rafmacalaba/data-use-mentions-extended huggingface dataset:
|
|Price of an intra-Africa cal|l per minute|0.29–0.33|0.7|0.57|non applicable|

Source: Mali 2005 data together with benchmarks are taken from the AICD Database. Mali 2007/8 is compiled from a variety of World Bank sources in order to give a sense of the progress made during the last four/five years (including Information and Communications for Development database). Source: Ampah and others 2009.

Derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data — = data not available. n.a. = not applicable. Numbers reported in the table as of 2007.

**Table 14. Mali’s skyrocketing mobile teledensity**

|Subscribers per 100 people||||||
|---|---|---|---|---|---|
|Country|2005|2006|2007|2008|Average annual growth|
|Benin|14.10|19.20|23.7|27.7|25.50|
|Burkina Faso|5.00|7.60|11.5|14.4|42.84|
|Ghana|12.10|19.30|29.4|34.6|43.17|
|Mali|6.70|12.90|21.1|35.4|74.63|
|Niger|2.70|4.40|6.7|8.5|47.37|
|Nigeria|14.40|24.40|34.7|44.2|46.35|
|Togo|8.10|10.70|13.2|15.6|24.55|

Since 1992, private flows to Mali’s ICT sector have increased from almost zero to $19.
who were employed in transport and utility sectors. Similarly, temporary migrants employed in transport and utility sectors while abroad do not disproportionately transition into that sector after return. Overall, descriptive evidence on sectoral patterns lends little support for human capital accumulation as a driver of temporary migration from Bangladesh.

Table 4: Distribution of temporary migrants across sectors of activity

||Before Migration|During Migration|After Return|
|---|---|---|---|
|Sector|%|%|%|
|Agriculture|22.2|3.0|25.9|
|Construction|23.8|66.6|9.6|
|Manufacturing|2.5|5.1|1.7|
|Retail, Hotel, Restaurant|37.9|11.1|42.1|
|Transport, Utility|10.4|10.3|19.4|
|Other services|3.3|4.0|1.4|
|Total|100.0|100.0|100.0|

_Note:_ The sample is restricted to employed males aged 18-59. _Source:_ Bangladesh Return Migrant Survey 2018/19.

23
# **I. Introduction**

There has been substantial work in better understanding the role of fiscal policy in Poland. Some of the highlights are work looking at the labor incentive effects of tax reforms (Morawski and Myck, 2010; Myck et al, 2013) and analyzing the combined effect of direct taxes, pensions, social security contributions, and social benefits (De Agostini et al 2014; Paulus, 2015; Brzeziński, 2015). There are well known country-level microsimulation models using the Household Budget Survey that have separately undertaken microsimulation analysis of specific reforms including changes in family benefits (Myck 2015a); Value Added Taxes (Myck, 2015b); and others. Existing work has shown the dominant role of pensions in overall incidence (Paulus, 2015) and the relatively small role of policy in reducing inequality (Myck and Najsztub, forthcoming).

Despite the large literature on the tax-benefit system in Poland, most of the existing analysis that has aimed to combine the impacts of taxes and spending has not included indirect taxation in the overall analysis, despite the fact that it makes up about half of total tax collections. Similarly, most studies have not included the impact of spending on education and health, despite the fact that they make up more than a third of total social spending and are an important component in terms of improving equality of opportunities across the population.

