rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 16: Indicators of institutional trust in the Dominican Republic**

||Rank (of<br>18)|DR (0-100<br>scale)|Regional<br>Average|
|---|---|---|---|
|Is thegovernment rulingon behalf of the wholepopulation?|18|15|30|
|Publicpolicies enhance livingconditions|17|28|44|
|Progress made in fight against corruption|15|23|37|
|Level of confidence in the Congress|10|32|34|
|Level of confidence in the Political Parties|10|22|23|
|Level of confidence in Justice|10|28|32|
|Level of confidence in the Army|14|33|45|
|Level of confidence in the Government|14|34|45|

Source: Latinobarómetro 2010 Red: indicator for the DR below regional average; <u>green, above regional average</u> In this context, the Dominican middle class seems to be among the most skeptical societal group. Espinal et al. (2006), conducting analysis of institutional trust in the Dominican Republic, find some evidence pointing to << _a U-shaped relationship between socioeconomic status and institutional trust, with the poorest and wealthiest trusting government institutions more than middle-income sectors_ >>. This lack of confidence in the effectiveness of public sector bodies and institutions together with polarization and lack of consensus may be some of the reasons why the middle classes are less active in demanding an improvement in public services<sup>15</sup> . There is also evidence that those that enjoy a better economic situation are less likely to seek assistance from the local government. Instead, as illustrated in the previous section, they usually opt for individual solutions to complement insufficient infrastructures (in health, education, power supply...).

The perception of lack of citizen demands is well illustrated by the following newspaper joke, referring to recent citizen movements in Spain, and claiming that in the Dominican Republic there are not protesters:

> 15 In the literature there is not strong evidence of the expected positive relation between civic engagement (and collective action) and institutional trust (Espinal et al., 2006; Finkel et al., 2000). Conversely, here we will try to see if the low levels of institutional trust may burden collective action (see, for instance Levi and Stoker, 2000).

[25]
**Table 6 - Direct Effect of Price Increase through Taxes (%), by Decile**

|_Price shock scenario_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Complete pass-through|-0.09|-0.08|-0.06|-0.07|-0.07|-0.09|-0.06|-0.06|-0.05|-0.03|
|Low-bound elasticity|-0.03|-0.04|-0.03|-0.04|-0.04|-0.05|-0.04|-0.04|-0.04|-0.02|
|Medium elasticity|-0.01|-0.02|-0.02|-0.02|-0.02|-0.03|-0.02|-0.03|-0.02|-0.02|
|Upper-bound elasticity|0.01|0.00|0.00|-0.01|-0.01|-0.01|-0.01|-0.01|-0.01|-0.01|

_Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ The table shows the share of total consumption for each decile. Complete pass-through refers to elasticity equal to zero; consumers pay all the increased prices, and this does not affect the quantity purchased.

**Figure 2 – Income Gains: Direct Effect of Tobacco Taxes (Increase in Expenditure because of tobacco taxes), by Decile**

<!-- Start of picture text --> 0.04<br>0.02<br>0.00<br>-0.02<br>-0.04<br>-0.06<br>-0.08<br>-0.10<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br>Complete pass-through Low-bound elasticity<br>Medium elasticity Upper-bound elasticity<br>Source: Estimates based on Russia Longitudinal Monitoring Survey–HSE 2016.<br>Note: Estimates assume a price shock of 25 percent.<br>Income Gains (%)<br><!-- End of picture text -->

# **Medical expenses**

Table 7 and figure 3 report the income effect of a reduction in medical expenses. As noted above, the model assumes that the health effects of tobacco-related diseases will immediately diminish with the reduction in tobacco consumption. Even though this assumption is implausible in the short term because changes in the effects of tobacco-related diseases take some time to materialize, it provides an upperbound estimate of the effects of tax increases. The overall results indicate that the reduction in medical expenditures is highly progressive, disproportionally benefiting lower-income groups. This derives from two factors: (1) the higher price elasticity and (2) a lower income base that massively benefits from the reduction in medical costs.

