rafmacalaba/data-use-mentions-extended huggingface dataset:
population. Our benchmark budget is set to equal 5% of this total value.

7 Following Foster, Greer and Thorbecke (1984) the FGT class of poverty measures take the following form: where xi is per capita expenditure for those individuals with weight wi who are below the poverty line and zero for those above, z is the poverty line and ∑<sup>_w_</sup> _i_<sup>is total population size.αtakes a value of 0 for the</sup> Headcount Index, 1 for the Poverty Gap and 2 for the Squared Poverty Gap. For further discussion, see Ravallion (1994).

8 The consumption distribution is constructed on the basis of the average, across _r_ replications, of householdlevel predicted per-capita consumption in the population census.

25
fraction of the world’s population on which one lacks data is _x_ , then one should expect to be 0.37* _x_ standard deviations away from the true mean, and as much as _x_ standard deviations from the mean at times. Here the standard deviation is based on the distribution of country-level estimates. As data producers might not be used to thinking in standard deviations from the mean, we provide examples of what such deviations imply.

In further results we show how these errors change (i) if one is interested in regional statistics, (ii) if data are imputed, (iii) if the probability of data missing is correlated with the indicator of interest, (iv) if one uses the share of countries rather than the share of population as a coverage threshold, and (v) if one has specific coverage requirements for populous countries, such as India. We end with recommendations on when to produce global statistics. We hope these recommendations can be used to ensure that global statistics are only made available when the accuracy is deemed sufficiently high. This has implications for international organizations and researchers producing cross-country data sets that are aggregated to create a global statistic. By consequence, the recommendations have implications for any users of such global statistics, including academia, the media, and policy makers.

To our knowledge, we are the first to study when there is enough data to create a global statistic. Yet, our paper relates to several streams of literature, such as the challenges of measuring the SDGs (MacFeely 2018; Sachs 2012), missing data on SDG reporting (Dang & Serajuddin 2020; World Bank 2021), and making inference with missing data (Dang et al. 2019).

# Method

To quantify the impact of data availability on the precision of global statistics, we rely on the World Bank’s World Development Indicators (WDI). The WDI is arguably the world’s largest database of relevant countryyear indicators spanning a wide range of topics. The WDI contains information
score [WAZ] < -2). Overall, livelihood conditions in the study area were similar to those in rural areas elsewhere in Guatemala, Central America and Mexico, where households often turn to migration in the face of limited local economic opportunity.

We conducted a household panel survey in 2008 and 2012, beginning with a representative survey in areas purposively selected to reflect the geoclimatic heterogeneity of the region across four municipalities.<sup>iii</sup> In early 2008, a census in the selected areas generated a complete enumeration of approximately 5,500 households, including whether each household had former members (head, spouse of head or child) living abroad. Using the census, a random sample stratified on migration experience was drawn. Twenty-four households—evenly split between those with and without a former member currently living abroad—were randomly selected from each of 52 clusters,<sup>iv</sup> yielding a target sample of 1,248 households. Between April and August 2008, 1,222 households (97.9 percent) were interviewed.<sup>v</sup> In April 2009, we coordinated a qualitative and short quantitative follow-up in one typical survey community from each of the four study municipalities to investigate the local effects of the economic crisis on migration, remittances and the livelihood patterns of households and their communities. Led by anthropologists, the study held seven focus groups with women drawn from the previously surveyed households and carried out 25 semi-structured interviews with conveniency sampled key informants and recently returned migrants. Focus group and semi-structured interview notes were coded and analyzed using WinMax (Kuckartz 1998; Saenz de Tejada 2009). In addition, a short quantitative survey updating information for original panel households was administered in the four communities.

From May to August 2012, a follow-up panel survey targeted all originally interviewed households. If only some of the original members of a 2008 household were located, all other
28 Vladimir and 26.7% in Gorodets (Fig. 2.8). Among married couples with children, 31.8% and 22.6% respectively would like to get a loan. The effect of the income level ius demonstrated in Fig. 2.9.

