rafmacalaba/datause-encoder-data huggingface dataset:
|**TABLE 12: SATISFACT**|**ION WITH CH**|**ILD HEALTH C**|**ONSULTATIO**|**NS REPORTE**|**D DURING FA**|**CILITY EXIT I**|**NTERVIEWS†**|
|---|---|---|---|---|---|---|---|
|Panel A|(1)<br>Overall<br>satisfaction|(2)<br>Reasonable<br>registration|(3)<br>Reasonable<br>lab fees|(4)<br>Reasonable<br>medicine|(5)<br>No<br>additional|(6)<br>Clean<br>health|(7)<br>Reasonable<br>wait time|
||score|fees||fees|payment|facility||
|Post indicator|-0.036|-0.054|-0.143|-0.045|-0.004|-0.141|-0.111**|
||[0.026]|[0.061]|[0.118]|[0.067]|[0.055]|[0.099]|[0.055]|
|PBF/Post interact|<br>0.099***|<br>0.112|<br>0.347**|<br>0.043|<br>-0.007|<br>0.227*|<br>0.110|
||[0.037]|[0.101]|[0.175]|[0.124]|[0.117]|[0.133]|[0.097]|
|Control<br>1/Post|<br>0.054|<br>0.019|<br>0.331*|<br>0.111|<br>-0.020|<br>0.136|<br>0.143|
|interact|[0.040]|[0.076]|[0.167]|[0.103]|[0.077]|[0.118]|[0.097]|
|Control<br>2/Post|<br>0.022|<br>0.074|<br>0.420**|<br>0.033|<br>-0.055|<br>-0.049|<br>0.021|
|interact|[0.045]|[0.080]|[0.166]|[0.120]|[0.090]|[0.131]|[0.100]|
|p-value PBF vs. C1|0.280|0.325|0.925|0.595|0.908|0.403|0.777|
|<br>p-value PBF vs. C2|0.092|0.709|0.685|0.945|0.696|0.019|0.442|
|p-value PBF vs. C3|0.009|0.268|0.050|0.731|0.953|0.090|0.259|
|<br>Baseline mean C3|0.881|0.957|0.846|0.854|0.904|0.868|0.943|
|N|614|488|369|544|605|612|608|
|Panel B|(8)|(9)|(10)|(11)|(12)|(13)||
||Enough<br>privacy<br>during visit|Adequate<br>hours|Courteous<br>health staff|Good<br>health<br>worker<br>communic|Sufficient<br>visit time<br>with health<br>worker|Easy to<br>get<br>prescribed<br>medicines||
|||||ation||||
|Post indicator|-0.098|-0.046|0.062|0.017|0.091|-0.002||
||[0.098]|[0.052]|[0.062]|[0.073]|[0.066]|[0.059]||
|PBF/Post interact|<br>0.336***|<br>0.085|<br>-0.012|<br>0.053|<br>-0.094|<br>0.055||
||[0.124]|[0.068]|[0.079]|[0.092]|[0.087]|[0.082]||
|Control<br>1/Post|<br>0.202|<br>0.036|<br>-0.080|<br>-0.080|<br>-0.018|<br>0.068||
|interact|[0.131]|[0.068]|[0.077]|[0.105]|[0.111]|[0.087]||
|Control<br>2/Post|<br>0.093|<br>-0.116|<br>-0.101|<br>0.055|<br>-0.031|<br>0.112||
|interact|[0.115]|[0.081]|[0.082]|[0.103]|[0.104]|[0.106]||
|p-value PBF vs. C1|<br>0.256|<br>0.427|<br>0.323|<br>0.170|<br>0.461|<br>0.890||
|<br>p-value PBF vs. C2|0.021|0.007|0.214|0.988|0.507|0.594||
|<br>p-value PBF vs. C3|0.007|0.210|0.876|0.566|0.279|0.498||
|<br>Baseline mean C3|0.774|0.942|0.887|0.830|0.830|0.925||
|N<br>* = p < 0.10, ** p < 0.05,<br>†Results from differenc|612<br>*** p< 0.01<br>e-in-difference|608<br>s regression|613<br>models examini|606<br>ng the effect|609<br>of PBF on sat|610<br>isfaction with|child health|

<mark>† Results from difference-in-differences regression models examining the effect of PBF on satisfaction with child health consultations reported by mothers during facility exit interviews</mark> . <mark>Regression models adjusted for individual (child age, child sex, maternal literacy, marital status, education level) and facility-level control variables (type of health facility public/private/religious, urban/rural). Standard errors were clustered at the health facility level.</mark> 45
- iv -

# Broadening the Scope

xiv. To an extent, these results reflect CIATA's narrow scope, and could be counteracted by broadening the range of technical assistance offered. CIATA has, in fact, recently added a collection monitoring component in an attempt to forestall declines in collection efficiency.

xv. But it also reflects the political environment confronting the property tax. Local politicians' decisions to reduce effective tax rates or relax collection enforcement are responses to an environment that discourages the effective use of local taxes.

