rafmacalaba/datause-encoder-data huggingface dataset:
suffer the least from any incentive problems in record keeping. Data were also collected through visual checks by enumerators, including measuring absenteeism using unannounced visits.

Figure 3 provides details on the timing and scope of the data collection effort. The posttreatment survey collected data from 75 health facilities and roughly 7,500 households. While all surveys included a core set of modules, including socio-demographic characteristics, households’ health outcomes and health facility performance as experienced by the household in the household survey, additional modules were added in the later rounds. For example, the household survey in 2006 included modules on child mortality and anthropometric measurements (height of children aged below 5 and weight of infants). The household survey in 2009 also included an additional module on women’s health, including prenatal and antenatal care, and collected detailed birth and death statistics for children from 2004 and onward.

In addition to facility and household surveys, we have information from the action plans for the treatment group in the _participation & information intervention_ at the start of the intervention in 2005 and mid-way through when they were given the opportunity to revise their plans. We have similar data for the treatment group in the _participation intervention_ at the start of the intervention in mid-2007.

# **3.4 Outcomes and statistical framework**

We divide our empirical investigation into two parts. We start by analyzing the impact on the main outcome of interest; i.e., whether the intervention resulted in improved health outcomes. We have four sets of health measures: child mortality, pregnancy, birth and anthropometric measurements of children. Then, we turn to the quantity of health care. To measure utilization, we use data from the health facility (daily patient registers) as well as utilization data from the household survey.

The second part focuses on the channels through which the intervention may have influenced health outcomes and the demand and supply of health care. That is, we assess changes in all steps in the accountability chain; i.e. whether there is evidence of increased monitoring activities from the communities and information exchanges, using information derived from direct observations at the facility and data collected at the household level, and whether there is evidence that the health facility staff responded through improved treatment practices and overall management of the health clinics.

We use the same set of outcome measures as those used in the short-run evaluation of the _participation & information intervention_ (Björkman and Svensson, 2009). In addition, we report the findings on the extent to which clinical guidelines for the examination of patients were followed, with a focus on antenatal and postnatal care.<sup>10</sup>

> 10These data were only collected in 2009.

8
> We can also compare the consumption-based poverty ( _P_ 0 _C_ ) and income-based poverty ( _P_ 0 _I_ ) measures. To make the consumption-based and income-based poverty measures comparable, we set the poverty line so that they have the same poverty rates of 37.4 percent (see World Bank, 2001). We set the poverty line at VND 3,452.06 per day per capita.2

The poverty rates are identical by construction, but there may be regional differences. This can be checked by looking at Columns 3 and 7. It turns out that the spatial distributions of income and consumption poverty are reasonably close, though there are two notable differences. First, in the Red River Delta, the income poverty is much higher than the consumption poverty. On the other hand, in Mekong River, the consumption poverty is much higher than the income poverty rate. Overall, income and consumption measures show a similar pattern of spatial distribution, though income measure is on average a much noisier measure than consumption.

# **4 METHODOLOGY**

Estimation of poverty and other economic indicators at the level of small geographic areas is generally constrained by the availability of representative data. In Vietnam, the VLSS data do not support reliable poverty measures even at the provincial level because the sampling strata are more aggregated than provinces. However, the small area estimation (SAE) developed by Elbers et al. (2002, 2003) has enabled us to reliably estimate measures of poverty and inequality at a spatially disaggregated level.

The SAE approach typically combines survey and census data source. Consumption or income regression models are estimated with the survey data set. The regressors contain only the variables in the geographic data set or the variables that also appear in the census data set. The left-handside variable is then imputed to each census record and aggregated to obtain poverty and inequality measures of interest. Using a Monte-

> 2 According to the World Development Indicators, the Purchasing Power Parity conversion factor (for 1998) was USD 1=VND 2,673.

