rafmacalaba/data-use-mentions-extended huggingface dataset:
A Review of Efficacy and Effectiveness of Nutrition Interventions”. Asian Development Bank, Nutrition and Development Series # 5, Manila.

- Arellano, M. and S. Bond. (1991).  Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. _The Review of Economic Studies_ 58: 277-297.

- Attanasio O. and A.M. Vera-Hernandez (2004), “Medium and Long Run Effects of Nutrition and Child Care: Evaluation of a Community Nursery Programme in Rural Colombia”, unpublished paper

- Baltagi B.H. (2001), “Econometric Analysis of Panel Data”, Chichester ; New York : John Wiley, 2001.

14
DHS). We combine these with an extensive array of data on the geo-location of road expansions (Jedwab and Storeygard, 2021) and electric grids in 27 African countries.

The high costs and potentially large benefits of infrastructure investments mean that the placement of electrification projects and new roads typically correlates with both economic and political characteristics of locations (Blimpo et al., 2013; Burgess and Donaldson, 2017; Okoye et al., 2019; Selod and Soumahoro, 2019). To address the potential endogeneity of infrastructure investment, we use two identification strategies: a baseline regression strategy and an instrumental variable (IV) approach. In the baseline model, we control for country and year fixed effects and a wide range of individual and local factors. In the IV model, we instrument electrification with topography data (Dinkelman, 2011) and the density of lightning strikes, which are a key source of damage to electricity lines and a source of disruption in service, particularly in Africa (Manacorda and Tesei, 2020).

We propose a new instrumental variable for the proximity to the road network, inspired by postindependence leaders’ idea of constructing roads to govern the entire expanse of their countries from the capital city - the center of political power - and to promote ethnic and regional integration and nation building. One of the main goals of African leaders as their countries’ gained independence was to extend the exercise of their authority over all locations. In the wake of the arbitrary partition of Africa over 1884 and 1885, African leaders also sought to integrate different ethnic groups and regions into their countries (Herbst, 2014; Deng, 1997). One way to achieve both policy goals was to construct roads to both 2
market distortions, the reform process can be combined with increases in public investment to address private sector underinvestment. Our findings are also a warning to policy makers that cutting public investment may undermine private investment.

The rest of the paper proceeds as follows. Section 2 presents a simple theoretical model linking public investment to private investment. In section 3, we discuss the empirical strategy used to quantify the crowding-in effect and present summary statistics, stylized facts of our investment data and instrumental variable, and. Section 4 presents the main results. Section 5 presents sensitivity, validity, and robustness exercises. In section 6 we

> 6 Boehm (2020) provided evidence of crowding-out of private investment in OECD countries.

> 7 raises the marginal product of private investment and hence, the level of private investment. Real world examples of such complementary public investment include: the construction of a highway linking rural and urban centers, which increases productivity of private capital; safe public water delivery, which can induce higher private investment in sanitation; and reliable irrigation, which can improve private agricultural activities.

4
## Summary: Interpretation of results

In sum, the SDI surveys from Kenya, Sierra Leone and Tanzania identify some strengths of the existing health system along with some areas for improvement. For example, the potential repurposing of lowerlevel facilities and available resources for inpatient care during the current pandemic can inform resource allocation strategies at national and sub-national levels. Assessing the availability of a variety of resources, from number of medical personnel to availability of medical supplies, can help prevent and preemptively address potential challenges such as facility overcrowding, health care worker safety, burnout and psychological well-being, and supply chain management for essential medicines and medical supplies. Likewise, while the importance of handwashing for infection prevention is made salient across the globe and citizens are urged to repeatedly wash their hands throughout the day, a concerningly large proportion of health facilities across these three countries lack appropriate handwashing facilities for patients. These humbling statistics compel policy makers to reevaluate priorities for health system strengthening, during and beyond the pandemic period.

