rafmacalaba/data-use-mentions-extended huggingface dataset:
vey (NLSS), the most recent official survey for measuring welfare and poverty in Nigeria. The NLSS sample comprises approximately 22,000 households and is representative of Nigeria’s 36 states and the Federal Capital Territory (FCT), aside from the state of Borno.<sup>18</sup><sup>_,_19</sup> The 2018/19 NLSS is a multipurpose household survey that allows us to compare monetary poverty and overall expenditure to poor food access using either the FIES or FCS for each household.

The consumption aggregate used to identify monetary poverty includes information on consumption of food and non-food items, and expenditures on health, education, housing, and meals consumed outside the home.<sup>20</sup> The consumption aggregate is deflated spatially and temporally using unit values from the food consumption module, and can then be compared with the national poverty line of 137,430 naira per person per year to calculate poverty statistics.

We further follow FAO guidance on the construction of the FIES, which suggests that — under certain conditions it is possible to use the “raw score” the sum of the eight — dummy variables that comprise the FIES module to classify households’ food access status. These conditions are met using the NLSS 2018/19 and, following other analysis on Sub-Saharan Africa (e.g., Wambogo 2018), we classify those households with a raw score of 7 or 8 as severely food insecure and those with a raw score of 4, 5, or 6 as moderately food insecure.<sup>21</sup><sup>_,_22</sup> Furthermore, the 2018/19 NLSS contains the full FCS module. The FCS is calculated in the typical way, providing a direct measure of dietary diversity and food access at the household level. Each food group is given a score from zero to seven, depending on the

> 18Following the guidance of Nigeria’s National Bureau of Statistics (NBS)
<!-- Start of picture text --> T<br>1 — (———<br>g(r,T) = (ce)<br>+r<br><!-- End of picture text --> <!-- Start of picture text --> So, under certain assumptions, the Flores et al. approach gives the same answer as the (lifetime-<br>consistent) lifetime money metric method. The challenge with the Flores et al. method (and the<br>LMMU method) is that with the typical cross-section household survey, we do not have the<br>PRE —<br>0<br>PRE == —— TG. T) + + CCposr<br>LMMU = FKOV = —————° <t(r,T) = ° t(r,T)<br>PRE t(r,T) + Cposr<br>So, for example, if -=10%, 7-1=25, C =90 and MM =10, then the regular consumption-based<br>catastrophic spending ratio is 10%, while the LMMU and FKOV measures (under the assumptions<br>we have made) are only 1.1%. In empirical work, one would of course have to make an assumption<br>loans (perhaps specifically for medical expenses), one may also have to make an assumption about<br><!-- End of picture text --> <!-- Start of picture text --> consistent with studies that find that people borrow to finance medical expenses (Demirguc-Kunt<br>and Klapper 2012) and that nonhealth consumption falls after a health shock (see e.g. Gertler and<br>Gruber 2002; Jack and Suri 2014). The evidence is consistent, in other words, with one implication<br>consumption over time, or
areas. This finding calls for an analysis of the drop in FLFP in terms of other factors, such as level of education, marital status, socio-economic status and spatial heterogeneity.

# **3.2 Level of Education and Female Labor Force Participation**

To better understand the effect of educational attainment on FLFP rates, we consider two aspects. First, we assess the changes in demographic profile by level of education (Figure 2), and second, we look at patterns in the relationship between the FLFP rate and levels of educational attainment (Table 9). In terms of education, the composition of the working age female population has undergone vast changes during the period between 1993-94 to 2011-12, with increasing proportions of secondary and higher secondary educated women, who in turn have a lower propensity to participate in the labor force. In general, the share of illiterates among the female population have decreased, while the share of women with higher education, especially in urban areas, has increased substantially (Figure 2).

**Figure 2: Level of Education of Working Age Females in India**

<!-- Start of picture text --> 6.9 11.62.7 4.8 8.8 11.4 15.6<br>19.9<br>28.4 21.6 22.2<br>36.8 29.0<br>38.0<br>37.2<br>38.3<br>33.6<br>63.7<br>49.0<br>37.3 32.5 28.1 21.8<br>1993‐94 2004‐05 2011‐12 1993‐94 2004‐05 2011‐12<br>rural Urban<br><!-- End of picture text --> Source: Authors’ calculation from NSSO data.

Now, turning to the relationship between the FLFP rate and levels of educational attainment
Source: Authors’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017).