10
7

# 2. INDUSTRIAL POLLUTION ESTIMATES

# 2.1 Methodology

In the present section the reaults of the calculation of some pollution indicators for the Mexican manufacturing sector are presented for the 1950-1989 period. As no emission figures are available for Mexico's manufacturing industries, the estimates are based on fixed US emission coefficients collected from USEPA emissions databases converted by Wheeler 3 into the following 7 compound industrial pollution indicators:

1. total quantity of TRI (Toxic Release Inventory) chemicals released or transferred (AVTRIT)

2. linear acute human health and terrestrial ecotoxicity (AVHUML) 3. exponential acute human health and terrestrial ecotoxicity (AVHUMX) 4. linear acute aquatic toxicity (AVQAC) 5. exponential acute aquatic toxicity (AVQAX) 6. cancer risk (AVCANC) 3 The inventory contains measures of releases of various substances. The 7 alternative indicators mentioned here are weighted sums of substances released. Indicator number two uses risk weights 1 through 4 where 4 denotes the substances deemed to pose the highest risk to human health. For an explanation of the indicators and an exposition of the assumptions on which they are based see Wheeler (1991b). The values of the indicators by ISIC class are listed in Annex 3.
utilities in 2005-2010 was accepted in 2004. The program foresaw dissolution of monopolies operating in the utilities sub-sectors in the long run, but the actual progress of the demonopolization has been very limited.

In parallel with the services liberalization, the WTO negotiations also led to further liberalization of trade in goods. As mentioned earlier, it could have created a problem of disentangling the effects of services liberalization on productivity from the effect of trade liberalization, which is positively linked to an increase in productivity in the literature (Pavcnik, 2002; Amiti and Konings, 2007; Khandelwal and Topalova, 2011). However, by 2001 Ukraine had already substantially liberalized its trade in goods. The average MFN tariff of Ukraine in 2002 was 7 percent and declined to 4.7 percent in 2008<sup>3</sup> . The view that the effect of trade liberalization for Ukraine had a limited impact can be further backed by the results of a computable general equilibrium analysis of the potential gains of WTO accession provided by Rutherford et al. (2005), who concluded that more than 70 percent of welfare gains of the – – WTO accession for Russia a country similar to Ukraine in many respects would come from liberalization of services. Taking into account the similarities of the two economies and the fact that Russia is less trade-liberalized relative to Ukraine<sup>4</sup> , it is reasonable to assume that the effect of services liberalization for Ukraine was even more pronounced. Still, in the empirical analysis we control for the effect of exporting on productivity and interact it with the effect of services liberalization to control for potential complementarities.

# **3.3 Performance of services in 2001-2007**

Liberalization of the Ukrainian services sector in 2001-2007 was accompanied by increased share of output produced by private and foreign-owned firms. The privatization process was limited by the fact that, by 2001, most services sub-sectors in Ukraine already had a high share of output produced by private firms, including more than 90 percent of output produced by private firms in the retail trade, financial, insurance, and business services sub-

> 3Data on MFN tariffs are from UNCTAD - TRAINS (Trade Analysis and Information System) database.

> 4The average Russian MFN tariff in 2002 was 9.6 percent.

10
_Tax Reform in Transition_ 13 reduction in the number of tax brackets broadening their coverage; (ii) reduction in the highest marginal tax rates; and (iii) consideration of alternative income threshold at which a positive tax payment must be made. These changes aim to tap the information on the underlying 1992 level and distribution of income to ensure equal or better revenue as well as protection of poor than under the 1992 system. In a nutshell, the counterfactual results show that the 1992 income tax system could be surpassed in its revenue raising and re-distributional effects by a much simpler tax system.

# **3.1. A Low, Flat Rate Tax System with High Exemption Can Raise More Revenue and Increase Protection of the Poor**

Consider a flat rate system with higher exemption level which incorporates better potential revenue and equity effects. Given the income levels and distribution provided by the survey, a very simple tax structure that exempts the poor (lowest 20 percent of the population) and levies a flat rate of 11 percent on the rest of the population would raise approximately the same revenue as the 1992 system (see panel a of Table 3). That is, the flat rate yields a potential tax revenue per taxpayer of 1,881 Lev, compared to 1,850 Lev under the 1992 system, i.e., revenue-neutral reform. This means that a linear system with a rate that is approximately half the lowest current statutory rate could generate approximately the same revenue as the 1992 tax system. The reason why the surprisingly low flat tax rate is found to be feasible from the revenue perspective lies in the fact that the underlying income distribution is not adequately taken into account in designing the 1992 tax rates and tax brackets (see Figure 1). An alternative to this low flat rate would be a somewhat higher uniform rate of 20 percent along with the same, higher exemption for the poor. This linear rate system, which could be justified on revenue grounds, has a significantly higher revenue potential of 3036 Lev per taxpayer, compared to 1,850 Lev under the 1992 personal income tax schedule (see panel b of Table 3).
**<u>Table 5: The Kitagawa-Oaxaca-Blinder Decomposition of Business Revenues</u>**

