rafmacalaba/datause-encoder-data huggingface dataset:
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Manning, W., J. Newhouse, et al. (1987). "Health insurance and the demand for medical care: evidence from a randomized experiment." The American Economic Review **77** (3): 251-277.

MoLHSA (2007). Household Survey on Health Service Utilization and Expenditure Draft Report. Georgia Primary Health Care Development Project, MoLHSA, Department of Statistics, MoED, Oxford Policy Management, UK and Curatio International, Tblisi, Georgia.

Newhouse, J. and The Insurance Experiment Group (1996). Free for all? Lessons from the <u>RAND health insurance experiment.</u> Cambridge, MA.

Ruiz, F., L. Amaya, et al. (2007). "Progressive Segmented Health Insurance: Colombian Health Reform and Access to Health Services." Health Economics **16** : 3-18.

Trujillo, A. J., J. E. Portillo, et al. (2005). "The Impact of Subsidized Health Insurance for the Poor: Evaluating the Colombian Experience Using Propensity Score Matching." International <u>Journal of Health Care Finance and Economics</u> **5** (3): 211-239.

Wagstaff, A. (2007). "Health Insurance for the Poor: Initial Impacts of Vietnam's Health Care Fund for the Poor." The World Bank, Policy Research Work Paper Series: 4134.

Yates, R. (2007). "The Impact of Abolishing User Fees in Africa—Recent Developments in Six African Countries." Social Science Research Network, Working Paper Series, The World Bank.

21
that there is not always a one-to-one relationship between ethnicity and native language (see discussion below).

# **_3.1. Intervention_**

Once the participants were selected, they were randomly assigned to one of four groups to watch a 2:34 minutes-long 360-degree VR experience using an Oculus Go VR headset (see Appendix A for still images from the VR experiences or follow the video links provided below for each treatment group). Immediately after watching the videos, each participant was asked to take a brief computer-assisted survey. The four 360-degree VR experiences in the experiment included:

- i. <u>Control group: Video of a well-known public park (Burns Garden) in its natural state, with no</u> modifications;

- ii. <u>Treatment-1</u> (urban design): Video with 3D urban design elements added to the park;

- iii. <u>Treatment-2</u> (diversity): Video with a diverse group of park users added to the park;

- iv. <u>Treatment-3</u> (urban design + diversity): Video including both the additional 3D urban design elements and the diverse group of park users.

The control group was shown a video of a well-known public park called Burns Garden in its natural state, while the three treatment videos were enhanced with additional content identified through primary and secondary research. An online survey was carried out on Facebook in Urdu to collect information on people’s perceptions about public parks in Karachi.<sup>5</sup> The survey was complemented with an extensive review of the literature on barriers that hinder the use of parks (see Appendix B). The Facebook survey indicated that public parks in Karachi were generally perceived as dull and unsafe places. When asked about the reasons for feeling unsafe, respondents mentioned lack of proper lighting at night, presence of drug users, fear of harassment (especially among women), and parks often being deserted. Respondents indicated that they wanted to see clean and fun parks that include various amenities, such as a library, bookstore, flower market, community garden, and tea kiosk. The characteristics most commonly mentioned in the comments left on the Facebook page included “green”, “clean”, “water”, “trees”, and “flowers”.

> 5 The survey was run for a period of five days (February 27 to March 3, 2019), reaching a total of 323,708 Facebook users between the ages 18-26 years. Of these, 7,356 users clicked on the link and only 182 completed the survey in full (100 of whom were men and 82 were women). A tablet was offered as a prize to one randomly selected participant to encourage participation.