As the Sudanese sample surveys IDPs living in the Abu Shouk and El Salam camps, we must understand these deprivations with the background that these settlements were created as emergency and crisis responses rather than durable, long-term solutions (Sudanese Government’s Joint Mechanism for Durable Solutions 2019). Although Sudan does have a national electric grid that supplies electricity to the urban and peri-urban areas of the nearby city El Fasher, IDP communities living in the camps report limited connection to the city’s electricity supply, reflected in the high deprivations in the electricity and cooking fuel indicators. The ad-hoc construction of dwellings in the two camps explains why 71% of the IDP households in Abu Shouk and 65% in El Salam live in tukuls or other permanent mud or wood structures (Sudanese Government’s Joint Mechanism for Durable Solutions 2019: p.50), both of which register as unimproved housing types. Bank branches in El Fasher have limited capital for small businesses as their headquarters in Khartoum regard the area as too great a risk and IDPs themselves as riskier investments than their host community peers (UNHABITAT 2009: p.8). Exposure to unclean cooking fuels and inadequate housing can lead to poor health outcomes, while lack of access to electricity and a bank account further excludes individuals from labor market integration and livelihood opportunities that would empower forcibly displaced persons to overcome their multiple, overlapping deprivations. Clearly, displacement status puts individuals at a greater risk of poverty than their host community neighbors, and we can unpack those risks in greater detail using the MPI.

Results can also be broken down to show the percentage contribution of each indicator to multidimensional poverty (see Figure 2). Among refugees in Ethiopia, lack of a bank account is the largest contributor to poverty, while among host communities, the largest contributor is years of
We find that receipt of a PSG grant of $86 per ha had a significant, though quantitatively modest, impact on area cultivated with summer crops. Interestingly, this impact was largest for farms located closest to active fighting and in the smallest size group (below 20 ha), two sub-groups with the greatest difficulty in accessing alternative sources of finance so that grant resources may have been important to bridge shortterm gaps. On the other hand, the size of the grant is well below the level of demand for credit articulated in the farm survey. To help farms, especially those further from the front, diversify into higher value crops, it will be important to explore how grant resources can be used to leverage larger amounts of bank finance. This will require using the digital platform built on the SAR to reduce the transaction cost of accessing credit, including by allowing banks to validate applicant information, farmers to access technical support, and linking to other factor and output markets. Beyond the methodological contribution of this paper, further research along these lines could help to appreciate the size and incidence of benefits from a digital farm registry and the implications for public investment and cost recovery.

We contribute to three strands of literature. First, a large body of literature shows that real-time use of administrative data can greatly improve targeting and effectiveness of public programs. Examples include use of biometric IDs to identify beneficiaries (Muralidharan _et al._ 2016), transfer of cash that is coordinated with actual implementation (Banerjee _et al._ 2020), or use of administrative data to solicit user feedback to help monitor public service providers (Muralidharan _et al._ 2021). Our finding that, <mark>even in a conflict environment where accountability and capacity have traditionally been limited, verifiable data from official sources can help transparently target benefits to groups that have been underserved in the past adds to this literature. It reinforces the notion that,</mark> especially in crisis or conflict settings, non-traditional data sources including mobile phone data
Policy Research Working Paper 5991

# **Abstract**

The majority of firms in most developing countries are informal. The authors of this paper conducted a field experiment in Sri Lanka that provided incentives for informal firms to formalize. Offering only information about the registration process and reimbursement for direct registration costs had no impact on formalization. Adding payments equivalent to one-half to one month’s profits for the median firm led to registration of around one-fifth of firms. A larger payment equivalent to two months’ median profits induced half the firms to register. The main reasons for not formalizing when offered incentives included issues related to ownership of land and concerns about facing labor taxes in the future. The degree of bureaucracy in the registration process also seems to matter for those with the incentive to register, with response to the incentives higher in Colombo, where the registration process was easier, than in Kandy. Three follow-up surveys, at 15 to 31 months after the intervention, measure the impact of formalizing on these firms. Although mean profits increased, this appears largely due to the experiences of a few firms that grew rapidly, with most firms experiencing no increase in income as a result of formalizing. The authors also find little evidence for most of the channels through which formalization is hypothesized to benefit firms, although formalized firms do advertise more and are more likely to use receipt books. In qualitative interviews owners of formalized firms also feel their businesses have more legitimacy. Finally, formalizing is found to result in a large increase in trust in the state.

Their focus is largely on the private costs and benefits of existing firms formalizing. Within their sample they cannot measure broader impacts of formalization on other firms (who may prosper from not having to compete against informal firms not paying taxes), nor impacts of easier formalization on entry of new firms. Nevertheless, our results suggest that although most informal firms do not want to formalize, given the current private costs and benefits of formalizing, policy efforts that lead to relatively modest increases in the
Data was collected on all students, teachers, and schools. After the intervention, a student survey was conducted to collect socio-demographic data in order to check whether the randomly created groups of schools were comparable.<sup>15</sup> The Science test was administered after schools had completed their 12-week intervention, followed by a student questionnaire designed to measure fidelity of implementation, students’ perceptions on the teaching they had experienced, and their attitudes toward Science. These questions were later used to build an index that measured if learning was interesting and relevant as well as if teaching practices inspired curiosity (see Appendix 4 for a detailed description of the index).<sup>16</sup> These questions were based on a validated instrument, the Tripod Survey for Students (MET Project 2012).

