|0.003|0.007|0.38|0.705|-0.011|0.016|
|team 1|0.009|0.005|1.77|0.076|-0.001|0.020|
|team 2|0.019|0.008|2.36|0.018|0.003|0.035|
|team 3|0.048|0.010|4.77|0.000|0.028|0.068|
|team 4|0.041|0.011|3.65|0.000|0.019|0.063|
|consume alcohol and tobac|co||||||
|team 1|0.019|0.007|2.76|0.006|0.005|0.032|
|team 2|0.002|0.005|0.45|0.650|-0.007|0.012|
|team 3<br>|0.012<br>|0.009<br>|1.42<br>|0.156<br>|-0.005<br>|0.029<br>|
|team 4|0.014|0.009|1.49|0.136|-0.004|0.031|
|exenditure on alcohol an|tobacco||||||
|p<br>team 1|<br>0.005|0.005|1.05|0.294|-0.004|0.014|
|team 2|0.007|0.007|0.94|0.347|-0.007|0.021|
|team 3|0.006|0.006|1.10|0.270|-0.005|0.018|
|team 4<br>expenditure on alcohol an|0.027<br>d tobacco (condit|0.009<br>ional)|3.10|0.002|0.010|0.045|
|<br>team 1|<br>0.067|<br>0.031|2.16|0.031|0.006|0.128|
|team 2|0.016|0.020|0.82|0.413|-0.022|0.054|
|team 3|0.028|0.033|0.85|0.395|-0.036|0.092|
|team 4|0.041|0.013|3.15|0.002|0.015|0.066|
|assets index|||||||
|team 1|0.040|0.009|4.49|0.000|0.023|0.058|
|team 2|0.009|0.007|1.29|0.197|-0.004|0.022|
|team 3|0.034|0.009|3.60|0.000|0.015|0.052|
|team 4|0.024|0.010|2.29|0.022|0.003|0.044|
|assets index (cattle)|||||||
|team 1|0.001|0.006|0.09|0.929|-0.011|0.012|
|team 2|0.003|0.005|0.54|0.590|-0.007|0.012|
|team 3|0.077|0.018|4.29|0.000|0.042|0.112|
|team 4<br>|0.014|0.029|0.47|0.636|-0.043|0.070|
|education level|||||||
|team 1|0.000|0.005|0.06|0.948|-0.009|0.010|
|team 2<br>team 3|0.009<br>0028|0.007<br>0010|1.35<br>270|0.176<br>0007|-0.004<br>0008|0.023<br>0048|
|<br>team 4|.<br>0.012|.<br>0.014|.<br>0.91|.<br>0.361|.<br>-0.014|.<br>0.039|
|open to vote for DP|||||||
|<br>team 1|0.402|0.018|21.82|0.000|0.366|0.439|
|team 2|0178|0018|1007|0000|0143|0212|
|<br>team 3|.<br>0.154|.<br>0.020|.<br>7.66|.<br>0.000|.<br>0.114|.<br>0.193|
|team 4|0334|0049|685|0000|0238|0430|
|<br>open to vote for FDC|.|.|.|.|.|.|
|<br>team 1|0.338|0.018|18.43|0.000|0.302|0.374|
|<br>team 2|<br>0.133|<br>0.017|<br>7.79|<br>0.000|<br>0.100|<br>0.167|
|team 3|0.150|0.019|7.97|0.000|0.113|0.187|
|team 4<br>open to vote for NRM<br>|0.223<br>|0.034<br>|6.61<br>|0.000<br>|0.157<br>|0.289<br>|
|team 1<br>team 2|0.050<br>0.012|0.010<br>0.007|5.25<br>1.78|0.000<br>0.075|0.031<br>-0.001|0.069<br>0.025|

34
## **Figure A7.2: Comparison of Colombian World Management Survey Management Score to Other Countries**

