rafmacalaba/datause-encoder-data huggingface dataset:
an explicit rationale for including Cost Efficiency analysis and just under half explicitly reported the limitations of the Costing exercise. Our analysis suggests the results of available Cost Efficiency analyses from WB impact evaluations should be used carefully. Such analyses often quantify a portion of the program’s benefits, and frequently suffer from missing information and missing cost data.

In sum, our analysis reveals the need to elevate reporting transparency and the need to develop standards of reporting so that the available evidence can accumulate and be used to improve the use of resources in development cooperation. In addition, our analysis suggests the need to examine the root causes underscoring the wide variation in cost-efficiency reporting across evaluators. It is difficult to know why VFM analysis is so infrequent in the absence of further inputs from IE producers and consumers.

# **QUESTION 2: WHAT ARE THE EXISTING INCENTIVES AND BARRIERS FACED BY PRODUCERS AND USERS, BOTH AS INDIVIDUALS AND INSTITUTIONS, FOR VFM INCORPORATION?**

The descriptive analytic findings on the state of VFM in IE are symptoms of underlying problems faced in their production, dissemination, and use. We found three primary reasons for the underproduction of VFM in IE—organized around three principal actors: impact evaluators, policymakers, and institutions.

**Finding 1:** The expected payoffs to producing efficiency analyses is dulled by the (inaccurate) perception that policymaker demand for VFM is low when it is more accurately described as uncertain.

As shown in Table 3.1, our sample of IE producers most frequently cite a lack of policymaker demand as the reason for little VFM observed in IEs. IE-producing interviewees supported this argument with three main points: first, interviewees questioned the cost data’s relevance to a policymaker’s individual contextual considerations. Second, many interviewees felt that the political calculus, rather than a project’s investment case, dominates a policymaker’s decision-making. This view of political economy dynamics undercuts incentives to generate efficiency analysis and often detrimentally influences the analysis itself. Third, IE and VFM producers felt uncertain about what policymakers need and want to know and when they need to know it; furthermore, producers felt unsure about how to go about gaining that insight.

**Table 3.1 Why is VFM so Infrequently Incorporated into Impact Evaluations?**

|**_Main reasons_**|**_IE_**<br>**_Evaluator_**|**_IE_**<br>**_Funder_**|**_CE/CBA_**<br>**_Methodologist_**|**_Policy-_**<br>**_maker_**|**_Sum_**|
|---|---|---|---|---|---|
|Policymakers do not demand VFM analysis|6|3|3|3|15|
|There is little incentive for academic impact<br>evaluators to produce VFM|4|3|3|3|14|
|Impact evaluators lack consensus on how to<br>apply CEA and CBA methods; there is no<br>agreement on the"right"approach|6|4|||10|

15
_"Bena Nakayima has buriedfour of her children, deadfrom AIDS, beneath the bananas next to her packed, mud homestead. ... Two more of her children are buried elsewhere... In her 70s and a widow, [she] has come to be the caretaker for 35 grandchildren orphaned by her children 's deaths. At an age when she expected to be "laughing with my children ", she is instead searching for ways to feed her grandchildren. "'_

# **I. Introduction**

High HIV infection rates in Sub-Saharan Africa are producing dramatic increases in the mortality of prime-aged adults. As of the end of 1999, an estimated 24.5 million Africans were living with HIV/AIDS, accounting for more than 70% of all global infections (UNAIDS 2000). In Tanzania an estimated 1.3 million people out of a total population of nearly 33 million were believed to be infected with HIV and 140,000 had already died of AIDS. The estimated infection rate among prime-aged adults (aged 15-49) was 8.1%, or about one in every 12 adults. In neighboring Uganda, a study of Masaka District found that when HIV prevalence reached this level, two-thirds of all adult mortality was due to AIDS (Nunn et al 1997). The probability of death for a 15 year old in Masaka before reaching 60 had already reached 61%, while in the absence of HIV/AIDS it would have been only 24% (Boerma et al 1998).

The high mortality of prime-aged adults due to AIDS has left in its wake orphaned children and elderly in households with fewer breadwinners to support them (Bamett and Blaikie 1992; Hunter and Williamson 1998; National Research Council 1996; World Bank 2000). African couples have large families in part because of the role of adult children in supporting their parents in old age. However, as a result of the AIDS epidemic, the elderly are often the caregivers for their adult children stricken with HIlV/AIDS, the guardians of orphaned grandchildren, and substitute workers for ill or deceased adults in the home and on the farm. In Uganda, nearly a third of AIDS patients were cared for by their parents in 1995 (Ntozi and Nakayiwa 1995). In Northwestern Tanzania, more than a quarter of sick household members are cared for by those 50-65 (Beegle 1998). In Thailand, elderly parents were the main source of finance for medical care for 60% of the AIDS patients who lived with their parents while ill, for _40%_ of the AIDS patients who lived adjacent to their parents and for about 25% of the AIDS patients whose parents lived elsewhere (Knodel et al 2000).2 Up to now, however, most of what is known about the impact of AIDS on the elderly is anecdotal, based on case studies of those who are highly affected and without comparison to a control group. Are these impacts typical? Which elderly are most seriously affected? What policies can be most cost-effective in helping them?