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<!-- Start of picture text --> A. Index for Number of Vehicles<br><!-- End of picture text --> <!-- Start of picture text --> Daytime (5:00 - 17:00) Pre-Curfew (17:00 - 20:00) Curfew (20:00 - 5:00)<br>1.5<br>Vehicle 4.0<br>Index<br>0.5<br>0.0<br>Jan Feb Mar Apr May Jun Jan Feb Mar Apr May Jun Jan Feb Mar Apr May Jun<br><!-- End of picture text -->

# B. Average Speed from Google Maps

<!-- Start of picture text --> Daytime (5:00 - 17:00) Pre-Curfew (17:00 - 20:00) Curfew (20:00 - 5:00)<br>50<br>Average<br>Speed<br>(km/h) 4°<br>30<br>Jan Feb Mar Apr May Jun Jan Feb Mar Apr May Jun Jan Feb Mar Apr May’ Jun<br><!-- End of picture text -->
household member, unemployment and bankruptcy of business, and the loss of housing, business, crop and livestock due to climate-related events.

The third stage constructs income levels associated with the probabilities of falling into poverty using the same independent variables as in the previous step. For this purpose, the average of the independent variables gets calculated for an array of estimated probabilities of falling into poverty. The resulting coefficients were then used to produce the predicted income associated to each probability—a mean, conditional on characteristics, with lower volatility than the observed average income (Lopez-Calva and Ortiz-Juarez, 2014). Based on these models, the methodology yields monetary estimates expressed in PPP terms.

The authors proposed a 10% probability of falling into poverty as a dividing line between economic security and vulnerability, and defined the predicted income associated to that probability as the upper bound of vulnerability—or the lower bound for the middle class—with the lower bound being the $4 a day poverty line. The resulting per capita incomes for non-poor individuals with a 10% probability of falling into poverty was $9.8. Thus, a household is defined as vulnerable if it faces a more than 10 percent likelihood of falling back into poverty, which is equivalent to living on $4-10 a day. Figure 1 shows the distribution of income in Mexico in 2012 and the dividing lines for the identified groups under this methodology. A recent report lead by Ferreira et al. (2013) carried out a “validation” of the threshold by looking at income levels that are consistent with selfperceptions of middle class status, showing that $10 a day corresponds to the lower envelope of such income levels.

**Figure 1: Distribution of income; Mexico 2012**

<!-- Start of picture text --> $4 $10 $50<br>0 5 10<br>Log of per capita income<br>.5<br>.4<br>.3<br>Density<br>.2<br>.1<br>0<br><!-- End of picture text --> _Source_ : Author’s estimations based on ENIGH 2012 The resulting incomes are then applied to the National Household Income and Expenditure Survey (ENIGH), which are regularly used to assess poverty in the country. The ENIGH survey is undertaken by the Institute of Statistics and Geography (INEGI), and it is a nationally representative survey, covering urban and rural areas, which contains detailed information on income (including direct transfers), different categories of expenditures (after taxes), and in-kind transfers. This survey is available for 1984 and 1989, and for every 2 years since 1992. In order to measure the size of the 6
multidimensionally poor when her income is below the low poverty line zj<sup>e′,regardlessof</sup> her achievements in the other dimensions. In contrast, when the income of an individual i is above the low poverty line zj<sup>e′butbelowthecutoffpovertylinezj′,iisidentified</sup> as multidimensionally poor only if she also suffers from (enough) deprivation in nonmonetary dimensions, as is the case in Figure 5.

<!-- Start of picture text --> x2 x2<br>xi′<br>xi′<br>z2 z<br>z2<br>z<br>xi<br>u<br>u<br>z1 e z1 x1 (money) z1 x1<br><!-- End of picture text -->

**Figure 5:** Deprivation-based identification makes fewer mistakes.

**Figure 6:** Achievement-based identification makes exclusion errors on extremors

Observe that Eq. (4) uses information on the depth of deprivation (in the cardinal variable) in order to improve the identification of the multidimensionally poor. This approach, which deviates from the “counting” approach, could also be used when measuring the intensity of multidimensional poverty, although here we only considered its incidence.