Fig. 2.8. Willingness to take a loan for the purchase of housing, by level of provision with housing (by quintiles based on per capita provision with total floor space; % of each quintile) <!-- Start of picture text --> 40-.<br>36<br>35<br>25<br>20<br>.5 4';$ I s:4 5<br>40 1.5<br>0.<br>Vladimir CGorodets<br><!-- End of picture text --> Note: I - lowest quintile, 5 **-** highest quintile **Source:** Vladimir and Gorodets Longitudinal Household Survey, December 1996.

**Fig. 2.9.** 15~~~~~~ Willngness **to** take a loan **4for** the purchase of t **h** uint les ous **i** ng (by **3** based on per capita income; **4'M** % of each quintile)

<!-- Start of picture text --> 40 -<br>35<br>30<br>25 22 21<br>20 1<br>1iis e a i7 13 3 13.4<br>VI2 3 4 5 1 2 3 ts4 5<br>Vladimir ~~~~~~Gorod*t<br><!-- End of picture text -->

**Note: 1- lowest quintile, 5 highest quintile Source- Vladirnir and Gorodets Longitudinal Household Survey, December** 1996
quantities can occur on the demand or supply schedules, whichever is smallest, at a given interest rate). Assuming an excess supply or demand at a given point, the researcher infers the likelihood of the observed quantity being on the supply or demand regimes. Empirical studies based on aggregate time series data and applied to business loans include Laffont and Garcia (1977) for the Canadian market, Sealey (1979) and King (1986) for the US market. Pazarbasioglu (1997) and Kim (1999) investigate whether there was a credit crunch, respectively in Finland following the banking crisis of 199192, in Korea following the financial crisis in December 1997. In Latin America, Catao (1997) and Braun and Levy-Yeyati (2000) assessed credit contraction for Argentina, Berrospide and Dorich (2001) for Peru, Carrasquilla, Galindo and Vasquez (2000) and Barajas, Lopez and Oliveros (2001) for Colombia, Barajas and Steiner (2002) for Colombia, Peru and Mexico, and Hurlin and Kierzenkowski (2007) for Poland.
the various groups studied have the lowest overall mortality rate in both high and low HIVprevalence countries. On the other hand, men with less than primary education have the largest increase in mortality. In high HIV-prevalence countries these men have mortality rates that are substantially higher than men with more education, and than women.

One group for which the higher mortality among men does not hold is among young women (ages 15-24) especially those with less education. For this group, mortality rates are higher than all other groups of the same age. This is consistent with the age profile of HIV/AIDS, but is also consistent with findings by others that younger more educated women appear to be engaging in more “protective” behaviors as knowledge about HIV and AIDS is disseminated.

Last, perhaps unsurprisingly, countries that have experienced conflict or episodes of internationally recognized genocide, display heterogeneous patterns in the time trends and socioeconomic patterns in mortality. The experience from several countries suggests that male mortality is typically more responsive to these events—and in several cases it is urban or more educated males for whom mortality increases most. At the same time, it is important to recognize that increased mortality during these episodes of conflict is not restricted to men—in all cases women’s mortality is affected as well.

# **References**

Bicego, George. 1997. “Estimating Adult Mortality Rates in the Context of the AIDS Epidemic in Sub-Saharan Africa: Analysis of DHS Sibling Histories.” _Health Transition Review_ 7(Suppl 2): 7-22.

- Case, Anne and Christina Paxson. 2010. “The Changing Role of Education in the AIDS Crisis”. Center for Health and Wellbeing. Princeton University. Mimeo. August 2010.

- Conley, Dalton, and Rebecca Glauber. 2005. “Sibling similarity and differences in socioeconomic status: life course and family resource effects.” NBER Working Paper 11320.