<!-- Start of picture text --> United States 3.308<br>Japan 3.230<br>Germany 3.210<br>Sweden 3.188<br>Canada 3.142<br>Great Britain 3.033<br>France 3.015<br>Australia 2.997<br>Italy 2.978<br>Mexico 2.899<br>Poland 2.887<br>Singapore 2.861<br>New Zealand 2.851<br>Northern Ireland 2.839<br>Portugal 2.826<br>Republic of Ireland 2.762<br>Chile 2.752<br>Spain 2.748<br>Greece 2.720<br>China 2.712<br>Turkey 2.706<br>Argentina 2.699<br>Brazil 2.684 Africa<br>India 2.611<br>Vietnam 2.608<br>Asia<br>Colombia 2.578<br>Kenya 2.549<br>Nigeria 2.516 Oceania<br>Nicaragua 2.397<br>Myanmar 2.372 Europe<br>Zambia 2.316<br>Tanzania 2.254<br>Latin America<br>Ghana 2.225<br>Ethiopia 2.221<br>Mozambique 2.027 North America<br>1.5 2 2.5 3 3.5<br>Average Management Scores, Manufacturing<br><!-- End of picture text --> Source: World Management Surveys, Nick Bloom.

# **WMS 2016 Data Collection**

In September 2016, we asked _Innovations for Poverty Action_ (IPA) to conduct a second round of the _World Management Survey (WMS)_ . The LSE provided support in training the four analysts who conducted the interviews, the two supervisors and the research associate responsible for the survey. All material was provided by the LSE and the training took place in October 2016.

Since the WMS is designed for larger firms, we chose as a sample frame the 109 firms in our sample that had at least 25 employees at baseline. This consisted of 37 control, 41 group treatment, and 31 individual treatment firms. Of these 109 firms, we were able to collect data on 70 firms (20 control, 31 group, 19 individual), of which 50 firms had also been interviewed in 2013 (14 control, 22 group, 14 individual). This response rate of 64% is double the standard WMS response rate, reflecting the pre-existing contacts with these firms through the project. Of those companies not interviewed, 3 had closed down, and the remainder either refused, or repeatedly rescheduled and could not be interviewed.

