rafmacalaba/data-use-mentions-extended huggingface dataset:
separate sampling frame. The resulting list was stratified implicitly by pre‐war region. 42 enumeration areas were selected with probability proportional to size to reach the desired oversample.

# 2.3. Sampling of the nomadic population

Nomadic households, who make up around a quarter of the Somali population according to UNFPA’s Population Estimation Survey (PESS) of 2014, are inherently difficult to sample because, by definition, they have no permanent place of residence (Kalsbeek, 1986; Soumare et al., 2007). Himelein et al. (2014) use a random geographic cluster sample approach, in which points are randomly selected from a map and all nomadic households within a radius around the point are interviewed. The SHFS followed a different approach. The strategy for sampling nomadic households relied on lists of water points used by nomadic households to water their livestock, which served as the primary sampling units. UNFPA’s 2014 PESS took a similar approach to estimate the nomadic population (UNFPA, 2014). The SHFS project deployed 200 purpose‐designed tracking devices to nomadic households who gave consent, which track their movements for two years. This will improve the understanding of the patterns of movement of the nomadic population in Somalia, which will facilitate sampling this population in the future.

## _Nomadic sampling frame_

Nomadic strata were defined at the federated member state level ( **Error! Reference source not found.** ), with the population count for each stratum provided by PESS. The list of water points was divided up by stratum. The list was put together from a combination of two sources. First, the list of water points used in PESS. Second, a regularly updated list of water points kept by the UN Food and Agriculture Organization (FAO). Given this combination of sources, the resulting list of water points used as sampling frame was 13
25 Customs Agency to submit invoices even for equipment transfers from corporate offices in other countries to Bulgaria as excessive and counterproductive.

There have been, however, some indications of progress in customs efficiency. Customs clearance time has been significantly reduced in pilot sites under the Trade Facilitation in South Europe (TFSE) project. Between June 2004 and the average for 2002 it fell 34 percent and was the shortest among selected SEE-economies (Table 11). Moreover, the scope of random inspection of shipments has been significantly expanded. While in December 2001 all shipments were examined by customs, in June 2004 only 2.3 percent were inspected.

**Table 11** _: Reduction of Clearance Time at selected Borders Crossing Points and Inland Clearance Terminals (in minutes and percent) in 2001, 2002 and June 2004 (in minutes and percent)_

||Pilot site|2001|2002|Jun-04|Reduction, June 04<br>over 2002|Final target|
|---|---|---|---|---|---|---|
|Albania|Tirana|270|102|73|28%|60|
|Bulgaria|Plovdiv|222|90|59|34%|40|
|Croatia|Jankomir|318|180|148|18%|40|
|Romania|Constanta|258|180|113|37%|120|

Source: TFSE Indicator Workbook (June 2004).

As for other areas not covered by four indicators of trade facilitation, two observations on transport and technical standards can be made. First, the road transport has been fully privatized and both forwarding and transport services are available on competitive basis. The improvements in customs and port efficiency would further cut in the cost of these services.

Second, the EU Regular Report commends Bulgaria for having had made “good progress” in aligning standards with the EU, although the process of fully aligning them to European standards has not
# **1 Introduction**

In economics, health sciences, and other disciplines, data on the outcome variable of interest is often costly to collect. The measurement of poverty for instance relies on household consumption expenditure data. Collecting this data involves long questionnaires administered over an extended period of time which substantially adds to the data collection costs. Individual-level data collection that involves physical examinations also tends to be costly. This has implications for the sample size and the frequency with which the data is collected. In many developing countries for example, particularly in Sub-Saharan Africa, estimates of poverty, malnutrition and health are obtained highly irregularly and as a result are often outdated.<sup>1</sup> The demand for bigger and more frequent data has motivated researchers to explore ways to substitute predicted data for real data. Consider the application to poverty measurement by Douidich et al. (2015) where household consumption poverty is predicted into annual labor force surveys in order to increase the frequency of poverty estimates from once every 7 years to every year.<sup>2</sup> Similar applications can be found in Stifel and Christiaensen (2007) and Christiaensen et al. (2012). Prediction methods have also been used to dramatically expand the sample size of poverty and health data. A prominent example of this is the small area estimation of welfare where the outcome variable of interest (i.e. consumption poverty) is predicted into a population census which covers (almost) all members of the population unlike typical household surveys. This allows researchers to obtain estimates of poverty at a highly disaggregated level such as districts, communities, and towns. These small-area estimates are often plotted in the form of a map known as a poverty map. The small-area estimation, which provides the methodological foundations for poverty mapping, was pioneered by Elbers et al. (2003) and extended by e.g. Tarozzi and Deaton (2009), Tarozzi (2011) and Elbers and van der Weide (2014).
observed comparative statics. As noted in the context section, worker-level data from the 752 garment making firms in the **?** baseline dataset reveals that 91% of the workforce in that baseline sample are unskilled workers. Though skilled workers comprise only 9% of the labor force, they earn 61% of all wages paid by firms in the baseline data. Skilled and unskilled workers average similar hours of work per week and skilled workers earn many multiples the hourly and weekly wage of an unskilled worker.