This paper aims to present a more comprehensive analysis of the impact of fiscal policy on inequality and poverty in Poland relative to earlier studies. Building on existing work done in Poland, the analysis covers the impact of the contributory pension system, direct taxes and transfers, the impact of value added and excise taxes, as well as the impact of education and health spending. The analysis assesses the progressiveness of each fiscal instrument and its contribution to reducing poverty and inequality. The approach follows the Commitment to Equity (CEQ) methodology (Lustig and Higgins, 2013), allowing comparisons between Poland with other countries where the CEQ methodology has been applied.
Equation (5) enables us to decompose the variability of commodity prices into various components by selecting the appropriate frequencies, _k_ .<sup>**8**</sup> In the context of the present analysis, we decompose the transitory component into two cycles ( _k_ ∈ [8-20] and [2-8]) and short-term fluctuations ( _k_ ∈ [0-2]). Thus, identity (1) becomes: [8,20] [2,8] ≡P . (6) tt tt tt tt tt P , which represents the permanent component introduced earlier, could be character-pp + TTPP + TTPP + SSTT ized by a linear trend, perhaps subjected to structural breaks, if price behavior is analyzed tt in the context of declining Terms-of-trade hypothesis (Baffes and Etienne 2016), or it could include non-linearities consistent with supercycles (Cuddington and Jerrett 2008; Fernández, Schmitt-Grohé, and Uribe 2020). The empirical section elaborates further on [8,20] the nature of P . The second component, , denotes the medium-term cycle with a periodicity of 8-20 years as proposed by Blanchard (1997) and popularized by Comin tt tt TTPP[2,8] and Gertler (2006). The third component, , represents the business cycle with a periodicity of 2-8 years, following traditional definition applied by the National Bureau of tt TTPP Economic Research (Burns and Mitchell 1946). The last component, captures fluctuations which may reflect short term movements in the economic activity or other macroett SSTT conomic variables (such as exchange rates and interest rates) or seasonality and weather patterns (in the case of agriculture). Often these fluctuations are analyzed with Vector Autoregression models (Kilian and Murphy 2014; Baumeister and Hamilton 2019) and GARCH models by utilizing high-frequency data, focusing mostly on volatility (Engle 1982). Following decomposition, the logarithm of the price was regressed on each component to measure its relative contribution to price variability. For example, the from [8,20] the regression
b) Cumulative projected growth differentials with actual growth <!-- Start of picture text --> 10.0%<br>8.0% MTBPS<br>BR<br>6.0%<br>4.0%<br>2.0%<br>0.0%<br>‐2.0%<br>‐4.0%<br>% pts<br><!-- End of picture text --> Source: Budget Reviews (BRs), Medium Term Budget Policy Statements (MTBPSs) and authors’ calculations.

Note: First print of outer-year forecast.

# **_An empirical investigation of the_ cycle is the trend** **_in South Africa_**

We now empirically look at whether the cycle is the trend mattered in South Africa. For the case study of South Africa, we develop a more sophisticated measure of the structural budget balance than in the panel analysis of EMDEs above, which is easier in a simple time-series setting. Details about deriving the structural balance can be found in Annex 2. Figure 9 plots our estimate of South Africa’s structural budget balance. It suggests that although South Africa saved between 2006 and 2008 (a positive _overall_ budget balance), the _structural_ balance was already in deficit by 2005, and deteriorated significantly between 2007 and 2009. Since 2008, both South African _overall_ and _structural_ balances were in deficit and have not recovered since. The sustained _structural_ deficits since 2005, together with corresponding _overall_ budget deficits since 2009, was chiefly responsible for South Africa’s large and sustained increase in debt since 2009, significantly reducing fiscal space.

**Figure 9: Estimates of South Africa’s budget balances**

(budget balances in percent of potential GDP) <!-- Start of picture text --> 2.0%<br>0.0%<br>‐2.0%<br>‐4.0
)|(0.035)|(0.026)|
|Growth in GRP per capita|-0.009|-0.014|-0.006|-0.001|0.000|0.002|
||(0.013)|(0.009)|(0.011)|(0.006)|(0.001)|(0.002)|
|Observations|271|272|272|272|272|272|
|R-squared|0.589|0.489|0.837|0.561|0.393|0.837|
|Year dummies|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author’s calculations using Sakernas data Notes: Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1 21
described further in the following sections. The key outcome variable is per capita emissions of CO2. We form a control group capable of replicating Australia’s pre-existing trend in emissions per capita prior to implementation of the new climate policies (as well as matching on other relevant characteristics). Candidate pre-period characteristics include emissions shares from different sectors, GDP per capita, and energy consumption of different emitting fuels (coal, natural gas, and petroleum products).