Before and after the intervention took place, teachers provided further additional information. As a baseline, a survey gathered background characteristics of teachers and general information about their Science class. In the post-intervention survey, teachers responded a set of questions to assess the fidelity of implementation of the intervention, as well as perceived changes in class dynamics and teaching practices.

Finally, administrative information was collected at the school level. This information included data on school and seventh-grade enrollment, number of classrooms and teachers, repetition rate, promotion rate, over-aged rate, location of the schools, and the Language score in the local end-of-primary exam of seventh grade (FEPBA).

# 4. The Sample

> 15 The student survey was collected in the classroom and contained information on students’ characteristics, their family, and their socioeconomic background.

> 16 See, for example, the Measures of Effective Teaching (MET) project, where results show that student surveys produce more consistent results than classroom observations or achievement gain measures (Met Project 2012).

15
mean that endogeneity may remain a problem. We take account of this however, by obtaining two-stage least squares estimates which use lagged minimum wages as an instrument for minimum wages in addition to our OLS estimates.

With respect to the unemployed workers included as part of the uncovered sector in our analysis, we are limited in that we do not have complete occupation data for all years in the period. We have the required industry data for all individuals used in the analysis. However, for the unemployed who have worked before, we have occupational data about their last job for only eight of the eleven years we consider.<sup>12</sup> These workers however do not constitute a large number of individuals in the sample and are more likely than not to be workers in the occupational classes covered by ROs. The implication of this minor data limitation is thus that, in the years 2004, 2006 and 2007, for which we do not have occupational data for the

> 11 See Table 1 above which shows the number of wage rates there were in 2016. Ideally, we would want to assign each worker their very specific wage rate to fully exploit the variation in minimum wages in the complex remuneration orders system to obtain estimates that do not raise any endogeneity concerns with regard to the minimum wage variable.

> 12 In Appendix Two we explain how we have used occupational and industry codes to assign workers and unemployed workers who have worked before to remuneration order categories.

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a 7.3 percent higher labor income, while the gain for final imports is 4.9 percent. Additional analysis by region and country is shown in Appendix Figures 9.1 to 9.3.

# 2.2 Which sectors are associated with the highest job and income increases?

Manufacturing sectors show the highest job and income growth associated with expanding exports in a sector, in particular those with high-medium technology intensity. Focusing on broad sectors reveals that export-employment elasticities are driven by manufacturing. A 10 percent growth in manufacturing exports is related to a 5.3 percent job growth (Figure 2). The potential for job creation through exports is particularly high in high-medium technology intensity manufacturing, including in motor vehicles, electrical equipment and computer and electronics (Figure 3, upper panel). This is despite the higher capital intensity of high-tech sectors, implying that capital and labor in these sectors are complementary.<sup>14</sup> Similarly, intermediate import growth in high-medium tech manufacturing sectors is related with high job growth which, to a large extent, is also used for export production (Figure 2).

**Figure 2: Trade-employment elasticities, by broad sector**

<!-- Start of picture text -->
1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>Agriculture, Mining Manufacturing Mfg - low tech Mfg - medium Mfg - high- Business Other services<br>fishing tech med. tech services<br>Exports Intermediate imports Final imports<br><!-- End of picture text -->

Source: Own computations. Data: OECD TiE and TiVA data, covering 48 countries (see Appendix 2) and 45 sectors for the period 1995-2018. Note: Trade-employment (trade-income, resp.) elasticities at
).

The intra-firm character of the technology transfers that take place through trade in capital equipment and other products is less apparent, but still distinguishable. What we know about MNC involvement from statistics on goods trade is that between 70 and 80 per cent of the goods exports of both the US and the UK - the main suppliers of embodied technology together with Japan and Germany - are accounted for by MNCs (UNCTC, 1988, p. 90). Moreover, a significant share of the exports and imports of the major home countries (between one fifth and one third overall, and more for complex and technologically sophisticated goods that supposedly embody _5_
peace talks with the government broke in late 2001 with the Maoists attacking an army barrack in one of the mid-Western districts. The Nepal Army was deployed overnight and the conflict took a very violent turn. In 2002 alone, over 4,600 people were killed. By 2006, when the insurgency eventually ended, over 13,000 people were killed and more than ten times as many displaced.