# Increasing Project Selectivity. Altering Incentives

xvi. What does this imply for the design of such technical assistance programs? First, it implies a need for greater selectivity in the choice of participating jurisdictions. Some CIATA projects would not have been viable-even from a financial standpoint--under any plausible range of tax rates and collection efficiencies. Such projects could have been identified and excluded through a preproj6ct analysis of the tax base and its administrative cost implications.

xvii. Second, it suggests a need to accompany technically-focused interventions, such as CIATA, with broader policy reform. In Brazil's case, the disincentives for local taxation arise from the vague division of functional responsibilities between levels of government and from the existing system of intergovernmental transfers. Reform should not aim at encouraging local tax effort per se: a high level of local tasation is not an end in itself. But it should aim at removing disincentives to local taxation and providing taxpayers with an unbiased basis on which to judge the case for an increase in local taxation.
- 17 -

manufactured exports, assuming no omitted quality change in other groups, of almost a half of one per cent per year. In other words, the estimated upward bias from the omission of quality change is about a third for U.S. exports of machinery and transport equipment and almost 20 per cent for U.S. exports of all manufactures.

If we estimate the average quality correction from an equation rather than from the change from beginning to end years, the relative decline in the adjusted price index for the U.S. is around .4 per cent per year for all manufactures and .7 or .8 per cent per year for SITC 7.

There are several possible ways to calculate the implication of these quality adjustments for world price changes, of which we show two here. The most conservative is to adjust only the U.S. price index and combine the adjusted U.S. price index with the conventional indexes for the other countries. That calculation, referred to below as "adjusted for U.S. only," must understate the quality change bias in our conventional indexes.

The other adjustment is performed by assuming, in effect, that for these products the law of one price holds in time-to-time form. It involves substituting the U.S. hedonic price index for a subgroup for whatever is reported by each country and also using that U.S. hedonic price index where no conventional price index is reported. The assumption flies in the face of the strong evidence that the law of one price is frequently violated. But it is more reasonable than the alternative assumption that the prices of these goods move as indicated by conventional indexes or, since most of these products are not covered by most countries' price indexes, the assumption that these prices move in the same way as those of all covered products. Some support for our assumption of common worldwide price changes is that the price index for U.S.
10% points in the 10 years of the 1980s (from 52% to 42%), and a further 17% points in the 16 years from 1990 to 2005.

It is notable that 2002-05 suggests a higher (absolute and proportionate) drop in the poverty rate than other periods. Given that lags in survey data availability mean that our 2005 estimate is more heavily dependent on non-survey data (notably the extrapolations based on NAS consumption growth rates) there is a concern that this might be exaggerated. However, that does not seem likely. The bulk of the decline is in fact driven by countries for which survey data are available close to 2005. The region for which non-survey data have played the biggest role for 2005 is Sub-Saharan Africa. If instead we assume that there was in fact no decline in the poverty rate over 2002-05 in SSA then the total headcount index (for all developing countries) for the $1.25 line in 2005 is 26.2%—still suggesting a sizeable decline relative to 2002.

China’s success against absolute poverty has clearly played a major role in this overall progress. Panel (b) in Tables 4 and 5 repeats the calculations excluding China. The $1.25 a day poverty rate falls from 40% to 28% over 1981-2005, with a rate of decline that is less than half the trend including China; the regression estimate of the trend falls to -0.43% per year (standard error of 0.03%; R<sup>2</sup> =0.96), which is almost identical to the rate of decline for the non-China developing world that we had obtained using the 1993 PPPs (which gave a trend of -0.44% per year, standard error=0.01%). Based on our new estimates, the projected value for 2015 is 25.1% (standard error=0.8%), which is well over half the 1990 value of 35% (Table 4). So the developing world outside China is not on track to reaching the MDG for poverty reduction.

