rafmacalaba/datause-encoder-data huggingface dataset:
43 installment payment options, drugs were always in stock, patients did not have to wait long before he saw them and his attitude and manner reassured them. As a result, they would recommend his dispensary to other people. His patients frequently passed by several other facilities, both in the private and public sectors, in order to come to his dispensary. Similarly, the rural midwife in private practice reported charging low fees with instalment payment options, and offering solicitous service that gratified her patients.

ii) Public sector providers. The government health workers substantially confirmed public perceptions of the problems of the state health service. First, all agreed that fee increases had hurt. But on the other hand, nurses and midwives reported that even before recent fee increases, patients had been leaving the general hospitals. All government health workers also recognized the shortage of drugs and hospital supplies and the lack of equipment as serious problems. Concerning the attitudes of health care providers, the nurses and midwives conceded that nurses, attendants and ward orderlies used to be seen as rude, but maintained that they had begun. to change since the Commissioner for Health assembled them to discuss this problem. Evidence of professional conflict in the hospital between doctors and nurses also emerged, with each side stating a case for controls on the autonomy or authority of the other. On the question of private practice and diversion of patients from the general hospitals, the resident doctor confirmed that several government doctors also had private practices. As for informal surcharges, the nurses and midwives reported that when they asked patients for the formally stipulated fees, often the patients would protest that they had already paid the doctors.

# Conclusions

Questionnaire surveys might have revealed the problems of access to care, high cost of services, and lack of supplies and equipment in this case. But they would not have elicited adequate information about health provider attitudes, nepotism, diversion of services and supplies, and unofficial surcharges for care. A project design which was targeted at resolving the problems uncovered by a questionnaire survey might have focused on construction of additional health facilities and supply of drugs and equipment--and would almost certainly have had little impact on community health. The value of the focus group method of interviewing was that it uncovered critical information from service users about the attitudes and behavior of health care providers, which in this case was a more important determinant of program performance than technical or financial constraints.
# V. Timing of early warning and drought response

## V.1 Timeline of drought impacts and early warning information

In responding to drought events, the timeliness of the response is key in preventing harmful welfare impacts of the drought. The existing early warning tools in Ethiopia can be used to facilitate early action during the onset of a drought, thereby preventing the adverse welfare impacts of droughts.

Early action could be based on variables obtained from the existing early warning systems. For instance, as detailed in section IV.1.1, the LEAP tool produces the intermediate outputs of planting dates, WRSI and crop loss, as well as the final output of beneficiary numbers. Intermediary outputs are available for every dekad (10 day interval) as rainfall data is collected on a continual basis. While outputs can be produced by the LEAP tool at any point in time, the accuracy of estimates increases as the season progresses and the rainfall pattern during the season emerges. If intermediate outputs – such as crop loss – or the final output of beneficiary estimates are computed early during any given season, the LEAP tool assumes a normal rainfall pattern for the remainder of the season, leading to less accurate estimations. LEAP output variables are most accurate when computed in December for the _meher_ season, and in July for the _belg_ season. However, it would be possible to use the LEAP tool to predict drought as early as August / September in any given year, thereby enabling a timely drought response as early as December<sup>53</sup> . This could make a critical difference in protecting lives and livelihoods, as detailed in Section V.2 below.

Currently, the LEAP early warning information is produced jointly by GoE and the WFP, based on available rainfall data. LEAP outputs are developed and monitored within the NDRMC (former DRMFSS) as part of the GoE’s overall drought monitoring and assessment framework. While LEAP data constitutes a key component of Ethiopia’s early warning framework, at present, the data is not made available publicly.

As detailed in section IV.1.2, LIAS data have been collected on a bi-annual basis as part of the seasonal needs assessment process. LIAS data are collected during the months of November for the _meher_ season, and in June for the _belg_ season, and have been collected and held within the LIU at DRMFSS. Additionally to DRMFSS, LIAS data have been held within DP institutions, NGOs and private sector companies.