In estimating effects, our ideal thought experiment is a comparison between the true emissions trajectory of Australia and a counterfactual Australia which never experienced the rapid changes in environmental policy around 2010 that were truly observed (but otherwise equivalent in all respects). To form an appropriate donor pool for estimation of this counterfactual emissions trajectory by synthetic control, we exclude from the donor pool any economy subject to a nationallevel carbon price or ETS implemented from 1980-2017. This approach has some weaknesses: For example, we are unable to extract any information from variation in policy stringency or sectoral coverage, and we risk measurement error by omitting subnational policies or renewable generation incentives that may be present among donor economies. However, our approach also has some strengths: It is relatively transparent and simple, and appropriately ignores many policies that offer weak incentives for market participants and have little practical implication for nationallevel emissions (including sub-national policies that may suffer from significant within-border leakage). Additionally, for potentially omitted policies in the donor pool, our strategy is conservative in the sense that any policy effective in reducing emissions that shows up in the donor pool should bias our estimates toward finding a null effect (a ‘control’ economy that is ‘treated’).

# **3.1 Data**

The outcome variable for the main analysis is the emissions of CO2 per capita. Emissions data come from the _Emissions Database for Global Atmospheric Research_ (EDGAR). The EDGAR dataset uses a bottom-up methodology to calculate CO2 emissions in the power, industry, buildings, transport, and agriculture sectors. The methodology
# Weathering Storms: Understanding the Impact of Natural Disasters on the Poor in Central America<sup>1</sup>

## _Oscar A. Ishizawa_<sup>_2_</sup> _and Juan Jose Miranda_<sup>_3_</sup> _The World Bank_

JEL Classifications: O11, O12, O44, Q51, Q54, R11 Keywords: Hurricanes, Poverty, Natural Disasters, Central America, Economic Growth, Vulnerability 1 This work was funded by The World Bank’s Global Facility for Disaster Reduction and Recovery Program (Trust Fund # TF018258). We thank in particular Johanan Rivera Fuentes for the overall coordination, technical inputs and for drafting the first version of this paper. We also thank Luis Felipe Jimenez, Andrea Villamil and Xijie Lv for outstanding research assistance. Gonzalo Pita produced the hurricane windstorm data based on the hurricane windstorm model developed under the WB LCR CAPRA Program (Pita et al., 2015). Participants at the World Bank’s Workshop on “Aggregated Shocks, Poverty and Ex‐ante Risk Management in Latin America and the Caribbean”, and the 2015 Poverty Learning Event on Risks provided excellent comments and suggestions.

2 Social, Urban, Rural and Resilience Global Practice (GSURR): oishizawa@worldbank.org.

3 Environment and Natural Resources Global Practice (GENDR): jjmiranda@worldbank.org.
4 The remainder of this paper proceeds as follows. Section 2 discusses the role of the financial system in economic development more generally. Section 3 discusses the difficulty associated with measuring the operation of financial systems and the paper‘s development of indicators of financial depth, access to finance, the efficiency of financial systems, and the stability of financial systems for both financial institutions and financial markets—the 4x2 measurement framework—as a strategy for empirically characterizing financial systems around the world and tracing their development over time. The section also introduces the Global Financial Development Database, an extensive world-wide database that combines and updates several financial data sets. Section 4 uses this database and the ―4x2‖ measurement framework to examine and compare financial systems. Section 5 summarizes the key findings.

# **2. The Concept of Financial Development and Its Importance**

There has been a considerable debate among economists on the role of financial development in economic growth and poverty reduction, but the balance of theoretical reasoning and empirical evidence points towards a central role of finance in socio-economic development. Economies with higher levels of financial development grow faster and experience faster reductions in poverty levels. This section introduces the concept of financial development and provides a brief review of the literature on the linkages between financial development, economic growth, and poverty reduction.

# _2.1 Concept of Financial Development_

Markets are imperfect. It is costly to acquire and process information about potential investments. There are costs and uncertainties associated with writing, interpreting, and enforcing contracts. And, there are costs associated with transacting goods, services, and financial instruments. These market imperfections inhibit the flow of society‘s savings to those with the best ideas and projects, curtailing economic development and retarding improvements in living standards.

