rafmacalaba/data-use-mentions-extended huggingface dataset:
preferences are quasi-homothetic, the indirect utility function can be written where _γ_ denotes the ‘subsistence’ bundle and _φ_ ( _·_ ) is an exact (K ̈onus) cost-of-living index, which includes the opportunity cost of leisure, even if this does not vary. Hence, Aggregating over all households in the village yields the total ‘commercial’ benefit _B_<sup>_c_</sup> = _M_<sup>_c_</sup> _H_ . It is seen that transactions between villagers, whether in the form of tenancy, or labor services, draught animals, animal feed, or whatever, simply net out; so that the resulting sum is just the village’s domestic product plus any net factor income from its dealings with the rest of the economy, that is to say, gross village income (analogously to gross national income). Any changes in the aggregate production plan involve mainly changes in the purchases of inputs that the village cannot supply itself, such as artificial fertilizers, pesticides, motor fuel, certified seeds and the like, with an allowance for the costs of additional credit when needed. There is, however, a snag in employing (5) in practice. Whereas an exact cost-of-living index will duly incorporate the value of leisure, the standard indices do not; nor, moreover, is leisure measured in the surveys available for present purposes. Changes in the real cost of the subsistence bundle are also ignored. Hence, estimates of _B_<sup>_c_</sup> derived from (5) using, say, the consumer price index for agricultural laborers (CPIAL) and data on household incomes, as the surveys define income, will involve errors; but if the changes in leisure are small, these are unlikely to be serious.

Each of these alternatives is now illustrated with actual examples, drawing on Bell (2009a). The community in Himachal Pradesh enjoys a good standard of living and is heavily commercialized, the principal crop being apples. A stretch of
11 measure. We preferred consistent data series, preferring the same survey and the same adaptation of it. In some cases, we had no choice but to switch survey, e.g. because a survey was discontinued or because we had limited access to the surveys. After excluding datapoints on quality grounds, and after selecting among the remaining datapoints, we retained 612 of the 1,000 surveys, covering 146 of the 154 countries. The estimates presented below are for the latest year for which we have data; country-level trends will be analyzed in a future paper.

# **New global estimates of out-of-pocket health expenditures – results**

In this section we present our new global estimates of out-of-pocket health spending using the measures in Table 1. We use maps to show the international variation in our measures and, for selected measures, tables showing the means by region and World Bank income group. In addition, to shed light on the sources of international variation in our measures, we present the results of multiple regressions that show the correlations between each of our measures and selected macroeconomic and health system variables.<sup>10</sup> We discuss the charts and regressions results below, going through the measures in Table 1 in turn.

## <u>Expenditure in absolute terms</u>

The top left map in Figure 2 shows per capita out-of-pocket health expenditures in 2011 international dollars for the latest year for which we have survey data. These numbers, it should be reiterated, are based entirely on our estimates from household surveys. They contrast with estimates presented in international health accounts databases, which are sometimes interpolations between datapoints, and sometimes estimated from non-survey sources such as user fees revenues reported by providers; the latter are not always reliable, with providers often reporting less than households say in surveys they spent (see e.g. Lieberman and Wagstaff 2009).

The map reveals large differences across countries. Tables 3 and 4 suggest the differences are driven in large part by international
121<br>(0.088)|
|Outcome mean|1.772|1.640|
|Observations|789|644|

_Notes._ The outcome in column 1 is the number of strategies the female set and committed to during the goal setting activity, ranging from 1 to 3 strategies; the outcome in column 2 is the number of strategies that the female reports she achieved at the endline survey, ranging from 0 to 3 strategies. The covariates are from the baseline survey (age, currently enrolled in school, completed school, household has electricity, household has earthen floor) or the goal setting activity, 1st visit (major depressive disorder likely, self-efficacy, will take risk). Major depressive disorder likely is a binary indicator measured using the Patient Health Questionnaire-2 (PHQ-2), where a score of 3 or higher is indicative of depression. Self-efficacy is measured using the General Self-Efficacy Scale developed by Schwarzer and Jerusalem (1995). This scale generates a total self-efficacy score that ranges from 10-40, which we standardized using the mean and standard deviation among females in ELA only communities. Will take risk is based on a question that asked females how willing they were to take risks on a scale from 1 to 10. We generate a binary indicator equal to one if the female gave an answer in the top quintile of responses. Age is age in years at the time of baseline, and currently enrolled in school, completed school, household has electricity, and household has earthen floor are binary indicators. Standard errors, clustered at the club level, are presented in parentheses below coefficient estimates. .01 ***, .05 **, .10 *. _Source._ Column 1: Female goal setting participants, 1st visit. Column 2: Female goal setting participants, endline data.

