rafmacalaba/data-use-mentions-extended huggingface dataset:
**Individual Eligibility**

An alternative approach is to base the eligibility for the subsidy on an assessment of individual household circumstances. While reliable information about poverty levels at the zonal level may be obtained based on census or survey data, it is much harder to obtain a reliable estimate of household income and expenditure in order to assess eligibility for individual subsidies. Consequently, it is necessary to find proxy variables which are closely associated with poverty, but which may be easier to observe and harder to falsify.

In Panama, an extensive search for proxy variables was conducted by making crosstabulations between candidate proxies and poverty levels, using data from the national Living Standards Measurement Survey. The results for the most successful proxy variables are reported in Table 4.2. These were as follows:

- the presence in the house of poor-quality floor materials (cement, earth, bamboo);

- the absence of a connection to the telephone network;

- the head of household not educated beyond primary level;

- the toilet facilities located outdoors _and_ most basic in nature (i.e., pit or latrine).

A successful proxy variable must have both a high correlation with poverty and a low correlation with not being in poverty. As the table illustrates, it is difficult to find a single variable that performs well on both these counts.

- For example, low-quality floor materials are highly correlated with poverty; 88.6% of those in extreme poverty live in houses with low-quality floor materials. However, lowquality floor materials are also quite prevalent among those who are not poor, affecting as many as 56% of this group. Hence, this variable is effective in identifying who should be
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 21. Funding gaps by sector**

<u>US$ millions per year</u>

||ICT|Power|Transport|WSS|Total|
|---|---|---|---|---|---|
|Needs|(178)|(302)|(236)|(311)|(1027)|
|Spending traced to needs|171|143|154|68|536|
|Within-sector reallocation|2|12|5|0|19|
|Potential efficiency gains|0|157|0|43|200|
|(GAP) or surplus|(6)||(77)|(200)|(283)|

Source: Derived from Foster and Briceño-Garmendia 2009.

Note: Potential overspending across sectors is not included in the calculation of the funding gap, because it cannot be assumed that it would be applied toward other infrastructure sectors. Inefficiency estimations based on operational performance data up to 2007 — = data not available.

# What else can be done?

The most obvious way to address Mali’s funding gap is to raise additional finance. At the current level and pattern of spending even if all inefficiencies are corrected, Mali would need to increase its spending by 50 percent what have been seen in recent years. Additional finance might be hard to secure. Even if additional finance is not secured, Mali can reduce the infrastructure funding gap through its own policy choices, and in particular through the technology choices to meet its infrastructure targets. The largest measure that the country could take to reduce its infrastructure spending needs would be to integrate itself more closely within the West Africa Power Pool. If it were to do so, the country would become a net importer of power, importing up to 12.7 terawatt-hours of electricity by 2015, which would reduce annual investment requirements by $124 million (
# **<u>3.2. How has the supply of teachers changed over time?</u>**

Any discussion of teacher earnings makes more sense if we understand the relative supply of workers in the field (which is, in turn, endogenous to teacher earnings). We track the proportion of primary school teachers relative to the overall labor force along with the ratio of primary school teachers to students across the countries in our sample (Figure 1).<sup>27</sup> There are no countries in which the fraction of teachers in the labor force has fallen substantively. The proportion has risen sharply in several countries (e.g., Burkina Faso, Democratic Republic of Congo, Sierra Leone, Tanzania, and Zambia), risen modestly in some (e.g., Côte d’Ivoire and Malawi), and stayed roughly constant in others (e.g., The Gambia). Furthermore, the teacher-student ratio has also risen in many countries (i.e., smaller class sizes), so the rise in teachers cannot entirely be explained by rising enrollment. The average rise across the continent suggests a combination of increasing demand for teachers and that teaching is seen as a desirable profession.

# **<u>3.3. What do teachers and other workers earn in Africa?</u>**

We assess teachers’ earnings relative to other wage workers in two ways. In this section, we compare monthly earnings, hourly earnings, hours worked weekly and the proportion having a second job (Table 4). In the next section, we report the results of multivariate analyses that simultaneously control for age, gender and urban/rural residency. For this comparison of earnings, we restrict the sample to wage workers with secondary education or more.

The median earnings of teachers identified in our study surveys are about $680 per month (measured at the 2011 purchasing power parity (PPP) exchange rate)
, regulatory reform processes work best when institutions are strong and promote effective competition in services. Some types of services can lag due to market failures, requiring special attention from competition agencies and domestic regulators. OECD competition policy indicators show that Turkey performs well vis‐à‐vis peer countries (left panel of Figure 5.1), suggesting that future service reforms could be effectively enforced. Another institutional factor that has proven to have a strong impact on the effectiveness of services reform is rule of law. Turkey shows room for improvement in this area, as seen in the right‐hand panel of Figure 5.1.