62
|Chad Education Sector Reform Project Phase 2<br>WalyWane|
|---|
|Chad Education Sector Reform Project Phase 2<br>WalyWane|
|Chad Education Sector Reform Project Phase 2<br>WalyWane|
|Chad Education Sector Reform Project Phase 2<br>WalyWane|
|Chad Education Sector Reform Project Phase 2<br>WalyWane|
|Chad Education Sector Reform Project Phase 2<br>WalyWane|
|Chad Education Sector Reform Project Phase 2<br>WalyWane|
|Chad Education Sector Reform Project Phase 2<br>WalyWane|
|Chad Education Sector Reform Project Phase 2<br>WalyWane|
|Chad Education Sector Reform Project Phase 2<br>WalyWane|
|Chad Education Sector Reform Project Phase 2<br>WalyWane|
|Partial Risk SharingFacilityin EnergyEfficiency<br>Ashok Sarkar|
|Partial Risk SharingFacilityin EnergyEfficiency<br>Ashok Sarkar|
|Partial Risk SharingFacilityin EnergyEfficiency<br>Ashok Sarkar|
|Partial Risk SharingFacilityin EnergyEfficiency<br>Ashok Sarkar|
|Partial Risk SharingFacilityin EnergyEfficiency<br>Ashok Sarkar|
|Partial Risk SharingFacilityin EnergyEfficiency<br>Ashok Sarkar|
|Partial Risk SharingFacilityin EnergyEfficiency<br>Ashok Sarkar|
|Yunnan HighwayAsset Management Project<br>Xiaoke Zhai|
|Yunnan HighwayAsset Management Project<br>Xiaoke Zhai|
|Yunnan HighwayAsset Management Project<br>Xiaoke Zhai|
|Yunnan HighwayAsset Management Project<br>Xiaoke Zhai|
|Yunnan HighwayAsset Management Project<br>Xiaoke Zhai|
|Yunnan HighwayAsset Management Project<br>Xiaoke Zhai|
|Yunnan HighwayAsset Management Project<br>Xiaoke Zhai|
|Yunnan HighwayAsset Management Project<br>Xiaoke Zhai|
|Yunnan HighwayAsset Management Project<br>Xiaoke Zhai|
|Yunnan HighwayAsset Management Project<br>Xiaoke Zhai|
|Yunnan HighwayAsset Management Project<br>Xiaoke Zhai|
|Sustainable Livelihoods and Adaptation to Climate Change<br>Priti Kumar|
|Sustainable Livelihoods and Adaptation to Climate Change<br>Priti Kumar|
|Sustainable Livelihoods and Adaptation to Climate Change<br>Priti Kumar|
|Sustainable Livelihoods and Adaptation to Climate Change<br>Priti Kumar|
|Sustainable Livelihoods and Adaptation to Climate Change<br>Priti Kumar|
|Sustainable Livelihoods and Adaptation to Climate Change<br>Priti Kumar|
|Sustainable Livelihoods and Adaptation to Climate Change<br>Priti Kumar|
|Sustainable Livelihoods and Adaptation to Climate Change<br>Priti Kumar|
|Sustainable Livelihoods and Adaptation to Climate Change<br>Priti Kumar|
|Sustainable Livelihoods and Adaptation to Climate Change<br>Priti Kumar|
|SAARC Development Fund - Capacityand Institutional StrengtheningJan Erik Nora|
|SAARC Development Fund - Capacityand Institutional StrengtheningJan Erik Nora|
|SAARC Development Fund - Capacityand Institutional StrengtheningJan Erik Nora|
|Bangladesh SafetyNet Systems for the Poorest Project<br>Kenichi Victor Nishikawa Chavez|
|Bangladesh SafetyNet Systems for the Poorest Project<br>Kenichi Victor Nishikawa Chavez|
|Bangladesh SafetyNet Systems for the Poorest Project<br>Kenichi Victor Nishikawa Chavez|
|Bangladesh SafetyNet Systems for the Poorest Project<br>Kenichi Victor Nishikawa Chavez|
|Bangladesh SafetyNet Systems for the Poorest Project<br>Kenichi Victor Nishikawa Chavez|
Table 8: Voice vs. exit

||paid bribe<br>(1)|bribe<br>bur-<br>den<br>(2)|informal<br>gift<br>(3)|bribe%sales<br>(4)|corr.<br>per-<br>ception<br>(5)|
|---|---|---|---|---|---|
|dec. index|-0.25***|-14.76**|3.45|-17.8|-0.78|
||-0.06|-5.72|-2.74|-28.07|-3.59|
|dec.index*(# units)|0.00***|0|0|0.00*|0|
||0|0|0|0|0|
|LG area|0.01***|0.54|-0.06*|0.22|0|
||0|-0.36|-0.03|-0.37|-0.01|
|dec.index*LG area|-0.23***|-10.07|0.85*|-6.01|0.1|
||-0.05|-6.09|-0.5|-7.14|-0.12|
|dec.index*GDP/capita|0.06|0|0|0.00**|0.00***|
||-0.05|0|0|0|0|
|# units|-0.00***|0|0|0|0|
||0|0|0|0|0|
|1 tier|-0.15***|-6.78|-0.12**|-0.45|0.31|
||-0.06|-6.64|-0.05|-0.53|-0.39|
|2 tiers|-0.17***|-7.47|-0.16***|-0.29|0.12|
||-0.05|-6.51|-0.05|-0.37|-0.23|
|3 tiers|-0.17***|-6.46||||
||-0.05|-6.71||||
|UK legal origin|-0.08***|-3.05***|-0.02|-0.05|0.36|
||-0.01|-0.86|-0.06|-0.54|-0.29|
|fractionalization|0|-0.36|0|0.23**|-0.03|
||0|-0.29|-0.02|-0.11|-0.08|
|start business|-0.01***|-0.17*|0|0.13|-0.09**|
||0|-0.09|0|-0.08|-0.04|
|enforce contract|-0.00***|0|0.01*|-0.02|-0.03|
||0|-0.06|0|-0.04|-0.02|
|# countries|54|56|56|56|101|