Our estimates suggest less progress (in absolute and proportionate terms) in getting above the $2 per day line than the $1.25 line. The poverty rate by this higher standard has fallen from 70% in 1981 to 47% in 2005 (Table 4). The trend is about 0.8% per year (a regression coefficient on time of -0.84; standard error=0.08); excluding China, the trend is only 0.3% per year (a regression coefficient of -0.26; standard error=0.05%). This has not been sufficient to bring down the number of people living below $2 per day, which was about 2.5 billion in both 1981 and 2005 (Table 5). Thus the number of people living between $1.25 and $2 a day has risen sharply over these 25 years, from about 600 million to 1.2 billion. This marked “bunching up” of people just above the $1.25 line suggests that the poverty rate according to that line could rise sharply with aggregate economic contraction (including real contraction due to higher prices).

24
and no imports from Brazil will take place. U.S. ethanol is essentially a nontradable good within the price band. If demand expansion pushes the U.S. price to the Brazilian price inclusive of the tariffs, then Brazilian imports take place and the U.S. price moves in tandem with the Brazilian ethanol price. Note that small ethanol imports also occur under a tariff rate quota (TRQ) allowed by the Caribbean Initiative. These imports are quite limited and the TRQ mostly goes under filled. In sum, a key policy element to correctly model the land allocation under a U.S. biofuel surge, is the U.S. tariff regime blocking Brazilian ethanol imports over a large price band (Elobeid and Tokgoz (2008)). This key policy element is incorporated in our analysis.

# **3. Definitions of Baseline and Scenarios**

The baseline<sup>3</sup> represents continuation of business as usual for economic development, population growth and biofuel development. Existing biofuel policies (e.g. already implemented mandates, subsidies and import duties) are part of the baseline<sup>4</sup> . Future mandates for increased biofuel use compared to existing mandates are included in the scenarios. Like in most dynamic CGE models, three variables -- population growth, savings and investment and productivity -- are the key drivers to generate a baseline. Population growth, depreciation rate of capital stock and productivity growth are exogenous to the model. Growth in aggregate labor supply tracks the working age population (defined as those between 15 and 65 years). Savings and investment determines the overall level of the capital stock along with the rate of depreciation. Sectoral productivity growths are consistent with recent trends (World Bank, 2009). Another exogenous variable in the model baseline is the growth of energy prices, which are calibrated with projections made by the Energy Information Administration of the U.S. Department of Energy<sup>5</sup> .

For the purpose of the study we define two scenarios. The first scenario considers the implementation of biofuel use targets consistent with what countries already have announced (hereafter “AT” scenario). Please see Column 3 in Table 1 for shares of biofuels under the AT scenario in 2020. The second scenario generally considers a doubling of the announced targets

> 3 The base year for calibrating the model is 2004, the year for which the social accounting matrices, the main database for the model, were constructed. Although our base year is 2004, the model is calibrated to replicate major historical statistics until year 2009.

> 4 Impacts of removal of subsidies and import duties have been analyzed in detailed in a separate paper.

> 5 A module that can represent both conventional and un-conventional oil and gas reserves and production would be ideal; however, the model used here does not have that capacity. Hence, we used energy price forecasts from other sources instead of generating them endogenously in the baseline.

19
loss by calving. In most glaciers, mass loss is observed at mid-ablation zones with negligible loss at glacier termini. Likewise, based on the radioactive isotope analysis of ice cores from a high altitude (6050 masl) Naimona’nyi Glacier, located near northwest corner of Nepal in the Chinese Himalaya, Kehrwald et al. (2008) concluded no net mass accumulation since 1950.

In the Spiti/Lahaul region in Himachal, most glaciers show distinct thinning at low elevations, even on debris-covered tongues (Bertheir et al., 2007). Overall annual mass loss of - .7 to -0.85 m w.e.a<sup>-1</sup> is observed during 1999-2004 (Bertheir et al., 2007), ranging up to -1.0 m w.e.a<sup>-1</sup> in Chhota Shigri Glacier (2002-2006; Wagnon et al., 2007). Negative mass balance is also observed in Garhwal Himalaya (Uttrakhand Himalaya) (0.32 m w.e.a<sup>-1</sup> ; 1992-2000) (Dobhal et al., 2008). In contrast, some Karakorum glaciers have not shown a change in mass (Matsuo and Heiki, 2010), and some show positive mass balance (Hewitt, 2005). On a larger spatial scale, based on Gravity Recovery and Climate Experiment (GRACE) satellite observations, Matsuo and Heki (2010) found that average ice loss from Asia’s high mountain region during 2003-2009 has been twice as fast as the average loss rate over the previous four decades.