- Dahan, Momi, and Alejandro Gaviria. 2001. “Sibling Correlation and Intergenerational Mobility in Latin America.” _Economic
# **1. Poverty and Trade Background**

## **1.1   Poverty trends during the period 1996-2001**

Cameroon undertook two household surveys during the last decade, in 1996 and 2001. Between these two years, Cameroon recorded a drastic fall in poverty prevalence. The headcount index, i.e. the proportion of people that are counted as poor, decreased by 13.1 percentage points; from 53.3 percent in 1996 to 40.2 percent in 2001. The poverty gap, which measures the degree to which the poor fall below the poverty line on average, also declined from 19.1 to 14.1 percent during this period. The squared poverty gap, which evaluates the extent of severe poverty among the poor, also declined from 9 to 7 percent.

A breakdown of poverty indicators shows that this sharp poverty alleviation is largely attributable to economic growth, rather than redistribution (Republic of Cameroon 2003). Economic growth contributed about 90 percent of the reduction in the headcount index, supporting the view that “growth is good for the poor”, as underlined in studies such as Dollar and Kraay (2000). Indeed, changes in the income distribution are found to have actually increased the poverty gap and squared poverty gap, suggesting that some emphasis on redistribution is required to better alleviate poverty.

The decrease in all three poverty measures was more substantial in urban areas than in rural Cameroon, further increasing the rural-urban poverty divergence. Indeed, the difference between the percentage of poor in rural and in urban areas has risen from 18.2 points in 1996 to 27.8 points in 2001. With regard to the poverty gap, this rural-urban differential has gone from 6.8 to 12 points. Similarly, the squared poverty gap has gone from being 3.2 higher in rural areas in 1996 to 6.6 points higher in 2001.

5
changed is the composition: in earlier periods labour reallocation out of agriculture was more important, while in more recent periods labour reallocation out of the state sector was more important.

Will factor reallocation continue to contribute to productivity growth in China over the next 20 years? The answer to this depends on the extent to which intersectoral differences in marginal products of factors remain, and the scope for future reallocation of factors to those sectors where marginal products are relatively high. In Figure 7 we present some very rudimentary evidence on this using more recent data for China. In the top panel we report the ratio of value added per worker in the private sector relative overall industry in China. The relative labour productivity advantage of the private sector in China has fallen somewhat over time, but remains quite high with the private sector having labour productivity around 2.5 times that of the industry as a whole. This is a comparison of average, rather than marginal products, and it is the latter that matter for intersectoral reallocation. However, if we assume a Cobb-Douglas production function for value added, the only difference between marginal products and average products is the output elasticity of labour. It seems clear from Figure 7 that the output elasticity of labour would have to be vastly smaller in the private sector than in industry as a whole for these labour productivity differences not to translate into sizeable differences in marginal products. We also note that although the share of the private sector in total employment has grown very rapidly, it still is quite modest at only 20 percent of industrial employment.

In the bottom panel of Figure 7 we report some evidence on the scope for reallocation of capital across ownership forms. We draw on data for large and mediumsized industrial enterprises reported in recent issues of the China Statistical Yearbook. This source provides information on value added and fixed assets, disaggregated by forms of ownership. Consistent with the top panel of this figure, we identify the private sector as all non-state and non-collective firms, and we again report the ratio of the
average, there are about 7.5 plots per holding in 93 villages in the four tehsils for which data was made available. The averages mask wide variations in the extent of 'scatteredness', however. In Talab village of Rengali tehsil, for example, there were 14.5 plots per holding prior to the start of the land consolidation operation.

A total of 146 villages falling under six police stations were taken up for consolidation after the promulgation of the OCH & PFL Act, 1972. One village failed to satisfy the minimum conditions for consolidation to proceed, and landowners in a further eight villages failed to cooperate with the proposals for land reallocation following the preparation and publication of the land register. As a result, consolidation operation in these 8 villages had to be called off. At the time of our survey, land consolidation had been completed in 134 of the remaining 137 villages, and is ongoing in three villages.

