rafmacalaba/data-use-mentions-extended huggingface dataset:
# _Measuring skills requirements by occupation_

We calculate two measures of skills requirements. The first measure is the skills count, that is the average number of times a skill from a given skill category appears in job postings mapped to a given occupation. We use this measure to explore complementarities among the nine subsets of skills categories (advanced, intermediate, and basic digital skills; three subsets each of cognitive and socioemotional skills and their subsets) within occupations.

Our second skills measure is the average relative frequency with which a skill category appears in an occupation. The measure is calculated by averaging for each occupation the number of times a skill category appears in the job posting mapped to that occupation divided by the total count of skills in the job postings for that particular occupation (that is, expressed as a share of total skills rather than the raw skill count).

# **3.2. Creating occupational skills profiles and digital occupation levels**

# _Using the online job postings data to create occupational skills profiles_

We take two additional steps to create our final data set. First, we map each job posting classified with the 2013 Malaysia Standard Classification of Occupations (MASCO) and its related skills profile to the 2008 International Standard Classification of Occupations (ISCO-08; see ILO 2012), the international occupational classification system.<sup>15</sup> This gives us occupational skill profiles for the occupational codes in the employment data from other countries.

Second, we impute the occupational skills profiles of 27 occupations. Among the 127 3-digit ISCO-08 occupations, the online job postings data did not include job postings for 16 occupations — primarily agricultural workers and street vendors — and 11 did not have sufficient job vacancy postings to generate a skills profile for that occupation.<sup>16</sup> These 27 occupations roughly represent between 20 and 40 percent of employment in the four countries studied.<sup>17</sup> Omitting them would distort the distribution of digital skills in a country. Instead, we
Nigeria suffered from slow growth and a series of shocks, even before the arrival of the COVID-19 crisis. Nigeria has consistently been affected by climate shocks, especially for farmers relying on rain-fed agriculture and livestock herders, as well as conflict, especially for communities in the north of the country, for several decades. In 2016, the collapse of global oil prices pushed Nigeria into recession, with real GDP dropping by 1.6 percent, given Nigeria’s continued dependence on oil: fuel has made up more than 80 percent of exports since the 1970s. Growth remained subdued during the 2017-2019 period, below the growth rate of peer economies and the rate of population growth, resulting in a steady decline in per capita incomes. The onset of the COVID-19 pandemic and the corresponding drop in global oil prices has only weakened Nigeria’s macroeconomy further, with real GDP dropping 1.8 percent in 2020.

Despite Nigeria’s size and its importance for regional and global poverty reduction, the infrequent availability of survey data on household welfare makes it difficult to track how poverty evolved in the country in the decade before the COVID-19 crisis. The two most recent official household surveys used for poverty measurement, the 2009/10 HNLSS and the 2018/19 NLSS, capture household consumption very differently and therefore produce incomparable poverty estimates. In particular, the module used to collect information on food consumption was changed from a daily diary, handed to enumerators during four visits over the course of one month, in the 2009/10 HNLSS, to a seven-day recall, in the 2018/19 NLSS. Additionally, the approaches for measuring meals outside the home and own-produced food were changed between the two surveys. Key non-monetary variables were also collected differently in the 2009/10 HNLSS. Evidence suggests that changes to the survey questions used to capture consumption can alter poverty estimates considerably (Deaton 2005, Beegle at al. 2012). The poverty headcount rate estimated using the 2009/10 HNLSS is 56.4 percent. Given these differences
(2006) find that larger countries may find it tougher to control corruption. In contrast, using Transparency International’s Corruption Perceptions Index for 1998, Root (1999) finds that in a cross-section of 60 countries, higher population is significantly associated with less corruption. Root attributes the findings to economies of scale in governance. Similarly, Anoruo and Braha (2005) find that in Africa, population growth and corruption are negatively correlated. Knack and Azfar (2003) demonstrate that the corruption measures typically used in the literature suffer from sample selection bias and correcting for this bias causes the relationship between country size and corruption to become much weaker or disappear. Similarly, when using total land area as a measure of country size, Goel and Nelson (2010) also find that country size has no significant impact on corruption.

