rafmacalaba/datause-encoder-data huggingface dataset:
18 made for the central government to target an increasing share of central government resources to the poorest provinces.

While a larger proportionate increase in government spending on health in the poorer provinces than in the richer provinces will clearly promote _equity,_ its relative effect on _efficiency is_ not immediately obvious. Indeed, the latter depends much on the _marginal_ impact of increased public health expenditure on health outcomes. Table 5 reports the impact of a 1,000 yuan increase on infant mortality rates by quintile of provinces. An increase of 1,000 yuan in per capita provincial government health spending in high-income provinces is associated with drops in infant mortality of only 0.12 percent. Conversely, similar increase in per capita health expenditure leads to a decline of infant mortality by 3.04 percent in poorer provinces. Thus, a 1,000 yuan increase in health spending yields much faster decline in infant and maternal mortality in poorest provinces. Because poorest provinces account for more than 30 percent of the total population of China, a strategy of redistribution of central government health expenditure from richer to poorer provinces will generate a larger overall decline in infant mortality, and as such will improve the overall efficiency of public health financing.

_Table 5: Impact of a 1,000 yuan increase in provincial government health expenditure on infant mortality, 1993_

|_Quintile of_<br>_Provinces_|_Percentage_<br>_ofpopulation_|_Infant_<br>_mortality_|_Maternal_<br>_mortality_|
|---|---|---|---|
|I|30.8|-3.04|-1.01|
|lI|15.6|-2.45|-0.67|
|lII|17.2|-1.05|-0.44|
|IV|24.2|-0.43|-0.23|
|V|13.9|-0.12|-0.03|

_Source:_ Staff estimate from MOPH data.

_Restructuring User Fees._ The combination of recent worsening income distribution, reduced government health spending and price decontrols (resulting in higher prices for health care and other consumable) reduced utilization of health services, consequently worsening their health status particularly in poor provinces. The government is concerned about cushioning the poorest of the poor from further higher health costs stemming from changing age pattern of diseases and greater cost recovery in public health sector. Indeed, better targeting of public expenditure to improve access of the poorest to health facilities would depend on two policy instruments: reducing user costs associated with treatment and drugs, and improving the quality of care for those who actually gain access.

- 4The implied GDP elasticity of provincial government health expenditures varies from a low of -0.098 for Guizhou, the poorest province in China, to a high of 2.56 for Shanghai, the richest province.
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62
_bolster those "pillars of integrity" that are functioning well. The Index empowers donors, both bilateral and multilateral, by offering a platform for evidence‐based reform efforts._ ” Similarly, the World Bank, which produces the very detailed Doing Business indicators of the business regulatory environment, notes that the goal of this exercise is “ _to provide an objective basis for understanding and improving the regulatory environment for business.”_<sup>_2_</sup> The World Bank has also developed considerable resources to developing and promoting other such “actionable” governance indicators (AGIs), of which they include Doing Business and Global Integrity as leading examples. According to the World Bank, the distinguishing feature of such “actionable” indicators is that they provide _“...convenient and replicable guidance on the features (rules of the game, organizational capabilities) for which reform interventions are likely to prove most helpful for improving the performance of particular governance elements”._<sup>_3_</sup> Realizing the promise of such detailed indicators to identify and prioritize specific reform efforts requires an understanding of the relative magnitude of the effects of each of the individual disaggregated indicators on the corresponding outcomes that policymakers might want to improve. For example, a policymaker interested in reducing corruption (or even just the perceptions of the prevalence corruption held by domestic or foreign investors) might want to know which of the over 300 individual measures comprising the Global Integrity Index would have the largest impact on corruption. Our premise is that a policymaker or an aid donor would not particularly care whether a country scores well on any specific disaggregated indicator (for example, the existence of an anticorruption commission), but rather cares whether improving such an indicator (for example, by creating an anticorruption commission) will actually reduce corruption. Similarly, a policymaker looking for the most “bang for her buck” in the area of business regulatory reform would want to know the magnitude of the partial effects of each of the many indicators in the Doing Business dataset before choosing the few on which she would like to expend her political capital to seek improvements in these areas.<sup>4</sup>

> 2 See http://www.doingbusiness.org/Documents/DB10_About.pdf.

> 3 <u>http://www.agidata.org</u>

> 4 Of course, the process of developing and implementing governance‐related reforms is much more complex that simply identifying those areas with the highest impact and acting on them.  In reality, policymakers must balance all of the political and financial costs and benefits of reforms in particular areas.  All we attempt to do here is to shed some light on the difficulty of quantifying a narrow measure of the benefits of reforms, which is their estimated impact on outcomes.

3
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- Dustan, A. (2018), “Can large, untargeted scholarships increase urban high school graduation rates? Evidence from Mexico City’s Prepa Sı”, Vanderbilt University.