While SDI survey data fill some important gaps in the evidence base, the available data do not paint a comprehensive picture of preparedness and some critical chasms persist. For instance, as has been noted previously, a binary indicator for availability of personal protective equipment is a useful starting point for assessing preparedness, but not knowing the stockpile of supplies limits the scope of targeted, evidencebased action. Furthermore, these surveys were not designed for collecting comprehensive information on pandemic preparedness, and as a result, information on key aspects of preparedness is missing. As described in previous sections and illustrated in Appendix Table 1, SDI indicators could be mapped to only seven of 18 items identified in the WHO guidelines for health services pandemic preparedness planning. For example, SDI data lack any information on management of excess mortality, an emerging challenge even for high-resource health systems in this current pandemic. Collectively, the strengths and the limitations of the existing large-scale health facility surveys in assessing pandemic preparedness signal the pressing need to reflect on the future of health service delivery measurement,
# **1. Introduction**

The number of ministers appointed to the cabinet is a frequent concern in many developing countries, especially in Sub-Saharan Africa. In 2017, Ghana’s President, Nana Akufo-Addo, appointed the largest cabinet in a quarter of a century, prompting the opposition to condemn his “elephant size” government (Reuters 2017). Conversely, when in May 2019 South Africa’s President Cyril Ramaphosa cut the number of cabinet ministers from 36 to 28, many observers interpreted this as an attempt to tackle government corruption (Reuters 2019). Beyond these anecdotes, is there a systematic relationship between the number of ministers in the cabinet, or cabinet size, and governance outcomes? We focus on Sub-Saharan Africa, where public and media concerns about an excessive number of ministers are common, and where prior scholarship links cabinet appointments with corruption and poor governance.

Existing theoretical and empirical work documents a negative relationship between cabinet size and fiscal performance, due to the “common pool resource” problem in budgeting. In this literature, more ministers are associated with higher spending and larger deficits. Empirical work covers mostly industrialized democracies (Perotti and Kontopoulos 2002, Volkerink and de Haan 2001) but also wider set of countries including some emerging market economies and developing countries (Wehner 2010, Woo 2003). A study by LeVan and Assenov (2016) looks at SubSaharan African countries and finds that more ministries are associated with higher expenditure but not with deficits. With some exceptions discussed in the following section, outcomes beyond fiscal policy are less frequently considered. Here, we take a step towards addressing this gap.

To do so, we commence with an overview of the link between cabinet size and governance, drawing on the literature concerned with elite clientelism and related work on African politics. We then present new data on cabinet size in Sub-Saharan African countries, building on prior work by Arriola (2009), and assess their association with a series of governance measures related to corruption and the quality of public policy.
the pandemic, and 81 percent only partially.<sup>29</sup> In Indonesia, the increased reliance on government assistance among the bottom 40 (reaching 80 percent in November) may have helped families avoid resorting to the more destructive coping mechanisms. The share of households cutting down on food consumption declined from nearly 70 percent in June to less than 50 percent in November.<sup>30</sup> Yet, other losses -such as those of human capital- may be harder to rebuild.

As the economies in the region recover, unprecedented levels of fiscal stimulus will need to unwind. An informed assessment of which groups have been most affected and the extent to which government actions have mitigated these effects will be critical. Social protection responses originally designed to be of limited duration were often extended as employment and income shocks continued. Going forward, countries will need to consider how to support an inclusive recovery in the face of mounting fiscal pressure.

Finally, the roll-out of the vaccines will play a critical role in achieving a sustained recovery from the pandemic. Countries that have vaccinated their population more rapidly tend to be those with stronger recovery capacity (World Bank 2021c).<sup>31</sup> While vaccination levels are ramping up quickly in many EAP countries such as China, Cambodia, and Malaysia, they remain low in the poorest countries, including Myanmar and PNG, as well as some of the largest, such as Indonesia, Philippines, and Vietnam. Recent data from the region suggests that acceptance is relatively high in most countries, but that timely access to sizeable stocks is proving difficult. In addition, even when countries do manage to procure the needed vaccines, reaching poor and underserved populations may be challenging due to inadequacy in the distribution infrastructure. In addition, there is a risk that poorer households could opt out disproportionately if they are required to pay for vaccinations.