10
Table 10: What is the most cost-effective price per firm trained?

|_Total cost of recruiting_<br>_and training_<br>_1000 firms (US$)_|189474|188654|**175119**|182267|178448|225000|272727|183333|150475|137500|**118581**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Cost of<br>training<br>1000 firms|150000|142500|127500|112500|75000|37500|0|150000|112500|75000|37500||
|Cost per person<br>of training<br>($150 - price)|150|142.5|127.5|112.5|75|37.5|0|150|112.5|75|37.5||
|Cost of<br>demonstrations<br>($30 per head)|39474|46154|47619|69767|103448|187500|272727|33333|37975|62500|81081||
|Number needed<br>at demo to get<br>1000 firms trained|1316|1538|1587|2326|3448|6250|9091|1111|1266|2083|2703|s of the course.|
|Attendance<br>rate at this price|0.76|0.65|0.63|0.43|0.29|0.16|0.11|0.90|0.79|0.48|0.37|ing at least one clas|
|(US$)|0|7.5|22.5|37.5|75|112.5|150|0|37.5|75|112.5|ate attend|
|(J$)<br>Price charged:|**Panel A: BDM Sample**<br>0|1000|3000|5000|10000|15000|20000|**Panel B: TIOLI Sample**<br>0|5000|10000|15000|Notes: Attendance rate is r|

39
**Table 5: Distribution of Estimates of the Impact of Cash Transfers on Temptation Goods**

||**Negative &**<br>**significant**|**Negative (or 0)**<br>**& insignificant**|**Positive &**<br>**insignificant**|**Positive &**<br>**significant**|**Total**|
|---|---|---|---|---|---|
|**Panel A: All estimates**||||||
|Estimates|12|24|6|2|44|
|From [--] studies|6|14|5|2|19|
|From[--]interventions|6|12|5|2|13|
|**Panel B: Only expenditure levels**||||||
|Estimates|5|17|5|2|29|
|From [--] studies|5|11|5|2|14|
|From [--] interventions|5|10|5|2|11|
|**Panel C: All estimates - RCTs only**||||||
|Estimates|1|13|3|0|17|
|From [--] studies|1|6|2|0|8|
|From [--] interventions|1|5|2|0|7|
|**Panel D: Only expenditure levels - RC**|**Ts only**|||||
|Estimates|0|9|2|0|11|
|From [--] studies|0|4|2|0|5|
|From [--] interventions|0|4|2|0|5|

**Table 6: Percentage distribution of Estimates of the Impact of Cash Transfers on Temptation Goods**

||**Negative &**<br>**significant**|**Negative (or 0)**<br>**& insignificant**|**Positive &**<br>**insignificant**|**Positive &**<br>**significant**|**Total**|
|---|---|---|---|---|---|
|All estimates|27%|55%|14%|5%|100%|
|Only expenditure levels|17%|59%|17%|7%|100%|
|All estimates - RCTs only|6%|76%|18%|0%|100%|
|Only expenditure levels-RCTs only|0%|82%|18%|0%|100%|

**Table 7: Percentage distribution of Estimates of the Impact of Cash Transfers on Temptation Goods – Conditional Cash Transfers (CCTs) versus Unconditional Cash Transfers (UCTs)**

||**Negative &**<br>**significant**|**Negative (or 0)**<br>**& insignificant**|**Positive &**<br>**insignificant**|**Positive &**<br>**significant**|**Total**|
|---|---|---|---|---|---|
|All estimates, CCTs|29%|55%|13%|3%|100%|
|All estimates, UCTs|18%|55%|18%|9%|100%|
|All estimates, CCTs - RCTs only|8%|77%|15%|0%|100%|
|All estimates, UCTs-RCTs only|0%|75%|25%|0%|100%|

Notes: This table excludes the evaluation of The Kenya Cash Transfer Program for Orphans and Vulnerable Children, which imposed conditions in 3 of the 7 districts in which it was implemented.

25
controlling for the population-weighted _distance_ (ln) between the host country _i_ and country of origin _o;_ whether the origin and host country share a _common language_ , _common border_ , or _colonial history_ . Data on these bilateral variables were obtained from the gravity data set developed by Head, Mayer and Ries (2010).

We estimate a two-stage Heckman model to simultaneously examine investment at the extensive margin - i.e. whether to invest - and the intensive margin - i.e. how much to invest. The first stage analyzing the extensive margin consists of a probit model, where the dependent variable is 1 if a MNE invested in a host country in year _t_ and 0 otherwise. The second stage, examining the intensive margin, consists of an OLS-model estimating the amount of greenfield FDI (transformed using the inverse sine-transformation) for those firms that decided to invest. To avoid multicollinearity resulting from limited nonlinearity in the functional form, we use the colonial history dummy as our exclusion restriction. However, our results are also robust to using different exclusion restrictions.