Finally, we warn against one key limitation of another solution to increase the information content considered in the identification methodology. A seemingly natural solution is to take a weighted sum of achievements, namely identifying the multidimensionally poor with<sup>�</sup> j∈J<sup>card wjxij≥k.ThissolutionwasproposedinRavallion(2011).This</sup> solution would also seem to be a natural way of identifying the multidimensionally poor from multidimensional well-being indices proposed by Benjamin et al. (2014b) or from the total consumption approach (see Section 3.4.3). The limitation of this approach is that it assumes an infinite substitutability across dimensions and thus does not acknowledge the essentiality of well-being dimensions. As a result, it systematically makes exclusion errors on extremors. The intuition is easy to grasp in Figure 6, where extremor i<sup>′</sup> is (mistakenly) not identified as poor because her achievement vector is above the linear identification contour associated to such an achievement-based identification methodology (in red).

It would be interesting to study empirically the differences that these two solutions make when compared with a more traditional AF identification. For example, on could look at how well-being comparisons by the World Bank’s MPM would be affected by these solutions. In the remainder of this section, we shortly discuss how to quantify the impact of these solutions.

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Table 4b: Decision Making Power over Large Purchases - Correlation with Male Breadwinner Norm: Attitudes and Norms

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||Female in HH<br>has all the<br>power<br>(Yes=1/No=0)|Male in HH has<br>all the power<br>(Yes=1/No=0)|Joint decision<br>making with<br>spouse<br>(Yes=1/No=0)|No power at all<br>(Yes=1/No=0)|Female in HH<br>has all the<br>power<br>(Yes=1/No=0)|Male in HH has<br>all the power<br>(Yes=1/No=0)|Joint decision<br>making with<br>spouse<br>(Yes=1/No=0)|No power at all<br>(Yes=1/No=0)|
|||**_Male Breadw_**|**_inner Norm: Ex_**|**_penses are a ma_**|**_n's responsibility _**|**_even if his wife ca_**|**_n help him_**||
|Female|0.12***|-0.08***|-0.03***|-0.01***|0.11***|-0.09***|-0.02**|-0.02***|
||[0.00]|[0.00]|[0.01]|[0.00]|[0.01]|[0.01]|[0.01]|[0.01]|
|Personal Belief:Agree(No=0/Yes=1)|0.01***|0.09***|-0.08***|-0.01**|||||
||[0.00]|[0.01]|[0.01]|[0.01]|||||
|Personal Belief:Agree*Female|0.05***|-0.07***|0.02**|0.05***|||||
||[0.01]|[0.01]|[0.01]|[0.01]|||||
|Community Norms (0/1)|||||-0.01|0.03***|-0.03***|-0.01|
||||||[0.01]|[0.01]|[0.01]|[0.01]|
|Community Norms (0/1)*Female|||||0.04***|-0.03**|0.00|0.03**|
||||||[0.01]|[0.01]|[0.02]|[0.01]|
|Constant|0.24***|0.46***|0.25***|0.03|0.22***|0.44***|0.26***|0.03|
||[0.02]|[0.03]|[0.03]|[0.03]|[0.02]|[0.03]|[0.04]|[0.04]|
|Observations|43053|43053|43053|43053|36433|36433|36433|36433|
|Adjusted R-squared|0.06|0.11|0.20|0.20|0.06|0.09|0.20|0.21|
|Mean Female|0.17|0.09|0.55|0.19|0.17|0.08|0.56|0.19|
|Mean Male|0.07|0.22|0.50|0.18|0.07|0.21|0.52|0.18|
|Norms+Fem(Coeff)|0.06|0.02|-0.06|0.04|0.03|0.01|-0.03|0.02|
|Norms+Fem(SE)|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|
|Norms+Fem(P-value)|0.00|0.00|0.00|0.00|0.00|0.20|0.01|0.02|
|Sample|ALL|ALL|ALL|ALL|ALL|ALL|ALL|ALL|
|CountryFE|YES|YES|YES|YES|YES|YES|YES|YES|
the results to assumptions that one cannot externally validate. In our study we have to adopt such an approach for items like alcohol and tobacco taxation, health and free basic services.