> 29 - <u>Results of Mongolia COVID 19 Household Response Phone Survey (Round 3)
are not correlated with the opening of line B. Furthermore, I also estimate lower bounds of the effect using retrospective questions, and in the quantitative framework, I consider this channel by allowing migration within the city. I use both jobs’ and workers’ informality rates as dependent variables.

I test first for changes in informality in terms of the locations in which the workers live. I use data from the Population Censuses and estimate the following specification relating the transit shock to the change in the ratio between formal and informal workers: where _Lis_ is the number of individuals that live in census-tract _i_ and sector _s_ , T _i_ is one of four different treatment variables: log distance in meters, log distance in walking minutes using the network of roads, a dummy variable indicating whether the closest station is within the 10th percentile of the Euclidean distance, and a dummy variable whether the closest station is within 25 minutes, _δs_ ( _i_ ) are state or municipality fixed effects,<sup>20</sup> and _Xi_ is a vector of census-tract characteristics that include distance controls such as: the area in square kilometers, distance to other stations of public transit, a central business district dummy variable, and some productivity measure in the baseline year in which I include value added per worker and the number of firms to capture how good is the location in terms of jobs. This equation relates the transit shock to the log of the ratio between formal and informal workers.<sup>21</sup> I estimate equation 3.2 for the pool of workers and for different groups based on skills.<sup>22</sup> Table 1 reports the results for different specifications of equation 3.2, while Figure A3 in the Online Appendix depicts the three-point estimates of my preferred specification for the pool of all workers, low-skilled workers, and high-skilled workers, respectively. Overall, the results imply that locations close to the new subway line experienced a decrease in workers’ informality rates. In particular
the energy demand function can be inverted to proxy for unobserved productivity. _ωit_ = _h_<sup>_−_1</sup> ( _kit, lit, mit, χit, eit_ ) is the inverse demand function for energy.

Under the Ackerberg et al. (2006) method, no parameter is estimated in the first stage. The first stage is used to get a consistent estimate of expected output ( _φ_<sup>�</sup> _it_ ( _._ )).

where In addition to the firm variables in _h_<sup>_−_1</sup> ( _._ ), I also include year dummies to account for macro trends that could impact input demand. I use a full fifth degree polynomial in capital, labor, material inputs, labor and energy to get an estimate of _φ_<sup>�</sup> _it_ . Once _φ_<sup>�</sup> _it_ ( _._ ) is estimated, for any candidate candidate values of the coefficients, productivity can be written as For the candidate values of the coefficients, the productivity series can be used to estimate the

> 14Ackerberg et al. (2006) argue that the labor parameter cannot be consistently identified in the Olley and Pakes (1996) and Levinsohn and Petrin (2003) method.

> 15See Hasan et al. (2007) for a discussion on labor market regulations and rigidities in India.

> 16The exact ordering of the timing assumptions of when labor and material inputs are chosen does not impact the empirical estimation. The only assumption needed for the empirical estimation is that energy is chosen after observing other inputs and productivity.

14
an important factor – supply of agricultural credit. Though not a control for the universe of inputs, credit holds a special status as it is closely tied to the usage of more direct inputs like seeds, fertilizers, machinery, etc. Moreover, multiple studies have found that agricultural credit has enabled the use of purchased inputs and has affected the input mix in India (Bhalla and Singh, 2010; Narayanan, 2016).<sup>3</sup> Thus, productivity growth might be lagging in some districts due to relatively lower credit allocation.