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from the Climate Hazards Group InfraRed Precipitation Station (CHIRPS) in association with the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center. Several other satellite-based rainfall products lack geographical and temporal coverage. The CHIRPS data, however, has a relatively high spatial and temporal coverage and quasi-global coverage. We extract the 15-year monthly average precipitation for Afghanistan from September 2006 to September 2020.

_Vegetation Index (NDVI):_ The Normalized Difference Vegetation Index (NDVI) data is obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the NASA Earth Orbiting System Terra satellites. MODIS provides an NDVI estimate every eight days at an approximately 250-meter ground surface (pixel) resolution.<sup>12</sup> NDVI values range between -1 and 1. For this paper, we retrieved the average monthly NDVI between September 2006 to September 2020. NDVI relies on the light reflected by plants, in particular the different ways that near-infrared light and visible red light are absorbed and reflected (Huete et al., 2002; Fensholt and Sandholt, 2005), and is used as a tool to measure a wide variety of vegetation related metrics: land cover, crop type, and cover, famine, and drought warning, and indicators of biodiversity (see Morikawa (2014) for details). Higher NDVI values are associated with greener, healthier vegetation, while lower values are indicative of drier vegetation and bare soils (Hulley et al., 2019).

_Temperature (LST):_ The Land Surface Temperature (LST) was extracted from the NASA MODIS Terra satellite data. Temperatures are measured in Kelvin (which we converted to Celsius) at a 1-kilometer spatial resolution. We extract the 15-year monthly average temperature between September 2006 and September 2020.

# **3.3 Mapping Household Data with Remote Sensing Data**

We create a 2-km buffer around each household location to extract data for weather indicators around the buffer area. For the weather indicators, we combine descriptive statistics
interactive positively to further improve performance. In particular, we will empirically investigate whether good governance is less likely in local cities, and thus whether becoming a global city, and a globalized country, helps improve city governance.

Thus, in terms of schematic chart 1, we would expect that the worst performance would take place in cities in quadrant II (poorly governed local city). If it attempts to attain higher levels of globalization, that is unlikely to take place without pressures to take measures to also improve governance. Thus, a rapid and fully horizontal move out of quadrant II to quadrant IV, or a rapid and fully vertical move to quadrant I, are less likely than either the city staying within a low equilibrium trap, or moving out of such a low level quadrant II towards the good performance equilibrium in quadrant III via the diagonal route.

> 13 Taylor defines alpha cities as those with global significance in four key global city functions (international accountancy, advertising, banking, and legal services).  A beta city is one that has global significance in three of the four key world-city (global city) functions.  A gamma city is one that has global significance in two of the four key global city functions.  Using the network aspects of the GaWC is a key advantage that we consider in looking at the issue of governance, asking questions like: “Is it that less globalized cities are relatively less well governed and that results in them trailing even further behind?”  We group cities into two categories of “global” and “local” to present some of the results in charts that visually capture the main empirical findings.

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the school or teachers to try to alter the results.

Once the test is completed, students are asked about their personal characteristics (gender, age, educational history) and a set of questions related to their family background. In particular, the questionnaire asks the maximum level of education that each parent attained, the possession of more than a dozen assets, and access to basic infrastructure services. Of these, I will base most of the analysis to a question on the number of books at home as a proxy of family socioeconomic status. Among the family background questions, this is the one with the best response rate. In addition, this variable is highly correlated with parental income and education, and reading skills in various international surveys (Hanushek & Woessmann 2008, Ammermueller & Pischke 2009).

While parental education is generally perceived to be a reasonable measure of overall family background, I chose to exclude it from the main analysis because more than a third of the students in the survey did not report it. This is not unusual in these sorts of surveys; the proportion of missing values is similar to that in PIRLS (Progress in International Reading Literacy Study, for developed countries plus others), and in SERCE (Segundo Estudio Regional Comparativo y Explicativo, for seventeen Latin American countries). Still, the pupils who have missing values in parental education tend to come from relative less well-off families (lower number of books at home, durable assets, and services) and to perform worse than those who has completed information (in terms of test scores and grade repetition). For robustness, I will replicate the analysis using parental education and an index of assets.

Of the total number of schools surveyed, for the purpose of estimation I exclude schools with only one class per school year. This exclusion allow me to identify the peer-group effects in a convincing way.<sup>7</sup> The resulting sample includes over 7,000 schools (half of the schools in the country), 20
the region and the problem of outdated data remains. In addition, correlation between assets and monetary poverty could be poor in the region, especially in conflict-affected countries where asset ownership may not highly correlate with poverty.

