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# **IV. Validation of PSI against Ground-Based Data on Outages**

## **A. Validation in Maharashtra**

To evaluate the reliability and consistency of our new measure, we also compare PSI against officially reported power system reliability data from Maharashtra’s largest power utility, Maharashtra State Electricity Distribution Co. Ltd, or MahaDiscom. The company provides information on its website on the reliability of its feeder network. The most widely used measure of reliability is SAIFI, or System Average Interruption Frequency Index, which describes the average number of interruptions experienced by a customer over a prescribed period. MahaDiscom reports SAIFI for “circles,” which overlap closely with districts in each year over time. However, MahaDiscom’s SAIFI reports are not a perfect measure of outages because MahaDiscom does **_not_** include planned load shedding in their measure, only unplanned outages. Nevertheless, we would expect districts with higher SAIFI levels to be associated with higher PSI.

MahaDiscom provides power reliability indices at the circle level by month from 2009 to 2013. Circles are the largest geographic unit MahaDiscom uses, which roughly correspond to districts. Because PSI is calculated annually, annual averages of the reliability indices were calculated. In the few cases where multiple circles are contained in a district, their values were averaged. Because PSI is calculated for rural villages only, urban circles were omitted from the analysis. There are 33 district-circle matches, and five years of data. There are no data from MahaDiscom for two districts in 2010. The total _N_ for the regressions is thus 163.

We focus on the System Average Interruption Frequency Index (SAIFI), which is the average number of sustained interruptions a customer would experience in a given period of time. It is (total number of customer interruptions)/(total number of customers). It does not include planned outages (e.g., load shedding), or outages due to natural disasters.

While we expect SAIFI to correlate positively with PSI despite the fact that it does not encompass all outages, there are other factors associated with light output patterns. For example, agricultural electricity usage is less likely to result in light output than residential or industrial use. To control for these confounders, we gather district estimates from the 2011 Indian Census on: logged population; logged area; % illiterate; % work in agriculture; % forested; % population rural; and % area rural. We use these because they might impact actual electricity use (e.g., poverty, as approximated by the literacy rate and proportion of people working in 13
# _Reviewing thc results_

This chapter begun by saying that there is something of a consensus over certain key relationships. But it has been argued here that cross-country regressions are not the best way to carry out the analysis - rather a country-specific modelling approach is required. Does the consensus stand up in the face of the evidence to date from such studies? The answer to this question is "not particularly" - here a range of the factors that need be considered are listed.

# (a) Growth

In Chapter 2 skepticism was expressed over the possibility of adequately explaining growth using cross-country analysis. Country-specific economic models may also be poor predictors of growth, unless exogenous factors are both included in the model and their time paths accurately fbrecast. Nonetheless, such models may still indicate whether certain policies are "good for growth" (bearing in mind the possibility that changing external conditions may require different policies). The model developed at the OECD to analyze the impact of adjustment on poverty (discussed in Chapter 6) has tcnded to find a positive impact of adjustment on growth (compared to a base run of the types of external shock typical to developing countries in the 1980s) - though purely contractionary policies (which may be best characterized as stabilization) do not always have such a favorable impact.

# (b Investment

Investment ratios have undoubtedly fallen in the 1980s (hence the before-after approach finds adjustment to be bad for investment). It is worth remembering that this fall was from a historical peak in investment ratios reached by most countries around 1980 - this peak was fuelled by the excess international liquidity of the 1970s and was not necessarily very productive. Nonetheless, the apparent failure of adjustment policies to stimulate private investment has rightly been a cause for concern and led to a renewed interest in private investnent in developing countries.

The conclusion that can be drawn from this literature is that the determinants of private investment are many, and the actual driving fbrce likely to vary from country to country. Some main debates are: crowding in versus crowding out: some argue that the falls in government investment associated with adjustment and/or stabilization are a main cause of declining private 78
27 In the countries where the research took place, gender was not an important determinant of absenteeism, so responses usually did not differ by gender. The survey did not explore whether students skipped classes and left early, as was often found to be in a 2004 study of rural schools in Bangladesh (Chapter 2).

It would be desirable to estimate the amount of learning time available after accounting for student absenteeism, but the latter is not a subset of teacher absenteeism. Students may go to school and find teachers absent, or they may be absent on days that teachers are present. Observations also suggest that two types of students exist: those who are up to date with the classroom material and attend regularly and those who know too little to keep up and attend sporadically. Without more detailed data, the amount of total learning time given student given absenteeism could not be calculated.

# **HOW MUCH OF THE CLASSROOM TIME WAS USED FOR LEARNING ACTIVITIES?**

The Stallings Classroom Snapshot, a well-known U.S. instrument, was adapted for international use. The system helps record and classify activities as interactive (instruction, discussion, questions), passive (copying and seatwork), and management (various activities peripheral to instruction). It also records a rough percentage of students who appear to be off-task (Table 3-3). Classrooms engaging students interactively are more efficient and may be more likely to foster memory consolidation than classrooms where students spend more time on non-instructional activities or are off-task.