Note<sup>∗</sup> - significant at 5% level,<sup>∗∗</sup> - significant at 3% level,<sup>∗∗∗</sup> - significant at 1% level. Dependent variable: column (1) - paid bribe; columns (2) - paid bribe; columns (3) - informal gift; columns (4) - bribe%sales; column (5) - corruption perception, 2007 (see definitions in Table 2). Estimation method: linear probability (OLS). Standard errors are reported in brackets: columns (1), (2), (5) - heteroscedasticity robust; columns (3), (4) - clustered by country. Additional controls are included in all regressions, but not reported here due to a lack of space. These are regional dummies, year dummies, individual and firm controls, population, openness, log (GDP per capita), # of units, LG population size.

29
|taxes with VAT|_E_<br>_Reform F_|607.3<br>605.5<br>-0.3|39.3<br>38.5<br>-2.0|66.8|70.6<br>5.8<br>6.5<br>6.4<br>-1.8||
|---|---|---|---|---|---|---|
|border|_Reform_|607.3<br>607.9<br>0.1|39.3<br>38.8<br>-1.3|**ue**<br>66.8|65.8<br>-1.4<br>6.5<br>6.4<br>-1.4||
|involving simplifcation of|_B_<br>_Reform C_<br>_Reform D_|**Impact on imports**<br>607.3<br>607.3<br>610.9<br>604.9<br>0.6<br>-0.4|**Impact on revenue**<br>39.3<br>39.3<br>39.8<br>39.4<br>1.1<br>0.1|**Total import tax reven**<br>66.8<br>66.8|59.3<br>71.6<br>-11.2<br>7.2<br>**Collected tarif rates**<br>6.5<br>6.5<br>6.5<br>6.5<br>0.5<br>0.6|of _σP_ = 0_._5 and _σES_ = 1_._5.|
|ious reforms|_A_<br>_Reform _|607.3<br>620.1<br>2.1|39.3<br>40.4<br>2.5|66.8|40.4<br>-39.6<br>6.5<br>6.5<br>0.4|ticity parameters<br>f SYP.|
|ct of var|_Reform _|607.3*<br>614.2<br>1.1|39.3<br>40.1<br>1.8|66.8|53.1<br>-20.5<br>6.5<br>6.5<br>0.7|suming elas<br> in billions o|
|impa|||pre<br> post|pre|post<br>t|ated as<br>iven in|
|Trade<br>ion_†_||pre<br> post<br>(%)|venue <br>venue <br>(%)|venue|venue <br>(%)<br>te pre<br>te pos<br>(%)|s simul<br>es are g|
|Table 8: <br>introduct||Imports <br>Imports <br>Change|Tarif re<br>Tarif re<br>Change|Total re|Total re<br>Change <br>Tarif ra<br>Tarif ra<br>Change|_†_ Scenario<br>* All valu|

21
# 1. **Introduction**

Although the HIV virus appeared later in Southern Africa than elsewhere on the continent, Southern Africa now has the highest rates of HIV prevalence in the world. Approximately one adult in five is HIV positive (see Figure 1-1), and Southern African countries face the prospect of the reversal of hard won progress in development, resulting in deepening human poverty and social dislocation. Valuable human resources are being lost, affecting all sectors, public as well as private. HIV/AIDS will hamper governments' ability to manage their economies and to deliver social services.