It is the existence of these costs—these market imperfections—that creates incentives for the emergence of financial contracts, markets and intermediaries. Motivated by profits, people create financial products and institutions to ameliorate the effects of these market imperfections. And, governments often provide
emerging economies to create such technologies, but also on the potential for these economies to supply needed technologies to lower income countries.

Two papers consider flows of technology transfer among developing countries. Brewer (2008) suggests that policy should consider the possibility of South-South or even South-North technology transfers. He gives examples where developing countries play roles as technology leaders, such as biofuels in Brazil and subsidiaries of General Electric developing wind turbines in China. Doranova _et al_ . (2009) provide further evidence in a study of 497 CDM projects. They ask whether existing knowledge in the host country shapes technology sourcing patterns, focusing on projects using local technologies. Of the 497 projects studied, 56% use technologies of local origin. Moreover, some technology transfer is South-South. China; Malaysia; Taiwan, China; and South Africa all provided technologies in other developing countries. They use data on patents and publications related to climate friendly technologies to measure the knowledge base of a country. Countries with more publications more likely to use local technologies, but more those with more patents are more likely to use foreign technologies, either alone or combined with local technologies. Countries with more experience with a technology are more likely to use local or combined technologies. While these studies suggest that emerging countries can play a role meeting the research needs of developing country markets for environmental technologies, further research on the potential of emerging economies to close the technology distance gap cited by Verdonlini and Galeotti, would be beneficial. What incentives need to be in place to encourage transfer of needed technologies among developing countries? Similarly, as adaptation to climate change becomes a concern, what lessons can tropical and semi-arid countries provide for warming industrialized nations.<sup>20</sup>

> 20 I thank Stephane Hallegatte for this insight.

27
# **7. Figures**

_Figure 1._ Mean household per capita consumption expenditure in Rural India, across surveys <!-- Start of picture text --> 130<br>120<br>117.0<br>110<br>100<br>93.9<br>96.1<br>90 87.3<br>86.9<br>80.0 80.3<br>80<br>74.7 79.2<br>70<br>68.7 62.9<br>60<br>50<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>CES SCS health SCS education IHDS<br>2011 USD PPP per month<br><!-- End of picture text --> _Note_ : CES refers to Consumption Expenditure Surveys, SCS Health and Education to the Surveys on Social Consumption and IHDS to India Human Development Survey. Consumption expenditures are in 2011 USD PPP using price deflators as in Atamanov et al. (2020).

_Figure 2._ Mean household per capita consumption expenditure in Urban India, across surveys <!-- Start of picture text --> 160<br>152.0<br>150.3<br>150<br>143.7 147.7<br>140<br>131.4<br>130 127.1 128.4<br>125.0<br>120<br>111.0<br>110<br>106.6<br>100 98.8<br>90<br>80<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>CES SCS health SCS education IHDS<br>2011 USD PPP per month<br><!-- End of picture text --> _Note_ : CES refers to
The low level of interest of officials in EAP in school feeding is consistent with these same officials not seeing malnutrition and poverty as a major barrier to learning in the EAP region. The available data on malnutrition in young children the region (UNICEF, 2021), and evidence from other contexts show that nutritious school meals have positive benefits for both learning (e.g., Chakraborty and Jayaraman, 2019; Aurino et al., 2023), enrollment, as well as nutrition (Adelman et al., 2019; UNESCO, 2023). However, the lack of prioritizing of school feeding by officials aligns with the low levels of investment in school feeding programs by both EAP governments and the World Bank; there is currently only one school feeding program supported by World Bank investments in EAP (Cambodia).

_Figure 5: Officials in EAP prefer structured reading and in-service training relative to other interventions._ <!-- Start of picture text --> 90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Building Providing Providing Hiring extra Removing In-service Giving<br>hospitals laptops to free lunches specialist fees for teacher teachers<br>schools in primary teachers for secondary training structured<br>school students school early grade<br>with<br>disabilities<br>project<br>Non-EAP EAP Non-EAP EAP Non-EAP EAP Non-EAP EAP Non-EAP EAP Non-EAP EAP Non-EAP EAP<br>Share of respondants selecting the<br><!-- End of picture text --> Source: Authors’ calculations from the Discrete Choice Experiment as part of the CGD–World Bank survey 2022. See probit model results in the annex. The probabilities have been computed for a similar “average” respondent in all countries. About 50 percent of respondents select school feeding programs outside of the EAP region
clear that one religion is more supportive than another of economic productivity” (McCleary, 2007). We report formal tests of relevance and validity below.

