rafmacalaba/data-use-mentions-extended huggingface dataset:
Lanka’s IO table for 2000, produced by the Institute of Policy Studies (Amarasinghe and Bandara, 2005). We extract information on employee compensation and value added (at factor cost prices) for a variety of agricultural sectors: “paddy”, “minor export crops”, “highland crops”, “tea growing” (separately for high, medium, and low elevations), and “potatoes”. Section VI.A.3 describes how this data is used in estimation.

# **A.H Wages, Household Characteristics, Occupational Distribution, and Land Distribution**

We use the 2016 and 2019 editions of the HIES for data on wages, employment, land ownership, land distribution, income, and consumption patterns. The HIES is a yearlong survey conducted in consecutive monthly rounds, with a nationally representative sample enumerated in each round to capture seasonal and regional pattern variation. The HIES provides household-level statistical
Currently, over 107 countries have some form of explicit deposit insurance, up from 93 in 2013 according to the Bank Regulation and Supervision Survey (BRSS) conducted by the World Bank. Figure 1 shows the number of countries in each income group with explicit insurance in the 2013 and 2016 BRSS surveys. Figure 2 shows the percentage of countries with explicit insurance in 2016 as well as the increase compared to 2013, which are indicated by dashed lines on the graph. Over 80% of the countries in the high-income group have some form explicit deposit insurance in place. There has been an increase in the percentage of countries with explicit deposit insurance since 2013, especially in the low-income group. Figure 3 shows the average percentage of total bank liabilities that are covered by deposit insurance within each income group. Over 40% of liabilities are covered for the high and upper middle-income countries, while less than 10% of liabilities are covered in low income countries.

Some of the recent changes have been implemented in the aftermath of the global financial crisis. During the crisis, several countries including Australia and Singapore introduced explicit deposit insurance schemes for the first time. Other countries, like the United States and Spain, substantially increased the coverage limit, while others like Germany and Ireland introduced unlimited coverage in order to restore confidence in the markets. Figure 4 shows the percentage of countries that have implemented changes in their deposit insurance systems in response to the crisis. Most countries, especially those in the high-income group significantly increased both the limit and the type of accounts covered under deposit insurance.

Overall, explicit deposit insurance is widely adopted across the globe and is now a standard policy of bank regulators and supervisors. The recent crisis has reinforced the importance of deposit insurance in promoting stability and its role in the overall financial safety net provided to the banking sector.

# **4. Economic Benefits of Deposit Insurance**

As the preceding discussion suggests, banks are uniquely prone to runs because of the role they play in the economy in transforming short-term
8 government credibility and its willingness to respect property rights of individuals (see, e.g., Knack and Keefer 1995 and Acemoglu, et al. 2002, Clague, et al. 1996).

High bureaucratic quality is another government policy with public good characteristics. Lower bureaucratic quality implies that the quality of public services offered generally by government to the average citizen is low, and that the average citizen confronts more obstacles to doing business with government. At the same time, those obstacles can always be eliminated for favored constituents of the government. Hence, bureaucratic quality is a useful indicator of the tradeoff that governments make between the pursuit of general and private interests. It can, again, be measured using the eponymous variable from Political Risk Service’s _International Country Risk Guide_ .

# **_Measuring public or non-targeted good provision:  secondary school enrollment_**

These measures of public or non-targeted good provision are subjective. The best objective measure would be one that quantified government spending on public or nontargeted goods. Unfortunately, even when we can identify categories of government spending that appear to be untargeted or dedicated to public good provision, the actual destination of these funds is often targeted. For example, education spending can be aimed at raising achievement for all children (through curriculum reforms, testing, spending on high quality teachers); or it can be targeted (by building new schools in some areas, but not in others). Cross-country budget data on education do not distinguish which.

To circumvent this difficulty, an outcome indicator, secondary school enrollment, is used below to approximate the extent to which the public component of education spending predominates.<sup>6</sup> If politicians care relatively more about political targeting than they do about

> 6 Primary school enrollment could, in principle, also be used, but most countries send most of their children to primary school  The coefficient of variation in the sample used here is 17.6 percent for primary school enrollment, and 50.8 percent for
maximum efficiency colonial objectives of a predatory state. The system created a core of civil service elite which was highly educated and highly dedicated to serving the colonial rulers. Their loyalty to rulers and detachment from the common man was duly rewarded by allowing them preferential access to all public services through elite institutions and by ensuring them financial security through a system of cash rewards and land grants. Thus colonized countries such as 2 The population census scheduled for 199 1 was finally held in 1998.