Recently, black carbon has received greater attention as a factor triggering accelerating glacial mass loss in the Himalayas. Black carbon is produced by incomplete combustion of biomass, coal and diesel fuels (Kaspari et al., 2011). Ramanathan et al. (2007) suggest that warming trends in Asia are amplified by black carbon. Kaspari et al. (2011) show that black carbon concentrations have increased approximately threefold from 1975-2000 relative to 18601975 in the high elevation regions of the Himalaya. While atmospheric black carbon causes glacial melting through warming related to light absorption, black carbon deposited on snow and ice accelerates melt through reduced surface albedo (Kaspari et al., 2011). Xu et al. (2009) suggest that black carbon deposited on Chinese glaciers is an important factor contributing to observed rapid glacial retreat. Through numerical experiments, Lau et al. (2010) found that heating of the troposphere by elevated dust and black carbon aerosols in the boreal spring can lead to widespread enhanced land-atmosphere warming and accelerated snowmelt in the Himalayas. Based on atmospheric observations at the Nepal Climate Observatory-Pyramid (NCO-P) at 5079 masl, Yasunari et al. (2010) estimate black carbon concentration on snow surface and perform numerical experiments suggesting accelerated glacier mass loss on Yala glacier (Nepal) and related impact on water availability.

3
assistance and support for market linkages which has had good results. Additionally, it is important to look at household level data to explore the income dependency on forest activities.

Another major limitation for REDD+ in Tanzania is landownership. Most forests are on general land and even though villages are situated near to those forests and can potentially protect them, many villages do not have legal rights to their land. As a consequence, there are not incentives for forest conservation. The government has been surveying and registering villages around the country and giving them land tenure. Nevertheless, the process has not been a priority for the central government. Thus, lack of resources and discontinuity on the allocation of land use rights have characterized the process. REDD+ funding can potentially support the survey and registration process to resolve the land tenure problem that is key for the success of any forest conservation program as pilot projects have demonstrated. In addition, a map identifying surveyed villages and non-surveyed villages can be of great help to see what land is and will be out of the allocation of village land so that resources can be directed effectively.

REDD+ pilots and PFM have also shown that social norms within the villages allow for cooperation in forest monitoring and conservation. Villagers participating actively in performing the forest conservation duties are released from other duties in the community. These appointments change through time so everybody collaborates in the provision of forest conservation while everybody also receives the benefits. However, uncertainty of the international process on REDD+ provides perverse incentives as it is not clear if benefits of REDD will be delivered.

Finally, there are some concerns on how the money flows from the national structures to the local levels and villages. In addition, there seems to be large unrealized opportunity for improving practices and maximizing returns from land use reforms and conservation practices; in this sense it is not clear why the government does not access international loans 4
Lim, K., & Zabek, M. (2021). Women's Labor Force Exits during COVID-19: Differences by Motherhood, Race, and Ethnicity. Finance and Economics Discussion Series, 2021(066), 1–39. <u>https://doi.org/10.17016/FEDS.2021.067</u> Mision de Empalme de las Series de Empleo y Pobreza, MESEP, (2010), Metodología de Cálculo de las Cifras de Pobreza, Indigencia y Desigualdad 2009. MESEP, Bogotá, Colombia.

- Nwaze, E., & Mohammed, A. (2013). An impact evaluation of the engagement of traditional and religious leaders in the Nigerian polio eradication initiative. _Sch J Med, 3_ (5), 53-63.

- Ñopo, H. (2008). Matching as a tool to decompose wage gaps. _The review of economics and statistics_ , 90(2), 290-299.

- OECD. (2013). Gender equality, women’s empowerment and development effectiveness: issues brief 7. https://www.oecd.org/dac/gender-development/_%20Issues%20Brief%207.pdf

- Pitts, M. (2021). Where Are They Now? Workers with Young Children during COVID-19 (No. 10– 2021; Federal Reserve Bank of Atlanta Policy Hub Papers).

