rafmacalaba/data-use-mentions-extended huggingface dataset:
20 percent of utilities reported a net profit. As noted in Dobozi (2016), the main cause of the slow progress in access expansion in SSA is the poor financial viability of electricity utilities. The sector needs substantial capital infusion. The annual cost of addressing SSA’s power sector needs has been estimated at US$41 billion, including US$14 billion for new power generation additions (Eberhard et al. 2011). Other estimates have suggested US$17 billion of investments a year are needed just to achieve universal access targets (SE4ALL 2015a). Whether US$14 billion or US$17 billion, these investment requirements contrast with an estimated US$3 billion’s worth of new capacity coming on stream in 2013 found in this study. Although there are many reasons for this gap, poor sector fundamentals—policy, regulatory, and contractual framework meeting international benchmarks; enforcement of regulations and policies in place; the payment discipline of all parties; and the financial viability of utility operation—are largely responsible. And despite large financial gaps between the cost of service and cash collected, electricity consumers in SSA do not necessarily enjoy low tariffs. If anything, tariffs in the region are relatively high for a variety of reasons: small economies of scale, low concentrations of high-volume consumers with good payment discipline, continuing reliance on oilbased generation, and high operational inefficiencies.

Due to continuing financial shortfalls, not only is the power sector in most SSA countries unable to expand supply to new consumers at the desired pace, it cannot deliver reliable electricity to the existing ones. Acute power shortages act as a brake on economic growth. In surveys of firms in the region, 38 percent identified electricity as a major constraint to doing business and 50 percent reported owning or sharing a back-up generator. On average, firms reported losing 8.6 percent of the total annual sales due to power outages (Enterprise Surveys 2016). A new index ranging from 0 to 8 for supply reliability and tariff transparency tabulated by
Another often overlooked yet complementary aspect of climate migration studies is climate-induced immobility traps. This refers to the same climate shocks possibly creating the opposite outcome by reinforcing pre-existing vulnerabilities and aggravating liquidity constraints, forcing poorer households to stay due to a lack of necessary resources to cover the costs of migration. Under this alternative scenario, climate change would reinforce existing and generate new local poverty traps and vulnerability pockets in the most affected (especially rural) areas, which should make climate-related immobility as much of a concern as mobility (Findlay, 2012; Letta et al., 2023).

Gaining a firm understanding of these complex dynamics requires clarity about what we need to measure, at what scale and how and, more importantly, filling data gaps on climate migration at the micro level. This not only involves providing more accurate quantification of migration inflows and outflows, but also improving the ability to properly characterize the phenomenon and empirically assess the ultimate socioeconomic and welfare impacts and how they differ across population subgroups (e.g., What type of migration does climate change induce? How do those migrating differ from those who stay? What channels and contextual factors mediate different migration responses and the realization of different outcomes, and how do they interact with each other?). Conducting these measurements requires an adequate amount of sufficiently disaggregated information on multiple socioeconomic aspects and living standards. It also requires this information to be collected in a consistent and systematic way, made available and accessible in a timely manner, and updated regularly. Addressing these data needs in the immediate future is instrumental to building the evidence base needed to inform policies and development interventions aimed at improving climate change adaptation and promoting better forms of mobility.

Household surveys are one of the data sources traditionally used to measure and study migration. Despite some recognized limitations, _multi-topic_ household surveys hold great potential when it comes to analytically addressing complex, multidimensional phenomena such as climate and migration, which require data on multiple livelihood dimensions and possibly over extended periods of time. When georeferencing is used in
Liberia was associated with a 50 percent increase in the risk of IPV compared to residing in a district where there were no conflict-related fatalities. Women living in a district that experienced 4-5 cumulative years of conflict were also more likely to experience IPV. Gallegos and Guitierrez (2016) examine the effect of women’s exposure to the internal conflict in Peru in childhood and teenage years on the likelihood that they will perpetrate or experience domestic violence. Findings suggest that female exposure to the internal conflict in Peru increases their risk of being both a perpetrator and a victim of IPV. The study also finds that women exposed to more conflict events are more likely to justify violence against women and stay in a violent relationship. Østby (2016) using subnational data on armed conflict events with individual-level data on partner abuse from DHS surveys in 17 Sub-Saharan African countries finds that the intensity of armed conflict in the home region has an independent and significant effect on women’s risk of experiencing sexual IPV. Falb et al. (2013) examine the effect of <mark>conflict on past-year IPV among refugee women affected by the conflict in Burma (Myanmar). They find that women who experienced conflict victimization</mark><sup>15</sup> <mark>were 6 times more likely to report past-year IPV than women who had not experienced conflict victimization.</mark> Turning to drivers of IPV in conflict affected and fragile settings, results from a number of studies are consistent with the hypothesis that the impact of conflict outside the home spills over to affect individuals within the household. Bruck and Stojetz (2019) examine exposure to wartime sexual violence by armed groups and find exposure to sexual violence by armed groups against women makes male veterans about 30 percentage points more likely to commit physical – but not sexual – IPV 18 years later (on average). Saile et al. (2013) <mark>examine the prevalence and predictors of current partner violence experienced by women in post-conflict Uganda. The authors suggest that
Financial Income**||||||||||||||||||||
|Monetary Financial<br>Income|5.36|9.13|5.87|4.87|n.d.<br>4.87|3.67|6.17|4.08|3.13<br>n.d.|3.13|4.22|5.85|4.33|10.97|10.09|10.46|3.57|3.89|3.60|
|Non Monetary Financial<br>Income|0.00|0.00|0.00|0.12|n.d.<br>0.12|0.00|0.00|0.00|0.00<br>n.d.|0.00|0.03|0.09|0.03|0.01|0.11|0.07|0.01|0.02|0.01|