||(1)|(2)|(3)|
|---|---|---|---|
|VARIABLES|<br>Overall|<br>Endow.|<br>Coeff.|
|Male|11.849***|||
||(0.105)|||
|Female|10.913***<br>|||
||(0.061)|||
|Raw Difference|0.936***|||
||(0.121)|||
|Difference due to<br>|0.598***<br>|||
|Endowments|(0.179)|||
|Difference due to|0.338|||
|Coefficients|(0.221)|||
|Individual Lives in Urban Area||0.002|-0.169*|
|||(0.008)|(0.089)|
|Individual Age||0.102**|-4.747*|
|||(0.049)|(2.500)|
|Individual Age Squared||-0.096**|2.619**|
|||(0.044)|(1.245)|
|Number of Adults in HH||0.003|-0.074|
|||(0.003)|(0.200)|
|Number of Children Aged 0-5 in HH||-0.012|0.251*|
|||(0.013)|(0.135)|
|Individual owns a Financial Account||0.235***<br>|0.190*<br>|
|||(0.040)|(0.098)|
|Individual is Married||<br>0.001|<br>0.410|
|||(0.008)|(0.384)|
|Individual is Divorced/Married/Separated||-0.009|0.011|
|||(0.021)|(0.033)|
|Individual Completed Secondary Schooling||<br>-0.013|<br>-0.011|
|||(0.023)|(0.029)|
|Individual has a Vehicle (bicycle,||0.049|-0.015|
|motorcycle, car)||(0.040)|(0.217)|
|Business has Electricity||0.088***|0.050**|
|||(0.025)|(0.021)|
|Business has Running Water||-0.003|-0.006|
|||(0.027)|(0.018)|
|Business has a Phone||0.043**|-0.293|
|||(0.019)|(0.409)|
|Business Maintains Accounting Records||0.006|0.021|
|||(0.048)|(0.019)|
|Business is Registered||-0.028|-0.068*|
|||(0.022)|(0.037)|
|Business has Machines||<br>0.018|<br>0.001|
|||(0.022)|(0.041)|
|Number of Men Employees||0.037|-0.002|
|||(0.087)|(0.013)|
|Number of Women Employees||<br>-0.001|<br>-0.036***|
|||(0.010)|(0.009)|
|Number of Days HH Male Member Worked||<br>0.715***|<br>0.034|
|||(0.142)|(0.052)|
|Number of Days HH Female Member||-0.446***|-0.016|
|Worked||(0.071)|(0.070)|
|Business is in Food Sector||<br>-0.065**|<br>-0.152*|
|||(0.031)|(0.091)|

39
# 3. Base scenario

The base scenario is designed to generate a plausible business-as-usual path for Yemen’s economy during the period 2020-2030, i.e., a path that assumes that fighting continues along the lines of the past eight years. As a result, economic growth is slow and living conditions do not improve. The subsequent analysis compares the base and non-base scenarios during the period 2022-2030. The solution for 2020 replicates the information in the SAM while, in 2021 and 2022, GDP grows at rates that are estimated or projected by the IMF (2022).<sup>6</sup> The assumptions are kept as simple and transparent as possible. Most importantly, it is assumed that (a) all international (export and import prices) are constant in real terms; and (b) for most institutional payments, including receipt and spending items in the government budget, the GDP shares are constant, kept at shares computed from the 2020 SAM. At the macro level, it is assumed that the government budget is cleared via tax adjustments, that the real exchange rate clears the balance of payments, and that household savings adjust to help maintain domestically financed private investment with an unchanged GDP share.

Figures 3.1-3.7 summarize results from the base scenario (covering indicators related to the macroeconomy, sectors, household welfare, and poverty); Tables 3.1-3.6 provide additional information about both base and non-base scenarios, including macro data (growth and GDP shares), the government budget, the balance of payments, and data on economic structure in 2022 and for the base scenario in 2030.

