rafmacalaba/data-use-mentions-extended huggingface dataset:
Our results are consistent with MNREGA projects improving recharge rates by improving water conservation and/or irrigation infrastructure. Because much of this infrastructure is constructed with unlined bottoms which therefore allows water to percolate from the surface into the groundwater it leads to recharge and raises groundwater levels. It also appears, given the decline in water levels from the beginning of the rabi season to the pre-monsoon measurements, that some of this is drawn down by farmers and used for irrigation.

We therefore turn now to the impacts on farmers’ irrigation choices and direct measurements of soil moisture pre- and post-MNREGA implementation.

# **5.2 Impacts on irrigation**

To test whether farmers take advantage of the increased water stored in aquifers implementation of MNREGA, we examine whether soil moisture increases after implementation of MNREGA, whether farmers increase the area of crops that are irrigated, and what sources are used for irrigation preand post-MNREGA. If the projects that were constructed due to MNREGA raised groundwater levels the reliability of access to groundwater should have been increased. To the extent that the cost of irrigation depends on the height the water needs to be raised, the cost of accessing that water would also have declined. A possible consequence of the increase in reliability and decrease in access costs is a transition in what farmers plant. In particular, access to more reliable and lower cost sources of groundwater typically lead farmers to adopt higher value and more water intensive crops (Hornbeck and Keskin, 2014, 2015).

Starting with soil moisture and using our satellite based measures, we show that soil moisture (measured as the quantity of water in a cubic meter of soil) increases after the implementation of MNREGA. The event studies (Figure 3) for both annual soil moisture and soil moisture during the rabi season show substantial increases in soil moisture after the implementation of MNREGA compared to flat pre-trends in rabi soil moisture. Results in Table 2 indicate that the annual average level of soil moisture increases by 2% after implementation of MNREGA. But the effect in the rabi
number of days out of the past seven on which it was consumed. The FCS is then a weighted sum of these components. Households are classified as having poor food access if they have a poor or borderline FCS, which is defined as less than or equal to 42 (WFP 2009).

Importantly, the 2018/19 NLSS was carefully implemented to ensure the data used in this paper are of high quality. First, the questionnaire was brought in line with international best practices. For example, food consumption was collected through a set of seven-day recall questions. Second, the 2018/19 NLSS made extra effort to allow households to express their food quantities in non-standard units of measurement to help households better estimate consumption. And third, the 2018/19 NLSS was designed and collected using Computer Assisted Personal Interviewing (CAPI) software on tablet devices. This made it possible to conduct real-time data-quality checks to ensure accuracy. Consequently, the data collected in the 2018/19 NLSS were of significantly higher quality than in previous survey rounds.<sup>23</sup> The basic summary statistics for the 2018/19 NLSS are presented in Table 1 and illustrate a number of important patterns about welfare and food access in Nigeria. First, both poverty and poor food access are widespread in the country. At the national poverty line, 40.1 percent of Nigerians live in poverty. Furthermore, approximately 14.9 percent of Nigerians have poor food access according to the FCS. Given that the threshold commonly used to denote poor food access using the FCS is generally used to target emergency humanitarian assistance and has been associated with calorie consumption well below minimum daily energy requirements (e.g., WFP 2012; Mathiassen 2013; etc.), there is probably an even higher share of Nigerians that have poor food access according to recommended guidelines.

Second, the share of households that have poor food access using the FIES is significantly higher than that of the FCS. Approximately 57.3 percent of Nigerians
small area estimates of poverty rates in Burkina Faso using the latest census in the country. These estimates are used as part of the sensitivity analysis in Section 4.