n.d.: No data in the survey.

Source: Own calculations based on ENIGH96 .

18
checks**

Our identification assumption would be violated in the presence of confounding, unobserved, omitted determinants of labor supply that vary over time within census blocks. In other words, if there were omitted variables that have not been fully accounted for by the rich set of fixed effects (week and municipality-by-year). We address this concern in two ways. First, we add a richer set of time-varying controls, namely, quarter-by-year fixed effects and non-parametric trends interacted with observable characteristics, such as poverty status, number of income earners, and worker’s age. The results, however, are similar (see column 1 in Table 4). Second,

> 33We are, however, unable to examine the effect on adults and the elderly due to data limitations. The DHS only covers morbidity of children 5 years and younger, while the ENAHO’s health data covers a long reference period and does not distinguish respiratory diseases.

> 34The main limitations are that (1) the DHS does not have a panel sample, (2) covers fewer years (2007 to 2009), (3) the sample is not distributed over the whole year, and (4) household’s locations are randomly displaced. These features prevent the use of household or week fixed effects, and difficult matching the household to a given municipality.

> 35See notes of Table 3 for additional estimation details.

> 36Results are similar using a more flexible specification with a step function of PM2 _._ 5 similar to the one used in Section 4.3, see Table B.2 in the Appendix.

16
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE concession arrangements in East Africa, the first for TRC and the second for the for Kenya-Uganda railway. The concession arrangements have helped to boost operational efficiency for TRC in particular, and its performance is now substantially better than the region’s major publicly owned railways based on measures of labor and rolling stock productivity (table 2.4).

**Table 2.4 . Performance of East African railways, 2005**

||Labor<br>productivity|Carriage<br>productivity|Locomotive<br>productivity|Wagon<br>productivity|Freight yield|Passenger<br>yield|
|---|---|---|---|---|---|---|
|Djibouti, CDE|||||12.5|3.1|
|Ethiopia/Djibouti, CDE|71|3,037|11|156|||
|Kenya, KRC (RVRC)|203|1,159|24|218|3.8|0.6|
|Sudan, Sudan|77|329|9|225|||
|Tanzania, TRC|259|3,264|25|583|4|1.6|
|Tanzania, TAZARA|352|2,770|13|551|3|1.1|
|Uganda, URC (RVRC)|156||9|144|||
|Rail concessions|350|2,945|23|491|5|2|

Legend: Labor productivity = ̳000s traffic units per employee; Locomotive productivity = millions of traffic units per locomotive; carriage productivity = ̳000s passenger-kilometers per carriage; wagon productivity = ̳000s net tonne-kilometers per wagon. Source: AICD railways database.

According to the 2010 LPI, the quality of East Africa’s rail infrastructure is remarkably low compared with that of the other parts of Africa. This
the SITC data into standard System of National Accounts (SNA) categories of per the Broad Economic Categories (BEC) classification system.<sup>9</sup> Data for EIAs come from two primary sources. We make use of the World Trade Institute Design of Trade Agreements (DESTA) dataset as a primary data source, which comprises information on 601 bilateral, regional, and multi-lateral agreements for the period 1961-2013. One of the advantages of this dataset is that it provides information on EIAs across several dimensions: type of agreement, depth of agreement,<sup>10</sup> and type of provisions referenced in the text of each agreement (partitioned into seven categories).<sup>11</sup> Our binary EIA variable is equal to unity if the agreement is a partial scope agreement, full FTA, customs union, or services agreement. We exclude framework agreements, as these are extremely shallow and do not contain any specific provisions. The DESTA variable for depth is an additive index that ranges from zero to seven where each bump up the index represents an additional category of substantive provisions that is included in the agreement.<sup>12</sup> We disaggregate this into seven dummy variables to be used as controls, where each dummy corresponds to a different category of provisions.