**Figure 1-1. HIV adult prevalence rates and selected development indicators in Southern Africa**

|**Country**<br>__|**AdultHIV**<br>**Prevalence**<br>___<br>__**_**_<br>___<br>___<br>__|**Population**<br>**'000**<br>_____<br>___|**Adult**<br>**Population**<br>**000**|**|GDP/Capita**<br>**(PPP$)**<br>_<br>_<br>_<br>_<br>_|**Secondary**<br>**Seodr**<br>**Enrolment**|
|---|---|---|---|---|---|
|**SouthAfrica**|**19.9%**|**43,421**|**20,630**|**$8,908**|**56%**|
|**Zimbabwe**|**25.1%**|**11,343**|**5,771**|**$2,876**||
|**Zambia**|**20.0%**|**9,582**|**4,137**|**$756**||
|**Lesotho**|23.6%|2,143|998|$1,854|18%|
|**Namibia**|19.5%|1,771|790|$5,468|38%|
|**Botswana**|35.8%|1,695|775|$6,872|48%|
|**Swaziland**|25.3%|1,083|480|$3,987|38%|
|**OverallTotal**|**21.4%**|**71,038**|**33,581**|**$6,423**|**53%**|

Sources. UNAIDS website (HIV prevalence), UNDP (2001).

# _1.1. Approach of the Paper_

The principal goal of this paper is to provide a framework for comparing different AIDS vaccination strategies to guide the process of targeting, should that become necessary. The paper begins with a brief review of the health and developmental impacts of HIV/AIDS in Southern Africa, and the current responses in the seven countries considered with regard to prevention, treatment and vaccine development.

In section 2, the paper presents an analysis based on calculating the total number of cases averted per 100 entrants into a vaccination program, under the assumption that prior screening for existing HIV infection is impractical and is not carried out. This indicator is then calculated for each of 12 identified risk groups and used to rank them in order of effectiveness. This leads to a discussion of access and targeting issues.

In section 3, the paper presents evidence of existing ability and willingness to pay for an HIV vaccine, based upon expenditure on other health items or vaccines, and a brief analysis of economic motivation. The paper concludes with recommendations for vaccination strategies under differing assumptions about the cost, efficacy and duration of protection offered by any potential vaccine.

_1_
Because this paper focuses on the demand side, we assess a broad array of variables that influence the demands for national statistics and have dropped the SPI’s first pillar – Data Use from our analysis, given filling data gaps in the Data Use pillar underlying the SPI remains a work in progress.<sup>4</sup> For the remainder of the paper, we use this modified SPI comprised of the four remaining pillars that represent the supply-side activities related to NSS performance.

# **3. What the SPI Reveals**

One key improvement of the SPI over the SCBI is its ability to measure statistical capacity through a robust, internally consistent index score for any country in the world. For the first time, we can compare the strengths of well-regarded NSSs with the less-developed institutions in lower- and middle-income countries. With SPI scores available for the full spectrum of countries, we have a measure of the difference between low- and high-income countries in terms of their capability to produce and disseminate statistical products and services.

Figure 1 shows the histogram and nonparametric estimation of the four-pillar SPI’s density function for the sample of 215 countries. The distribution appears to be bimodal, with a group of high-capacity countries with SPIs falling in the range from 0.8 to 0.9. The remaining countries center on an SPI of 0.5. Few countries score between 0.62 and 0.75, suggesting that a discrete step may be necessary to reach the group of high-performing countries.

A cluster analysis of the four-pillar SPI scores indicates the presence of a stable cluster of 26 highcapacity counties with a mean SPI of 0.84 and another stable cluster of 30 low-performing counties with a mean SPI of 0.14. No clusters are identified in the region of 0.64–0.79, even when the number of clusters reaches 15.<sup>5</sup> This “statistical performance gap” is analogous to the concept of the middle-income trap in the development economics literature (see Gill and Kharas 2007).<sup>6</sup> A similar gap between the top-performing NSSs and the rest of the world is evident in the Open Data

> 4 See World Bank (2021).