The broader literature on the determinants of corruption highlights several factors (discussed in detail in section 2). Treisman (2000) finds that states of government, colonial history, trade openness, religious affiliation, and exposure to democracy have a significant impact on corruption. At the same time, greater ethnic fractionalization or diversity in the country has also been found to contribute positively to corruption (Dincer 2008, Glaeser and Saks 2004, La Porta et al. 1999). If members of one ethnicity are elected to a public position, they are more likely to maintain the position even if they display corrupt behavior. Goel and Nelson (1998) and subsequently, Kotera et al. (2012) report that larger government is associated with higher corruption. They find that in larger government, there appears to be lesser individual accountability and greater layers of bureaucracy which eventually leads to more corruption. Fisman and Gatti (2002) also find that fiscal decentralization in government expenditure is strongly and significantly associated with lower corruption. This was supported by a later study that found a negative 5
# **1. INTRODUCTION**

Improving health outcomes is a high priority in the vast majority of low-income countries. However, despite dramatic increases in public spending on health in the last decade, most low-income countries have made little progress and will not reach the 2015 Millennium Development Goal health targets (United Nations 2010 and 2011).<sup>1</sup> One reason is the poor performance of medical care providers. Absenteeism among providers is widespread (Chaudhury et al. 2006; Reinikka and Svensson 2009). Provider knowledge of proper clinic procedures for basic care is low (Das and Hammer 2004). And providers give a standard of care that is well below the state their clinical knowledge (Das and Gertler 2007; Das, Hammer and Leonard 2008; Leonard and Masatu 2010a and 2010b). In this context, finding ways to increase health provider productivity in terms of supplying higher quality care to more patients is critical.

One promising intervention designed to improve health provider productivity is to pay them for performance. So-called Pay-for-Performance (P4P) schemes give bonuses to providers that give higher quality of care to more patients. P4P affects health care provision in two ways; first, through incentives for providers to expend more effort in specific activities; and second, through an increase the amount of financial resources. Proponents highlight P4P incentives and argue that they strengthen the link between productivity and rewards (Levine and Eichler 2009).

In this paper we provide evidence on the effect of P4P incentives on health outcomes and the associated pathways through which the incentives work. The evaluation uses data from Rwanda, one of the poorest countries in the world, where we nested a prospective evaluation into the national rollout of P4P. In order to identify the P4P incentive effect separately from the increase in resources, the program agreed to hold constant the level of resources constant across treatment and comparison facilities. It did so by increasing the traditional input-based budgets of the comparison group by the average amount of P4P payments to treatment facilities. As
<!-- Start of picture text --> Predictive Margins of edu_attain with 95% Cls<br>oO<br>N<br><@<br>2+<br>Lo<br>eS<br>oo<br>ae<br>ae)<br>2<br>5<br>N<br>¥<br>& N Vv oe) & o o “A eo) 2 AS) es nv es se<br>&3° &ov &<br>Ss<br>SY<br>~~<br>awx<br>Highest edu attain [0-14]<br>—————_ Male ———— Female<br><!-- End of picture text --> <!-- Start of picture text --> Predictive Margins of edu_attain with 95% Cls<br>oO<br>N<br><9<br>2x<br>86<br>oo<br>zS<br>GO<br>2?<br>05<br>N<br>LF<br>& XN Vv “) &® ° © NY Co} 2 © NY wW s<br>oe a So<br>ps & &<br>SsSy<br>7<br>wwSs<br>Highest edu attain [0-14]<br>——— Male ——— Female<br><!-- End of picture text --> <!-- Start of picture text --> Predictive Margins of edu_attain with 95% Cls<br>©<br>N<br>a<br>£~+<br>Lo<br>Tit<br>go<br>ao<br>52<br>ao<br>a<br>5<br>N<br>5
<!-- Start of picture text --> oS+<br>Recall Data<br>Diary Data<br>a 8.75 USD<br>2<br>4=<br>o<br>I [)2<br>><br>a<br>a°<br>J<br>10 15 20 25 30<br>Weekly consumption (USD)<br><!-- End of picture text --> <!-- Start of picture text --> °=<br>8.75 USD<br>©<br>So<br>23<br>eS<br>><br>=Vv<br>a<br>cS)<br>oN<br>Recall Data<br>Diary Data<br>So<br>5 10 15 20 25 30<br>Weekly consumption (USD)<br><!-- End of picture text -->
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective" a yard tap, rather than an in-house tap. The in-house tap variable reported in Figure 1 thus understates the number of households with private water connections. It is only possible to identify households with yard taps in seven of the fifteen countries in this sample. In those seven countries, almost none of the poorest households had sewer connections or in-house connections and yard taps, but at higher income levels in-house connections and yard taps were much more prevalent than sewer connections (Figure 2).