Qian, Y., & Fuller, S. (2020). COVID-19 and the gender employment gap among parents of young Children. Canadian Public Policy, 46, S89–S101. https://doi.org/10.3138/CPP.2020-077 Singh, V., Shirazi, H., & Turetken, J. (2022). COVID-19 and gender disparities: Labour market outcomes. Research in Economics, 76(3), 206–217. https://doi.org/10.1016/j.rie.2022.07.011 Tribín, A., Gómez-Barrera, A., Pirela-Rios, A. (2022). Distribución del cuidado, roles de género y poder de negociación en Colombia: Un análisis a partir de la ENUT 2020-2021. Informe Quanta - Cuidado y Género. Recuperado de https://cuidadoygenero.org/distribucion-cuidado-enut

- Tverdostup, M. (2022). COVID-19 and Gender Gaps in Employment, Wages, and Work Hours: Lower Inequalities and Higher Motherhood Penalty. Comparative Economic Studies. https://doi.org/10.1057/s41294-022-00198-z

Urquidi, M., Valencia, H., & Durand, G. (2020). Brecha de ingresos laborales por género en Bolivia: un análisis de su evolución en el periodo 1993 a 2018 (No. 1986; Nota Técnica).

Viollaz, M., Salazar-Saenz, M., Flabbi, L., Bustelo, M., & Bosch, M. (2022). The COVID-19 Pandemic in Latin American and Caribbean Countries: The Labor Supply Impact by Gender (No. 15091; Discussion Paper Series). www.iza.org World Bank. 2023. From Infection to Inflation. LAC Poverty and Labor Brief. © Washington, DC.

Zhou, M., & Kan, M. Y. (2021). The varying impacts of COVID-19 and its related measures in the UK: A year in review. PLoS ONE, 16(September 09). https://doi.org/10.1371/journal.pone.0257286.
Policy Research Working Paper 4978

# **Abstract**

This paper reports on the 2009 update of the Worldwide Governance Indicators (WGI) research project, covering 212 countries and territories and measuring six dimensions of governance between 1996 and 2008: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. These aggregate indicators are based on hundreds of specific and disaggregated individual variables measuring various dimensions of governance, taken from 35 data sources provided by 33 different organizations. The data reflect the views on governance of public sector, private sector and NGO experts, as well as thousands of citizen and firm survey respondents worldwide. The authors also explicitly report the margins of error accompanying each country estimate. These reflect the inherent difficulties in measuring governance using any kind of data. They find that even after taking margins of error into account, the WGI permit meaningful cross-country comparisons as well as monitoring progress over time. The aggregate indicators, together with the disaggregated underlying indicators, are available at www.govindicators.org.

This paper—a product of the Growth and the Macroeconomics Team, Development Research Group—is part of a larger effort in the department to study the causes and consequences of governance for development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at akraay@worldbank.org, dkaufmann@brookings.edu, and mmastruzzi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Table 1. Bribes in selected countries**

||Faced|Reported|Bribery|Firms|Bribes as|CPI|
|---|---|---|---|---|---|---|
||bribery|corruption|depth (%|expected to|a share of||
||(% of<br>firms)|as a major<br>constraint (%<br>of firms)|of firms)|give bribes “to<br>get things<br>done” (%)|sales (%)||
|Sweden|1.9|2.7|1.5|-|-|84|
|Poland|5.1|18.6|4.5|14.5|-|60|
|Guatemala|7.3|64.6|4.2|12.9|12.4|28|
|Argentina|9.3|50.0|6.2|14.7|6.5|39|
|Brazil|11.7|68.8|8.4|12.5|6.6|37|
|Peru|16.5|51.2|15.9|15.5|7.2|37|
|Germany|-|3.8|-|-|1.1|81|
|All countries|18.0|32.7|14.1|22.5|-|43|
|LAC|8.7|36.3|6.3|10.0|-|43|

_Source:_ WBES and BEEPS 2005.

_Note_ : The CPI is measured from 0 to 100, and a higher number means a lower perception of corruption. BEEPS =The Business Environment and Enterprise Performance Survey; CPI = Corruption Perception Index; WBES = World Bank Enterprise Surveys.

An additional measure to consider is Transparency International’s Corruption Perception Index (CPI). This index ranks 176 countries on a scale from 0 (“highly corrupt”) to 100 (“very clean”) as determined by expert assessments and opinion surveys. In 2017, Denmark and New Zealand were perceived as the least corrupt countries in the world, while the country perceived as the most corrupt in the world was Somalia. The last column of table 1 displays the index values for the selected countries. We can see that Argentina ranks slightly below the Latin American and Caribbean average, and it ranks more than 40 points below developed economies like Sweden and Germany. Using this alternative measure, we can also conclude that corruption is an important problem for developing economies.

Figure 2 shows the correlation between the CPI and bribery incidence. As we can see, countries with a low CPI (high perceived corruption) tend to have a high percentage of firms facing bribery. The figure also shows that Argentina is below the linear predicted relationship between these two variables. This implies that the incidence of bribery in Argentina is lower than what a linear model 8