**Table 3-3.  Percentage of Time Spent in Instructional Tasks**

|Instructional Strategy|U.S Classroom|Pernabuco|Ghana|Morocco|Tunisia|
|---|---|---|---|---|---|
|% of time|Criteria|(Brazil)||||
|Interactive instruction|50% or more|**52.4**|**59.9**|**62.8**|**61.2**|
|Oral Reading||6.7|8.7|15.7|15.3|
|Teaching, Explanation||32.8|19.9|26.7|27.9|
|Discussion||6.3|24.1|6.6|6.2|
|Practice Drill||1.4|6.5|12.3|11.3|
|Passive instruction|35% or less|**19.6**|**10.3**|**19.9**|**25.6**|
|Seatwork||16.3|7.4|14.8|22.9|
|Copy||3.0|2.9|5.0|2.7|
|Total Instructional Time||**72.1**|**70.2**|**82.6**|**86.7**|
|Organizing/Management|15% or less|27.9|28.0|17.8|13.3|
|Student Off Task Rate|6% or less|**19.3**|**21.1**|**9.2**|**9.9**|

Observations showed that the most important block of time lost tended to be inside the classroom. Ghanaian teachers spent 70.2% of the time engaging students in learning, while Tunisian teachers spent 86.7% of the time. Overall the largest chunk of time is spent on instruction, either lecturing or explanations. This ranged from nearly 33% in Brazil to 20% in Ghana.

Teachers in all four countries addressed the entire classroom 70-90% of the time (Figure 3- 4). They mainly just talked and used the blackboard (“chalk and talk”), while students wrote
our sample. In Korea, 36.8% of establishments are managed by women, while only 13.5% of establishments have FTM in Senegal and 12.8% in Ethiopia. Although Chile has a relatively higher income, only 14.1% of Chilean firms are managed by women. Nevertheless, the share of FTM firms is positively correlated with GDP per capita at the country level. This suggests that as country grows there are more female participation in management of businesses.

Table 1: Percent of firms with FTM by country

||Percent of frms<br>with FTMs (%)|Observations|
|---|---|---|
|Korea, Rep.|36.84|1,520|
|Brazil|27.65|711|
|Viet Nam|25.77|1,499|
|Georgia|22.72|1,743|
|Poland|21.20|1,494|
|Kenya|16.54|1,297|
|Ghana|16.12|1,259|
|Chile|14.17|1,052|
|Burkina Faso|14.07|600|
|Senegal|13.47|1,785|
|Ethiopia|12.87|1,475|
|Total|19.44|14,435|

Note: This table shows the percents of firms with FTMs across countries. Shares are weighted by sampling weights. Number of observations are unweighted. Data from the FAT surveys in 11 countries including Brazil, Burkina Faso, Chile, Ethiopia, Georgia, Ghana, Kenya, Korea, Rep., Poland, Senegal, and Viet Nam. FTM = Female Top Manager.

There is a large sectoral heterogeneity in the firms having female top managers. Table 2 presents the percents of the firms with FTM across sectors. The results show that while only 2.4% of establishments in Motor Vehicles have FTM, the share increases substantially in other sectors such as Food processing (33.4%), Health services (32.6%), and Leather and footwear (54%). That is, women are concentrated in some specific sectors for some reasons such as self-selection or some entry barriers including access to finance, education, management, etc., which are examined in later sections.

6
Table I.3. Heterogeneous Effects by Level of Education

||Prosociality<br>Index<br>(1)|Attitudes<br>Index<br>(2)|Beliefs<br>Index<br>(3)|
|---|---|---|---|
|Game [=1]|0.244**|0.343***|0.130|
||(0.104)|(0.097)|(0.102)|
|Game x Higher Education [=1]|0.026|-0.017|0.058|
||(0.164)|(0.169)|(0.168)|
|Video [=1]|0.226*|0.304***|0.017|
||(0.115)|(0.105)|(0.110)|
|Video x Higher Education [=1]|-0.063|0.075|-0.003|
||(0.183)|(0.180)|(0.180)|
|Higher Education Completed [=1]|0.152|-0.017|0.066|
||(0.134)|(0.137)|(0.130)|
|P-value: Game + Game x Higher Ed. = 0|0.033|0.019|0.159|
|FDR q-value: Game + Game x Higher Ed. = 0|0.061|0.061|0.136|
|P-value: Video + Video x Higher Ed. = 0|0.253|0.010|0.925|
|FDR q-value: Video + Video x Higher Ed. = 0|0.179|0.061|0.436|
|Av. Dep. Var. (Control group)|0.000|0.000|0.000|
|Observations|827|826|826|