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23
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and inequality, we use both types of weather anomalies simultaneously. Following Sedova and Kalkuhl (2020), our measures of weather shocks are, therefore, variables that capture positive and negative temperature and precipitation anomalies during a time period of 90 days before a household’s EPH survey date. First, we construct a Standard Temperature Index (STI) and Standard Precipitation Index (SPI) that measure temperature and rainfall variability as the difference between short-term average temperature or precipitation during the time bin _t_ and the historical average (or long-term climate pattern) during this time of the year between 1981 and 2003, divided by the historical standard deviation (Patel, Chopra, & Dadhwal, 2007), where _Pdt_ is average precipitation during 90 days prior to the interview, _Pdh_ is the historical average precipitation during this time bin, and _σPdh_ is the historical standard deviation of precipitation during the same time bin, which equally applies to the STI. Historical values as reference period of long-term climate conditions cover the 23 years between 1981-2003, corresponding to the start of the CHIRPS and leading up to the start of the EPH data collection. In line with previous literature, we use the STI and SPI to capture weather shocks, as levels of temperature or precipitation matter less in an absolute sense but rather in proportion to a district’s historical variation (Heim, 2002; Hirvonen, 2016; Letta et al., 2018; Randell & Gray, 2016). Based on these indices, we construct four measures of positive and negative weather anomalies as: where a positive (negative) anomaly is defined as the absolute z-score of temperature or precipitation, if the index is larger (smaller) than zero, and zero otherwise. Therefore, in this study, the four weather shock measures are _T_ (+) _dt_ , a positive temperature anomaly (also referred to as heat shock), _T_ ( _−_ ) _dt_ , a negative temperature anomaly (cold shock), _P_ (+) _dt_ , a positive precipitation anomaly (flood shock), and _P_ ( _−_ ) _dt_ , a negative precipitation anomaly (drought shock). Figure 2 shows the averages of these weather shocks between 2004 and 2019. Eventually, our measure of weather shocks captures short-term temperature and precipitation extremes in relation to long-term climatic conditions in the respective districts. In doing so, we capture meteorological conditions as expressed by the SPI and STI, instead of more complex indices such as the Standardized Precipitation Evapotranspiration Index (SPEI) or other drought indices for several 11
likely to be in school than the children of women of other marital status particularly at younger and older ages. It is especially striking that these children drop our earlier (Figure 11). In contrast, at any age between 5 and 14, the resident dependents of widows have the highest probability of being in school in urban areas. Those least likely to be in school are the children of divorced women at ages under 8 or so and the dependents of previously widowed but now married women at older ages (Figure 12).

A further issue is whether daughters are treated differently from sons and whether such differential treatment is associated with a mother‘s marital situation. Figure 13 explores this question by breaking down the school enrollment data given in Figures 11 and 12 by the gender of cohabiting school aged children. The difference between the share of boys versus girls is plotted. Note however, that due to small sample sizes once disaggregation by age and gender is carried out, the children of current widows and current divorced women are not included in Figure 13. Likewise, there may be some small-sample problems with the married, previously widowed group in urban areas to keep in mind. In both urban and rural Mali, boys are overall more likely to be in school than girls conditional on a given marital status. However, the gap appears to be much less pronounced and increases only weakly with age among the children of once married women. The daughters of previously widowed or divorced women hold an advantage at very young ages in rural areas, but once they reach seven and a half or so, the gap reverses, rising quite steeply with age, particularly for the children of previously widowed women. The latter‘s pro-son gap overtakes that for the offspring of once married women from around age 10 for the children of prior widows and around age 12 for those of prior divorcees. In urban areas, the gender gap favors boys at all ages and is generally significantly larger for the children of previously widowed and divorced women. Reinforcing the regression results on mean differences, the evidence is thus strongly suggestive of negative human capital investment effects being worse for cohabiting daughters than for cohabiting sons in the event of their mother experiencing a marital status shock.

As we saw earlier, some of the children whose mothers remarry after widowhood or divorce, live separated from their mothers. Figure 14 compares the enrollments of children who 24
management of malaria surveillance records; and orientation on community and health center engagement. A typical visit by the NGO field worker lasted for one to two hours for each CHW. In treatment arm A, these activities were conducted in conjunction with the community mobilization activities described above.

# **Outcomes**

Intervention effectiveness was assessed through a comparison of outcome measures between the intervention and control arms. Main outcome measures were related to the reported consistent usage of LLINs and care seeking patterns of febrile cases. Specific measures included proportion of fever cases seeking care from a trained provider and receipt of test and treatment, if appropriate, for malaria within a day of developing symptoms; households owning at least one LLIN; and population sleeping under a bed net.

# **Evaluation**

A brief quantitative household survey instrument was implemented in 90 study villages before intervention activities and a more extensive household and community survey was conducted in all study villages at the end of the intervention period (November 2010-January 2011). Data instruments utilized the local language Odia and were piloted and modified before each survey. The baseline survey collected basic demographic data from 22 households per village. These data are mainly used to explore balance of socio-demographic characteristics across intervention arms.

8
approach to an assessment of the impact on poverty transitions of reforms in Hungary’s social safety net.