Though it is not possible to categorize workers as skilled or unskilled in the analysis panel dataset, the labor information in the February 2014 census differentiates workers by skill level. At that time, the sample employed 256 apprentices, 76 family workers, and 27 skilled workers, a workforce composition that was 92% unskilled workers, remarkably similar to the ratios in the national-level **?** data. The preponderance of unskilled workers in this setting implies that the first of the key comparative statics, that worker hours increase in response to outages, is likely explained by firms employing this type of worker. Likewise, the contextual similarities between this study and **?** (and the experience of the research team in the field) imply that skilled workers in this sample are also much better paid than their unskilled counterparts, suggestive evidence that large changes in firm-level weekly wages paid (the second key comparative static) are driven by firms employing skilled workers.

An alternative explanation of rising hours and falling earnings could be that in the presence of falling productivity, skilled workers being paid at a piece rate simply experience a reduction in hourly wages.<sup>18</sup> Though it is not possible to test for this directly given data limitations in the firm-level panel, in the third key comparative static of the paper the analysis fails to reject that outages have no effect on within-firm worker hourly wages, consistent with reductions in both wages and hours for skilled workers and inconsistent with the simplest version of this
calculated as where _r_ ̄ _f_ is the rank of the last technology that conforms the pure ladder. In words, if the most sophisticated technology that the company adopts in the function is below _r_ ̄ _f_ , then the EXT is computed as a pure quality ladder. If the company has adopted some technology that is in the tree top (i.e. with greater sophistication than _r_ ̄ _f_ ), then the index counts how many of those technologies the company uses, and adds them as in a horizontal relationship. We use formulas (B.1) to (B.4) to compute EXT indices at the function, company-level depending on the structure of the relationship between the technologies in each function.

All the function-company-level, the EXT indices defined so far take values in the closed interval [0,1]. This property is inherited by indices at greater levels of aggregation, since those are simple means of the function-company level indices. For some of the graphical presentations of our results, it is more appealing to re-scale the technology indices so that the lower bound is greater than zero. We therefore re-scale the technology indices in the following way where _T_<sup> ̄</sup> _j,a_<sup>_X_is the EXT (</sup><sup>_X_equal to the empty set) index for unit</sup><sup>_j_at the level of aggregation</sup> _a_ . Note that, the range of our EXT and MOST technology indices will be [1,5].

# **B.4 Regional-level analysis**

In this section, we explain how we examine the relationship between technology and regional productivity. A cross-country analysis of the relationship between technology and productivity using FAT data would face two limitations. First, we only three countries: Brazil, Vietnam, and Senegal. In addition, the data for Brazil covers only one region, whereas the data from Vietnam and Senegal cover the entire country. To overcome these limitations, we
eliminating the underestimated observations identified above (Set A) from the calculation of the non-food PL. After eliminating observations that were singled out as suspicious we ran the estimation procedure, and once the final results were obtained we compared these with the baseline scenario, including the results obtained using the full sample.<sup>29</sup> As expected, both the revised non-food shares and non-food PLs were much lower than the baseline in Southern Mozambique and urban areas, which suggests that the construction of the non-food PLs was seriously affected by underestimated observations.

The direction of the bias is quite straightforward. The elimination of observations characterized by excessively high non-food shares produces lower non-food PLs and, consequently, total PLs tend to be lower. This effect becomes even clearer when comparing columns 2 and 3 of Table 2 with columns 5 and 6. Due to our simple modification of the non-food component, the total PLs decrease 5 percent and 12 percent, respectively, in the greater Maputo urban area and in Maputo City.<sup>30</sup> We then turn to the question of whether our alternative PLs minimize this effect. Our simulations indicate that non-food PLs calculated at the national or nationwide urban/rural levels are relatively insensitive to underestimated observations. The number of observations used to calculate the poverty line is crucial to its robustness: the more specific the PL, the higher the risk of magnifying measurement errors.