Table 6 presents the results from the two-stage Heckman model. Column 1 shows the results for the regression of the intensive margin, where the dependent variable is the amount of greenfield FDI flows given that an MNE invests in a country. At the average geographic diversification level, a nationwide conflict has a significant negative effect on total FDI flows, whereas a localized conflict does not significantly affect greenfield FDI flows (Column 1). This is consistent with our hypotheses and main results. However, in the equation for the extensive margin there is no significant main effect of BRD, irrespective of the type of conflict. Hence, political conflict affects greenfield FDI flows through a decrease in the size of investment projects, whilst there is no evidence that conflict affects the probability that an MNE invests in a country. The diversification moderator is not statistically significant for the intensive margin, but it is positive and significant at the 10% level for the extensive margin (Column 2). At particularly high levels of geographic diversification, with presence in at least 26 countries, the effect of BRD becomes positive. This indicates that for MNEs that are better able to absorb the risk posed by political violence, the increase of conflict can actually positively affect the probability of investment. Although this might seem counterintuitive, it could suggest that MNEs that are sufficiently diversified have a competitive advantage in countries with high discontinuous risk and 33
# **<u>ANNEX 7: WORLD BANK CARBON FUNDS: AMOUNTS RAISED, SOURCES,</u>**

# **PARTICIPANTS, AND ERPAs SIGNED**

|**Fund**|**Inception**|**Fund**<br>**capital**<br>**(US$**<br>**mil.)**|**Private**<br>**capital**<br>**raised**<br>**(US$**<br>**mil.)**|**%**<br>**private**|**Leverage**<br>**of donor**<br>**support**<br>**(private:**<br>**donor)**|**Participants**|**MtCO2e**<br>**under**<br>**contract**<br>**(ERPA**<br>**signed)**|
|---|---|---|---|---|---|---|---|
|Prototype Carbon Fund|2000|180|115|63.9|1.8|23|31|
|Community Development<br>Carbon Fund|2003|128.6|58|45.1|0.8|25|7.1|
|BioCarbon Fund Tranche 1|2004|53.8|30|55.8|1.3|14|4.7|
|Danish Carbon Fund|2005|75.4|49.5|65.7|1.9|6|3.6|
|Spanish Carbon Fund|2005|308|69.9<sup>1/</sup>|22.7|0.3|13|15|
|Umbrella Carbon Facility<br>Tranche 1|2006|998.8|749.1|75|3|16|129.3|
|Netherlands CDM Facility|2002|..|..|..|..|1|..|
|Netherlands European Carbon<br>Facility|2004|..|..|..|..|1|..|
|Italian Carbon Fund|2004|155.6|47|30.2|0.4|7|15.4|
|BioCarbon Fund Tranche 2|2007|36.1|18.1|50|1|6|..|
|Carbon Fund for Europe|2007|70|14.0<sup>1/</sup>|20|0.3|5|..|
|**Total**||**2,006.30**|**1,150.60**|**57.4**|**1.3**|**117**|**206.1**|

_1/ Converted from € to US$ at the exchange rate of 1€=1.4US$._ 53
# 3 Data Description and Information Campaign

The analysis in this paper uses the data from the General Population Cohort study (GPC) of the Medical Research Council Programme on AIDS in Uganda. Since November 1989, this study surveys annually 15 villages in the Masaka District in Uganda.<sup>4</sup> In 1999—2000, for the eleventh round, 10 villages have been added to the cohort. The villages are located in a rural area in southern Uganda where the main cash crops are bananas and coffee.

The GPC is an open cohort: every individual who reaches age 13 is enrolled, as well as the persons migrating in the survey area. Every year, the participants in the cohort answer a sociodemographic and a medical questionnaire. The medical questionnaire includes anonymous HIV testing.<sup>5</sup> The coverage and compliance in the GPC have been very high. For example, there were 9,611 individuals in the resident adult population at round 12 (11/2000—09/2001): among them 6,594 (69 percent) were interviewed and 6,312 (65 percent) were bled and tested for HIV.

At least since round 5 (11/1993 — 9/1994), there has been a general decline in HIV prevalence among individuals older than 17.<sup>6</sup> Questions about the educational achievement of the participants were included in rounds 1, 7, 8, 9 and 11. As evidenced in the summary statistics presented in table 1, there is a general trend toward higher educational achievement over the period, corresponding to the openings of several schools in the area in the 1990s. The proportion of individuals with no formal education has decreased substantially while the proportion with secondary education increased dramatically. It should be noted that since 1996, the Ugandan Government has introduced the Universal Primary Education (UPE) which pays for public schools at the primary level. However, tuition is charged in secondary schools.