As a secondary dataset on EIAs and EIA depth, we also make use of an updated version of the BBF (2014) dataset. In short, this dataset provides an index of bilateral and regional EIAs for years 19602011, ranked from 1-6, where 1 represents the shallowest form of integration agreement (one-way preferential trade agreement) and 6 represents the deepest form of EIA (economic union). Our binary EIA variable used in this analysis is equal to unity if two countries have a two-way preferential trade agreement (TWPTA) or any deeper agreement, and zero otherwise. Results using this specification are presented in Appendix 1.

Data used to construct variables for economic proximity used in robustness checks and
penditure on a non-seasonally adjusted basis–are used. The government’s financial statements across economies may be reported at the central government or general government level. For government revenue, both general government or central government level, data are collected based on availability. When national accounts data are used for government spending, and given the identifying assumptions used in model based on Ilzetzki et al. (2013), the data is checked to make sure it is not interpolated and avoid spurious results.<sup>6</sup> Given the lack of tax only data for many economies, overall revenue including tax and nontax sources of revenue are used. To model monetary policy, this paper uses the ex-ante real interest rate defined as the three-month interest rate less one-year-ahead consumer inflation forecasts.

As a rule, when official data is not available in real terms (constant prices) it is deflated using the GDP deflator. When data is not available from official sources as seasonally adjusted, and seasonality is present, the X13-ARIMA-SEATS method to seasonally adjust the data is implemented (US Census Bureau, 2016). When data is monthly, like reported for government spending and revenue, the data is summed (or averaged in the case of interest rates) to get to a quarterly frequency.

Table 1: Data

|Variable|Defnition|Transformation|Sources|
|---|---|---|---|
|_Git_|Real public consumption expenditure, seasonally adjusted|Log level|Haver Analytics|
|_Yit_|Real GDP, seasonally adjusted<br>|Log level|Haver Analytics|
|_πit_|GDP defator, seasonally adjusted|Log level|Haver Analytics|
|_Tit_|Real general/central government revenue|Log level|Haver Analytics|
|_Rit_|Ex-ante real interest rate|Level|Haver Analytics; OECD|
|_Cit_|Real private consumption expenditure, seasonally adjusted<br>|Log Level|Haver Analytics|
|_Iit_|Real gross fxed capital formation, seasonally
from the satellite can provide near real-time estimates of flow. The policy application is to provide a probability of extreme flows in these transboundary basins to inform decisions regarding water obligations and to estimate the current probability of not meeting a particular treaty obligation.

# **METHODOLOGY FOR MODELING SURFACE WETNESS WITH FLOW**

# **Surface wetness (BWI)**

We utilize a surface wetness index, the Basist Wetness Index (BWI), due to its strong correspondence with flow and availability of monthly time-series data. BWI is derived from SSM/I observations to estimate runoff (Basist et al. 1998). The BWI is based on the emissivity adjustment associated with water in the radiating surface and has a strong correlation with the upper level soil moisture (Basist et al. 2001). This surface wetness is sensitive to liquid water from multiple sources (i.e. precipitation, irrigation, snow melt, rivers, swamps, and lakes), yet the satellite observations are largely insensitive to clouds. This allows measurements to monitor water near the surface under almost all conditions at 30 square Kilometers across the globe (Owe et al. 1992) with accurate measures of the amount of liquid water on or near the earth's surface (Vinnokov et al. 1999). The index ranges from zero, which represents no water detected near the surface, to all other values, which represent the percentage of radiating surface that is liquid water. The index takes the form of a linear relationship between channel measurements (Equation 1), where a channel measurement is the value observed at a particular frequency and polarization (i.e. the SSM/I observes seven channels).

where the change of emissivity, Δε, was empirically determined from global SSM/I measurements, _Ts_ is surface temperature over wet or dry land, _Tb_ is the satellite brightness temperature at a particular frequency (GHz), _υ n_ (e.g. _υ1 υ2 υ3_ ) is a frequency observed by the SSM/I instrument,
survival and find that selection on efficiency exists among larger firms but less so for small firms. Supporting these results, Shiferaw (2009) finds that the probability of firm exit is lower in expanding industries and that productive firms are less likely to exit among Ethiopian manufacturing firms.