An alternative approach to small area estimation with geospatial covariates is modeling directly the poverty rates using an area-level model (Fay and Herriot, 1979). In this case, both the direct estimates of poverty rates, denoted by , and the geospatial covariates, denoted by r , are aggregated at the target area level. The FH model consists of two stages: the sampling model<sup>rr</sup> rr<sup>d</sup> and the linking model. The combination of both stages results in an area-level linear mixed pp̂ XX model denoted by = r 1 2 ,<sup>rr</sup> where r represents the vector of unweighted averages of the geospatial variables selected for rr<sup>d</sup> rr rr rr pp̂ XX ββ + DD ββ + νν + εε the model at the target area level. represents a set of regional dummy variables, is a random effect specified at the target area level with XX ) and is the sampling error 2 rr 2 rr with ) . The sampling variance DD is estimated under the sampling design and is νν rr vv<sup>2</sup> rr νν ~ NN(0, σσ εε assumed to be known. The variance component of the random effect is estimated by maximum rr εεaa εεaa εε ~ NN(0, σσ σσ likelihood methods (e.g., the adjusted maximum-likelihood of Li and Lahiri, 2010) to guarantee positive variance estimates. The MSE of the estimator under the FH model can be obtained by analytic solutions (e.g. Prasad and Rao, 1990) or by bootstrap techniques (Gonzalez-Manteiga et al., 2007). In this paper the FH model is estimated using the Fayherriot Command in Stata (Halbmeier et al, 2019) with no transformation. The routine works similarly to
Svensson (2007), using data collected from 126 Ugandan firms, show that a 1% increase in the bribery rate implies a reduction of 3% in firm sales growth. Obviously, corruption may amplify the hampering role of credit constraints (see above) when it involves bank officials responsible for screening the entrepreneurial initiatives (see Beck, Demirgüç-Kunt and Maksimovic, 2005).

Finally, the lack of an adequate infrastructural endowment including roads and railways, basic utilities such as electricity and water supply, and ICT networks, is singled out by the literature as a significant shortcoming in preventing young and small firms in DCs from growing (see Aterido, Hallward-Driemeier and Pagés, 2009; Goedhuys and Sleuwaegen, 2010<sup>19</sup> ; Ghani, Kerr, and O’Connell, 2011b).

Having discussed regulatory, institutional and infrastructural conditions, the focus of this study is rather on those microeconomic and personal characteristics that _coeteris paribus_ may have a role in determining the post-entry performance of entrepreneurial new firms; the following section discusses this issue.

# **<u>4.2 Empirical microeconomic studies</u>**

From an empirical perspective, a relatively recent stream of literature has focused on the post-entry performance of firms and has investigated the survival, growth and early exit of newborn firms (among the early studies, see, for instance: Reid, 1991; Boeri and Cramer, 1992; Baldwin and Rafiquzzaman, 1995). Within this field of research, it is possible to analyze the relationship between the _ex-ante_ features of entry on the one hand, and both survival and, conditional on survival, the post-entry performance of newborn firms on the other. The following subsections are devoted to investigating what have been found to be the most important ‘ex-ante’ characteristics affecting the post-entry performance of new businesses.

> 19The authors, using data from the World Bank Investment Climate Survey covering 947 manufacturing SMEs in 11 Sub-Saharan countries, show that firms with their own transport facilities and their own website exhibit
due to security issues. Moreover, conflicts may affect poverty quickly and make existing data outdated and unable to capture the changes. There are also many countries in the MENA region which do not collect HBSs regularly or do not share the microdata with the public. All these issues restrict the ability of governments, academics, and international organizations to monitor welfare in the region and to have informed policy discussions (Demombynes and Sandefur, 2014; Serajuddin et al., 2015).

To our knowledge, there are several papers looking into the issue of availability of household budget surveys. Demombynes and Sandefur (2014) explored the supply of household surveys during 1980-2010 and estimated the costs of filling remaining gaps. Serajuddin et al. (2015) analyzed data deprivations globally, focusing on HBSs and poverty estimates during 1990-2011. Both papers provided very useful overviews of HBS availability, but neither conducted a detailed review in the MENA region nor discussed options to fill the gap using different methods to measure poverty and well-being.

