rafmacalaba/datause-encoder-data huggingface dataset:
their illness.<sup>25</sup> Given these patterns, we conclude that a soft-skills channel alone is unlikely to account for the impacts we observe.

# **5 Conclusion**

Consistent with research in other low-income settings, we document significant gaps in the quality of malaria care at Malian medical clinics. These gaps are especially striking given that easy-to-administer, low-cost rapid diagnostic tests (RDTs) for malaria are readily available.

We present descriptive and experimental evidence that providers’ beliefs about the quality of RDTs are part of the problem. Before the intervention, health workers were too pessimistic regarding the tests’ accuracy. We evaluate the impact of an information intervention that provided information to health providers about RDTs’ accuracy using data from quality assurance testing and field research in a Malian setting. In response, providers positively updated their beliefs about RDT accuracy.

Importantly, beliefs translated into diagnostic and treatment outcomes. First, we find changes in testing behavior that indicate providers were more comfortable relying on RDTs to establish a malaria diagnosis. Second, we find evidence that this led to improved quality – of care post-consultation, the match between underlying malaria status and prescribed treatment improved significantly. We estimate that the share of patients receiving the “right” malaria treatment (an antimalarial if malaria positive, no antimalarial if malaria negative) increased by 7-13 percentage points at treated clinics, relative to the 41-44 percent rate of correct treatment in the comparison group.

Despite these benefits, our results also highlight important risks of health information interventions, associated with the incorrect (and persistent) beliefs of _patients_ . First, even though the extended provider training improved patient knowledge, demonstrated by their increased ability to predict the outcome of their home malaria RDT, the training reduced patient satisfaction by 0.09 standard deviation units, with evidence that this effect was concentrated among patients who had incorrect pre-consultation beliefs about their underlying malaria status. Second, an intensive, one-on-one patient-centered information intervention designed to increase demand for correct diagnosis of malaria (via malaria tests) had few effects and, if anything, backfired: patient information reduced use of malaria tests by 6 percentage points (11 percent) and had no impact on prescription outcomes. Both these findings suggest that patients may have hard-to-move priors about their cause of illness and/or persistently distrust diagnostic tests. While our study cannot speak to long-term effects, there is a risk that dissatisfied patients could reduce their demand for clinic services,

> 25Poorer soft skills could, however, exacerbate the dissatisfaction patients felt when not prescribed what they wanted/expected.

21
We first examine the scale of foreign bank operations in each of the 80 countries. A bank is considered foreign, if at least 50 percent of its shares is foreign-owned. As measures of foreign bank penetration, we consider the importance of foreign banks in terms of numbers.and in terms of assets.

The data also allow us to consider how foreign banks differ from domestic banks in terms of interest margins, taxes paid, overhead expenses, loan loss provisioning, and profitability. This work extends the work on the accounting decomposition of interest margins by Hanson and Rocha (1986) and, more recently, Demirgii9-Kunt and Huizinga (1997) to look at foreign and domestic banks separately. While foreign banks have lower interest margins, overhead expenses, and profitability than domestic banks in developed countries (consistent with Terrell's (1997) findings), the opposite is true in developing countries. This suggests that the reasons for foreign entry, as well as the competitive and regulatory. conditions found abroad, differ significantly between developed and developing countries.

Next, we present some empirical results relating the share of foreign banks to the levels of the interest margins, profitability and overhead expenses (all relative to assets) of domestic banks. The equations, when estimated in first differences, similarly suggest that an increased foreign bank share reduces domestic bank profitability and overhead expenses. Foreign bank entry also reduces domestic banks' net interest margins (defined as net interest income divided by total assets), but this effect is not significant. Taken together, this evidence suggests that domestic banks become less profitable, because they lose market share to foreign entrants. In this scenario, domestic banking customers gain because their movement to foreign banks implies a revealed preference. Overall, the economy gains from the lower resource-cost of the domestic banking sector.

Interestingly, we observe the above results when we define the foreign bank share is in terms of numbers rather than asset size. The entry of foreign banks obviously precedes their gaining market share. Our results thus suggest that the benefits of foreign bank entry accrue rather early in time, before foreign banks have gained substantial market share.

The remainder of this paper is organized as follows. Section 3 describes the basic approach of the study. Section 3 discusses the data. Section 4 presents the empirical results. Section 5 concludes.