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9 EBRD transition indicators, converted for the purpose of this analysis along similar lines as for first generation reforms into a single index of structural reforms,<sup>4</sup> provide interesting insights into Bulgaria’s pace of institutional transformation since the collapse of central planning in 1990. The values of structural reform index show very little progress until 1997. An almost total absence of government commitment to structural reforms during the initial stages of transition is best illustrated by the fact that the value of this index for Bulgaria in 1994 was the same as for Poland in 1990, that is, when structural reforms barely started. Even then the achieved progress in 1995-96 would put Bulgaria on par with Poland’s achieved progress in 1992 (Table 4).

**Table 4:** _Progress in structural reforms revealed in values of EBRD-based aggregate index in 1991-2004 in selected transition economies (in percent)_

||1990|1991|1992|1993|1994|1995|1996|1997|1998|1999|2000|2001|2002|2003|2004|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Bulgaria|0.0|5.7|13.4|17.1|22.9|28.6|28.6|41.9|41.9|41.9|47.6|47.6|51.4|53.3|57.1|
|Poland|22.9|28.6|28.6|45.7|51.4|57.1|57.1|60.9|62.8|62.8|64.7|66.6|
child labor and child marriage— are still widespread and relatively common in Nigeria.

The International Labour Organization (ILO) defines child labor as work that deprives children under 18 of their childhood, their potential, and their dignity, and that is harmful to their physical and/or mental development. Data from the Multiple Indicator ́ Cluster Survey (MICS) conducted in Nigeria in 2016 2017 indicate that the prevalence ́ of child labor among children aged 5 17 was 50.8%, with 54.3% for boys and 47.2% for girls.

While child labor affects the school chances of both boys and girls, child marriage, in contrast, is a predominantly girls’ phenomenon. It is defined as the marriage of a girl under 18 years of age. In the case of Nigeria, DHS data show a 43% prevalence of child marriage in among married women aged 20 – 24—which is 6 percentage points above Sub-Saharan Africa’s average of 37%. Interestingly, Figure 1 built using Nigeria 2018 DHS shows significant regional disparities in the prevalence of child marriage both in terms of marriage before the age of 15 (Panel A) and marriage before the age of 18 (Panel B).

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Asia are estimated to remain below their 2008 levels by –1.2 and –1.6 percent, respectively. In contrast, the GDP of the remaining developing regions comfortably exceeded that of 2008 by 2010. All regions are expected to exceed their 2008 GDP levels by 2011, although with substantial heterogeneity: at one extreme, the 2011 level of GDP in Asia is expected to be 24 percentage points above its 2008 value; at the other extreme, GDP in advanced economies is expected to be just 0.87 percentage points above its 2008 value. Similar patterns emerge if the comparison is made against the GDP levels that would have been achieved had economies continued to grow at the average rates achieved during 2000–2007 (Figure 4, bottom panel). The difference between expected GDP, calculated based on forecast growth rates, and the counterfactual GDP levels is a rough measure of the output cost of the crisis. By far, the region most affected by the crisis is Eastern Europe and Central Asia, whose expected GDP level is about 14 and 16 percent below the counterfactual trend in 2010 and 2011, respectively.