' Drusilla Menaker, "Elderly are left to raise orphans: Disease's toll stretches their limited means", _Dallas Morning News,_ December 28, 1999.

2 This is not the pattem observed in the US, however, where only 6% of the caregivers for AIDS patients in the US were 60 or older, according to one study (Turner et al 1994).
52 countries in Asia. In addition, many Filipinos immigrated illegally especially to the United States. In fact, illegal immigration from the Philippines is so common that a special term has developed for these immigrants. They are known in the Filipino community and among immigration officers as TNT's, which is an acronym for the Tagalog expression meaning "hide and hide." The U.S. amnesty program for undocumented immigrants has also revealed a substantial influx of Filipinos. Offsetting these flows are an unknown number of Filipinos who emigrated from their countries of destination. Given this information, it would appear that the most recent World Bank figure of -250,000 for 1980-1985 is more supportable than the United Nations figure of -200,000. In the first three years of the 1985-1990 period, 150,596 Filipinos immigrated to the United States. If this level continues for the last two years of the period, the total would be 250,993. Again, this does not include legal immigration to other countries, illegal immigration, or emigration. It appears that in 1985-1990 net emigration from the Philippines will be at least as high as it was in 1980-1985 and it should approach a level of 275,000-300,000. A similar level should be maintained through the rest of the century.

# The Republic of Korea

The main destination of Korean emigrants is the United States. In 1980-1985, 163,088 Koreans immigrated to the United States. For the first three years of the 1985-1990 period, there were 106,878 such immigrants, or 178,130 immigrants for the five-year period if the same level of immigration were to be maintained. Koreans are less likely than Filipinos to move to countries other than the United States and they may be less prone to immigrate illegally as well, but they are also less likely to emigrate from the United States once they move there (Jasso and Rosenzweig, 1982). Therefore, given the same considerations mentioned in the case of the Philippines, I would recommend net emigration of 175,000 for 1980-1985 (halfway between the United Nations and previous World Bank estimates) and a similar figure for each fiveyear period through the end of the century.

# Other Asian Countries

The 1980-1985 levels for many of the remaining Asian countries seem generally reasonable but the rapid decline thereafter is not tenable. Given the arguments presented.in the United States section and the fact that Asia is the principal source of legal immigrants to the United States, it is clear that emigration from most Asian countries will remain stable if not increase for the rest of the century. This is particularly true from the largest sending countries (the Philippines, the Republic of Korea, and India), but also from such locations as Taiwan, Malaysia, and Thailand.

The outflow from Vietnam can be expected to decrease, but not as rapidly as previously proposed by the World Bank, particularly if the Orderly Departure Program to the United States picks up steam. Moreover, there is currently a backlog of 20,000-30,000 Amerasian children and their family members waiting to come to the United States. Arrivals of Amerasians have
# **Introduction**

Inorganic fertilizer use is considered low in Africa and many reasons have been invoked to explain this. These include limited or untimely availability of the input (Carlsson, et al., 2005; World Bank, 2006), imperfect markets (Abrar, Morrissey, and Rayner, 2004), lack of agronomic knowledge (Asfaw and Admassie, 2004), riskiness and credit constraints (Croppenstedt, Demeke and Meschi, 2003) and economies of scale in supply – which have all been invoked to give rise to “market smart subsidies”. While there are signs of an increase in fertilizer use, especially in those countries with subsidy programs (Nigeria, Malawi and Zambia) or other concerted support (Ethiopia), fertilizer use generally remains low (Sheahan and Barrett, 2014; Sommer et al., 2013; Monpellier, 2013; Banful et al., 2010; Liverpool-Tasie and Takeshima, 2013).

Importantly, the notion that fertilizer use is low is predicated on the assumption that it is profitable to use higher rates than is currently the case. However, there is little rigorous empirical evidence to support this notion. While various studies have explored the yield response of fertilizer in crop production, (Adedeji et al., 2014 ;Sommer et al., 2013; Omonona et al, 2012; Offodile, 2010; Akighir and Shabu, 2011; Xu et al., 2009), there are few studies that have actually explored the profitability of fertilizer use. Moreover, most studies on profitability are either outdated or largely based on case study areas; not nationally representative (Wopereis-Pura et al., 2002; Poussin, et al., 2003; Becker and Johnson, 1999; J.L.Adedeji et al., 2014 ; Omonona et al, 2012; Offodile, 2010; Akighir and Shabu, 2011; Dadi, Burton, and Ozanne, 2004).