> 53 At present, LEAP can produce WRSI extended to the end of the season based on climatology. A further enhancement of the tool could consist in the integration of probabilities of end of season results based on seasonal forecasts. By extending the LEAP tool in this manner, it would be possible to show higher risks of a poor end of the season, and hence a greater need to act earlier.

20
|**Table 2: Percentage of villages wi**|**th at least one**<br>|**NREGS proje**<br>**Phase 1**<br>|**ct completed**<br>|**by year and**<br>**P**<br>|**phase**<br>**hase 2**<br>|**Phase 3**<br>|
|---|---|---|---|---|---|---|
||**2006**|**2007**|**2008**|**2007**|**2008**|**2008**|
|Irrigation|0.24|0.60|0.76|0.13|0.46|0.01|
|Land and soil conservation|0.18|0.61|0.75|0.15|0.37|0.00|
|Road|0.04|0.12|0.20|0.00|0.00|0.00|
|Other|0.17|0.54|0.62|0.00|0.02|0.00|
|Any project|0.32|0.79|0.88|0.25|0.55|0.01|

_Source:_ Authors’ computation from administrative data.

17
**<u>Table 2: Fraud Victimization Differences</u>**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Difference bet|ween credit con|strained and un-c|onstrained|urban HHs|
|=1 if Fraud victim dummy|All urban|Urban|Urban|Diff.|t-stat.|
||HHs|constrained|un-constrained|||
|Approached urban HHs|0.057|0.079|0.053|0.026|5.240<sup>***</sup>|

Note: This table describes differences in the probability of fraud victimization after being approached by fraudsters. We focus on urban households with and without credit constraints. We report the probability of being a fraud victim after being approached by fraudsters for all urban households, urban households with credit constraints, urban households without credit constraints and the difference in columns (1) to (4). The last column reports the t statistics for t-test.<sup>*</sup> _p_ < 0.1,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

33
# Appendix Table A1: Distribution of Reported Earnings Before and After Childbirth

|||Event Tim<br>|e<br>|
|---|---|---|---|
||_−_12|_−_4|37_−_42|
|**Firm size 1-50**||||
|Minimum wage|14.2%|0.8%|6.4%|
|Skilled minimum wage|31.7%|1.7%|32.8%|
|Twice current minimum wage|3.3%<br>|26.7%<br>|4.8%<br>|
|Twice minimum wage of event time _−_4|1.7%|(26.7%)|12.6%|
|**Firm size 51+**||||
|Minimum wage|1.3%|0.0%|1.1%|
|Skilled minimum wage|6.3%|0.0%|4.9%|
|Twice current minimum wage|6.3%|11.3%|6.3%|
|Twice minimum wage of event time _−_4|3.8%|(11.3%)|5.5%|
|**Public sector workers**||||
|Minimum wage|0.0%|0.0%|0.0%|
|Skilled minimum wage|0.0%|0.0%|1.9%|
|Twice current minimum wage|18.5%|6.2%|11.6%|
|Twice minimum wage of event time _−_4|12.3%|(6.2%)|1.3%|

_Note:_ Table shows probabilities of reported wages at 95% to 105% of the minimum wage, skilled minimum wage, twice the minimum wage, and twice the minimum wage of event time _−_ 4. The sample is women employed in the private sector (top two panels) or the public sector (bottom panel) who gave birth between age 20 and 40 and were employed between twelve and four months before giving birth and at least in one month 37-42 months after childbirth, and received a top-percentile wage increase between four to nine months before giving birth. Firm size categories refer to 12 months before childbirth. (Number of individuals: top panel: 120; middle panel: 80; bottom panel: 65.) 32
Table **11.5**