_Food security and subjective well-being_ This section focuses on two alternatives to non-monetary measures of well-being: food security and subjective well-being. Phone and public opinion surveys often collect these measures and each type of survey is discussed below.

# **Phone surveys**

# <u>Method</u>

Remote forms of data collection, such as mobile and fixed-line phone surveys, are particularly effective in two contexts: i) settings where it is not safe or possible to perform face-to-face surveys, and ii) settings needing high-frequency data collection (Hoogeveen et al., 2014). Both settings are prevalent in MENA. Nearly 30 percent of the region’s population is exposed to either conflict or persistent violence, and the entire region is particularly vulnerable to political, climate, and other types of shocks where welfare monitoring at higher frequency could better inform policy making. During the COVID-19 pandemic, many

> 16 Households may have difficulties in remembering all expenditures they made in the past leading to recall bias. Some expenditures/consumption are also seasonal fluctuating across time. Therefore, data collection should ideally last the whole year to properly capture the seasonal variation.

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**14** **<u>│</u>**

At present, there is still no official source of information for the number of foreign multinationals in developing countries that can be used to verify the representativeness for the entire sample. The information available is compiled by the OECD in its Activities of Multinational Enterprises (AMNE) database. It contains the official data published by National Statistical Offices and Central Banks on activities of foreign multinationals. The coverage of AMNE data has been good for the United States and several European countries, none of which has a publicly available World Bank Enterprise Survey.

Recent analytical work at the OECD, however, has produced estimates of the share of foreign multinationals in total output for many more economies by combining information from two official sources: the AMNE analytical statistics and the Inter-Country Input-Output (ICIO) tables (Cadestin et al., 2018). This new data set highlights the contribution of domestic firms and foreign multinationals, as well as their foreign affiliates, to global trade and production. It essentially brings to enterprise analysis the same conceptual shift introduced by Trade in Value-Added statistics (TiVA) in trade analysis. Nineteen of the countries in the new data set have a World Bank Enterprise Survey, 14 of which we use in this study – BRICS and a number of economies in Eastern Europe, conducted in their majority around the same year, 2014 – offering a unique opportunity to assess the representativeness of foreign multinationals.

The share of foreign multinationals in aggregate output calculated by the World Bank Enterprise Surveys and the OECD are largely aligned (see Fig. 1), with a 45-degree line indicating equal shares from the two sources. The Pearson correlation coefficient, which measures the strength of association between two continuous variables (ranging from -1 to +1) reaches 0.86, statistically significant at the 1% level, indicating a high degree of correlation. However, this exercise only accounts for a limited number of countries in the sample. It also considers only a
13 Returning to Table 7 and looking across countries, a positive relationship between per capita income level and government consumption and expenditure shares, a generalized relationship often referred to as Wagner's "Law"<sup>1</sup> 4. s somewhat in evidence. In 1985, the value of median expenditure shares steadily increases across the four groupings of nations ranked from low-income to industrialized market economies. However, within any one group of countries, the range of both expenditure and consumption shares is broad enough so that a considerable overlap is present across all income groups.

A positive relationship between ince-ne level and government spending is also suggested by the movement in individual Ge (Gc) ratios over time. Ge (Gc) shares increase from 1972 (1965) to 1985 in 72% (69%) of the countries for which data are presented. However, sufficient numbers of cases are exceptions to this trend reflecting either: (a) serious misrepresentations in the data; (b) significant shifts in national political orientation; or, (c) counter-examples to Wagner's "Law". Caution again appears warranted in positing any unique relationship between government spending shares and per capita income level.

Beyond the WDR data, an examination of government expenditure trends in developing economies has been undertaken by Pluta(1981). Relying on fiscal data taken from the DiF's, International Financial Statistics (IFS), Pluta measures the growth in government expenditures for 20 non-African developing countries from, roughly, the early 1960's to the mid-1970's. IFS data rely on a country's own definition of government expenditures and are less comparable across countries than data obtained from the _GFSY._ Although Pluta does not
make the jobs workers already have more productive. These improvements could allow firms not only to raise the quality of existing jobs, but to create new ones as well.

A theme running through both parts of the analysis is the extent to which market reforms have facilitated productive job creation. Because the main concern of the paper is firm-level evidence, the focus is on policies that have affected firms: privatization, product and labor market liberalization, and obstacles to growth, particularly in the new private sector.<sup>1</sup> Have these policies helped create more productive jobs through between- and within-sector job reallocation, worker flows, and within-firm productivity growth? The evidence on these questions comes from studies employing firm-level data in Hungary, Romania, Russia, and Ukraine. Most of the data, and therefore most of the evidence, pertain to manufacturing, particularly large and medium-size firms inherited from the planning system. These data are comprehensive in coverage, but they contain relatively few labor market variables (principally, employment and wages) for each firm. The analysis is therefore supplemented by survey data, in Russia based on a stratified probability sample of manufacturing firms and in Romania based on a sample of micro and small enterprises in all sectors.