_Notes:_ This table presents OLS estimates derived from Equation 1 with interactions between the treatment exposure indicator variables and the indicator variable for high level of education, denoted as higher education completed [=1]. Higher Education Completed is an indicator variable equal to 1 if the respondent has completed education beyond high school, including technical, university, or higher studies. The dependent variables are constructed following the methodology outlined by (Kling, Liebman and Katz, 2007) for each group of outcome variables: (i) Prosocial Behavior (detailed in Table 2), (ii) Attitudes (detailed in Table 4), and (iii) Beliefs (detailed in Table 5). All columns control for gender and student status. Robust standard errors are reported in parentheses. The reported p-values, such as Game + Game x Higher Ed. = 0 and Video + Video x Higher Ed. = 0, represent the p-value of a Wald test for each linear combination. Additionally, the False Discovery Rate (FDR) q-values for Game + Game x Higher Ed. = 0 and Video + Video x Higher Ed. = 0 are provided. Significance levels are denoted as<sup>_∗∗∗_</sup> at 1%,<sup>_∗∗_</sup> at 5%, and<sup>_∗_</sup> at 10%.

61
# _An Explorative Analysis of Trends and Determinants_

Infant and child mortality levels in Sub-Saharan Africa are the highest in the world. In the median African country, more than 15 of 100 children die before their fifth birthday (Jameson et al., 2006). This compares to less than 25 out of 1,000 in the richer parts of the world. Not only are U5M levels very high; in addition, progress in reducing child mortality is very slow. Hence, Sub-Saharan Africa as a whole is seriously off track in terms of reaching MDG4. The latest report on MDG achievements by the United Nations (2008) puts U5M rate estimates for 1990, 2000, and 2006 at 184, 167, and 157 out of 1,000, respectively, for SubSaharan Africa.

Within the African continent however there is quite some variation in child mortality. This also holds for our sample of countries. As can be seen in Figure 3, U5M rates in our sample that covers the late 1990s to the early 2000s vary between 100 and 250.<sup>5</sup> In both the initial and the final year, the Sahel countries of Niger and Mali exhibit the highest U5M rates, initially considerably higher than 200. Only conflict or post-conflict countries, such as Angola, Sierra Leone, or the Democratic Republic of Congo, have similar or higher rates. In addition to the Sahel countries, Guinea and, despite quite a considerable reduction in recent years, Mozambique, stand out with U5M levels of around 150 in the final year.

> 5 We have produced our own estimates of child and infant mortality for each country/year and in some instances these figures differ slightly from those that a query on the DHS-website (http://www.measuredhs.com/) gives. The years when the Demographic and Health Surveys (DHS), which we use for our all subsequent statistics and analyses, were conducted are given in Table 1 in the Appendix. The shortest time span is covered by the Kenyan surveys between 1998 and 2003 with 5 years; in all other cases the time that had passed between the first and the second survey lies between 6 and 8 years.

17
massamba, 2010). Given the importance the government places on decentralized extension services and the lack of rigorous evidence to date, formally documenting the impact of this policy action seems warranted (Gautam, 2000).<sup>1</sup> In what follows, we describe the details of the status quo extension model operating at baseline in our study area.

# 2.2 Study Area

We worked in ve districts of central Mozambique: Mutarara (Tete Province), Maríngue and Chemba (Sofala Province), and Mopeia and Morrumbala (Zambézia Province; Figure 1). This area receives nancing from a large World BankGovernment of Mozambique investment to support the development of the extension network (Smallholder project ). The project provides three levels of agricultural technical assistance: each district has a facilitator, an environmental specialist, and eight EAs. A district is subdivided into four administrative posts (posto administrativo) that include about 810 communities (aldeia). EAs periodically receive training from the district specialists.<sup>2</sup> Each community has a designated contact farmer (CF) who receives direct assistance from the two EAs placed in his administrative post.<sup>3,4</sup> CFs receive visits from EAs monthly. They were instated to respond to other farmers' demands for technical assistance and provide advice through demonstration activities.

A CF model of extension may not foster learning and adoption among CFs. EAs are typically challenged to reach the communities they serve. Designating CFs may therefore not adequately address the supply-side constraints of extension services. Another concern is that information may get diluted from the central level to CFs. For instance, EAs may not cover all techniques, suciently train the CFs, nor adhere to the expected format. Since CFs do not know what curriculum their EA should follow, accountability may be low. Finally, periodic visits from the EA may not be sucient

> 1Recent work has employed a quasi-experimental design to evaluate the impact of extension and found a positive impact of extension on farm income in Mozambique (Cunguara and Moder, 2011)

> 2In October 2010 and November 2012, these trainings were dedicated to SLM.

> 3The ratio of EAs per administrative post in our study area is on par with the 2013 national average of 1.89 (Gêmo and Chilonda, 2013). This ratio is calculated using the 2010 gures from the Direçåo Nacional de Extenså Agraria (DNEA), available at the following URL: http://www.worldwide-extension.org/africa/mozambique/s-mozambique.

> 4EAs can choose which CFs to work with, and do not necessarily split responsibilities. Hence, a given CF may interact with both EAs in his administrative post. CFs are typically chosen by the community. In 2010, CFs had been in their position for three years on average, with a standard deviation of 3. This indicates the majority of CFs were already commissioned by the project prior to our intervention.

6
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