Another equally unexpected coalition emerged during the conflict that ended the insurgency in 2006. In a short duration, Gyanendra proved himself unpopular when he dismissed the elected parliament and started ruling the country through his handpicked cabinet of ministers since 2005. This presented an unusual opportunity for the Maoists to become a mainstream political party by teaming up with the other political forces in a fight against the King. This movement led to the demise of the monarchy and an election was held in 2008 to write the constitution for the newly formed republic. The Maoists emerged as the largest party, confirming its transition to mainstream political force.

The geography and the trajectory of the insurgency produces spatial and inter-temporal variation in conflict intensity, which I exploit in my empirical study. Conflict intensity was lower before year 2001, when census data were collected. The conflict took an unexpectedly violent turn after 2001, until its end in 2006. The threat of violent Maoist conflict ended in 2008 when the constituent assembly elections were held, the year when the NLFS-II data were collected. The period between 2008 and 2010 represents a post-conflict peace period after the Maoists emerged as the largest party in the elections. Figure 6 shows the stark increase in conflict intensity before and after 2001. Conflict intensity increased from 0.1 deaths per 1,000 population on average to 0.6 deaths per 1,000 population in this period. The figure also shows large geographic variation in conflict intensity in both periods. Further, post-election peace ended violent in all districts.

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and the associated matrix is: where each rij is the transitional probability if we assume that all workers were to leave their initial sector at the same rate.

# _Data_

Panel labor force surveys from Argentina, Brazil, and Mexico allow us to follow the behavior of individuals over time. These three countries have different labor market histories that are instructive to understanding youth employment in Latin America. For example, Mexico has the lowest unemployment rates in Latin America while Argentina has some of the highest rates and Brazil has moderate levels of unemployment. The structure of each country’s data provides us with some lessons, as well.

The Argentine _Encuesta Permanente de Hogares_ (EPH) for 1995-2003 is used. The national survey is carried out in urban areas and is designed such that a sample is selected in period t and its labor market status is observed. Six months later, the same sample is interviewed again. This process is repeated two more times which gives us, in the end, the labor market status at four points in time over a one and a half year period (t, t+6, t+12, t+18). In each period, there are four consecutive cohorts at different phases of the interview process. To limit attrition bias, we only track one transition. Further, we only track May to October transitions to avoid the summer vacation which falls during the October to May period. We pool the data, giving us a sample size of 368,453 over the eight years of data. While these data are useful since they show a long-term dynamic process, they are also limited in that it is unlikely that we are capturing short-run movements between labor market states.

The Brazilian _Pesquisa Mensual de Emprego_ (PME) for 1982-2002 is designed similarly to that in Argentina, but the same sample is interviewed once a month for four 8
its limitations. A critical observation here is that the Poverty Space, in its current iteration, does not fully emulate spillover networks in the strictest “causal” sense. Proper modeling of these networks demands longitudinal data, which is rarely available in developing economies and often inconsistent across regions. The Poverty Space navigates around this limitation by projecting a structure that can help to predict potential spillovers, but with the understanding that it might not precisely mirror the actual dynamics of these interplays in all settings.

While imperfect, this approach offers a new way to begin answering critical policy questions. This paper offered a first look at how these methods can enrich our understanding of multidimensional poverty, and points toward fresh avenues for deeper exploration. In particular, looking at dimensions that are relevant in specific contexts or regions (without the limitations of cross-country comparability) may allow more granularity in terms of variation over time and across space is a key area where future research could help to advance our understanding of the interconnected and dynamic structure of multidimensional poverty. If the world is to get back on track in its efforts to eradicate poverty, efforts to accelerate progress will be necessary. Acupuncture-like approaches, based on the complex interactions among poverty dimensions, considering the underlying structural interdependencies between deprivations, appear promising as one innovative means for amplifying development impact.

# **References**

Alkire, S. _et al._ (2021) ‘Global multidimensional poverty and COVID-19: A decade of progress at risk?’, _Social Science & Medicine_ , 291, p. 114457.

Alkire, S. and Ballon, P. (2012) ‘Understanding association across deprivation indicators in multidimensional poverty’, in _Research Workshop on ‘Dynamic Comparison between Multidimensional Poverty and Monetary Poverty’. OPHI, University of Oxford_ .

Alkire, S. and Foster, J. (2011) ‘Counting and multidimensional poverty measurement’, _Journal of public economics_ , 95(7–8), pp. 476–487.

Athey, S. (2017) ‘Beyond
|
|Pseudo R-squared|0.99|0.99|0.99|0.99|

Note: Dependent variable is the stock of immigrants in country _j_ originating from _i_ . Data on bilateral stock of migrants by macro-occupation are from the OECD DIOC dataset `https: //www.oecd.org/els/mig/dioc.htm` . Skilled immigrants are those in ISCO occupation 1, 2 and 3. Robust standard errors in parentheses. ***, **, * significantly different from 0 at the 1%, 5%, and 10% levels respectively.