Second, information on sewer connections is only available for twelve of the fifteen countries. When households in only those twelve countries are pooled and divided into quantiles of 5 percent, it becomes clear that sewer coverage lags behind coverage of in-house water taps as expected (Figure 3). Sewer coverage is consistently about 10 percent lower than in-house water tap coverage for households with incomes (as approximated by the consumption aggregate) under US$400 per month; above US$400 per month, the gap between in-house water service and sewer connections actually widens. As in Figure 1 electricity coverage is higher than coverage of other infrastructure services at all income levels. In the remainder of the paper we present results for the pooled sample of households from all fifteen countries (as in Figure 1), and, except where noted, we use coverage figures for in-house water taps (rather than in-house connections and yard taps).'<sup>5</sup> 3.2. Coverage in urban and rural areas As anticipated, a smaller percentage of rural than urban residents had infrastructure services in their homes.<sup>16</sup> Fewer rural households had electricity (46 percent vs. 89 percent in cities), in-house water taps (12 percent vs. 59 percent), sewer connections (7 percent vs. 61 percent), and telephones (8 percent vs. 3 8 percent). The poor live disproportionately in rural
# 1. Introduction

In recent years, the growing interest of academics and policymakers in the extent, causes and consequences of governance and misgovemance has been reflected in the proliferation of cross-country indicators of various aspects of governance. In an accompanying paper (Kaufmann, Kraay, Zoido-Lobat6n (1999)) we present a large database compiling several hundred cross-country indicators of various aspects of governance, produced by thirteen clifferent organizations, and covering 178 countries. These indicators report subjective perceptions on a wide range of issues relating to govemance, ranging from the extent to which corruption in the political system affects foreign investment, to the efficiency of public services delivery, to the likelihood that citizens of a country resort to extrajudicial means to settle disputes.

In this paper, we take the view that many of these indicators serve as imperfect proxies for one of a much smaller number of fundamental concepts of governance. Given this view, there are considerable benefits from combining related indicators into a small number of aggregate governance indicators. First, the aggregate indicators span a much larger set of countries than any individual source, permitting comparisons of govemance across a broad set of countries. Second, aggregate indicators can provide more precise measures of governance than individual indicators. Third, it is possible to construct quantitative measures of the precision of both the aggregate govemance indicators and their components, allowing formal testing of hypotheses regarding crosscountry differences in governance We realize these benefits by constructing aggregate governance indicators using an unobserved components model. This model expresses the observed data as a linear function of unobserved governance plus a disturbance term capturing perception errors and/or sampling variation in each indicator. The main advantage of this method is that it allows us to obtain estimates of the variance of this disturbance term for each indicator. These can be interpreted as a measure of how informative each indicator is about the broader concept of governance it measures. We then compute the mean of the conditional distribution of govemance given the observed data for each country as a natural point estimate of the level of governance in that country
, which in the LPM regression was not the case. Note as well that the coefficient sizes increase substantially compared to the LPM results.