We supplement religious composition with one additional exogenous instrument, from the “State Antiquity Index” project of Bockstette, Chanda and Putterman (2002). Specifically, we use their estimate of (the log of) number of years of statehood from 1 AD to 1950 AD.<sup>20</sup> These authors suggest that a more extensive history of statehood “may support the development of attitudes consistent with bureaucratic discipline and hierarchical control,” but also fosters a common language and sense of identity, facilitating trust and harmonious social interaction.

When instrumenting for multiple endogenous variables, it is important that the excluded instruments not be related in too similar a way to both the morality proxy and the morality-ability correlation. A weak instruments problem can be present even if they have strong explanatory power in both first-stage regressions, if the fitted values from those regressions are highly correlated with each other. In other words, the excluded instruments must discriminate reasonably well between the two endogenous variables. We report formal tests for this weak instruments problem below. With few exceptions, theory does not provide much guidance as to which instruments might predict the morality-ability correlation but not the morality proxy. According to McCleary (2007), upper-caste Hindu men have a duty to “earn a living responsibly,” suggesting percent Hindu may be positively correlated with the morality-ability correlation. In general however our strategy is simply to let the data determine whether the instruments can discriminate sufficiently between the two endogenous variables.

> 20 Coding is based on three key questions for territory corresponding to the boundaries of modern nations: (1) was there a government above the tribal level? (2) Was the government locally based or foreign (e.g. a colony)? (3) How much of the modern territory was ruled by this government? We follow Bockstette, Chanda and Putterman (2002) in
-> Landed households, roots & lowland<br>+ | '<br>c 1 1<br>2 ' '<br>a f i}<br>E | \<br>Bn | |<br>88 }\ |'<br>£ | |<br>2 |<br>©ad i} i}<br>Bo<br>=<br>8 | |<br>a \ 1\<br>x \ \<br>'<br>-2 0 2<br>NDVI anomaly<br>——— Linear ———— Quadratic ———— Cubic<br><!-- End of picture text -->
ibid).

**Moreover, the country is vulnerable to the spread of epidemics, and crime rates are high.** The country's high population density makes it prone to epidemics and the spread of illness (Robayo-Abril and Barroso, 2022). Additionally, crime rates are high. In 2018, El Salvador was the country with the highest homicide rate globally (World Development Indicators, 2021). The intentional homicide rate is 52.0 per 100,000 people (ibid). Property crimes are also prevalent in the country, accounting for 47 percent of all crimes (OSAC, 2021). Additionally, El Salvador is marked by violent, well-armed street gangs, further increasing individuals' vulnerability (ibid). All these characteristics add up to a generally high exposure to human hazards.

**In addition, El Salvador has high exposure to natural hazards.** El Salvador is vulnerable to natural hazards, with an INFORM Index of 6.5 out of 10 (DRMKC, 2021). Earthquakes are especially pertinent, with an index of 9.7, followed by Tsunamis with an index of 8.2. Also, El Salvador is highly vulnerable to climate change, and vulnerability has increased over the years. The country ranks 106 out of 181 on the NDGAIN Index<sup>5</sup> (World Bank Group, 2021a). This exposure is mainly due to its location, which increases the probability of extreme weather events. The complex environmental, social and institutional situation adds to the overall challenging situation. El Salvador's economic dependency on climate-sensitive economic activities, such as agriculture, forestry, and economic activities in coastal areas, further exacerbates the situation.

**When compared to other countries in the region, El Salvador is marked by high levels of vulnerability.** At a subnational level, several of El Salvador's departments report high vulnerabilities compared to Latin America and the Caribbean (World Bank Group, 2021b). El Salvador's