# 2. · _The approach_

Below, we present information from the income statements of domestic and foreign banks. As a measure of bank efficiency, we consider the accounting value of a bank's net interest income over total assets, or _net margin/ta._ To reflect bank profitability, we consider the bank's before-tax profits over total assets, or _before tax profits/ta._ From the bank's income statement, _before tax profits/ta_ satisfies the following accounting identity: 3
The weakness of current evaluation practice can be illustrated no better than by this critical assessment, found in the Implementation Completion Report of a recent World Bank project in the area of export promotion: _Although the design of the M&E system was appropriate, both Bank and Government project teams had difficulty measuring the achievements of the project using the broad indicators cited in the PAD._<sup>_46_</sup> _[B]y current standards, they were insufficient and incomplete._ _[...] M&E, particularly important as a learning objective, was weak. It was slow to start and did not deliver. The M&E staff [...] lacked the capacity and experience to carry out the monitoring activities, and the Unit was unable to carry out baseline and impact surveys of randomly selected farmers in both project and non-project areas, i.e., survey to gauge key interest groups’ response to the outputs generated by the pilot activities. The M&E Unit’s ability to collaborate with other implementing agencies to collect information and data was also ineffective. Implementing partners did not regard the M&E exercise as a learning process but instead, conducted their promotion activities without consulting or collaborating with the M&E unit._ As the reviewers noted, the learning function of evaluation tends to be overshadowed by the ―monitoring‖ function for implementing agencies.

In order to overcome these hurdles, several avenues must be considered. First, the burden imposed on project managers should be relieved by making impact evaluation a separate exercise carried out by specialists, albeit in collaboration with project managers. Project managers should be involved at the right time — that is, during project design and from then on, as much as possible, left in peace. The World Bank has moved in this direction through the creation of the DIME unit, which provides expertise and help with IE financing.

At the same time, governments in the countries receiving trade assistance must buy into the process. This means sharing knowledge and building capacities for a proper interpretation of IE results and, over the long run, for governments to build their own IE capabilities as part of public-services delivery improvements.

Also, every effort should be made to reduce the cost of IEs. For small-scale activities, the cost of an IE can be as great as that of the activity itself. This is excessive. Local resources — in particular universities and graduate students — should be involved, producing a double benefit: costs are reduced and local capacities are strengthened.

Finally, the exploitation of IE results should prioritize learning over monitoring. That is, donors and implementing agencies should tread cautiously in using IE results to frame incentive systems. Care is needed in the interpretation of IE results because premature conclusions could

> 46 M&E stands for Monitoring and Evaluation while PAD stands for Project Appraisal Document.

37
- 8 -

but also quality of roads has been low. About 40 percent of roads in Nepal remain to be paved (Figure 2).

**Figure 2. Road density in South Asia**

<!-- Start of picture text --> 180 166<br>160 148 Road densityPaved road density<br>140<br>120<br>120 112<br>100<br>80<br>60 53<br>40 34<br>20 16 22 1711 12 7 6 2<br>0<br>Km per 100 km2 of land area<br>Bangladesh Sri Lanka India Pakistan Bhutan Nepal Afghanistan<br><!-- End of picture text --> Note: Figures are the latest available data in 2002-2007. Source: World Development Indicators.

In Nepal there are several important rural road reconstruction and rehabilitation projects assisted by multi- and bi-lateral donors. While the World Bank has been assisting the Rural Access Improvement and Decentralization Project (RAIDP) in 20 districts mainly in the Terai area, other donors, such as Asian Development Bank and UK Department for International Development (DfID), have been supporting other 45 districts under the Rural Reconstruction and Rehabilitation Sector Development Project. The remaining 10 districts are planned to be covered by scaling up the RAIDP.

In the RAIDP, the 20 districts were selected according to the predetermined performance evaluation framework composed of a wide range of criteria, such as project preparedness, existing infrastructure gap and poverty profile. The framework aimed at creating institutional incentives to compete for resources among districts. The funding allocation was also designed on a performance basis. However, the original performance criteria were found unsuitable, given the prevailing conflict unstable political conditions, and low implementation capabilities at the district level. Therefore, the criteria were somewhat simplified, focusing on financial and physical progress of the works carried out.

Three entities share responsibility for the rural road development and maintenance in Nepal: the Ministry of Local Development (MOLD), the Department of Local Infrastructure and
7 incomes. The selection of explanatory variables is restricted to those variables that can also be found in the census (or some other large dataset) or in a tertiary dataset that can be linked to both the census and survey. The parameter estimates are then applied to the census data, expenditures are predicted, and poverty (and other welfare) statistics are derived. The key assumption is that the models estimated from the survey data apply to census observations.

Let _W_ be a welfare indicator based on the distribution of a household level variable of interest, _yh_ . Using a detailed household survey sample, we estimate the joint distribution of _yh_ and observed correlates _xh_ . By restricting the explanatory variables to those that also occur at the household level in the population census, parameter estimates from this “first stage” model can be used to generate the distribution of _yh_ for any target population in the census conditional on its observed characteristics and, in turn, the conditional distribution of _W_ . Elbers et al (2002, 2003) study the precision of the resulting estimates of _W_ and demonstrate that prediction errors will fall (or at least not rise) with the number of households in the target population, and will also be affected by the properties of the first stage models, in particular the precision of parameter estimates. A general rule of thumb is that welfare estimates obtained on this basis will be estimated fairly precisely as long as the target population comprises at least 1,000-5,000 households.