In Table 3 we extend the sensitivity test to the full PL methodology and compare the results with a national-basket alternative. Table 3 describes the revised PLs and their variation from the official figures. As noted above, whereas for the non-food PLs the impact of underestimation is easily detectable, for the food PLs the impact can be ambiguous: the bias can run in either or both directions depending on what type of information is missing.

The findings in Table 3 support our hypothesis. Location-specific PLs tend to be more strongly affected by the presence of underestimated observations in the data. The elimination of these observations

> 29
latest nationally representative labor force survey, most household entrepreneurs in Sierra Leone are traders or shopkeepers (84.0 percent), compared with 75.4 percent of the youth in the control group. The program increased the probability of involvement in trading or shopkeeping businesses by 7.9 percentage points, which means that 83.2 percent of the household enterprises owned by youth were of this type (appendix A, table A2, column 5). However, this outcome is driven purely by women because no change among men was observed, and the effect is significantly different by gender. This aligns with the high concentration of women in self‐employment in the program’s absence, and the effect was thus expected to be seen in the profile of existing businesses. However, because it relies on conditional estimates, this result should be interpreted with caution; similar effects are not found in the unconditional estimates, possibly because of the large proportion of households not engaged in any household enterprise.<sup>17</sup> An examination of the impacts across treatment groups suggests that the entrepreneurship results are driven by treatment group B, which is consistent with the business skills content of this intervention package. The impacts by treatment are significantly larger among group B than among group C in terms of the likelihood of first‐time entrepreneurship and raising profits during bad months. This result suggests that the business skills training package was more effective than the full package at promoting entrepreneurship.

> 17 Based on balance tests, the conditional results show no evidence of differential selection bias. Thus, the restricted sample is balanced on observables across treatment groups.

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Policy Research Working Paper 9230

# **Abstract**

This paper estimates the impacts of mobile broadband coverage on household consumption and poverty in Nigeria, the largest economy and mobile broadband market in Africa. The analysis exploits a unique dataset that integrates three waves of a nationally representative longitudinal household survey on living standards with information from Nigerian mobile operators on the deployment of mobile broadband (3G and 4G) coverage between 2010 and 2016.

The estimates show that mobile broadband coverage had large and positive impacts on household consumption levels which increased over time, although at a decreasing rate. Mobile broadband coverage also reduces the proportion of households below the poverty line, driven by higher food and non-food consumption in rural households. These effects are mainly due to an increase in labor force participation and employment, particularly among women.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at crodriguezc@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
4 machines and increase their demand for skilled labor (Acemoglu, 2003; Behrman, Birdsall, and Szekely, 2000; Harrison and Hanson, 1999). Furthermore, many developing countries are rich in natural resources. Trade would stimulate this sector rather than labor-intensive ones.

From a dynamic perspective, economic growth is key to sustained poverty alleviation and trade liberalization is argued to lead to the needed increases in productivity to sustain growth. Freer trade provides greater incentives for investment, the benefits of scale and competition, limitation on rent-seeking activities favored by trade restrictions and openness to new ideas and innovations (Berg and Krueger, 2003; Grossman and Helpman, 1991; Lucas, 1988).

Empirically, cross-county growth regressions have produced mixed evidence. Using either trade shares or indices of trade liberalization, Sachs and Warner (1995), Edwards (1997), Frankel and Romer (1999) Dollar and Kraay (2001) and Lee et al. (2004) find a positive association between liberalization and growth. However, noting that trade liberalization often occurs at the same time than other policy reforms, Rodriguez and Rodrik (2001) have criticized the literature which associates trade openness measured by trade shares with more rapid growth. Considering trade policy measures instead, Harrison (1996), Edwards (1997), and Irwin and Terviö (2002) find a significant negative effect of trade on economic growth, while Vamvakidis (2002) still shows that trade enhances growth.

Examining the effect of trade on poverty more directly, Krueger (1983) shows in her case studies that developing countries’ manufactured exports were, indeed, labor-intensive, but that the employment effects of freer trade policies were generally rather limited. A number of cross-country studies on poverty, while not dealing with trade explicitly, incorporate trade openness as a control variable and showed similar results: at best the benefits of greater trade openness seem to have bypassed the poor.

Looking at developing countries only, Beck et al. (2007) and Kpodar and Singh (2011) find
just over 28 percent (106 jobs per project) with FDI in the primary sector playing a negligible role in job creation. South Asia, notably India, is a large recipient of services-sector FDI.