Annual GDP data suggest that emerging economies recovered similarly to developed economies, at least during 2010. However, analysis of higher-frequency IP data suggests that emerging economies did recover more strongly than advanced economies. While the collapse in IP was similar in advanced and emerging economies, the recovery began earlier in emerging economies. The peak in IP took place around April 2008 for both advanced and emerging economies. But by January 2009, emerging economies had started a sustained recovery; while advanced economies started to rebound only in May 2009 (Figure 3 and Table 1). Thus, the downward phase of the cycle lasted, on average, nine months for emerging economies and 13 months for advanced economies. As a consequence, emerging economies achieved their precrisis levels of IP by November 2009, whereas advanced economies were still well below their 13
We take the City of Cape Town, South Africa, as a case study. Cape Town is a rapidly sprawling city, with an extremely high level of inequalities (income-based Gini index of 0.63 in 2011, UN-Habitat Global Indicators Database 2020<sup>1</sup> ). This translates into transportation and segregation issues; low-income households often living far from employment centers and being limited to use slow transportation modes as they cannot afford private cars. In addition, the housing market of the City of Cape Town is characterized by the coexistence of formal, subsidized, and informal housing, resulting in strong locational constraints for the people that cannot afford formal housing because informal and subsidized housing is only possible in some locations in the city.

We find that spatial inequalities can reinforce income inequalities. In the short run, households are more negatively impacted by the fuel tax if they earn low incomes or if they live far from employment centers. In the medium and long term, there is a persistence of spatial and income inequalities: the poorest households, living in informal settlements or subsidized housing, have few or no ways to adapt to changes in fuel prices by changing housing types, adjusting their dwelling sizes or locations (as informal or subsidized housing is only possible in some locations), or shifting transportation modes and employment locations. Low-income households living in formal housing are also impacted by the tax over the long run, due to complex effects driven by the competition with richer households on the housing market. These mechanisms are summarized in Table 1 and will be detailed throughout the paper.

|Timeframes|**Short term**|**Medium-term**|**Long te**|**rm**|
|---|---|---|---|---|
|Mechanisms|Direct impacts<br>of the tax|Job location<br>and transport<br>mode<br>adjustment|Rents adjustments|Dwelling
(0.046)|
|Physical abuse in last year|0.109|0.089*<br>(0.049)|0.041<br>(0.049)|0.148|0.012<br>(0.041)|
|Force sex in last year|0.085|0.037<br>(0.041)|0.017<br>(0.041)|0.102|0.000<br>(0.029)|
|Observations|201||637|459|637|

_Notes._ Column 1 shows control means for females in ELA only communitues and column 4 shows control means for females not assigned to the _Goal_ treatment. Columns 2–3 and column 5 shows the difference between means in the corresponding community- or individual-level treatment arms and the corresponding control group means, controlling for randomization strata. Estimates in columns 2–3 come from a single regression, and estimates in column 5 come from another. See Table 1 for indicator definitions. Standard errors, clustered at the club level, are presented in parentheses below the coefficient estimates in columns 2–3 and column 5. .01 ***, .05 **, .10 *. _Source._ Female baseline data, balanced panel, but restricted to females who had ever had sex at baseline.

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variables for asset and animal ownership, and variables reflecting coping strategies. As for the ProGres data set, the Targeting data set does not contain information on consumption or expenditure. However, it does contain information on wealth.

The last set of data is the Post-Distribution Monitoring (PDM) data set, which is from a sample survey that covers similar themes as the Targeting data set. The PDM data set, which was collected in 2017 by the WFP, aims to provide better understanding of how refugees use food assistance and contains data on consumption and expenditure. The sampling design is a two-stage stratified random sample, where the first stage includes the selection of camps and the second stage the selection of households. Beforehand, the different camps are stratified in three zones: <mark>(i) North East (Ourecassoni, Amnaback, Iridimi Touloum), (ii) Centre-East (Goz Amir, Djabal, Gaga, Teguine, Bredjing, Farchana), and (iii) South (Amboko, Dossey, Gondjé, Belom, Moyo) (Map 1).</mark> In addition, the sampling takes into account the kind of humanitarian assistance that is provided to refugees (in-kind, food voucher, or cash). The survey includes two <mark>consumption aggregates</mark> 8
lower than the control). Agricultural income from sales of crops is no higher, and household-level enterprise profits and assets are not improved. Hence, this picture suggests that the flow of income from TASAF group enterprises is being invested in livestock assets, but has neither translated into flow consumption benefits nor helped the household to increase other sources of income. The groups receiving training do not show superior outcomes on any of our primary measures, a theme to which we return later.