This paper fills the gap by focusing only on the MENA region<sup>2</sup> and explores in detail the availability, access and quality of the HBSs collected during last 15 years (2004-2018). The paper gives an overview of nontraditional methods of data collection in cases when collecting an HBS is not possible. It also offers a comprehensive list of potential options to estimate either monetary poverty or other indicators of wellbeing in the region. Survey-to-survey imputation, multidimensional poverty indexes (MPIs), phone

> 2 The MENA region includes here only 13 developing countries (Algeria, Djibouti, Egypt, the Islamic Republic of Iran, Iraq, Jordan, Lebanon, Libya, Morocco, the Syrian Arab Republic, Tunisia, West Bank and Gaza and Yemen) and excludes rich Gulf countries.

2
<!-- Start of picture text --> 100<br>80 75 73.9 70<br>— 67 68<br>oO 65 64<br>£ 62 61 61 61 61<br>S 58 5g °°<br>= 60 5 54° aS 3<br>E 49 pO<br>-_ 44<br>2 eo o UF S| cd | ie 4 39<br>8 40 3 pa | Mere | coe J 4<br>{0} 30 30<br>a 26 27 26<br>21 23<br>20 16 lea<br>0<br>0 1 2 3 4 5 6 7 817 18-27 28+ Don't All<br>Know Firms<br>Firm age (years)<br>MM ELMPS 2012 inest. MMMM ELMPS 2018 in est.<br>YY) Economic Census Establishment Census<br><!-- End of picture text -->
||Tariffs|Trade-weighted average applied tariff|WEF|
|---|---|---|---|
||Openness<sup>f</sup>|Defined as the sum of export and import over GDP.|WEO|
|Pillar 8: Labor<br>market|CBR labor<br>regulation index<br>Women (% of<br>population)|Calculated as the average of sub indicators pertaining to laws and<br>regulations related to work time.<br>Women (% of population)|Cambridge Center<br>for Business<br>Research database<br>WDI|
|Pillar 9: Financial<br>system|Domestic credit to<br>private sector (% of<br>GDP)<sup>g</sup>|Resources provided to the private sector by financial corporations|WDI|
|Pillar 10: Market<br>Size|GDP_or_volume of<br>imports|GDP_or_volume of imports [dropped as highly correlated with<br>dependent variable]|WDI|
|Pillar 11: Business<br>dynamism|Cost to start a<br>business<sup>h</sup>|Number of days and procedures to start a business|Doing Business<br>database|
|Pillar 12:<br>Innovation<br>capability|Availability of latest<br>technology|The extent to which the latest technology is available in the country.|WEF|

_Note_ : a Results are robust to the use of other variables, incl. quality of roads index (WEF), which reflects the perceived quality of roads in the country; infrastructure (WEF), which reflects the perception of quality of infrastructure in the country (transport, telephony, energy). Also, an infrastructure index is used, built as weighted average of energy and internet use.

b Results are robust to the use of International Internet bandwidth (kb/
.58|1.73|1.89|2.05|2.22|1.88|||||
|High-income countries|1.46|1.34|1.26|1.40|1.62|1.81|1.96|2.25|2.44|2.04|||||
|<br>Developing countries|1.02|0.88|0.86|0.95|0.93|0.99|1.09|1.10|1.11|1.05|||||
|Asia|1.30|1.04|0.98|1.09|1.16|1.29|1.45|1.46|1.42|1.36|||||
|Africa|0.90|0.80|0.78|0.83|0.80|0.80|0.85|0.90|0.97|0.91|||||
|Latin America|1.01|0.92|0.92|1.08|1.06|1.10|1.28|1.16|1.03|0.99|||||

a Unweighted average of national NACs. Source: Authors’ calculations based on updated NRA estimates from Anderson and Valenzuela (2008).
at improving the GQ network, the North-South and East-West (NS-EW) Corridors, Port Connectivity, and other projects in several phases. The total length of national highways planned to be upgraded (i.e., 4 For example, Brown et al. (2008), Ulimwengu et al. (2009), Baum-Snow et al. (2012), Banerjee et al. (2012), Roberts et al. (2012), Baum-Snow and Turner (2013), Faber (2014), Xu and Nakajima (2017), Qin (2017), and Aggarwal (2018).