As intended, to facilitate comparisons to non-base scenarios, the picture is one of stable growth starting from 2023. GDP growth is simulated at an annual rate at 2.0 percent between 2022 and 2030 (Figure 3.1).<sup>7</sup> Absorption (the sum of domestic final demands) is boosted by Yemen’s trade deficit, permitting it to exceed, by a wide margin, GDP at purchasers’ prices and, even more so, GDP at factor cost (Figure 3.2). Among domestic demands, household consumption dominates (on the left-hand axis in Figure 3.3), followed by government consumption (which includes consumption financed by donors and managed by NGOs and others), private investment, and government investment (right-hand axis in Figure 3.3). In terms of GDP shares (Table 3.2), Yemen’s private consumption represents 108.6 percent while the sum of the other domestic final demands is as low as 28.1 percent, values that are extreme by international standards.

The macroeconomic aggregates covered in Figures 3.1-3.3 grow at annual rates of 1-2 percent (Figure 3.4). The growth rate for household consumption coincides almost exactly with the population growth rate, leaving per capita consumption unchanged with a headcount poverty rate of 74.4 percent both in 2022 and 2030. Among the sectors, the private service GDP is more

> 6 While GDP growth is exogenous for the base scenario, it is endogenous for all non-base scenarios, deviating from the base due to the shocks, policy and other, that are imposed.

> 7 Unless otherwise noted, the growth rates are geometric averages for the period 2022 to 2030.

12
|PPR|Working Paper Series|||
|---|---|---|---|
|Title|Author|Date|Contact|
|WPS174 Trade Restrictions<br>with Imported<br>Intermediate<br>Inputs: When Does||||
|the Trade Balance Improve?|Ramon E. Lopez<br>Dani Rodrik|March 1989|M. Ameal<br>61466|
|WPS175 An Integrated<br>Model of Perennial<br>and Annual Crop Production for||||
|Sub-Saharan<br>Countries|Robert 0. Weaver|May 1989|A. Daruwala<br>33716|
|WPS176 Credit Rationing, Tenancy,<br>Productivity,<br>and the Dynamics<br>of Inequality|Avishay Braverman<br>Joseph E. Stigiltz|||
|WPS177 Cash-Flow or Income? The Choice of<br>Base for Company Taxation|Jack M. Mintz<br>Jesus Seade|April 1989|A. Bhalia<br>60359|
|WPS178 Tax Holidays and Investment|Jack M. Mlntz|April 1989|A. Bhalla<br>60359|
|WPS179 Public Sector Pricing InaFiscal<br>Context|Christopher<br>Heady|April 1989|A. Bhalla<br>60359|
|WPS18O Structural<br>Changes In Metals<br>Consumption: Evidence from||||
|U.S. Data|Boum-Jong Choe|April 1989|S. Lipscomb<br>33718|
|WPS181 Public Finance, Trade and Development:<br>What Have We Learned?|Johannes F. Linn<br>Deborah L. Wetzel|April 1989|M. Colinet<br>33490|
|WPS182 The Experience<br>of Latin America<br>With Export Subsidies|Julio Nogues|April 1989|S. Torrivos<br>33709|
|WPS183 Private Investment<br>in Mexico:<br>An Empirical Analysis|Alberto R. Musalem|April 1989|L. Spear<br>30081|
|WPS184 Women and Forestry: Operational Issues|Augusta Molnar<br>Gotz Schrelber|May 1989|M. VIllar<br>33752|
|WPS185 Uniform Trade Taxes, Devaluation,<br>and the Real Exchange Rate:<br>A Theoretical<br>Analysis|Stephen A. O'Connell|<br>April 1989|A. Oropesa<br>61758|
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cushion the impacts on the most vulnerable. To that end, using the World Bank’s FY2021 FCV list of countries, we disaggregate the total poverty increases in Africa into countries that are in the FCV group and those that are not. Out of the 54 countries in Africa, the World Bank’s FY2021 FCV list designates 21 to be affected by some form of conflict or fragility.<sup>7</sup> Of the 21 fragile and conflict affected countries, two (Burundi and Eritrea) had minimal to no reported social protection responses, and only eight (38%) had major social protection responses, where we define “major responses” as those including an increase in cash transfers or unemployment insurance that persisted for more than three months. The remainder had moderate social protection responses such as one-off cash transfers increases, or increased food aid. By contrast, of the 32 non-conflict affected states, all had at least moderate social protection responses and 15 (47%) had major social protection responses (Gentillini et al., 2021).