Finally, the literature examines the impact of macroeconomic and other shocks on private sector behavior. For instance, large macroeconomic disturbances are found to affect the firm turnover process. A study of new firms in the U.S. finds that startup rates are substantially shaped by both macroeconomic fluctuations as well as industry-specific characteristics; macroeconomic expansion serves as a catalyst for startup activity, but new-firm startups are promoted by a low cost of capital as well as high unemployment rates (Audretsch and Acs, 1994). Levinsohn (1999) investigates employment patterns in Chilean manufacturing firms following a major trade liberalization between 1975-1979, which made the economy more outward-oriented, removed quantitative trade restrictions and slashed tariff rates, privatized firms, relaxed government controlled prices, and reformed financial markets. He finds that in the years following trade liberalization, firm size matters for job creation and destruction; job churning is very high in both expanding and contracting industries; and job creation and destruction varies with whether the firm is operating in the tradable or non-tradable sectors. The importance of firm size in determining survival is supported by another study that examines firms in Ghana, Kenya, and Tanzania during the 1990s, when market reforms were implemented to eliminate protection of the domestic firms (Harding et al., 2004). To summarize, the literature in developing countries generally finds that the main determinant of firm exit is size, with small firms having much higher exit rates than large firms.

# **3. Macroeconomic background and government reforms**

In 2010, Cote d'Ivoire ranked 149 out of 177 in the UNDP "Human Development Index" (UNDP, 2010).<sup>7</sup> This followed a period of political instability and civil war (2002 to
response to outbreaks by human, animal, and wildlife sectors in place. (9) Coordinated disease surveillance systems in place in the animal health and public health sectors for zoonotic diseases/pathogens identified as joint priorities. (10) Mechanisms for responding to infectious and potential zoonotic diseases established and functional. (11) Outbreak/pandemic emergency risk communication plan and activities developed and tested.

23 Gage and Bauhoff. “Health Systems in Low-Income Countries Will Struggle to Protect Health Workers from COVID-19”. Blog post. Published March 31, 2020. https://www.cgdev.org/blog/health-systems-low-income- <u>countries-will-struggle-protect-health-workers-covid-19</u> 24 Measure DHS Service Provision Assessment Survey Inventory Questionnaire <u>https://dhsprogram.com/pubs/pdf/SPAQ1/INVENTORY_06012012.pdf</u> 21
extant literature.<sup>5</sup> Using the events following the 2005 earthquake in Pakistan together with a specially designed household census and survey of more than 28,000 households implemented by the authors in 2009 in four earthquake-affected districts, we are able to provide the first such evidence. We find that mirroring the provision of foreign assistance, trust in foreigners increases dramatically in households living closer to the 2005 earthquake activated fault-line.

The 2005 Pakistan earthquake provides a particularly suitable environment to quantitatively estimate the effects of _particular types_ of foreign assistance on the “hearts and minds” of the local population. The powerful 7.6 magnitude earthquake that struck the NorthEastern region of Pakistan on October 8<sup>th</sup> , 2005 along the Himalayan Frontal Thrust Fault resulted in unprecedented destruction of property and loss of life. An estimated 74,000 Pakistanis died, 70,000 were seriously injured, and over 2.8 million were left homeless. Between October and January, there was a desperate “race against winter”—an all-out effort to get households into some sort of shelter before the harsh Himalayan winter resulted in even higher casualties. Given the severely correlated shock that ruled out any sort of local risk-sharing, it is likely that assistance received during this time had a very high marginal utility and would thus have an especially strong impact on individual attitudes towards the aid provider.

Immediately following the earthquake, there was a remarkable outpouring of aid from all over Pakistan and around the world. This aid came in many shapes and forms. Apart from financial support for the Pakistani government, organizations and individuals provided logistical and technical assistance. A multitude of people ranging from doctors, nurses to trained personnel specializing in excavation arrived from around the world into the affected region. Till March 2006, foreign organizations continued to provide support in terms of emergency shelters and

> 5 In contrast, there is a large and heavily debated literature on the effectiveness of aid in terms of influencing poverty, governance or other economic outcomes
Policy Research Working Paper 8968

# **Abstract**

Although low tax morale hits developing countries hardest, little is known about its determinants in those countries. This paper examines the impact of trust in public institutions and the neighborhood on individual tax morale in four African countries: Algeria, Ghana, Morocco, and Nigeria. First, the paper provides theoretical foundations of such a relationship. Further, the paper uses the World Value Survey to estimate the effects of trust in public institutions and the neighborhood on individual tax morale. The identification strategy employs the instrumental variables method and relies on historical data on the slave trade and the literature on the cultural heritage of trust. The paper finds that trust in public institutions and the neighborhood are largely associated with tax morale in the African countries under consideration. The findings are robust to an alternative identification strategy, additional controls, and a falsification test.

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

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team