> 5 We used the Stata cluster _kmean_ routine to perform this analysis (StataCorp 2019).

> 6 The term _middle-income trap_ refers to countries that experienced rapid growth and quickly reached middle-income status but then failed to catch up to high-income countries. Statistical capacity may exhibit the same phenomenon. The SPI, the first capacity building indicator to measure all countries, is in its infancy. It will be some years before there are sufficient time series data to robustly test the statistical performance gap hypothesis.

8
20 regulations in the first place, and like those loose regulations, exacerbate the magnitude of crisis.

One other key government decision at the time that bank insolvencies occur is whether to compensate all, some or none of the depositors and other creditors in insolvent institutions, whether to pursue the assets of delinquent bank borrowers, and whether to recapitalize the insolvent banks. The resulting fiscal transfers to the financial sector are likely to be positively correlated with government sensitivity to special interests. First, the transfers are bounded from above by the difference in value between bank assets and liabilities. That is, where the influence of special interests has been less profound and the magnitude of banking losses is small, so also are fiscal transfers. Second, fiscal transfers to recapitalize banks or repay depositors are larger to the extent that governments make only feeble efforts to recover assets from delinquent bank borrowers - who are often special interests that benefited from earlier lax prudential regulation. Third, fiscal transfers are larger to the extent that governments compensate not only small depositors, but also large depositors and creditors of banks - again, those who tend to be special interests in their own right and who are most likely to have benefited from earlier lax government oversight of the financial sector.2° The foregoing discussion identifies numerous government decisions that might contribute to banking crises and over which special interests might exercise influence. The empirical tests below focus on two of them: the magnitude of fiscal transfers in the event of 19 Akerlof and Romer, 1994, introduced the term and discuss the phenomenon. 20 jy might be argued that special interests have no influence on fiscal transfers, because transfers are driven only by the (exogenous) magnitude of the banking crisis. The scanty evidence that exists on crisis magnitudes suggests that this is not the case. In Chile, the assets of insolvent institutions amounted to approximately 22 percent of GDP, but bailout costs were twice as high, 41 percent. In Colombia, assets amounted to § percent of GDP, but the fiscal costs of resolving the crisis were lower, 5 percent, Deposits in insolvent institutions were approximately 5 percent of GDP in Uruguay, where bailout costs were 7 percent of GDP; they were 9 percent of GDP in Malaysia in 1985, where the bailout amounted to only 4.7 percent of GDP (asset and deposit information from Caprio and Klingebiel, 1997 and Beck, Demirgit¢-Kunt and Levine).
**Notes:** This figure shows the share of government purchases accounted for by procurement firms, i.e., firms with at least one procurement contract, falling into different parts of the distribution of _k_ . In the data, we have used data for the year 2006. Very similar patterns are observed in different years.

**Figure 9.** Distribution of government purchases across procurement firms

(and thus, financially constrained firms) and public procurement, most importantly the dynamic “crowding-in” benefits of winning public procurement contracts, we take the results in Panel (a) at horizons _h_ ≥ 1 as evidence that the model performs remarkably well in this regard.

**Sales to the private sector.** We next look at the evolution of firms’ sales to the private sector in Panel (b) of Figure 8. Similar to the data, our model predicts a “crowding-out” effect on impact and a “crowding-in” effect from _h_ = 1 onwards. As discussed in Section 4.6, this is because at impact constrained firms have to split their scarce collateral between the two sectors (a financial problem that is only partially alleviated by the extra credit generated by selling to the government). However, the new profits generated from procurement allow the firm to accumulate more net worth over time. This higher level of net worth will ease the firm’s financial constraint (lowering _λ_ ) and hence allow it to increase output in the private sector in the subsequent periods. Similarly to the effect of procurement on credit, our model over predicts the “crowding-out” effect at impact (∼35% in the model vs. ∼13% in the data) but does a better job in replicating the “crowding-in” effect from _h_ = 1 onwards (firms increase sales to the private sector by around 8% at _h_ = 1 and around 2-3% at _h_ = 4 both in the model and the data).