The typical indicators produced by incidence analysis are measures of progressivity such as incidence –i.e., the share of taxes (transfers) paid (received) as a proportion of the pre-tax (pre-transfer) income-- and concentration coefficients which measures the share (by decile or quintile) of specific, or overall, taxes and transfers. In addition, fiscal incidence studies report inequality and poverty indicators --such as the headcount ratio and the Gini coefficient—before and after taxes and transfers. Also, some studies include indicators of horizontal equity to capture how policy impacts individuals who are equal before fiscal intervention (see below). This study produces information on all these indicators.

Source: Lustig and Higgins (2013).

We follow Lustig and Higgins (2013) and measure per capita income before and after each fiscal intervention (figure 3).

<!-- Start of picture text --> BENEFITS<br><!-- End of picture text --> <!-- Start of picture text --> TAXES<br><!-- End of picture text --> <!-- Start of picture text --> Market Income<br>Wages and salaries, income from capital,<br>private transfers; contributory pensions<br><!-- End of picture text --> <!-- Start of picture text --> Personal income<br>and payroll taxes<br><!-- End of picture text --> <!-- Start of picture text --> Net Market Income<br><!-- End of picture text --> <!-- Start of picture text --> Direct transfers<br><!-- End of picture text --> <!-- Start of picture text --> Indirect subsidies<br><!-- End of picture text --> <!-- Start of picture text --> Indirect taxes<br><!-- End of picture text --> <!-- Start of picture text --> In-kind transfers (free government<br>services in education and health)<br><!-- End of picture text --> <!-- Start of picture text --> Co-payments, user<br>fees<br><!-- End of picture text --> <!-- Start of picture text --> Final Income<br><!-- End of picture text -->

- Market income<sup>17</sup> comprises pre-tax wages, salaries, and income earned from capital assets (rent, interest or dividends) and private transfers.

17In South Africa, we take net market income reported by each household in the IES and impute each direct tax paid to arrive at market income. This figure is then divided by the number of members in each household to arrive at per capita market income. We did not include the value of own production (sometimes called autoconsumption). Statistics South Africa did not measure auto-consumption in the conventional way; instead their variable captured both home production and business inventories and we had no way of separating out 10
129~ ~ ~ ~~~1 <!-- Start of picture text --> 19Y: 19B1 1952 1953 IMU 1955 1990 1987<br><!-- End of picture text -->

- Figure 2: RPXYF: relative price of exports to home goods before imposition of export subsidies.

- XPXPROD: Producer price of exports, in terms of home goods and after imposition of a 204 ad valorem export subsidy.

- XPXPROD: (Foreign) consumer price of exports, in terms of home goods and after imposition of a 204 ad valorem export subsidy.

The figure demonstrates that the subsidy is mostly shifted backward to producers for most of the simulation period. Initially more than two thirds of the subsidy goes into increased profit margins. a percentage that after seven years still stands at a little under fifty percent. There is, however, a significant affect on fictitious exports and overimvoicing: the ratio of reported over actual exports goes up by no less than 5 percentage points on average, making for a much stronger response of recorded than of actual exports. Thus the conclusLon that export subsidies, in spite of an apparent effect on recorded exports, are not a very effect:Uve tool to increase export volumes; most of the benefits accrue for many years to home producers rather than to foreign producers.

Consider next a change in the relative price of bome goods in terms of imported goods, what we have referred to as the real exchange rate. A 10 percent real depreciation will, for given relative price of exports to home goods, substantially increase demand for exports, thus putting upward pressure on the price ratLo of goods sold abroad versus goods sold at home. The not effect again depends on demand and supply elasticities. The model run suggests that the real depreciation initially goes for almost 7% out of 10 into higher export prices in terms of home goods, a share that declines gradually to 5% after seven years. In terms of foreign goods the reverse happens;
90% of the observed consultations. Measurement is higher in the PBF group by 8 percentage points (p = 0.107).