10
induced vulnerability, which encompasses chronically poor households, and second, risk-induced vulnerability, which covers households that are not now poor, but that face a substantial risk of falling into poverty. The approach is widely favored among practitioners because it facilitates the generation of vulnerability estimates in the absence of panel data. Notable instances of such an application are described by Atamanov, Mukiza, and Ssennono (2022), Rude and Robayo-Abril (2023), and Skoufias, Vinha, and Beyene (2024).

While the Günther and Harttgen (2009) method offers the advantage of requiring only a single crosssectional household survey, the vulnerability estimates are representative at the same geographical level as the household survey. The method thus does not provide vulnerability rates at the spatial resolution necessary to implement the effective, highly disaggregated targeting sought for social programs. The proposed approach overcomes this limitation by recovering poverty rates and the shares of households at risk of becoming poor from welfare indicators imputed from census data. This information is then used to create a highly spatially disaggregated vulnerability map. As in Günther and Harttgen (2009), a household is considered vulnerable if it exhibits a probability of falling into poverty of more than 50 percent over the next two years. Methodological details are provided in the data and methods section.

# The concept of vulnerability

According to Dercon (2005, 2010), vulnerability is often defined as “the risk of households falling into or remaining in poverty because of either idiosyncratic hazards (due to characteristics of the individual household) or covariate aggregate hazards (external to the household).” This definition highlights several characteristics of vulnerability. First, both poor and nonpoor households can be vulnerable. Second, risk is a critical component of vulnerability. Third, risk is related to household- and community-specific characteristics.

Transforming this general definition into measurable concepts requires a precise understanding and the modeling of the income-generating function of households, particularly the expected mean and variance of income and consumption under different realizations of the potential shocks. Overall, there are three
# **2 and Economic Growth**

Civil wars affect economic conditions in two stages: during conflict and during the postwar period. We start our analysis by exploring the first stage, i.e. the relationship between violence and economic activity during conflict. We then turn towards the recovery from conflict. It turns out that the contemporaneous cost of conflict is essential to comprehend the observed lack of long term development in FCS. This is because many FCS do not experience stable peace. Economic recovery happens on average but it does not last long enough. This is why understanding the contemporaneous effects of conflict is essential to understand long term development.

However, the direct economic cost of conflict incidence is not the only channel through which civil wars impact the development of the affected territories. In this report we explore two ways in which the detrimental effects of conflict spread to the recovery period: the humanitarian crisis triggered by wars (sections 4 and 5) and the contraction of investments due to instability (section 6). Both of these effects will force growth in the recovery period downwards. This is important as it indicates that the contemporaneous effects we turn towards now might be an underestimate.

The macroeconomic impact of civil wars has been studied extensively. Collier (1999) analyzes the effects of violence on GDP growth using cross-county data on internal wars which occurred between 1960 and 1992. He finds that civil conflict is correlated with a contemporaneous reduction of GDP per capita growth of 2.2 percent.<sup>2</sup> On a regional level, Abadie and Gardeazabal (2003) investigate the economic effect of the Basque terror campaign and estimate a reduction of the GDP per capita of approximately 10 percent.

There are several channels through which violence retards development. During conflict there is a direct cost caused by the destruction of resources that would have been employed in production. This directly impacts contemporaneous economic performance in the affected territory and can affect recovery as well. In addition, the economic activity of countries involved in
Malawi has been engaged for several years in rollout programs for prepaid meters, having achieved enrollment of 47 percent of customers by 2014, which helped to achieve a 93 percent bill collection rate in 2014.

There is a strongly statistically significant correlation between T&D losses and bill collection losses. The correlation coefficient of 0.68 is significant using a 1-percent test. This may suggest that operational inefficiencies tend to be pervasive rather than found in isolated pockets.

# 6.3 Utility staffing

Staffing data were available in 36 countries. There are at least several important dimensions to consider with respect to utility staffing, including staff numbers, staff skills, attendance (reporting to work), and salaries and benefits. A utility may appear overstaffed, but if many staff members on the payroll are not showing up for work—a known problem in several utilities—the utility may actually be chronically understaffed with large and unproductive overhead costs that have a strong political dimension. Staff skills may also be a concern, with many utilities suffering from lack of appropriately trained and experienced staff.

Figure 11 summarizes utility staff costs, which include employee salaries and associated costs such as health benefits and pensions. Staff costs can represent a significant portion of operating costs for a utility: on average US$27,000 per employee per year in constant 2014 US dollars, although this result is heavily skewed by South Africa where the staff costs average US$61,000 per employee. Excluding South Africa, staff costs are on average US$13,000 per employee. Staff costs represent a median of 14 percent of operating costs. Staff costs represent the lowest share of opex in Tanzania (5 percent). At the 46
11 political freedom are not perfectly correlated, but the correlation is expected to be highly positive. For the sample of 89 countries for which data are available for 2000, the correlation between the negative of the corruption perception index and the Gastil index is 0.66.<sup>14</sup> The fact that the correlation between the Gastil index and the two available proxies, albeit strong, is far from being perfect should not be viewed as disappointing since the Gastil index itself is an imperfect discrete measure of the underlying concept of democracy.