Of the 134 villages in which the operation had been completed and _pattas_ distributed, however, landowners in 47 villages (35 percent) have refused to take possession of the new holdings, since they are unwilling to undertake the large-scale exchange of plots that would be required to effect the consolidation process. Some of these villages are located at the tail-end of the Hirakud irrigation system where there is considerable fluctuation in water availability. It appears that landowners do not have confidence in the way the proposals were prepared in practice, and fear that the method adopted has not adequately taken account of variations in land quality (see Box 10 for a more detailed village case study). In spite of the undulating terrain and widely varying soil types, 75 percent of landowners have been allotted land in a single compact block (chak), and 20 percent in just two chaks. Only five percent households have been allotted land in three chaks. Diversification of holdings across land types is regarded by local farmers as essential under the prevailing cropping systems. It is suggested by informed observers that such conditions, accounting for much
exception of certified midwives, all clinicians in this setting, are equally qualified to provide high-quality care.

Although we already know that capacity has a one-to-one relationship with knowledge in this setting, we can still examine the role of equipment in the know-can gap in Table 3. This regression corresponds to equation 5, _C_ = _α_ 2 + _β_ 2 _I_ + _ν_ 2 _K_ ( _T_ ) + _γ_ 2 _K_ ( _T_ ) _∗ I_ + _E_ 2. In the analysis of the relationships, it is important to include both the inputs of knowledge _K_ and equipment _I_ and the interaction between the two inputs. The level of equipment might help to explain the intercept on Graph 4 of the Three-Gap model: do increased levels of equipment improve the overall capacity of health workers? The interaction of know and the measure of infrastructure can help to explain the slope: as knowledge increases, does it make a difference if the equipment score is higher? In this case, unsurprisingly, the level of equipment available does not significantly alter either the intercept or the slope. This does not mean every facility has adequate equipment, only that their performance is not explained by the level of equipment.

To examine the determinants of the can-do relationship, we summarize the results of 54 questions on the attitudes and motivation survey, coming up with five summary measures of motivation: **self-satisfaction** , **feeling valued by the facility** , **job satisfaction** , **positive** 4 **facility characteristics** and **positive worker behavior** . The individual questions that make up the summary statistics are listed in Appendix B and each score is described briefly here:

> 4When the survey was conducted, the answers to the motivation questions were scored on a scale of 1 to 5, where 1 corresponds to “strongly disagree” and 5 corresponds to “strongly agree.” Afterwards, we flipped the scores of all negatively worded motivation statements for which answering “strongly disagree” indicated high motivation
# **1. Introduction**

A growing body of literature has documented the impacts of internet access on a host of economic outcomes.<sup>1</sup> Some empirical evidence suggests that the internet—like other skill-biased technological innovations—tends to benefit more educated workers but may lead to displacement effects among their less educated peers. This mechanism has often been cited to explain why poverty reduction has been sluggish among the least developed areas of the world despite significant improvements in internet connectivity.<sup>2</sup> On the other hand, more recent evidence has shown that the internet has enabled faster job creation and increased economic activity in Africa, as well as higher productivity and export growth among firms in Africa (Hjort and Paulsen, 2019). However, little is known about the effects of the internet on household and individual welfare, especially among the poorest populations.

In this paper, we provide new evidence on the impacts of mobile broadband coverage on household consumption and poverty in Nigeria, the largest economy and mobile broadband market in Africa. Using a difference-in-difference strategy, we exploit a unique data set that integrates three waves of a nationally representative longitudinal household survey on living standards—the General Household Survey or GHS—with information from Nigerian mobile operators on the deployment of mobile broadband coverage during the 2010-2016 period. The data allow us to match the location of each household with the coverage provided by each mobile site, to determine with precision the time when individual households began receiving coverage. Individual coverage of mobile broadband is defined as the provision of 3G or 4G coverage, which enables high-speed access to the internet, and excludes 2G coverage as it only provides for limited internet browsing and applications.