# Growth of Firm Size in Four Sectors

|Employment Size<br>at Start-Up|Employment S<br>149<br>_50-99_|ize in**1991**<br>100-199|200**+**|**TO!al**|
|---|---|---|---|---|
|Woven Textiles|||||
|1-49|_5_<br>6|4|_5_|20|
|50-99|4<br>1|0|0|**_5_**|
|100-199|2<br>4|4|1|11|
|200 +|1<br>0|1|2|4|
|Total|12<br>11|9|8|40|
|Auto Parts|||||
|149|3<br>5|2|5|15|
|50-99|1<br>1|1|0|3|
|100-199|1<br>0|0|1|2|
|200 +|0<br>0|0|0|0|
|**Total**|5<br>6|3|6|20|
|Electronic Parts|||||
|1-49|4<br>5|_5_|1|15|
|50-99|0<br>0|4|0|4|
|100-199|0<br>1|0|0|1|
|200 +|0<br>0|0|0|0|
|Total|4<br>6|9|1|20|
|Factory Automation|||||
|1-49|17<br>10|4|1|32|
|_50-99_|0<br>1|2|2|5|
|100-199|0<br>0|0|0|0|
|200+|**0**<br>**0**|**0**|**0**|**0**|
|Total|17<br>11|6|3|37|

49
are key in the detection of the main causes of child mortality in resource-poor settings, and used this data to construct illness spells and estimate demand for formal health care during the spell. Building on WHO guidelines for CHWs, we can derive when parents would be instructed to seek a formal medical evaluation by a doctor for their child, based on the duration and type of symptoms.<sup>2</sup> The guidelines we apply are used by public health systems and NGOs in over 80 countries, including our partner NGO Mali Health, who based their CHW training on them. Given that the optimal solution to the social planner’s problem of health care timing cannot be directly observed, their embedded assessment of when a child should seek formal care provide a better understanding of the effect of the interventions on the allocation of care. First, they deliver a ranking of symptoms by severity from a medical perspective, and therefore of relative value of care: a policymaker who wants to improve population health should aim to increase care seeking on days when care is required according to these guidelines over other days, all else equal. Second, they allow us to measure whether the CHW actually improve adherence to the guidelines they apply and teach (see also Section 3 for a discussion).

We organize our empirical results with the help of a framework that treats care seeking as a stopping problem, where parents decide to seek care on any day of a given illness spell conditional on spell length and symptoms. This captures the intuition that a set of initial symptoms will become more concerning as the illness persists or new symptoms arise. The value of waiting instead of seeking care immediately lies in the possibility of avoiding a facility visit altogether. We treat the WHO guidelines as the preferences of the policy maker. The policy maker may disagree with the parent at what point care should be sought, e.g. because of a different cost-benefit trade-off or a better understanding of the illness.

> 2We used a set of symptoms as well as care-seeking guidelines derived from the Integrated Management of Childhood Illness (IMCI) guidelines for community healthworkers. The IMCI was developed by Unicef and the World Health Organization. The version adapted for the use by community healthworkers is called C-IMCI, see WHO (2014); WHO Department of Child and Adolescent Health and Development (2005); UNICEF Health Section (1999); Rosales and Weinhauer (2003) and Arifeen et al. (2009). The guidelines are quite intuitive – for example, they prescribe immediate care if symptoms indicate a risk of malaria, but treat coughing or diarrhea as harmless unless they last unusually long or cause complications that point to pneumonia or dysentery (see section 3).

4
competitors three years before the survey. We therefore focus on the results using the 2005 data only; by using lagged values available in 2005 we can somewhat mitigate the endogeneity problem as applied to the market structure variables, but not others such as elasticity of demand and various pressures.

We use the 4-component index of deep restructuring; the results are similar using other measures. The regressions are reported separately for all groups of countries, including the pre-2001 EU members Germany and the cohesion countries. The explanatory variables include measures of ownership (state-owned and privatized; the omitted category is new private), size (log employment), location (big city dummy), number of competitors (omitted category is 1-3 competitors), price elasticity of demand (omitted category is highly price elastic), pressure to innovate from domestic competitors, foreign competitors, customers, and sector and country dummies. The results are shown in Table VI.3.