Using the credit data, I ask whether the supply of credit in the districts forming the lagging clubs has declined during 2012–17 relative to the districts in the leading clubs using a difference-in-difference approach. I do not find any evidence of the credit supply shrinking in the former relative to the latter. Credit expansion – both in absolute value and share, is higher in lagging rice clubs relative to the most productive club. The test also does not lend support to the credit hypothesis for the other two crops, and its presence can be reasonably rejected. The finding does not imply that productivity is unresponsive to credit, especially given the evidence that it encourages the use of inputs. Instead, in line with Das _et al._ (2009) – who find a positive but variable impact across states, the analysis here suggests that a similar phenomenon holds at the district level in Bihar. Credit might be more efficient in some districts and future research should focus on explaining the heterogenous impact as it has crucial policy implications.

This paper is most closely related to papers that study agricultural productivity convergence in the context of developing economies. For instance, McErlean and Wu (2003) and Wu (1995) test for regional convergence in agricultural productivity in China. Martin and Mitra (2001) consider cross-country

> 3 Yet, the evidence on the credit-growth relationship is varied. For instance, Binswanger and Khandkher (1995) and Subbarao (2012) estimate a positive effect,
those that report lack of money as the main barrier to account use, government policies to promote inclusion can increase the likelihood that individuals perceive financial services as being within their reach.

The rest of the paper is organized as follows. Section 2 introduces the survey data and summarizes our main variables of interest. Section 3 details the empirical approach we use to test the underpinnings of the use of accounts and the factors correlated with perceived barriers to account use. Section 4 presents the empirical results. Section 5 concludes.

# **2. Measuring Financial Inclusion**

# **_2.1 Survey Methodology_**

The data were collected by adding a new module on financial inclusion to the 2011 Gallup World Poll (GWP) survey, which has been conducted annually since 2005. Our module included questions on ownership of an individual or joint account, the use of the account for saving, and the frequency with which the account is used. Additional questions asked the unbanked for reasons why they do not use an account. In our analysis, we focus on 123 countries and over 124,000 individuals. We drop data for 25 countries because of missing demographic information, such as education and income. Table 1 lists all the countries included in our sample.

The survey was conducted in the major languages of each country.<sup>6</sup> The 2011 GWP surveyed at least 1,000 individuals per country in 148 economies—representing approximately 97 percent of the world’s population—using randomly selected, nationally representative samples.<sup>7</sup> The target population was the entire civilian, noninstitutionalized population aged 15

> 6 Detailed country-level information about the data collection dates, sample sizes, excluded populations, and margins of error can be found at http://go.worldbank.org/IGRTPHK660. 7 In some economies, oversamples are collected in major cities or areas of special interest. Additionally, in some large economies, such as China and Russia, sample sizes of
# **Who Benefits Most from Rural Electrification? Evidence in India**

## **1. Introduction**

The goal of rural electrification programs in developing countries goes beyond providing rural households affordable modern energy at a cheaper price than inferior alternatives over the long run. Rural electrification is expected to improve rural people’s quality of life and spur growth on a range of socioeconomic fronts. Various examples can be given to substantiate such expectations. As a replacement for kerosene-based lighting sources, electric lighting substantially reduces indoor air pollution and carbon emissions. In addition, it allows school-going children to read during evening hours, thus encouraging more hours of study. Furthermore, it benefits income-generation activities through business operations being able to stay open longer and promoting productive uses. The large body of literature on the benefits of rural electrification claims that rural electrification greatly contributes to the welfare growth of rural households (e.g., ADB 2010; Barnes, Peskin, and Fitzgerald 2003; Cockburn 2005; Khandker 1996; Martins 2005; World Bank 2008). But most of these findings are based only on the correlation between rural electrification and development, without taking any selection or program-placement biases into account. Some recent studies, however, have attempted to ascertain the welfare gains caused by rural electrification (e.g., Dinkelman 2008; Khandker, Barnes, and Samad, forthcoming).