The variables used in the analysis, for which summary statistics are reported in table 1, in addition to education level and HIV status, include gender, marital status, religion and a measure for wealth. The proxy for wealth included is the type of materials used to construct the house.<sup>7</sup>

> 4 See Mulder and others (1994); Mulder and others (1995); Nunn and others (1994); Whitworth and others (2002); Nunn and others (1997).

> 5 The MRC program also offers voluntary testing and counselling services organized independently of the GPC.

> 6 HIV prevalence is calculated taking each round as a separate cross-section and does not involve interpolations.

> 7 The distinction is made between houses made of soft materials (mud or wattle with grass/papyrus roof), a

7
database (FGGD). The FGGD 2005 data includes a suitability index for rain-fed crops measured at a spatial resolution of 5 arc-minutes (10 km) (Van Velthuizen et al. 2007).<sup>16</sup> We also include the production of plant biomass using the measurement of Net Primary Productivity (NPP), which is the result of all the carbon used in photosynthesis minus the loss of carbon from the maintenance respiration. Google Earth Engine provides the cloud-corrected MODIS product (MOD17A3) data at 1km spatial resolution summarized at the annual level from the 45 8-day Net Photosynthesis (PSN) products. Evaluating the mean level at the administrative level yields the variable _npp_ .

Naidoo and Iwamura (2007) derive a global map of gross economic rents from agricultural lands (in 2000 US dollars per hectare per year) by integrating spatial information on crop productivity, livestock density, and producer prices from FAOSTAT (http://faostat.fao.org; accessed June 2005). We extract these economic rents by pixel and summarize the mean over the administrative unit ( _agr_ _~~o~~ pp_ _~~c~~ st_ ).

# **Climate:**

Climate provides the initial conditions to agricultural production and human settlement. Increasing change in climate will arguably affect conditions to support agricultural production. In a low production scenario, Hertel et al. (2010) find that the prices for major staples rise 10–60% by 2030, which would have heterogenous impacts on the poor depending on local changes in agricultural production and the sources of income for households in the area. In the case of drought and urban floods, Winsemius et al. (2015) find evidence that the poor already are overexposed to natural hazards of floods and droughts and are vulnerable to changes in climate.

BioClim is a global gridded database at 30 arc-second (1km2) spatial resolution of bioclimatic variables that are derived from monthly temperature and rainfall values from cleaned weather station data and elevation. The bio-physical model interpolates these meteorological variables and provides derivative variables of seasonality over the base period of 1960 to 1991 (Hijmans et al. 2005)<sup>17</sup> . We extract these 1km2 pixels for annual mean temperature ( _bio1_ in degrees Celsius * 10), temperature seasonality ( _bio4_ in standard deviation * 100), annual precipitation ( _bio12_ in mm * 10), precipitation seasonality ( _bio15_ in coefficient of variation * 10), precipitation of wettest quarter ( _bio16_ in mm * 10) and precipitation in driest quarter ( _bio17_ in mm). We aggregate the data to obtain mean values for each administrative area. In Figure A2.4 (left panel), we see the effect of elevation in the higher temperature of the Great Rift Valley

> 16We modified negative values that represent pixels classified as urban, closed forest or irrigated to equal zero for the mean calculations by administrative area.

> 17For the interpolation of noisy multi-variate meteorological data, the authors use thin plate smoothing splines from the ANUSPLIN program with latitude, longitude, and elevation as independent variables.

44
**Figure 1:**

<!-- Start of picture text --> Level of Proficiency - Math , Indonesia<br>Level 4, 1%<br>Level 4, 2%<br>Level 3, 5%<br>Level 3<br> 10%<br>Level 2<br>14%<br>Level 2<br> 20%<br>Level 1<br>27%<br>Level 1<br>32%<br>Below Level 1<br>52%<br>Below Level 1<br>35%<br>2003 2006<br><!-- End of picture text --> Most developing countries score at the bottom of the scale in most international achievement tests. Until recently, there were very few if any examples of developing countries that had achieved significant improvements in these tests. Critics argue that the international development community has focused almost exclusively on increasing enrollment in the education sector and has ignored the need for that education to be of adequate quality. However, Indonesia is a rare case of a developing country that has achieved some progress.

In order to find out what lay behind Indonesia’s exceptional improvement in 2006, we looked at how family, student, school and institutional inputs may have affected the increase in the test score of 15 year olds in math. We decomposed the increase over time into its constituent components using the traditional Oaxaca-Blinder method, based on the estimation of a cognitive achievement production function. Our decomposition results suggest that almost all of the test score increase was unexplained or, in other words, was due to changes in the _returns_ to characteristics rather than to changes in the characteristics themselves. We found that most of the positive change was due to the increased returns over time to the variable representing a student’s age, which varied only by months in this case (as the PISA is administered to a randomly selected sample of students who are between the ages of 15 years and 3 months and 16 years and 2 months at the time of the test).