The fact that the variable of foreign licensed technology is significant may raise potential suspicion. For instance, one could suggest that the foreign technology that is licensed may also include software, which therefore may be correlated with our data policy index. This is because of the multiplicative term of the data‐linkage variable in equation (2) also includes the extent to which each sector uses software. However, a closer look at the Enterprise Survey variable description states the survey question as: “Does this establishment at present use technology licensed from a foreign‐ owned company, excluding office software?” Hence, we are assured that no artificial or spurious correlation is being picked up in our regressions.<sup>10</sup> On the contrary, given that the coefficient results are significant, it seems likely that foreign licensed technology as part of a firm’s innovation activity is related to a country’s framework of regulatory policies in data. Moreover, higher levels of data restrictiveness found in countries appear to hamper firm‐level innovation in sectors that are more intense in using software.

> effectively means that mathematical computations of our econometric performance exceed the limit for the largest number representable when an attempt is made to calculate the binomial coefficient. 10 See the Enterprise Surveys Indicator Description, page 112, which can be found here:

‐ <u>https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Indicator Descriptions.pdf.</u> 12
36 FERNANDES-KLENOW-MELESHCHUK-PIEROLA-RODR ́ıGUEZ-CLARE when exports from a given origin to a given destination are high, exports per firm are responsible for, on average, at least 40% of the high exports. This finding is robust to looking at all destinations or only the largest destinations, including all firms or ignoring very small firms, including all country pairs or only ones for which more than 100 firms export, and to disaggregating across industries. When we look at average exports by percentile of exporting firms (rather than average exports per firm), we find the intensive margin is more important the higher the percentile.

Although variation in fixed trade costs across country pairs can make the Melitz-Pareto model fit the intensive margin in the data, such fixed trade costs would need to be negatively correlated with distance. Moreover, variation in fixed trade costs does not reproduce the pattern of a steadily rising intensive margin across exporter percentiles. Allowing firms to export multiple products or taking into account granularity does not reverse these implications.

In contrast, moving away from a Pareto distribution and assuming that the productivity distribution is lognormal resolves the puzzles. Specifically, we estimate a Melitz model with lognormally distributed firm productivity and idiosyncratic firm-destination demand shifters and fixed costs. We estimate this model using likelihood methods on the EDD firm-level data. Our estimated Melitz-lognormal model is consistent with the positive intensive margin overall and with the intensive margin rising by exporting firm percentile. This specification also implies Since the trade elasticity is no longer a constant in the full Melitz-lognormal model, one would expect from the analysis in Arkolakis et al. (2012) that the welfare effects of a trade cost reduction would be different from those in the Melitz-Pareto model (see Melitz and Redding, 2015). However, extending exact the hat algebra approach popularized by Dekle et al. (2008) to our estimated full Melitz-lognormal model, we find that the Melitz-Pareto model provides a remarkably good approximation for the welfare effects of trade liberalization.

Looking ahead, moving from Pareto to lognormal firm productivity may matter more when taking
.

# 2.2 Poverty in Nigeria

There exists limited evidence on aggregate poverty and child poverty in Nigeria. As in the literature in general, there is a lack of evidence on child poverty that connects the findings of studies focusing on specific poverty indicators and or in specific regions in isolation. Related to education Kazeem and Musalia (2016) highlight the historical colonial roots of the ethnic and socio-economic education discrepancy observed based on data from 2004. Western style education brought by Christian missionaries initially only spread in the Southern Igbo and Yoruba ethnic group dominated regions, whereas it was longer resisted by the Muslim Hausa and Fulani dominated ethnic groups with nomadic lifestyles in the North. Besides, poorer children needing to provide for their family face higher opportunity costs of completing

> 2 “Recognizes that children living in poverty are deprived of nutrition, water and sanitation facilities, access to basic health-care services, shelter, education, participation and protection, and that while a severe lack of goods and services hurts every human being, it is most threatening and harmful to children, leaving them unable to enjoy their rights, to reach their full potential and to participate as full members of society” (UN, 2007, paragraph 46).

4
to be representative at Statistical Office and World Bank (2012). For a more general discussion on spatial (and temporal) price deflation and real consumption measurement, see e.g. Gibson et al. (2017) and van Veelen and van der Weide (2008).