To provide further impetus on the welfare gains of electricity, this paper analyzes the impact of electrification on a wider range of household outcomes in rural India and determines who benefits most from rural electrification. With its long history of rural electrification programs, diverse population, and geographic spread, India presents an ideal case for this study, which has benefitted from a large, nationally representative data set. We apply an instrumental variable (IV) method in a fixed-effects (FE) framework to obtain unbiased estimates of the impacts of rural 2
owning a phone, but whose number does not match to any number in the CDR provided to us by the single mobile operator.<sup>9</sup> Results in Panels B and C of Table 2 show the performance of each targeting approach on the balanced and full sample, respectively. Note that as described in Section 2.5, different targeting quotas are applied for each panel based on the proportion of each sample that is ultra-poor. In the CDR-based and combined approaches, we report performance when the households without CDR are targeted first (i.e. households without CDR are targeted in a random order and then the households predicted to be poorest are targeted until the quota is reached) as well as when households without CDR are targeted last (i.e., after the 535 households with phones are targeted, households without phones are included in a random order until the quota is reached).

Unsurprisingly, these results suggest that CDR-based targeting is not effective when a large portion of the target population does not own a phone (e.g. Panel C of Table 2, where only 16% of the sample has matching CDR). However, when we simulate more realistic levels of phone ownership in Panel B (84% of the households, based on our survey data), CDR-based targeting is once again comparable to asset- or expenditure-based targeting, particularly when households without phones are targeted first (AUC = 0.72, 0.70, 0.68 for assets, consumption, and CDR, respectively). On the other hand, if households without phones are targeted last (for example, if program administrators base targeting wholly on CDR and provide no benefits to any household without a phone), the CDR-based method only improves marginally on random targeting.<sup>10</sup>

# **3.5 Additional Tests and Simulations**

Our main analysis considers the household head to be the unit of analysis. As described in Section 2.3
|Yes|
|_N_|22764|22864|18378|18378|21451|21547|17201|17201|
|R-squared|0.076|0.969|0.792|0.950|0.076|0.972|0.796|0.953|
|Dep. Variable [mean]|0.029|283.912|0.247|3.626|0.030|273.537|0.240|3.642|

Notes: (1) The table shows the regression results across districts in Africa during 2002–2007. (2) The results show the local economic impacts of China’s WTO accession based on the nature of mining activities in Africa. (3) The standard errors, clustered at the country level, are in brackets;<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. (4) Population density is measured as population per square kilometer. (5) Growth, Gini, and Sen Index are measured using lights data as described in the data section. (6) Fixed effects include time-fixed effects, district-fixed effects, and country-year-fixed effects. (7) Climate is measured using the standard deviation of rainfall distribution at the district level.

Source: Authors’ estimations Table 8: List of countries

|Algeria|Gabon|Mozambique|South Sudan|
|---|---|---|---|
|Angola|Gambia|Namibia|Sudan|
|Benin|Guinea-Bissau|Niger|Tanzania|
|Botswana|Kenya|Nigeria|Togo|
|Cameroon|Liberia|Republic of Congo|Tunisia|
|Chad|Madagascar|Rwanda|Uganda|
|Côte d'Ivoire|Malawi|Senegal|Zambia|
|Democratic Republic of the Congo|Mali|Sierra Leone|Zimbabwe
our sample households were private connections to the piped water network, private wells, and water from neighbors. Households indicated that their dwellings are close to the piped water network. A typical household can access at least three sources, with approximately 54 percent of households claiming that they could get their water from a private piped connection if they wanted. Typically, a household uses just one source of water. Only about 4 percent of the sample relies exclusively on a combination of community sources—including neighbors, public taps, and public wells.

> 7    Our consumption survey module focused on major items and not all the micro details covered in the HIES. We made three adjustments to our consumption data—calibrated our food expenditure, imputed our housing expenditure and adjusted our ‘other expenditure’ category—allowing us to define poverty in a manner that was comparable to the Sri Lankan government’s official statistics.

12