We find that mobile broadband coverage had large and positive impacts on household consumption levels. Households that had at least one year of mobile broadband coverage experienced an increase in total consumption of about 6 percent. These effects increase over time, though at a decreasing rate. Coverage also reduces the proportion of
other developing countries, individual government officers seem to have considerable discretion in deciding which rules to enforce, on whom, when, and, sometimes, how. In many cases inspection visits are arbitrary or excessive, and are viewed by business owners as punitive, or as a veiled demand for bribes. Often the latter is a price worth paying for avoiding the disruption to production plans or the loss of staff time that more frequent or more intrusive visits would otherwise bring about.

A useful proxy of the cost of imposed by predatory administration or enforcement of regulations is the frequency of visits that government officials make to business premises. On average, inspectors visit factories around 10 times each year to see if they comply with various regulations. These include taxes (sales tax, income tax, customs, and excise duty), labour and social security; fire and building safety; and environment regulations. Regulator visits, however, vary considerably across cities and states (see Figure 1). On average, firms in cities such as Delhi, Gurgaon, Chandigarh and Nagpur report less than 5 regulator visits per year. On the other hand, firms in Chennai report 17 visits, Mumbai 15, Ahmedabad 16, and Cochin 19 visits.

# **_Provision of Infrastructure_**

_Utilities_ : The investment climate survey (ICS) data show that over 30 percent of Indian firms find power supply to be the most important infrastructure bottleneck or obstacle to growth.. India’s present difficulties with power supply (shortages, costs, unreliability) stem more from transmission and distribution deficiencies than from generation. In the analysis of the next section, we use the frequency of outages as our indicator of the quality 9
0.1||**0.3**|2.0|
|Landline|subscribers/100|people<br>0.8||**0.9**|9.4|
|Mobile phone|subscribers/100|people<br>15.1||**30.2**|86.7|
||**Keny**|**a**<br>Countries wi<br>access to sub<br>cables|thout<br>marine<br>|Other de<br>regi|veloping<br>ons|
|Price of monthly mobile basket||**15.9**|11.12||9.9|
|Price of monthly fixed-line basket||**21.0**|13.58||—|
|Price of 20-hour Internet package||**81.5**|67.95||11.0|
|Price of a 3-minute call to the United States||**2.00**|2.59||2.0|
|Price of inter-Africa calls, mean||**1.04**|0.72||n.a.|

Source: Minges and others 2009, derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. — = data not available. n.a. = not applicable.

**Table 8. High international call charges driven both by technology and market power**

|US$|Percent of<br>cases|Call within<br>region|Call to U.S.|Internet<br>dial-up|Internet<br>ADSL|
|---|---|---|---|---|---|
|Without submarine cable|67
. For instance, McKinsey estimates that “Open Data can help unlock between US$3 trillion to US$5 trillion in economic value annually across seven sectors, including education, transportation, consumer products, electricity, oil and gas, health care, and consumer finance.”<sup>5</sup> Likewise, previous work by the World Bank has suggested that Open Data can yield economic benefits across five archetypical types of businesses, including suppliers, aggregators, developers, enrichers, and enablers of Open Data.<sup>6</sup> Other economic analyses of the topic include: a European Union study that estimated aggregate direct and indirect economic benefits of €200 billion<sup>7</sup> ; a UK study that “conservatively” estimated the direct economic benefits of public sector information at around £1.8 billion a year<sup>8</sup> ; and a study produced in Spain that found that the “infomediary” sector employs around 4,000 people in the country and generates €330 million to €550 million annually.<sup>9</sup> Compared with the attention given to these financial and economic benefits—however mind-bogglingly large they are purported to be—considerably less effort has been made in providing a sufficiently critical evaluation of the public-sector benefits, including the potential of _Open Government Data_ (OGD). As a subset of Open Data, OGD is that which is “held by national, regional, local, and city governments, international governmental bodies, and other types of institutions in the wider public sector”.<sup>10</sup> As defined by the OECD, Open Government Data can be seen as comprised of two elements: “(i) Government Data,

> 2 - See “Big Data Universe beginning to explode,” at http://www.csc.com/insights/flxwd/78931 <u>big_data_universe_beginning_to_explode. Accessed September 15, 2015.</u>