> 5The food poverty line in the IHS3 is Kwacha 22,956, the monetary cost of 2,400 calories per person per day, where the price of a calorie is estimated from the population in the 5th and 6th deciles of the aggregate distribution for consumption. The non-food poverty line (non-basic food needs) in the IHS3 is kwacha 14,045, and is estimated as the average non-food consumption of the population whose food consumption is close to the food poverty line.

13
Brown, Drusilla, Alan Deardorf, Alan Fox and Robert Stern (1996), ―Liberalization of Services Trade,‖ in W. Martin and L. A. Winters, eds., _The Uruguay Round and the Developing Countries_ , Cambridge: Cambridge University Press.

- Brown, Drusilla and Robert Stern (2001), ―Measurement and Modeling of the Economic Effects of Trade and Investment Barriers in Services,‖ _Review of International Economics_ , 9(2): 262-286 _._

- Central Bureau of Statistics (2007a), Economic Survey, 2006, Nairobi: Ministry of Planning and National Development, Republic of Kenya.

- Central Bureau of Statistics (2007b), Statistical Abstract, 2006, Nairobi: Ministry of Planning and National Development, Republic of Kenya.

- Chinitz, B. (1961), ―Contrast in agglomeration: New York and Pittsburgh,‖ _American Economic Review, Papers and Proceedings_ , 51:279-89.

- Cox, David and Richard G. Harris (1986), ―A Quantitative Assessment of the Economic Impact on Canada of Sectoral Free Trade with the United States,‖ _The Canadian Journal of Economics,_ Vol. 19 (3), August, 377-394.

Daniels, P.W. (1985), _Service Industries: A Geographical Appraisal_ , New York: Methuen & Co.

- Dee, Philippa, Kevin Hanslow and Tien Phamduc (2003), ―Measuring the Costs of Barriers to Trade in Services,‖ in Takatoshi Ito and Anne Krueger (eds.), Trade in Services in the Asia-Pacific Region, Chicago: University of Chicago Press.

- Dihel, Nora, Ana Margarida Fernandes, Aaditya Mattoo and Nicholas Strychacz (2010), ―Reform and Regional Integration of Professional Services in East Africa,‖ Economic Premise No. 32, September, The World Bank. Available at: www.worldbank.org/economicpremise.

- Dixit, A. and J. Stiglitz (1977), ―Monopolistic Competition and Optimum Product Diversity,‖ _American Economic
combining advanced natural language and data mining techniques to monitor news at the global level in real time. The project gathers meta-data from online news sources globally (every 15 minutes) and translates it into English from more than 65 languages In our analysis, we use GDELT 2.0 Global Knowledge Graph (GKG), the most comprehensive version of the GDELT dataset. GDELT identifies for each news article its source, its nationality, and its main topics.<sup>17</sup> We define a news article to be migration-related if one of its top 3 topics is one of the following: migration, refugees, borders, displacement. GDELT thus allows us to identify all the news articles related to migration for the period 2017-2020 for any of the destination countries of the migrants located in all the FMPs in Libya.

An important feature of GDELT is that it provides a numerical variable measuring the tone of each news article. The tone is calculated as the positive score minus the negative score, where the positive (negative) score is the share of all words in the article found to have a positive (negative) connotation. In practice, a news article has a positive (negative) tone score if the share of words with a positive connotation is larger (smaller) than that with a negative connotation. This tone variable has been shown to perform similarly to human-coded sentiment

> 16A Key Informant (KI) is a “person within the community who, due to his or her position, has access to specialized knowledge about the situation and context of the populations residing within. KIs can be local officials, religious, community, or tribal leaders, government administrators, local humanitarian workers, representatives of displaced groups, or others.” As an example, during Round 18 (February-March 2018), 1360 KIs interviews were conducted, in all 100 Baladiyas (i.e., districts) of Libya (IOM, 2018a).

> 17The GDELT algorithm extracts from the content of the news the major topics