Examination of the profitability of fertilizer use requires an understanding of 1) fertilizer agronomics, i.e. the yield response, and 2) fertilizer economics (the output/input price ratio as well as quantities and costs of inputs such as seed, chemicals, labor and transportation. This requires detailed information on agricultural practices and input costs. The Nigeria Living Standard Measurement Study-Integrated Survey on Agriculture (LSMS-ISA) data set provides a unique opportunity to explore the profitability of fertilizer use in Nigeria. It is a nationally representative panel data set with detailed agricultural information at the plot level. This makes it possible to specifically address the profitability of fertilizer use in a production function framework.

Furthermore, while various studies have explored the yield response of fertilizer in crop production, very few address the fact that there are likely unobserved characteristics that affect 2
Table 8: Local spillovers on earnings

|(1)<br>Earnings in<br>wage Employment<br>(monthly)|(2)<br>Earnings in<br>self Employment<br>(Profts, monthly)|
|---|---|
|Panel A. Pooled Estimates||
|Treatment (ITT)<br>-1984.77<br>(1734.89)<br>PDS Lasso selected controls<br>Yes|1141.09<br>(3964.52)<br>Yes|
|Department X (Urban/Rural)<br>Yes|Yes|
|Mean in Control<br>6382.68|32513.35|
|Observations<br>1,099|1,102|
|Panel B. Treatment Arm Estimates||
|VSLA (T1) (ITT)<br>-1389.97<br>(2032.09)|1347.10<br>(5791.10)|
|Cash Grant with repayment (T2) (ITT)<br>-2773.07|969.78|
|(1744.84)|(4537.98)|
|Cash Grant (T3) (ITT)<br>-1232.74|1148.59|
|(2437.79)|(6034.99)|
|PDS Lasso selected controls<br>Yes|Yes|
|Department X (Urban/Rural)<br>Yes|Yes|
|Mean in Control<br>6382.68|32513.35|
|p-value T1=T2<br>0.37|0.95|
|p-value T2=T3<br>0.41|0.98|
|p-value T1=T3<br>0.94|0.98|
|p-value T1=T2=T3<br>0.57|1.00|
|Observations<br>1,098|1,102|

Robust standard errors clustered at locality level. Earnings and revenues are in CFA franc and winsorized at 99%.

The specification includes control variables chosen using post double selection (PDS) Lasso based on the set in Table A6.

> _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01
15 Figure 4: kWh of electricity consumed per month by expenditure quintile and location <!-- Start of picture text --> 120<br>Bottom quintile Quintile 2 Quintile 3 Quintile 4 Top quintile<br>100<br>80<br>60<br>40<br>20<br>0<br>Madagascar Mozambique Rwanda São Tomé and Príncipe<br>kWh per month<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data. _Note_ : The survey in São Tomé and Príncipe had many households reporting exceptionally low monthly consumption, including those in the top quintile. They are reported to illustrate difficulties encountered in obtaining information on electricity consumption. Rwandan consumption is calculated from expenditures and the unit price in effect at the time of the survey.

The results show the predictable pattern of increasing consumption with increasing quintile, and significantly higher consumption by the top quintile in the first three countries where the results seem more plausible than in São Tomé and Príncipe or Togo. Rwanda, the only country in which kWh consumed was calculated from reported billed amounts, shows low consumption compared to Madagascar or Mozambique. In addition to seeming confusion about units for electricity consumption, another reason for caution in interpreting the results is the very small sample size in lower quintiles, especially in rural areas, leading to results that are not meaningful. For example, the bottom three quintiles in rural Mozambique had a total of only 11 households reporting kWh consumed. The bottom two quintiles in Rwanda similarly had only 7 and 12 households, respectively, connected to the grid with positive expenditures.

# Spending on Electricity

The share of household expenditures spent on electricity among those with non‐zero spending on electricity provides an indication of how much households are willing to spend, balancing affordability with the perceived value of electricity. The results are summarized in Table 2 for the share of household expenditures that include imputed values of freely acquired items, largely home‐grown food. The corresponding results by quintile are given in Table A.4 in the annex. The median expenditure share is about 3 percent, and not markedly higher among the poor. The findings are consistent with households not spending much more than 5 percent of their total expenditures on electricity. However, there was considerable variation across the countries, ranging from a mere 0.3 percent in Malawi to 9.3 percent in Swaziland. Notable exceptions include the poor in Botswana and Swaziland. Urban households spent a larger share in 13 out of 22 countries.
data on cognitive performance or noncognitive skills, nor sufficient background data that can be used as instruments to estimate a simultaneous equations model, we do not explore this relationship. By controlling for _C_ and _NC_ in the log-earnings equation, however, we assume that the estimated returns to years of schooling (and experience) are purged of the covariation of skills and schooling (and experience), an approach that has been used previously in the literature.<sup>25</sup>

# **_Expanding the measure of gender disadvantage_**

A typical approach used by many of the studies in Table 1 is to estimate the gender difference in earnings by the coefficient _ẟ_ on a dummy variable ( _G_ in equation [3] below), where _G_ signifies whether or not the worker is female. Because the function already controls for the level of human capital which is assumed to determine earnings, a negative sign is indicative of female disadvantage.