For the child consultations, we find some significant differences in the conduct of physical examinations. Overall, weight and height of the children was measured in 85 percent of consultations. In the PBF facilities, these measurements were more likely to be conducted by 12 percentage points (p = 0.037). For the 13 core physical examination procedures, the share of completed procedures were higher by eleven percentage points in the PBF group. PBF providers were statistically significantly more likely to observe the children’s ears, perform skin pinch tests for dehydration, check for lethargy, and count radial pulse. We do not find significant impacts on duration of these consultations, whether vaccination history was checked, and whether providers washed their hands prior to conducting the physical examinations.

Results from the analysis of the data collected at the health facilities show that the PBF project had a significant impact on the quality of care. As a result of the program, availability of equipment and medical supplies increased. We detect improvements in facility infrastructure and procedures related to infection prevention and control. We find positive impacts on provider competence and even some improvements in the content of curative care of children under-5. Although we find these positive impacts, substantive gaps in quality of care remain. For example, many providers could not correctly diagnose the clinical vignette cases and many providers did not properly wash their hands before conducting physical exams. There may be further improvements in quality with time, but additional intervention targeting quality of health may be needed. Next, we turn to analysis of the household data to evaluate whether the project was successful in increasing utilization of health services.

# Health outcomes and health-related behaviors

In Table 7, we present results on the impact of the PBF intervention on health outcomes and health behaviors. As seen in Panel A, we do not find impacts on anthropometric measures of children. We also do not find a significant impact on the rate of adults over 40 with elevated blood pressure (Panel B). Adults in the PBF group were more likely to self-report being hypertensive prior to the measurement conducted by the survey teams (p = 0.063). This is likely to be a result of the increased measurement of blood pressure in the PBF group. Among adults over-40 with selfreported hypertension, we do not find any impact on rate of medication prescription or whether an 14
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where _Gm_ k and _Gdk_ are Ginis for market ( _m_ ) and disposable ( _d_ ) income inequality in country _k_ , and _Ψ_ indicates country-level control variables including country dummies, and εk= normally-distributed error term. We expect _α1_ >0 (note that the dependent variable is defined as _reduction_ ).

The second part of the hypothesis is tested by regressing the sharegain against market income share for _each_ decile, that is.

where _Sm,ik_ is the share of _i_ -th decile in market (pre-redistribution) income in country _k_ , _Sd,ik_ is the share of the _same people_ in disposable income, and _uik_ is the error term.<sup>3</sup> The difference _Sdi-Smi_ is the sharegain. The key expected result is that the coefficient _βi_ should be negative for all deciles, that if for all _i_  (1,10). It means that if a _given_ decile is better off pre-redistribution, it will gain less through redistribution.<sup>4</sup> Of course, we may be particularly interested in what happens to the poorest deciles, but the negative relationship ought to hold for all deciles.

Both (1) and (2) are readily testable and are broader hypotheses than the median voter hypothesis. (1) and (2) jointly ask whether greater redistribution is likely to happen in more market-unequal environments, but they do not address the exact mechanism

> 3 Note that the people who are in the poorest decile according to pre-redistribution income need not be, and generally will not be, in the poorest decile according to disposable (post-redistribution) income. In effect, many of them are likely to climb much higher. However, we are not interested in comparing the poorest decile according to pre-redistribution income to the poorest decile according to post-redistribution income because this is not the object of  the hypothesis. We are interested in finding out how  much, on average, the people in the poorest pre-redistribution decile gain through government redistribution process.

> 4 My 2000 paper also showed that the coefficient _β1_ for the bottom decile and quintile is not significantly different from unity. This can be interpreted as indicating that if market income distribution “moves” against the poorest decile, the redistributive system will exactly compensate for that shortfall. Scervini (2010) has argued that with the more recent LIS data this result no longer holds and that rich countries have become less redistributive.

7