The equation to be estimated is thus: Since this study focuses on the long-term determinants of the degree of fiscal decentralization rather than on explaining short-term deviations from the mean, equation (1) is estimated by applying the _between_ panel estimator that exploits the cross-sectional variation of the time averages for each region.<sup>15</sup>

**2.4. Data** . To test the five hypotheses, we assembled a new dataset on Russian regions. The data cover the period 1994-2001 and 86 Russian regions (the cities of Moscow and St. Petersburg are excluded as incomparable with the rest of the regions for the purpose of the analysis; and Chechnya is excluded due to data unavailability).<sup>16</sup> The data are taken mostly from official Russian statistical yearbooks and the Ministry of Finance database and augmented from other sources listed in Appendix 1. The data for certain years and regions are unavailable, for

> 14 Higher values of the corruption perception index compiled by Transparency International correspond to lower corruption and hence the negative of the index is taken. 15 See Wooldridge (2002) for details.

> 16 The data before 1994 are unreliable due to hyperinflation and poor coverage (e.g., GRPs were not computed). The period after 2001 appears to be characterized by much stronger centralization, particularly in the political sense, so that the degree of fiscal decentralization within a region can no longer be thought of as
_ANDR EE_<sup>_ ́_</sup> _DECEMBER - 2021_ _4_ by replacing a subset of expensive surveys with targeted predictions, and advance broader economic monitoring in data-poor regions.

The remainder of the paper has been structured as follows. The next section provides a brief overview of the type of price surveys that can be used to deploy the new monitoring capabilities and discusses some basic challenges encountered when trying to read inflation from the raw data. Section II details the imputation strategy and section III presents results. For those interested in analyzing the subnational price and inflation estimates further, all results for the 25 countries analyzed are available to be interactively explored.<sup>5</sup> Section IV provides concluding remarks and makes several recommendations for future data gathering.

# **I. Food Price Survey Data**

Subnational food prices have been surveyed in many countries for years by humanitarians to inform their country operations. Well-known data bases are those from the WFP, FEWS NET and the Food and Agricultural Organization (FAO).<sup>6</sup> The paper focuses on raw monthly data from the WFP, but parts of the discussion, and particularly the methods developed here, could apply to similar data sets.<sup>7</sup> The paper gathered all end-of-August data available from the WFP Vulnerability Analysis and Mapping (VAM) unit as of September 21, 2021. The WFP data reports prices as measured in different market locations throughout each country. Price quotes are supplied by WFP country and regional offices, and local partners including the FAO. In total, the data base covers over 2,000 markets across 99 countries, and reports monthly data on the prices of goods that vary by country and market. Price monitoring dates back to early/mid 2000s in most countries, while in few countries monitoring began already in the 1990s.

The number of markets varies strongly by country, ranging from 1 to well over a 100. The methods developed by the paper are
people were also shocked to learn that there were about one billion people in the world living on less than $1 per day, at purchasing power parity (World Bank, 1990).<sup>22</sup> Such data, and (probably more importantly) credible poverty data for specific countries, have been the empirical foundation of domestic and international efforts to fight poverty since the 1980s.

> 19 Marshall (1907) estimates that 21 shillings was equivalent to three-quarters of a bushel of good wheat. At 13.5% moisture by weight a bushel of wheat weights 60lb according to the Wikipedia entry on “bushel.” I assume a household of 4.5 people, which is the lower bound of the range 4.5-5 given by Booth (1993, Chapter 4) for the average size of working men‟s families at the time.

> 20 See Bronislaw Geremek, (1994, Chapter 7).

21

> On Booth‟s influence see the Wikipedia entry on Charles Booth and the Archive maintained by the London School of Economics. On Hunter‟s work see

> 22 This calculation was based on household surveys for just 22 developing countries (Ravallion, et al., 1991). The $1 a day line was a typical poverty line for low-income countries. Since then there has been a massive expansion in survey data collection and availability, and refinements to the methodology; the latest estimates can be found in Chen and Ravallion, 2010).

15
been producing by the end of 2020 according to early forecasts, and some should have already been producing for several years by now. The reality has been very different.