The main messages from a review of available evidence are as follows. Market reform has resulted in a very large increase in the pace of job reallocation. This was mainly in the form of job destruction in the manufacturing sector during the early years of reform, but job creation has also increased in recent years as the economies recovered. The depth of the reallocation process is signified by a relatively large proportion of job reallocation occurring between sectors (compared with OECD economies, see the summary in Davis and Haltiwanger, 1999). But the pace has also been fast and increasing among firms within the same narrow sectors.

Firms appear to have changed their employment levels primarily, however, by adjusting separations rather than hiring rates. Over the course of the transition period, job reallocation became
insurance payout is given by the insurance cover and the actual damage in that year, AA<sup>II</sup> = θθ(1 + μμ<sup>II</sup> )GGDD . BB<sup>II</sup> II Historically, hurricane damage is zero in most years so that the insurance payout will also be zero in tt tt BB = θθGGDD most cases. Typically, the insurance premium depends on the variance of the insured events. Equation 4.2 in (Gray & Pitts, 2012) specifies a commonly used functional form μμ<sup>II</sup> where ���� are expected damages, AA<sup>II</sup> = θθ∗�DD is the standard deviation of damages and<sup>�</sup> + ββ∗SSDD(GGDD)�, is a parameter. From this, the markup can be derived as GGDD SSDD(GGDD) ββ ���� . ���� and are drawn from the empirical distribution of hurricane events of (World Bank, 2018) μμ<sup>II</sup> = ββ<sup>SSDD(GGDD)</sup> GGDD and the mark-up is an increasing function of the variance parameter β . GGDD SSDD(GGDD)

# 3.3. A contingency fund

A contingency fund is a separate account administered by the national government for the purpose of having funds available for immediate use after a hurricane disaster. The fund is assumed to receive regular payments from the government. If a share of hurricane damage is to be covered by the contingency fund, the regular payment must be θθ ����. Since the government administers the fund itself, the administration cost AA<sup>CC</sup> = θθ(1 + μμ<sup>CC</sup> )GGDD should be lower than the markup paid to the insurer (although poor investment decisions or inappropriate use of the fund could
self-<br>No highereducation 22.2 recognition (black<br>and similar)<br>Men<br>Women<br><!-- End of picture text --> Note: ethnicity information is for 2020 due to availability of information. children <12 years at home.

Figure 16. Average real monthly labor income by socioeconomic characteristics <!-- Start of picture text --> Men 2019 Women 2019<br>Men 2021 Women 2021<br>Real labor income (thousands)<br><!-- End of picture text --> <!-- Start of picture text --> Men 2019 Women 2019<br>Men 2021 Women 2021<br>Real labor income (thousands)<br><!-- End of picture text --> <!-- Start of picture text --> Men 2019 Women 2019<br>Men 2021 Women 2021<br>Real labor income (thousands)<br><!-- End of picture text --> <!-- Start of picture text --> Men 2019 Women 2019<br>Men 2021 Women 2021<br>Real labor income (thousands)<br><!-- End of picture text -->
<!-- Start of picture text --> .015<br>.01<br>.005<br>0<br>0 50 100 150<br>Density kdensity dist_closest<br><!-- End of picture text --> Figure 7: Kernel density (with histogram) showing the distribution of households at distances from closest refugee settlements (Panel 2009-2012) We then exploit the spatial and time variation in the presence of refugees and the related changes in several outcomes of interest. Such variation is obtained through the use of fixed effects to account for unobserved heterogeneity across the households. For this strategy, the regression model follows a two-way fixed effect specification: Each outcome variable _Y_ of household/individual _i_ in year _t_ and cluster _c_ is regressed on the refugee index _Refugee_ in year _t_ and cluster _c_ . To ease interpretation, the refugee index is transformed into logarithm (adding one in case of zero values). We use Ordinary Least Squares as the main method of estimation. To deal with the so-called Moulton problem (Cameron & Miller, 2015; Abadie et al., 2017), we cluster the standard errors at the EAs level.

Causal identification is nonetheless a challenge given the potentially endogenous nature of the presence of refugees. Indeed, the localization of settlements as well as the number of refugees they host can be influenced by unobserved attractiveness of the considered area. Refugees are likely to be located in peripheral areas, mostly rural. These areas may feature less dynamic labor markets, pushing e.g. the coefficient between the presence of refugees and some welfare indicator downward. The descriptive statistics indicate that the level of welfare tends to be lower in refugee-hosting districts 17