5 For example, Fernald (1998), Chandra and Thompson (2000), Lahr et al. (2005), Baum-Snow (2007), Michaels (2008), Holl and Viladecans-Marsal (2011), Hsu and Zhang (2014), Duranton and Turner (2012), Fretz and Gorgas (2013), Holl (2013), Duranton et al. (2014), Donaldson and Hornbeck (2016), and Donaldson (2018).

6 The first part of this section is taken from Ghani et al. (2016).

7 Source: National Highway Authority of India website: http://www.nhai.org/. The Committee on Infrastructure continues to project that the growth in demand for road transport in India will be 1.5-2 times faster than that for other modes. Available at: http://www.infrastructure.gov.in. By comparison, highways constitute 5% of the road network in Brazil, Japan, and the United States and 13% in the Republic of Korea and the United Kingdom (World Road Statistics, 2009).

4
Points)|
|Proposed roads in Karnali province|Karnali provincial government<br>(via RAP)|Aug 2019|Vector (Lines)|
|Terrain|NASA Shuttle Radar<br>Topology Mission|Global|1 arc-second<br>(≈30m × 30m)|
|Landcover of Nepal|Integrated Center for Mountain<br>Development (ICIMOD)|2020|1km × 1km|
|Rivers of Nepal|OpenStreetMap|Dec 2019|Vector (Lines)|
|Health facilities|Nepal Ministry of Health|2016|Vector (Points)|
|Karnali municipal headquarters|World Bank|2019|Vector (Points)|
|Formal fnancial institutions<br>(classes A - D)|Nepal Rastra Bank<br>(central bank)|2017|Vector (Points)|
|High Resolution Settlement|CIESINdFbk|2018|1 arc-second|
|Layer Maps (HRSL)|an aceoo||(≈30m × 30m)|

Geospatial data for health services and formal financial institutions were sourced from the Ministry of Health (2016) and Nepal Rastra Bank (2017), respectively, while municipal headquarters were compiled from assorted Nepali-language policy documents and then geolocated with reference to OpenStreetMap. This basket of services was selected as a representative of a cross-section of developmental, economic, and political needs; Figure 3(a) shows their current distribution versus existing roads. Effective health care is an essential input to human development, particularly in the region of Nepal most afflicted by food insecurity and consequent malnutrition. Formal financial institutions (hereafter referred to as financial institutions) contribute to economic development by enabling the growth of local small to medium enterprises (SMEs) – unfortunately, no suitably comprehensive data set of informal financial institutions exists. Finally, municipal headquarters not only provide essential administrative services but are acceptable proxies for principal local markets. Education was considered for inclusion but a suitably comprehensive geospatial data set could not be located.

The Rural Access Programme (RAP), a donor-funded infrastructure support program
**_Figure 4: Distribution of foreign worker nationalities by state (percent, an average during March 2017- February 2018)_**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Bangladesh Indonesia India Myanmar Nepal Philippines Pakistan Thailand Others<br>Johor Kedah Kelantan Melaka Negeri Sembilan Pahang Pulau Pinang Perak Perlis Sabah Sarawak Selangor Terengganu W.P. Kuala Lumpur W.P. Labuan W.P. Putrajaya<br><!-- End of picture text --> _Source_ : MOHA 2017-18.

# **3. Data and methodology**

## **3.1.Data**

**The principal data source used is a transaction-level data set of remittances obtained from BNM.** Since 2016, BNM has compiled outbound remittance transaction data reported by remittance service providers (RSPs) as part of its banking supervision responsibilities. These include transactions carried out by post offices, money changers, and money wholesalers, but exclude those by commercial banks. Furthermore, the data set does not include informal remittance transactions such as cash carried out by a traveler or sent through an informal money transfer channel (e.g., _hawala_<sup>§</sup> ).