Analysis of a set of secondary outcomes in Table A4 arrives at a similarly pessimistic conclusion. This analysis has the same structure as the previous one except that we do not have baseline observation of these outcomes and so it is a simple midline cross-section rather than an ANCOVA. We examine outcomes related to food security, durable consumption, savings, borrowing, schooling of children, and health, and find no impacts significant at the 95% level for either treatment, even when we use the F-test to look for joint significance of the two treatments. We can also focus our analysis on the wellbeing of the specific vulnerable individual who caused the household to be eligible for TASAF. A core purpose of programs with elaborate targeting criteria that attempt to draw in vulnerable individuals is the desire to improve the health and consumption of these individuals specifically. To examine whether household-level impacts could be muting a stronger impact on the actual vulnerable group member, we exploit an individual-level survey module to report on the health and the consumption of the actual VG group member at the time of the round 2 survey, before the late-treated group is treated. These results, in Table A5, examine illness, hospitalization, morbidity, self-reported health quality, and individual food insecurity for the targeted individual. The results provide no evidence that the VG group members who had benefited from 12 months of TASAF treatment enjoyed better health or superior individual-level consumption.

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not expect these challenges would affect our identification strategy for two main reasons. First, even with these limitations, the SIRE data replicates well the dropout patterns observed in Guatemalan household surveys (Adelman et al, 2018). Second, there is no reason to believe that this potential measurement error would be different across randomly selected control and treatment groups. In short, we expect relatively small and random measurement error in dropout, creating (if anything) a small downward bias to our estimates.

# **Data on potential mechanisms**

The data on mechanisms was mostly collected via online questionnaires. There were some challenges with the response rates of the online questionnaires capturing the potential mechanisms, 17
.5.

Fifth, to create a measure of _Road Density_ for 2011, 2016, and 2020, we use two data sources: the Global Roads Open Access Data Set gathered by the NASA Socioeconomic Data and Applications Center from 2010, and the World Food Programmes road networks shapefile from OpenStreetMap for 2017 and 2020. For each parish polygon, we extract the total length of roads (km), weighted by the speed limit of each type of road. There are six types of roads in Uganda, ranging in speed from trail to highway. See SI Section S1.6 for more details on road density data construction.

Lastly, to assess public opinion, we use the Afrobarometer surveys Rounds 3–8, which roughly correspond to our study years. The Afrobarometer is an in-person, nationally representative survey. For Uganda, each round has roughly 2,400 adult citizen respondents, so for this analysis our units are respondent-years (as opposed to parish-years). Note that this survey data is repeated crosssections, not panel, and not all questions are asked every round. We evaluate the following questions

> 14For HC-I, we use a binary indicator of whether a parish has an HC-I rather than the distance, because many parishes have one or more.

16
the media can play in disseminating information to hold government to account.<sup>89</sup> In addition to investigating the role of OGD, the present research also seeks to build on previous efforts (Joshi 2013) investigating the efficacy of transparency and accountability initiatives, the findings of which are mixed in terms of service delivery.<sup>90</sup> To be sure, Gaventa and McGee (2014) have evaluated the efficacy of a number of transparency and accountability mechanisms,<sup>91</sup> and note that where there is “positive evidence in one setting, this is often not corroborated—and sometimes even contradicted—by findings in another setting where different, or even similar, methods have been used.”<sup>92</sup> Likewise, Carothers and Brechenmacher (2014, 2018) note that evidence on the impact of accountability, transparency, participation, and inclusion is “often long term, indirect, and difficult to isolate from other factors, and the evidence base to date is still too thin to arrive at firm conclusions.”<sup>93</sup>

> 85 Priyanka Pandey, Sangeeta Goyal, and Venkatesh Sundararaman, “Public Participation, Teacher Accountability, and School Outcomes: Findings from Baseline Surveys in Three Indian States,” World Bank Policy Research Working Paper Series (November 1, 2008).