In Tanzania, the first publicized timeline for the development of the planned LNG project comprising blocks 1, 2 and 4 was from the IMF in 2012, which suggested that first gas by 2020 was achievable (IMF, 2012b). This prediction was subsequently echoed in company and government statements. However, delays in passing key legislation and identification of a site for the planned LNG plant and a fall in gas prices caused this timetable to slip. Sweeping legislative reforms in 2017 and a government review of existing production sharing agreements (PSAs) (including for blocks 1, 2 and 4) in 2019 have complicated negotiations and pushed back the timeline further.<sup>16</sup> In early 2019, the government stated that it hoped to agree to key terms by September 2019, reach an FID in 2022 and first gas in 2028 (Ng’wanakilala and Dausen, 2019). However, with key terms still yet to be agreed and negotiations currently suspended, further slippages are almost certain (Materu, 2019b).

Uganda’s journey to first oil has been similarly challenging. The first operator, Tullow, initially targeted first oil in 2009, and this timeline was reflected in IMF reports. However, it was soon pushed back to 2010 and then repeatedly delayed thereafter. The IMF, for example, has changed its first oil estimate eight times since its initial projection (see Figure 3). The most recent estimate is first oil in 2023 to 2024 (IMF, 2019b, p. 8), but this is likely to also prove over-optimistic with an FID unlikely before 2021 (Perkins, 2020).

Our analysis of oil and gas asset data between 1960 and 2018 suggests that, on average across all projects worldwide, it took 7.5 years to get from discovery to production. In Sub-Saharan Africa, the 15 There
aspirations, and many of the existing ones employ such diverse methodologies that meaningful comparisons become challenging. As a result, there is a lack of comprehensive data regarding the aspirations of the general population, especially among young individuals. A key contributing factor to this data gap is the absence of standardized instruments and guidelines for measuring youth aspirations. Incorporating an aspirations module into household surveys can help capture aspirations over time and establish connections with household characteristics. Collecting individual-level data within nationally representative multi-topic household surveys enables the identification of various attributes concerning young people and their families. This, in turn, enriches our comprehension of how aspirations impact the future prospects of youth.

However, it is crucial to acknowledge that the measurement of youth aspirations requires ongoing research and refinement. Rigorous testing of assessment tools and their adaptation to local contexts are essential steps, as they can incentivize countries to comprehensively assess the state of youth aspirations. This comprehensive understanding is vital for elucidating how aspirations influence future outcomes, including how they may evolve with age, respond to external shocks, and adapt in light of available coping mechanisms. Furthermore, delving into the connections between current youth aspirations and family-related factors, such as the educational achievements of their parents or the socio-economic background of the household, can significantly contribute to a more nuanced policy discourse aimed at aligning aspirations with viable job opportunities.

# **7. References**

Akosah-Twumasi, P. et al., 2018. A Systematic Review of Factors That Influence Youths Career Choices—the Role of Culture.. _Front. Educ,_ p. 3:58..

Appadurai, A., 2004. The Capacity to Aspire: Culture and the Terms of Recognition. In: _Culture and Public Action._ Washington: World Bank, pp. 59-84.

Bandura, A., Barbaranelli, C., Caprara, G. V. & Pastorelli, C., 2001. Self-efficacy beliefs as shapers of children's aspirations and career trajectories. _Child Development ,_ p. 72(1)
measure business opportunities in the host-country. Data for both of these variables comes from the _World Development Indicators_ produced by the World Bank.

_Host-Country Risk_ refers to four different types of host-country risk indexes reported by the International Country Risk Guide (ICRG in its _country risk index._<sup>_14_</sup> ). These variables are _Economic Risk_ , _Political Risk_ , _Investment Profile Risk_ , and _Country Risk_ (composite index). The _Economic Risk_ rating assesses a country’s economic strength or weakness as a function of variables such as the country’s GDP per capita, real GDP growth, annual inflation rate, budget balance, and current account balance.<sup>15</sup> The _Political Risk_ rating provides a summary of the degree of political stability in each host country. _Investment Profile Risk_ , a subcomponent of the _Political Risk_ index, takes into account factors such as contract viability, profits repatriation, payments delays measurements, etc. that affect those who invest in the host country. Finally, the _Country Risk_ is a composite index that includes both economic and political risk variables and other variables such as exchange rate stability and total foreign debt as percent of GDP. We change the sign on these variables from how ICRG reports them, so that higher values indicate greater potential risk.

The type of organizational form explained by equation (1) can only be observed for a given host-country once the parent bank decides to establish operations in that country. Thus, in estimating equation (1) there might be a selection bias, unless we also consider how parent banks make decisions on where to establish foreign operations. We take this into account by estimating a Heckman probit model where the first stage or selection equation is modeled following

> 14 This index is defined by ICRG so that higher values mean less risk, but in our estimations we reverse the sign on this variable to give it the more intuitive interpretation that higher values mean more risk.

> 15 Although the ICRG Economic Risk index includes the host-country