**The data are individual daily transactions for the period of March 2017 through February 2018** . The RSP data include objectively known variables for each transaction such as unique identifiers of remitters, remittance amount, transaction currency, destination country of the transfer, and the state location of MSPs. They also contain other variables, such as occupation and employment sector of the remitter, which are recorded during the account registration process at the MSPs. We do not use these variables in this paper, as the information could be outdated or incorrect.

## **3.2.Methodology**

§ Hawala is an informal method of transferring money without the movement
data/datatopics/aspire. As the majority of surveys used in the ASPIRE database are the same surveys in the GMD, we can explore all three dimensions together (poverty, education, and social protection) at the household level in future analysis.

> 20 Social assistance includes unconditional and conditional cash transfers, social pensions (non-contributory), food and in-kind transfers, school feeding, public works, fee waivers and subsidies. Social insurance includes insurance schemes against old age, disability, death of the main household provider, maternity leave and sickness cash benefits, and social health insurance. Labor market programs include active labor market programs such as training or job search assistance and passive labor market programs such as unemployment insurance and benefits.

20
The first and third of these points may well be more important for the success of utility reform and independent regulation electricity than for telecoms – and more so again for water and railways.

However, in looking forward, we can identify some clear work priorities. These are:

- a) _The use of regulatory indices combining governance factors in future econometric and related statistical work_ .

We intend to follow up this conclusion in the estimates that we report for the impact of electricity regulation in our companion paper.

- b) _The need for better data, particularly on regulatory practice, showing the evolution of regulatory systems_ .

There is a great paucity of data on regulatory practices – snapshot data let alone successive observations. In addition, there is a clear errors-in-variables problem in the absence of data on regulatory practice. Our preliminary examination of this issue in the context of the Stern-Holder data set suggests that the problem is serious enough to cause concern but not sufficient to invalidate estimation of models using data on laws alone – at least until it can be supplemented by better data which will also greatly facilitate the testing of how much difference is made by not having data on regulatory practice.

- c) _Data on regulatory systems, their performance over time and their impact can be collected by informed researchers carrying out systematic and well-coded case studies_ .

This has been shown by the 1998 NERA study of regulation in six Asian developing countries and the Prayas Report. To provide well-based estimates of what works, why and what is most important requires such data to be combined with other industry and country data. These tasks are neither huge nor particularly novel in social research terms.

Rodrik and his co-authors, Easterly and Levine, Acemoglu and colleagues have all shown the returns from a similar kind of research structure to understand the roles of institutions and policy for aggregate growth. The task identified here is to do something similar for the institutions responsible for the growth of output and investment for electricity and other service utilities.

54
market it can be safely assumed that a household who does not desire more work on NREGS is not rationed in the private market.

# **4. Performance evaluation**

By comparing the distributions of unadjusted consumption Ci with adjusted consumption Ci∗ we are able to quantify the impact of allowing for the disutility of work on assessments of the program’s performance. There are two aspects of performance to focus on. The first is targeting <u>performance. Here we analyze the relationship between the participation rates in NREGS and the</u> position along the distribution of consumption per capita, for both the raw data and using locally weighted regressions.<sup>10</sup> Using the two different welfare measurements, Ci and Ci∗, we estimate f and f<sup>∗</sup> defined as non-parametric relationships between the participation rate ( _PR_ ) and the position along the distribution of these two measurements: and similarly for the adjusted consumption, for which the non-parametric function is denoted f<sup>∗</sup> . Thus PR(x) is the participation rate observed for a given level of consumption x and FC(. ) is the cumulative distribution function (CDF) of consumption and _f_ is a smooth non-parametric function to be determined empirically.<sup>11</sup> If the program is indeed well targeted in terms of adjusted consumption C<sup>∗</sup> then we should observe that the function f<sup>∗</sup> is decreasing for all _x_ .

Second, we study the impact of our adjustment on the gains from the scheme. For this purpose we use household-specific estimates of forgone income for men and women from the Bihar Survey. These are answers to the questions: _“If you were doing some other work instead of this during these days, how many days do you think you would have worked?”_ and _“If you were doing some other work instead of this