As figure 3 illustrates, the manufacturing sector has historically received the largest share of greenfield investments in Asia mainly in real estate; coal, oil, and natural gas; alternative/renewable energy; chemicals and automotive OEMs. However, after a sharp rise in 2018, greenfield FDI to the region fell sharply in 2019 to $230 billion from $323 billion the previous year. The decline was driven by a 50 percent reduction in investments in the manufacturing sectors most pronounced in chemicals and chemical products; computer, electronic, optical products and electrical equipment; and automotive OEM (figure 3). Consequently, the share of manufacturing fell to 44 percent, the lowest since fDi Markets data became available in
of all households. Since the 1960s, some have gained access to at least some land, but around a quarter of all households in Orissa still operate no land. Overall, in spite of land reforms, socio-economic and demographic change over the last half century, these trends suggest that formidable obstacles continue to prevent the rural poor from improving their access to private arable land.

**_Land revenue systems:_** Historically, different parts of the state inherited different land revenue administration systems from Bengal Province (northern Orissa), Madras Presidency (southern Orissa), Central Provinces (western Orissa), and the former princely states. Some 80 percent of the total area fell under _zamindari_ systems, in which many layers of 'intermediaries' between the landlord and cultivator were responsible for exacting land revenue. _Ryotwari_ (peasant-proprietor) systems prevailed over parts of southern Orissa that had been under Madras Presidency. Some of the complexity of land revenue administration in Orissa today may be attributed to the legacy of these diverse systems, which were brought under a unified legislative structure only following independence. The legacies of these distinct systems also have certain lasting effects on the ground. For example, land records tend to be more complete and accurate in the former _ryotwari_ areas in which, unlike in _zamindari_ areas, there were village accountants. This not-so-distant historical record can be important in resolving land disputes even today, in establishing the basis for contemporary land claims.

**_Main provisions_** _in_ **_land legislation:_** Orissa is one of a few states in India that has attempted legally to abolish tenancy (land-leasing), except in the case of persons of disability (the definition of which includes widows, divorcees, and other unmarried women). Land rights may pass to any cultivator who can demonstrate continuous occupation over a period of at least 12 years ('adverse possession'). While tenancy remains widespread, these restrictions have led to concealed forms (e.g. oral contracts) which give tenants little or
If e-commerce participation is random across the population, one may use the following model to assess the causal impact of e-commerce participation on household income: (1) where is household income per capita of household i, ii ii is a constant term, ii takes value one if YY = αα+ γγPP + εε household _i_ participates in e-commerce and zero otherwise, gives the difference in average ii ii household income per capita between the e-households and non-e-households, YY αα PP contains both the observed and unobserved (to an econometrician) characteristics of household γγ _i_ . Under the ii εε assumption that e-commerce participation is random, is independent of . As a result, captures the average treatment effect on the treated (ATT) of e-commerce participation on ii ii PP εε γγ household per capita income.

However, our survey data are collected in an un-experimental setting, where e-commerce participation is not random but determined by household _i_ ’s choice based on . The dependence between and introduces selection bias in the estimate of . In other words, the choice of ii ii PP εε treatment and control groups simply based on e-commerce participation is far from scientifically ii ii PP εε γγ rigorous. In addition, the linearity assumption in equation (1) can be strong and could lead to serious bias if specified inaccurately (Imbens 2015).

To remediate the un-experimental setting, we use propensity score matching (PSM). Rosenbaum and Rubin (1983) developed the use of the propensity score—the probability of receiving treatment conditional on covariates—to reduce the dimensionality of the matching problem. Since then, PSM has been widely employed in empirical economic studies (Heckman et al. 1997, Dehejia and Wahba 1999, and Davies and Kim 2003), and many other fields where there is a treatment, a group of treated individuals, and a group of untreated individuals. After matching, PSM does not assume any parametric relationship between outcome variables and participation decisions to calculate the treatment effects, which is another reason PSM is more reliable than the model in
measured using at-risk of poverty lines (AROP), fixed at the baseline scenario before the reform was implemented._

**When considering the combined effect of the Monthly Social Allowance (MSA) and the heating allowance, their aggregate impact on income inequality and poverty is still modest (Figure 7, panel b); this impact is slightly more pronounced than the effect of the heating allowance in isolation.** This observation underscores the limited magnitude of change brought about by these transfers in the broader context of income distribution and poverty reduction. Interestingly, one key takeaway is that the primary beneficiaries of this combined assistance are the households in the first decile. This is primarily because the MSA is exclusively directed toward this decile, augmenting the support provided by the heating allowance. In essence, these households in the lowest income bracket experience a more substantial increase in their total assistance due to the combined support of the MSA and the heating allowance (Figure 7 panel a).

21