The first-stage estimation is carried out using household survey data.<sup>7</sup> The empirical models of household consumption allow for an intra-cluster correlation in the disturbances (see Elbers, Lanjouw and Lanjouw, 2002, 2003, Elbers, Lanjouw and Leite, 2008, and Demombynes et al, 2007, for more details). Failing to take account of spatial correlation in the disturbances would result in underestimated standard errors in the final poverty estimates (Tarozzi and Deaton, 2009). Different models are estimated for each region and the specifications include census mean variables and other aggregate level variables in order to capture latent cluster-level effects. All regressions are estimated with household weights and with parsimonious specifications to be cautious about overfitting. Heteroskedasticity is also modeled in the household-specific part of the residual.

> 7 These surveys are stratified at the region or state level, as well as for rural and urban areas. Within each region there are further levels of stratification, and also clustering. At the final level, a small number of households (a cluster) are randomly selected from a census enumeration area.
deaths affected 644 panel households (in the sense that a member of the household suffered the loss of an adult living with them), on which we have full baseline and follow-up data, constituting nearly one quarter of the total sample of panel households.

# **Methodology and empirical specification**

Using the sample of respondents interviewed in 2004, the objective in this study is to evaluate the impact of adult mortality on consumption for surviving respondents, focusing on the growth in consumption between the baseline and follow-up survey. We use as the unit of observation the surviving panel household _i_ , defined as a household that includes at least one survey respondent who was interviewed in the baseline. The treatment of interest is the adult mortality shock _D._ The relationship of interest used in the evaluation can be written as the following general specification: Consumption (C) is measured in per capita terms at baseline, (period _0,_ which is the first KHDS 1991-1994 interview and for most households refers to an interview in late 1991 or early 1992) and in 2004 (period _1_ ).<sup>3</sup> Since consumption growth may be characterised by substantial heterogeneity among households, vector _X_ is a set of control variables, such as household characteristics of the respondents in the panel households, measured at baseline, including age, sex, wealth, education and other initial conditions. Death (D) captures the death of a previous

> 3 Consumption per capita is the annual per capita Tanzania shilling value of total household consumption, including actual consumer expenditures on food and non-food items as well as the valuation of foods grown by farm households or received as gifts. The consumption values are in terms of average price level of the KDHS villages from January-June 2004. The average exchange rate during this period was 1,107 Tanzania Shillings to $1 USD. We compute baseline per capita consumption based on the first interview of the 1991-1994 panel as the questionnaire in this round is consistent with the 2004 questionnaire. For the second-fourth rounds of 1991-1994 the consumption modules are not identical. Specifically, the recall periods of several major subcomponents of the consumption aggregate refer to the 6-month intervals between survey rounds rather than a 12-month recall.

12
# **Appendix D2: Changes in Averages and Decomposition of Effects, OLS**

||Change in<br>the obtained<br>coefficient<br>(2009-10)<br>DEVMENA|Change in<br>the obtained<br>coefficient<br>(2009-10)<br>Arab Spring|Change in<br>the averages<br>(2009-10)<br>DEV<br>MENA|Change in<br>the averages<br>(2009-10)<br>Arab Spring|Developing<br>MENAA<br>first-order<br>effect|Developing<br>MENA<br>second-order<br>effect|Arab Spring<br>countries<br>first-order<br>effect|Arab Spring<br>counties (4)<br>second -order<br>effect|
|---|---|---|---|---|---|---|---|---|
|Dissatisfaction with|0,064|0,072|0,028|0,091|-0,033|0,024|-0,106|0,023|
|Standards of living|||||||||
|People cannot get ahead by<br>working hard (Yes)|-0,033|-0,027|-0,012|0,004|0,003|-0,006|-0,001|-0,003|
|Dissatisfaction with efforts<br>of the government to<br>increase high quality jobs|-0,010|-0,024|0,033|0,070|-0,005|-0,007|-0,011|-0,015|
|Dissatisfaction with freedom|0,050|0,265|0,038|0,052|-0,001|0,019|0,006|0,083|
|to choose life|||||||||
|Dissatisfaction with|-0,006|0,014|-0,015|0,076|0,002|-0,002|-0,012|0,005|
|Educational system/schools|||||||||
|Corruption widespread<br>within government/business|0,043|-0,317|0,016|0,111|-0,001|0,033|-0,027|-0,215|
|(Yes)<br>Unemployed|0,258|0,339|0,001|0,030|0,000|0,017|-0,009|0,008|
|Working for the government|0,298|0,542|0,038|0,034|0,013|0,019|0,020|0,049|
|Income (1,000’s)|0,001|-0,011|-0,600|-1,306|-0,014|0,010|-0,025|-0,094|