Figure 5 reports the trends in poverty across these groups of countries. First, the levels of poverty in the two groups are drastically different. While 26% of the population (amounting to 196 million people) were living in extreme poverty in the non-FCV group of countries in 2019, there were 45% living in poverty in the FCV countries (amounting to 248 million people) in that same year. For the non-FCV group, an additional 11 million people were pushed into poverty in 2020 (equivalent to 1.5 percentage points increase) and 16 million in 2021 (equivalent to 2 percentage points increase) due to the COVID-19 pandemic. For the FCV group, the expected additional poor is about 12 million people in 2020 (equivalent to 2.1 percentage points increase) and over 14 million in 2021 (equivalent to 2.5 percentage points increase). Not only is the stock of poor higher in conflict ridden countries, the increases due to the pandemic are also estimated to be larger and more persistent.

> 7 https://thedocs.worldbank.org/en/doc/888211594267968803-0090022020/original/FCSListFY21.pdf **.**

12
- 19 -

<u>Table 7. Regression Results for Bids in Electricity Project Procurements</u>

||OLS|OLS|OLS|
|---|---|---|---|
|Entrant bid dummy 1/||-0.05|1.69 <sup>***</sup>|
|||(0.11)|(0.39)|
|Dummy for entrant facing with incumbent(s) 1/|||-1.72 <sup>***</sup><br>|
||||(0.39)|
|Share of incumbent bidders in auction 1/||0.59 <sup>*</sup>|2.53 <sup>***</sup>|
|||(0.35)|(0.63)|
|Number of contracts before 2002||-6.36|-4.31|
|||(9.46)|(6.57)|
|ln(_N_)|-4.14|-6.12|53.16 <sup>**</sup>|
||(12.77)|(12.80)|(20.11)|
|Turbine|83.39 <sup>*</sup>|69.29 <sup>*</sup>|32.21|
||(42.64)|(36.07)|(22.77)|
|Trans. dist. lines|8.13|11.83|-35.48 <sup>**</sup>|
||(15.40)|(14.81)|(15.40)|
|Substation|7.50|-2.95|20.04 <sup>**</sup>|
||(10.34)|(10.06)|(9.02)|
|Civil work|81.10 <sup>***</sup>|77.90 <sup>***</sup>|94.46 <sup>***</sup>|
||(23.32)|(21.01)|(17.70)|
|Installed capacity|0.12 <sup>*</sup>|0.11 <sup>*</sup>|0.18 <sup>***</sup>|
||(0.06)|(0.06)|(0.04)|
|Number of turbines|1.62|1.43|8.25 <sup>***</sup>|
||(3.36)|(3.30)|(1.96)|
|Trans. line voltage|0.06 <sup>*</sup>|-0.02|0.19 <sup>***</sup>|
||(0.03)|(0.06)|(0.06)|
|Trans. line length|-0.12|-0.07|-0.28 <sup>***</sup>|
||(0.09)|(0.08)|(0.10)|
|Contract duration|1.45 <sup>**</sup>|1.18 <sup>**</sup>|2.40 <sup>***</sup>|
||(0.58)|(0.50)|(0.68)|
|Donor 1|17.52|1.71|-16.66|
||(21.05)|(16.71)|(14.24)|
|Constant|-83.94 <sup>*</sup>|-68.70 <sup>*</sup>|-307.56 <sup>***</sup>|
||(45.14)|(40.53)|(69.57)|
|Obs.|99|99|99|
|R-squared|0.545|0.580|0.749|
|_F_-statistics|32.74|25.27|37.90|

1/ For presentation purposes, the coefficients are divided by 100. Note: The dependent variable is the bidding amount. The robust standard errors are shown in parentheses. *, ** and *** indicate the 10%, 5% and 1% significance levels, respectively.

Source: Author’s calculation.

# **V. CONCLUSION**

Asymmetric auctions are among the most rapidly growing areas in the auction literature. The traditional symmetric framework is not always applicable in practice because bidders are usually heterogeneous.

In public procurement auctions related to official development projects, an alleged concern is that the competition pressures at the auctions might be relatively weak. The potential benefits from improved auction efficiency are expected to be enormous.