**_Spillover effects._** A further way in which the classic impact evaluation problem often needs to be adapted to the needs of practitioners concerns its assumption that impacts for direct participants do not spillover to non-participants; only under this assumption can infer the counterfactual from an appropriate sample of the non-participants. Spillover effects are recognized as a concern in evaluating large public programs, for which contamination of the control group can be hard to avoid due to the responses of markets and governments, and in drawing lessons for scaling up based on randomized trials (Mofitt, 2003).

An example of spillover effects can be found in the Miguel and Kremer (2004) study of treatments for intestinal worms in children. The authors argue that a randomized design, in which some children are treated and some are retained as controls, would seriously underestimate the gains from treatment by ignoring the externalities between treated and “control” children. The design for the authors’ own evaluation avoided this problem by using mass treatment at the school level instead of individual treatment (using control schools at sufficient distance from treatment schools).

Spillover effects can also arise from the way markets respond to an intervention. Ravallion (2008) discusses the example of an _Employment Guarantee Scheme_ (EGS) in which the government commits to give work to anyone who wants it at a stipulated wage rate; this was the aim of the famous EGS in the Indian state of Maharashtra and in 2006 the Government of India implemented a national version of this scheme. The attractions of an EGS as a safety net stem from the fact that access to the program is universal (anyone who wants help can get it) but that all participants must work to obtain benefits and at a wage rate that is considered low in the specific context. The universality of access means that the scheme can provide effective insurance against risk. The work requirement at a low wage rate is taken by proponents to imply that the scheme will be self-targeted to the income poor.

The EGS is an assigned program, in that there are well-defined “participants” and “nonparticipants.” And at first glance it might seem appropriate to collect data on both groups and compare their outcomes either by random assignment or after cleaning out observable heterogeneity. However, this classic evaluation design could give a severely biased result. The gains from such a program are very likely to spillover into the private labor market. If the 14
**Table A.9. Bivariate regression coefficients – Beliefs about gender roles within the home and women in public spaces predicting women’s work for pay outside the home**

|**Theme**|**Norm**|**Core**|**Paid work**|**Paid work**|**Paid work**|
|---|---|---|---|---|---|
|||**elements**|**(Full sample)**|**(self) (Female-**|**(Female**|
|||**measuring**<br>**social norms**||**only sample)**|**spouse)**<br>**(Male-only**<br>|
||||||**sample)**|
||A woman’s primary|PNB|-0.10**|-0.14***|-0.04|
||responsibility is taking care||(0.04)|(0.04)|(0.03)|
||of HH|SNE|-0.10*<br>|-0.14**<br>|-0.03<br>|
||||(0.05)|(0.06)|(0.05)|
|||SEE|-0.09*|-0.14**|-0.03|
||||(0.05)|(0.05)|(0.05)|
|||Sanc.|-0.02|-0.04|0.00|
||||(0.03)|(0.03)|(0.03)|
||Financially providing is solely a|PNB|-0.13***|-0.17***|-0.07*|
|Gender roles in|man's responsibility||(0.04)|(0.04)|(0.04)|
|the household||SNE|-0.12**|-0.18***|-0.03|
||||(0.05)|(0.05)|(0.05)|
|||SEE|-0.10*|-0.15**|-0.05|
||||(0.06)|(0.05)|(0.07)|
|||Sanc.|-0.08**|-0.07**|-0.09*|
||||(0.03)|(0.03)|(0.04)|
||Women should not / do not|PNB|-0.05|-0.05|-0.05|
||leave children in the care of||(0.04)|(0.05)|(0.04)|
||someone else when they go|SNE|-0.05|-0.07|-0.04|
||to work or to look for work||(0.05)|(0.06)|(0.05)|
|||SEE|0.03|0.03|0.02|
||||(0.06)|(0.06)|(0.06)|
|||Sanc.|0.01|0.00|0.01|
||||(0.03)|(0.03)|(0.03)|
||Women should not / do not|PNB|-0.15**|-0.19**|-0.12*|
||work for pay outside the||(0.06)|(0.07)|(0.06)|
||house|SNE|-0.16**|-0.24***|-0.06|
||||(0.07)|(0.06)|(0.08)|
|||SEE|-0.21***|-0.20***|-0.22**|
||||(0.07)|(0.07)|(0.08)|
|||Sanc.|-0.05|-0.03|-0.07|
||||(0.04)|(0.05)|(0.05)|
||Women should not / do not|PNB|-0.08|-0.10|-0.06|
||leave the house go to places||(0.08)|(0.10)|(0.08)|
|Women in the|<br>such as the market|SNE|-0.04|-0.11|0.05|
|public space|unaccompanied by a family||(0.08)|(0.10)|(0.07)|
||<br>member|SEE|-0.05|-0.07|-0.02|
||||(0.05)|(0.04)|(0.06)|
|||Sanc.|-0.06|-0.12***|0.04|
||||(0.04)|(0.04)|(0.05)|
||Women going outside brings|PNB|-0.15*|-0.20**|-0.08|
||||(0.08)|(0.09)|(0.08)|

41