40
<!-- Start of picture text --> Study countrycode = Type of intervention ES (85% Cl) Outcome_variable<br>High Income - Basic<br>(Bitler et al 2074) USA (HS) - Subsidized Preschool 0.07 (40.07,0.09) = T, PPVT Score (8 yo.)<br>(Bitler et al 2074) USA (HS) - Subsidized Preschool 0.04 (40.04,0.12) 9 7, PPVT Score (7 ye.)<br>(Blair and Raver 2014) USA TT& Coaching & EF Curriculum 0.70 (40.00, 0.20) = T, Vocabulary (6-7 yoo.)<br>(Kline and Walters 2018) USA, (HS) - Subsidized Preschool O04 (40085, 0.14) 0 7, PPVT and WII Tests score (6-7 yoo.)<br>Low & Middle Income - Basic<br>(Brinkman et al. 2017} IDM Preschool Construction & TT & Community Outreach O05 (40085,0.18) 9 7, Language and cognitive development (ED) (8-9 y.0.}<br>(Martinez et al. 2017) Moz Preschool Construction & TT & Parental Engagement —[——— ts 1.43 (0.14, 0.72) T, Language and cognitive development (EDI) (6 y.o.)<br>(Ozter et al. 2018) MW TT& Coaching 0.03 (40.14,0.21) 71, PPVT(T yo)<br>(Ozer at al. 2018) hl TT& Coaching & Teacher Stipend 0.10 (40.08,0.30) T2, PPVT(7? yo.)<br>(Ozter et al. 2018) Na TT& Coaching & Parental Engagement 0.70 (4008, 0.30) 73, PPVT(? yo)<br>MOTE: Weights are from random effects analysis<br>718 0 719<br><!-- End of picture text -->
Page 49

# **Table V.2 Per Household Upgrading Costs**

|**Per Household Cost**|**2**|**011/12 Prices**|
|---|---|---|
|Management Structure||90|
|Project Preparation % of Civil Cost|1|470|
|Project management % of Civil Cost|2.5|1,175|
|Survey, Household Data||7,200|
|Community Consultation & Design|||
|Civil Works||47,000|
|TOTAL||55,935|
||Approx.|**56,000**|

Source: Authors’ calculations

# **Impact on the Budget of the AMC**

# **Table V. 3 AMC Budget 2008-09 through 2011-2012**

||||all figure|s in Rupees Thousands|
|---|---|---|---|---|
||**2008-09**|**2009-10**|**2010-11**|**2011-12**|
||FINAL|FINAL|REVISED|APPROVED BY AMC|
|**Total Capital Income**|**8,762,959**<br>|**10,365,515**|**17,762,152**|**28,607,000**|
|**Total Capital Expenditure**|**10,311,889**|**12,950,453**|**17,762,152**|**28,607,000**|
|**Total Revenue Income**|**15,541,668.00**|**16,169,373.00**|**22,353,514.00**|**31,975,350**|
|**Total Revenue Expenditure**|**9,104,426.00**|**11,559,960.00**<br>|**13,494,597**|**17,903,008**|
|**Total INCOME(CAPITAL+REVENUE)**<br>|**24,304,627**|**26,534,888**|**40,115,666**|**60,582,350**|
|**Total EXPENDITURE(CAPITAL+REVENUE)**|**19,416,315**|**24,510,413**|**31,256,749**|**46,510,008**|

Source: Published budgets of the AMC and authors’ calculations The Average Municipal Budget of Ahmedabad for Capital Expenditures has been around Rs. 1300 crores in the last 3 years (2008-2011). This provides an indication of their current capacity to undertake large infrastructure improvement projects.<sup>30</sup> The capital budget in 2011-12 is more than twice the average, reflecting very rapid capital budget growth over the last three years. The annualized project cost of the five year program is Rs. 603 crores which represents 21 percent of the 2011-12 capital budget, which seems a clearly manageable figure. The corresponding figures for the three year program are Rs. 895 crores and 31 percent respectively. There are clear advantages to setting an ambitious goal of completing the program in three years, but this could be a stretch for the AMC. It is up to them to assess whether this represents too much of a stretch for a rapidly growing budget. The five year implementation option appears quite manageable. As for the operations and management budget, experience from the SNP suggests that, since newly connected households pay into the network whereas they did not before, the program should not be a drain on the operating budget. This should be assessed in more detail.

> 30 *This does not include capital expenditure towards the Sabarmati Riverfront Development Project, since it is set up as an SPV and is funded through a loan from HUDCO. However, the AMC budget does include expenses for BRTS project related infrastructure improvements and bus procurements. The BRTS is operated by Ahmedabad Janmarg Limited which is also an SPV
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