**Endogenous persistence in sales to the government.** We also find that the procurement contract has a persistent positive effect on _b_ , which is the result of an increase in the firm’s net worth. In particular, we find an increase of around 8, 6, and 3% estimated for one, two, and three years, respectively, after the firm obtains a contract (see Figure F.1). This result identifies a channel through which the model endogenously generates some persistence in firms’ participation in procurement. To have a sense of how strong this channel is as compared to the data, we compute the probability of obtaining a contract over the next three years, i.e., _t_ + 1, _t_ + 2, or _t_ + 3, conditional on a firm having a contract in _t_ . In the data, this number is quite high, 75%, which compares to its model counterpart of 42%. That is, our model generates more than one-half of the persistence observed in the data.

31
;^"&w~oroh V',orking W- Paper Series

||Titise|tAuthor|Date|Contact<br>for paper|
|---|---|---|---|---|
|WP1952|Enterpl:<br>n<br>tkonporams<br>Trar.sAitcr o.non<br>as|SimeonDi'ankov|August 1998|R. Vo<br>33722|
|WPS1953|TradePoiicis andIncentivesin<br>Indian Agriculture:Methodology,<br>Backgrou1nd Statistics andProtection,<br>and IncenLive indicators,_1965-95-_<br>Background Paper 1, Sugar and<br>Sugarcane|GarrY Pursell|August1998|L. Tabada<br>36896|
|WPS1954|Politicians andFirmsin Seven<br>Centraland Eastern Europear,<br>Countries|Stijn Claessens<br>Simeon Djankov|August 1998|R. Vo<br>33722|
|WPS1955|AppraisingWorkfarePrograms|Martin Ravallion|August 1998|P. Sader<br>33902|
|WPS1956|Benefit Incidence and the Timing<br>of Program Capture|Peter Lanjouw<br>Martin Ravallion|August 1998|P. Sader<br>33902|
|WPS1957|Bidding for Concessions|Michael Klein|August 1998|J. Dytang<br>37161|
|WPS1958|Total Strangers or SoulMates?<br>Antidumping and Competition<br>Policies in LatinAmericaand the<br>Caribbean|_J._LuisGuash<br>SarathRajapatirana|August 1998|J. Troncoso<br>37826|
|WPS1959|Is There a Credit Crunch in<br>East Asia?|Wei Ding<br>Ilker Domag<br>Giovanni Ferri|August 1998|F. Femandes<br>80453|
|WPS1960|Calm After the Storms: Income<br>Distribution in Chile, 1987-94|Francisco H. G. Ferreira<br>Julie A. Litchfield|August 1998||
|WPS1961|Protecting the Environment and the<br>Poor: A Public Goods Framework<br>Applied to Indonesia|Gunnar S. Eskeland<br>Chingying Kong|August 1998|C. Bernardo<br>31148|
**Chart 6B: Uphill Flows of FDI in Manufacturing and Exports of Sophisticated Goods (Averages for 2003-2007)**

<!-- Start of picture text --> ISR<br>IND<br>MYS<br>ARG<br>KORMEX<br>ZAF<br>BRA PHL<br>TUR CHN TWN<br>IDN BGR<br>POL THA<br>ROMCHL HUN<br>0 .05 .1<br>Uphill exports of sophisticated goods<br>.02<br>.015<br>.01<br>.005<br>Uphill outflows of total FDI in manufacturing<br>0<br><!-- End of picture text --> _Sources: UN COMTRADE, Thomson Financial SDC Platinum database, and Financial Times’ FDI Intelligence database._ Uphill outflows of FDI in manufacturing and exports of sophisticated goods are all expressed as a share of a country’s GDP.

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