3
# **Corruption and Confidence in Public Institutions: Evidence from a Global Survey**

Bianca Clausen (World Bank), Aart Kraay (World Bank), and Zsolt Nyiri (German Marshall Fund)<sup>1</sup>

> 1 <u>bclausen@worldbank.org, akraay@worldbank.org, znyiri@gmfus.org. We would like to thank the</u> Knowledge for Change (KCP) Program of the World Bank for financial support, and Claudio Raddatz, Mary Hallward-Driemeier, and Alfonso Astudillo for helpful comments. We are also grateful to the Gallup Organization for enabling this project by providing access to the Gallup World Poll data, and especially to Gale Muller, Vice Chairman and General Manager of the Gallup World Poll, and to seminar participants at Gallup. The views expressed here do not reflect those of the Gallup Organization, the World Bank, its Executive Directors, or the countries they represent.
# **Appendix D: Subtreatments**

Table D1: Effect of Ordering of Yes and No Options in ACASI on Placebo Questions

||(1)<br>Farm work<br>(past year)|(2)<br>Market visit<br>(past week)|(3)<br>Maize/Rice<br>(next week)|(4)<br>Meat<br>(next week)|
|---|---|---|---|---|
|**Panel A. Malawi**|||||
|YES First|-0.02|0.05|0.06*|0.07*|
||(0.02)|(0.03)|(0.04)|(0.04)|
|NO First mean|0.94|0.54|0.58|0.34|
|Observations|854|851|708|615|
|**Panel B. Liberia**|||||
|YES First|0.00|0.03|0.01|-0.01|
||(0.03)|(0.04)|(0.02)|(0.04)|
|NO First mean|0.82|0.74|0.94|0.70|
|Observations|615|616|595|542|

Note: Includes only those who are in the ACASI group (FTFI group excluded). Regressions include country sample fixed effects and individual controls (including all variables in Table 1). Standard errors clustered at individual level in parentheses. ***, **, and * represent significance at 1%, 5%, and 10%, respectively.

35
# **2.2 KHDS**

The Kagera Health and Development Survey (KHDS) is a study into the long-run wealth dynamics of households and individuals within Northwest Tanzania. This study entails the resurvey of a panel of households, originally interviewed for 4 rounds from 1991 to 1994. Resurveys were then organized in 2004 and 2010. A multi-topic household questionnaire is administered to all split-off households originating from the baseline households, including those that have moved out of the baseline location.

This constitutes one of the longest-running African panel data sets of this nature and offers an unprecedented set of research opportunities for examining long-run (nearly 20 years) trends in and mechanisms of poverty persistence and economic growth in rural households. As the children of the original respondents have now formed their own households, intergenerational and migration issues can be addressed by the survey data. The data is of particularly high quality and the 2010 round of the survey was conducted using electronic survey questionnaires administered on handheld computers, with automated skips and validation checks run during the interview when errors could be corrected at source. The resulting improvements in data quality have been formally assessed by Caeyers et al. (2012).

KHDS has maintained a highly successful tracking rate. Table 1 shows that in 2010 88% of the original 6,353 respondents had either been located and interviewed, or, if deceased, sufficient information regarding the circumstances of their death collected.

In 2010, households were found in three cities: Dar es Salaam, Mwanza and Kampala. This is defining cities as agglomerations with more than 500,000 inhabitants. The 2012 census puts the population of Dar es Salaam at 4.36m and Mwanza at 0.7m.<sup>4</sup> There are a further 21 respondents who moved to areas that, while administratively recognized within Tanzania as cities, have a population below 500,000 (Arusha, Tanga and Mbeya). Results do not change

> 4For Mwanza this is the sum of Nyamagana Municipal Council (363,452) and Ilemela Municipal Council (343,001). More detailed analysis using 2002 census data (Wenban-Smith and Ambroz, 2014) showed that while the municipal districts of Mwanza counted a population of 596,885, only 65% of these lived in strictly urban wards (others lived in rural or mixed wards). The current census does not yet disclose the new categorizations, but applying the same rural-urban ratio of 65% to the current census figure gives us an urban population of 456,631. This estimate lies under 500,000, but we decided to treat Mwanza as a city based on the district-level figures.

5