The studies reviewed in Table 1 show that in low-income and middle-income countries, on average, women earn 4 to 58 percent less than men do, controlling for a set of covariates that include schooling, skills, work experience, as well as socioeconomic background, demographic characteristics, and location of residence. However, this approach captures only the gender differences in the intercept. It constrains the structural coefficients to be the same for men and women, whereas there are theoretical grounds and considerable empirical evidence that the effects of schooling, cognitive skills and noncognitive skills on earnings are likely to be different for men

> Kuziemko, 2006; Buchmann, diPrete and McDaniel, 2008). In some classroom settings, this disruptive behavior results in boys receiving greater, not less, attention from teachers (and parents) than girls (King and Winthrop, 2015). 25 Estimates   from 24 studies on the U.S., for example, indicate that the return to schooling, holding constant a measure of cognitive skills, is 20 percent smaller than the unconditioned return, implying that part of the return attributed to years of schooling is indeed due to the effect of skills on earnings (Bowles, Gintis and Osborne, 2001).

15
tobacco and two grew improved maize although the Lilongwe Land Development Programme offered free inputs, such as seeds and fertilizer, on credit as well as extension. Chipande argues that female-headed households cultivated smaller plots and had less labor, which meant that they could not easily repay (and therefore did not apply for) credit. Women did grow groundnuts, which are more labor intensive than maize, but the labor requirement for groundnuts is spread relatively evenly over the year, and there are no sharp peaks in labor intensity. For Tanzania, Tibaijuka (1994) simulates reductions in women’s time burdens and finds that this reduction has the potential to increase labor productivity by 15 percent and capital productivity by 44 percent and to increase farm cash incomes by up to 10 percent.

# **_Human Capital_**

Some estimates of male and female differences in productivity show that education is significant. Moock (1976) shows that in Kenya, returns to education in terms of agricultural output are greater for female-headed households than for male-headed households. Alene et al.’s (2008) study of Kenya shows that after controlling for access to land and education, the yield gap between male and female maize farmers disappears.

Education affects agricultural productivity through a number of channels in addition to basic skills. First, education is important for the adoption of improved technology. Evidence from Kenya shows much lower adoption rates of improved seeds and fertilizers for femaleheaded households than for male-headed households. These differences are explained in part by lower education levels (Kumar, 1994; Saito, Mekonnen and Spurling, 1994). Second, education, especially at lower levels, may be important for obtaining access to credit. Using 23
reporting electricity as a major constraint, and they consume more self-generated power. As expected, more firms in low-income countries face electricity constraints as opposed to high-income countries. According to the World Bank’s Enterprise Surveys, an average of 70 percent of firms in low-income countries reports electricity as a major constraint, compared to only 13 percent of firms in high-income countries (Figure 1).

It is important to note that the electricity constraints faced by firms in low-income countries might relate to both the lack of electricity and higher tariffs, whereas they primarily reflect high electricity costs in high-income countries. In addition, the average share of self-generated electricity by firms in low-income countries accounts for 26 percent of the total, while it comprises only 13 percent in high-income countries. Again, these numbers are not directly comparable, as firms might choose to self-generate electricity as part of their production process, not because of a lack of grid electricity. Finally, the IEA data show that electricity consumption is much higher in high-income countries as opposed to low income-countries because of availability of power, high production needs, and affordability for the general population, among other factors. In terms of sectoral consumption, manufacturing and services are more energy intensive than agriculture across all income groups.

Data examination shows that countries experienced much higher growth in electricity consumption compared to the value added in the long term, and these higher growth rates occurred primarily in lowincome countries. Figure 2 depicts the cumulative growth in the real value added and electricity consumption between 1996 and 2016 for three sectors: agriculture, manufacturing, and services.<sup>11</sup> Capturing growth rates instead of levels for the value added and electricity consumption reflects the selected empirical methodology, which uses first differences to estimate the relationship between these two indicators in addition to level regressions. As discussed above, the data set includes a limited number of low-income countries due to the lack of sectoral data for electricity consumption. Despite a large number of observations for advanced economies, the variation in growth of both value added and electricity consumption is primarily driven by other income groups in countries.

> 11 The IEA provides electricity consumption data until 2016, but PW9 data is limited to 2014 and earlier. The study uses electricity consumption data until 2016 whenever possible, but the estimation results rely on PW9 covariates and include data until 2014.

9