First, as should be expected, larger firms are more likely to have engaged in more deep restructuring measures than smaller firms. The impact of size is found in all country groups, including the developed market economies, and the magnitude of the impact is about the same in all. The ownership variables show that in TEs, not surprisingly, stateowned firms are less active than new private firms; privatized firms are less active than new private firms as well, but if anything more active than state-owned firms. The differences are not, however, huge: the coefficient on the SOE dummy ranges from about -0.07 to about -0.16. The index is an average of 4 measures and takes the values of 0 to 1. Although SOEs in TEs have engaged in less deep restructuring than private firms, they are not simply “dinosaurs”. The absence of any measurable ownership effects in Germany and the cohesion countries is due simply to the very small number of privatized and state-owned firms in the samples for these countries.

The most interesting results relate to the role of competition. First, we find no significant impact of market structure on restructuring. As suggested by Carlin, Schaffer and Seabright (2004), this null result may stem from the endogeneity of market structure.

40
markets. It takes a value of 1 if the kernelly estimated fatalities are greater than the median number of fatalities due to violent conflict near both households around the nearest market.

# **4.4. Fractionalization**

Conflict is another variable which, if not treated appropriately in a statistical regression, can lead to biases due to simultaneous causality. Conflict, for obvious reasons, can lead to lower investment levels, lower incomes, and lower welfare in general. At the same time, lack of economic opportunities and poor institutions (e.g. rule of law can) can also lead some to join rebel or insurgent militias.<sup>17</sup> This implies that conflict is likely to occur in poorer areas, and is also likely to depress these areas further, resulting in twoway causality. It is therefore essential to address these biases when estimating conflict’s effect on economic variables.

Social fractionalization, which measures religious, ethnic, and linguistic diversity within a country or region, can be used as a valid instrument for conflict. Earlier studies have found ethnolinguistic fractionalization to be a strong determinant of the probability and duration of conflict (Wegenast and Basedau 2014; Esteban and Ray 2012; Esteban and Schneider 2008; Schneider and Wiesehomeier 2008; Montalvo and Reynal-Querol 2005; Reynal-Querol 2002; Gurr 2000; Collier and Hoeffler 1998; Horowitz 1985). The validity of the instrument is based on the observed correlation between higher levels of fractionalization and levels of conflict.<sup>18</sup> Our fractionalization variable is likely to satisfy the exclusion restriction, that is, causality could not run from welfare to fractionalization, as the fractionalization variable is generated using demographic data published in 2001 (details of the methodology used to generate this variable are provided below) while the income measures we use are from 2006 and 2007. Income measures in these years cannot affect predetermined, and hence exogenous, ethnic fractionalization.

Typically, fractionalization indices are calculated using nation-wide census data. However, given that this is an intra-country study, using one index for the entire country would not be useful. Instead, we calculate micro-level ethnic fractionalization; i.e. fractionalization within a 50km radius of households and markets. The distance of 50kms was chosen because that is the same radius used as the bandwidth for the conflict kernels, so we measure conflict and fractionalization within the same area. The 50km radius around each household is used as an instrument for conflict around each household, and the 50km radius around each market is used as an instrument for conflict around markets.

> 17 The causes of conflict are not always cut and dry, however. While those who create conflict may originate from areas with low economic opportunities, often conflict arises around areas with wealth, as there are more opportunities for theft.

> 18 Using a similar argument, Mauro (1995) uses ethnolinguistic fractionalization to instrument for institutional quality, when estimating its effect on GDP growth.

15
<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>0.55 0.53<br>20% 0.40 0.42 0.39<br>10% 0.26 0.20 0.23<br>0%<br>Disability Survivor Unemployment Social care Ndhima Energy Health Education<br>Pension Pension Benefit services Ekonomike transfers<br>elderly/disabled<br>Poorest 2 3 4 Richest<br><!-- End of picture text --> Source: Own estimates using the Albania HBS 2015.