> 86 N. Awortwi and A. Nuvunga (2019) Sound of One Hand Clapping: Information Disclosure for Social and Political Action for Accountability in Extractive Governance in Mozambique, IDS Working Paper 523, Brighton: IDS

> 87 Alina Mungiu-Pippidi, “Quantitative Report on Causes of Performance and Stagnation in the Global Fight against Corruption.” available at http://anticorrp.eu/publications/quantitative-report-on-causes/ (2014).

88 Claudio Ferraz and Frederico Finan. "Exposing Corrupt Politicians: The Effects of Brazil’s Publicly Released Audits On Electoral Outcomes." _The Quarterly Journal of Economics_ (2008).

89
indicator variable for being 0-10 km from the GQ network with indicator variables for whether the district’s work was completed by March 2003, March 2006, or later. Of the 70 districts, 27 districts were completed prior to March 2003, 27 districts between March 2003 and March 2006, and 16 districts afterwards. In almost every case in Table 3b, the relative sizes of the effects by implementation date are consistent with the project’s completion taking hold and influencing economic activity. Given that our final data survey comes from 2007, it is not surprising that we do not yet see substantial activity in the districts completed after March 2006. On the other hand, expansions in activity are generally strongest for districts that were completed by March 2003. Again, the timing of the GQ upgrades is consistent with the results we observe.

# **Comparison of GQ Upgrades with the NS-EW Highway**

Table 4 compares districts proximate to the GQ network to districts proximate to the NS-EW highway network that was not upgraded. The idea behind this comparison is that districts that are at some distance from the GQ network may not be a good control group if they have patterns of evolution that do not mirror what districts immediately on the GQ system would have experienced had the GQ upgrades not occurred. This comparison to the NS-EW corridor provides perhaps a stronger foundation in this regard, especially as its upgrades were planned to start close to those of the GQ network before being delayed.

The upgrades scheduled for the NS-EW project were to start contemporaneous to and after the GQ project. To ensure that we are comparing apples to apples, we identified the segments of the NS-EW project that were to begin with GQ and those that were to follow in the next phase. We use separate indicator variables for these two groups so that we can compare against both. Of the 76 districts lying with 0-10 km of NS-EW, 40 districts were to be covered in the 48 NS-EW projects identified for Phase I. The empirical appendix provides greater detail on this division
more exposed to poverty due to a higher expenditure level as compared to the labor income flow of those who are working.

For that reason, pension systems are considered as an important component in social security systems where the main purpose is to relieve consumption (or income) during an individual's life cycle and to reduce the poverty incidence among those who are elderly.

In order to approach the importance of transfers from the income protection system to older people, when containing poverty incidence and distributive inequality, an arithmetical exercise for poverty in years 2006 and 2016 is presented. Based on information taken from the National Employment, Unemployment and Underemployment Survey, this exercise consists of calculating the incidence of poverty and income distribution based on age, taking into account the total household income with and without transfers from the two pension schemes: compulsory contributory insurance and solidarity pension.

These two estimations do not contemplate the effects that the transfers may have on the behavior of household members that are not affected by their presence or absence. Therefore, this analysis is inevitably partial, but it allows identifying the existence of trends related to the effects taken into account.

Figure 10 presents, for years 2006 and 2016, the percentage of people in poor households per age quintile, identifying the potential impact of deleting all kinds of transfers for pensions. Therefore, it is noticed that in 2006, poverty affected 30.6% of the population, but if there had not been these transfer programs, there would not have reached 32.4%. However, the distribution of age-based poverty incidence is not homogeneous. In effect, poverty is higher in two well-defined age groups: those under the age of 20 and those who are older than 65 years. In this sense, the contributory pension scheme has a higher impact on reducing poverty among people older than 65 years, since social pensions have a lower impact on age distribution as a whole.

17