**Spending on education is progressive and equalizing, although quality and efficiency are not accounted for in the analysis** . In particular, preschool and basic education are the most progressive education spending in Albania and also have the largest redistributive role, with a reduction in the Gini coefficient of 0.013 points (Figure 18 and 19). In fact, as found in other papers, the concentration coefficient of preschool/basic education is negative, suggesting that a larger spending share goes to the lower deciles, given that is disproportionately concentrated among the poor. Figure 17 above shows that overall education spending reaches people across the income distribution relatively equally, capturing the widespread use of public education services in Albania. As mentioned, these results do not capture heterogeneity in quality of services. The PISA results for 2015 show, for example, that a large share of 15-year-olds in Albania are considered functionally illiterate (50 percent with a score below 2 in reading), one of the highest among countries who take the test.<sup>41</sup> Moreover, within Albania there is a gap in PISA scores between urban and rural areas equivalent to, in some subjects, more than one year of schooling.

**Similarly, health spending is progressive and equalizing** . Health spending is more or less distributed across the population in absolute terms: About 20 percent of health spending goes to those at the bottom quintile of the distribution, while 15 percent is concentrated among the top (Figure 17). These differences may reflect high-income households opting out of the public system, Health spending contributes to reducing inequality by around one Gini point as it is slightly more concentrated among those at the bottom of the distribution, and its size is not insignificant.

> 41 World Bank (2016).

27
# Table IV-1 (zont'd)

|Country.<br>Title of Project|Funder, Status<br>of Activity|Project Smmary|Use Private<br>Distribution<br>System|Employee<br>eefit|Curacteristics<br> <br>Contrat<br>with Privat*<br>providera|of Projct<br>Training|Evaluation<br>CoM|
|---|---|---|---|---|---|---|---|
|0'wm<br>**WIT/Dln**|irSm-<br>**smvis**|(cnt'd)||||||
|Kore:<br>Kaus HoTire<br>Industrial<br>Compny|Kau Ho Tire<br>Industrial<br>Cony.<br>1972|Co pny Mngemmnt ad the union<br>aopted<br>nd pro oted f_ily<br>plAnnn<br>_and_mle It part of the<br>persomAl magemennt policy.<br>Compay oriented new employees<br>d educated existing employees<br>in the benefits<br>mid specifics<br>of<br>fmily pluming.<br>Incentivesare<br>inthe fom of priorities<br>for<br>house sdloas,<br>pd<br>holiday<br>after**a**vasctomy.<br>DLtacentives<br>include covered obstetric<br>care<br>through the third child. proso-<br>tioa restriction<br>for employee<br>with large famlies<br>and those<br>with sor tha 4 children sat<br>leave the firm.<br>Condoems pills.<br>nd sterllsation<br>offered.|No|**yes**|No|ND|lb|
|Sri Lanka:<br>Industrial<br>Sector Programs|Family Plannin<br>Asswlatloo<br>of<br>Sri Loam, 1972|UfO semasra witb FPASL urging<br>convinced labor unlons**Lo**press<br>for family plaraing**as a**bone<br>fit.<br>Thirty popalation educators<br>**va**<br>trained with paying salaries<br>for the 30 days. Eacb motivator<br>was given 4 days/mth<br>to educate<br>thelr fellO<br>employee.<br>Instituk<br>tional qestlomalrea<br>and follor-<br>up<br>otivtlon,al<br>activities<br>for<br>employees are provided.<br>Sterili-<br>at'lon_was_offered with Rs. 50<br>3 (vas-.:toay) or 10 (tubectomy)<br>days paid leave as an incentive<br>whether the employee or spouse<br>werE, scerilIJ.?-.|No|**YI"**|**Yes.**gover-<br>ment_fnAWd_<br>FFASL<br>provided<br>**ervlces**|**Ye,**for<br>motivators|No|