rafmacalaba/data-use-mentions-extended huggingface dataset:
0.31 to 0.39 percent|Common trends induced spurious<br> <br> <br>|
|**ational GDP)**|Munnell and<br>Cook (1990)|US States|1970 to<br>1986|Cobb-<br>Douglas;<br>Log levels|OLS|States that have invested more in infrastructure tend to have<br>greater output, more private investment, and more employment<br>growth. The coefficient on public capital is 0.15***|correlation;<br>Reverse<br>causality;<br>Endogeneity; Measurement error|
|**ut or reginal/n**|Evans and<br>Karras (1994)|US States<br>(Private<br>Nonagricultu<br>ral Sectors)|1970 to<br>1986|Cobb-<br>Douglas;<br>Log levels|FE|There is no evidence that government activities are productive<br>except investment in education. Government capital often has<br>negative productivity (-0.048)|Reverse<br>causality;<br>Endogeneity;<br>Measurement error|
|**ut (sector outp**||US Regions|||OLS<br>FE|Region-level estimates are essentially identical to those from<br>state data (FE estimate is -0.12*), suggesting no quantitatively<br>important spillover effects across states||
|**Outp**|Holz-Eakin<br>(1994)|US States|1969 to<br>1986|Cobb-<br>Douglas; Log<br>levels|OLS<br>FE<br>IV|Only OLS estimates of state production functions find<br>substantial productivity impacts (0.203***). Estimates of<br>production functions that use FE reveal no role for public-sector<
# 1. Introduction

Nigeria has been steadily growing and was considered one of the fastest growing economies in the world in the decade surrounding the global financial crisis. At the same time home to the largest number of people living in extreme poverty. In 2018, it surpassed India that had been leading this rank before. “End poverty in all its forms everywhere” by 2030 defined as the Sustainable Development Goal (SDG) 1 is thus an ambitious goal for Nigeria. Given the established consensus that children may suffer more and in different ways from poverty than adults (Abuda and Delamonica, 2018), it is important to study poverty distinguishing between these two groups. Along these lines, Newhouse et al. (2016) showed based on data from 2010 that Nigeria is facing one of the highest child poverty rates among a sample of 89 developing countries and that children are disproportionally more affected by poverty than adults. Considering that 44 percent of the Nigerian population is estimated to be below the age of 15<sup>1</sup> , reducing child poverty is therefore particularly important to achieve SDG 1. While some may argue that economic growth will bring along poverty reduction, there is a concern that Nigeria’s strong economic growth has not been inclusive, as its effects on poverty remain unclear (Ajakaiye et al., 2016a,b). This view is supported by Dang and Dabalen (2019) and Ichoku et al. (2012) findings, that Nigeria has experienced non pro-poor growth. Moreover, World Bank (2016) showed that despite estimating a poverty growth elasticity of -0.6 and a reduction in the share of the population considered poor, Nigeria nevertheless experiences an increase in the number of poor. This means that Nigeria’s population is growing at a higher rate, than the growth elasticity of poverty needed to result also in the reduction in the absolute number of poor people.

However, the United Nations definition of poverty extends beyond monetary poverty that may be directly affected through growth
# **Table A14: Cost allocation by income group**

|Income group|Contribution<br>(%)|Contribution<br>($US billion)|
|---|---|---|
|High income|59.53%|29,762.56|
|Upper-middle income|29.72%|14,862.37|
|Lower-middle income|10.14%|5,070.30|
|Low income|0.61%|304.77|
|Total|100%|50,000|

Notes: The income group is identified using the World Bank country classifications 2022-2023, available at: <u>https://blogs.worldbank.org/opendata/new-world-bankcountry-classifications-income-level-2022-2023</u> 62
to engage in innovation (Gebreeyesus, 2009) and exports (Rijkers et. al., 2010).<sup>19</sup> They may also benefit from stronger bank ties and hence have better access to finance (Shiferaw, 2016).<sup>20</sup> Firm age is also critical for growth, with young formal Ethiopian firms growing faster than their older counterparts (Bigsten and Soderbom, 2007). Innovation also matters. A cross-country study using the World Bank’s Enterprise Survey data shows that product innovation matters for growth once factors such as firm’s ownership of transport and internet connectivity through their own website are controlled for (Goedhuys and Sleuwaegen, 2010).

External industry environment and geographical factors also affects firm growth. Among industry characteristics, Ethiopian firms operating in industries with high competition from imports observed stunted job growth (Shiferaw, 2016). Studies focusing on spatial characteristics in Ethiopia suggest that enterprise clustering, when measured by the total count of firms producing the same product, has a positive effect on physical productivity (Bigsten et al., 2012). However, this relationship is not significant when agglomeration is among firms producing different products or sectors. In the case of Ethiopia, it seems that concentration of activity is a result of lack of accessibility to markets in places beyond a few cities. For example, although towns with initially large number of firms continue to attract more firms, nonetheless, accessibility to better roads contributes to a convergence in the overall distribution of manufacturing firms and a decline in the degree of geographic concentration (Shiferaw et al., 2015). This result suggests that firms do not foresee remarkable benefits from agglomeration if they have access to markets.<sup>21,22</sup> The quality of infrastructure and transport costs are important factors explaining the elevated performance of urban manufacturing enterprises in Ethiopia vis-à-vis their rural counterparts (Rijkers et. al., 2010).<sup>23</sup> Exploiting geographical variations
Accra-about a third higher than the hinterland rural areas. The rural-urban difference was somewhat lower when we look at smaller towns in the individual administrative regions and compare them with their rural hinterlands, and non-existent in the Volta and Western regions. The average size of households was lower in towns partly because of the greater proportion of singlemember households in town. Thus the level of urban consumption per capita exceeded the rural level by as much as one half. Part of this difference was, of course, accounted for the difference in the cost-of-living between town and country. Knight made an attempt to estimate the latter using the data from the sample survey, and estimated that the urban price level was 112 per cent of the rural price level using rural weights, and 104 per cent using urban weights (Knight, Table 4, p.209). Thus both the extent of the nominal income difference in favor of the urban sector, and of the cost-of-living difference were quite small in Ghana compared to what was prevalent in other parts of Africa. In Kenya, for example, the urban-rural differential was as high as 3:1, or even 4:1, and the cost-of-living was estimated to have been 60 per cent higher (Mazumdar 1993). There were two reasons for this difference. First, although we have mentioned that single member households were more important in the urban economy of Ghana, the 36
_Services PTAs_ services PTAs has surged against the backdrop of rising value chain trade, a stalemate of services negotiations at the multilateral level, as well as digitisation, which has positively affected the tradability of many services. All told, today’s large number of services PTAs provides the governance framework that underpins much of current services trade. This begs the question of how an agreement’s architecture, scope and depth affects trade and value added in services.

This paper offers evidence on those sets of provisions in services trade agreements that have a material impact on bilateral trade and value added in services. As a benchmark, and broadly consistent with earlier literature, we find that the value of trade between the two partners is on average 20% higher compared to non-preferential trade flows two years after a _bilateral_ services PTA came into force, whereas the effect of a _plurilateral_ PTA is much lower.

Yet the main findings derive from exploiting the rich policy information in the newly released World Bank Deep Trade Agreements 2.0 (WB-DTA) dataset. We find that ambitious configurations of provisions in areas such as the structure of the agreement, rules of origin for judicial and natural persons, and accountability increase bilateral services trade by 15-65%. At the same time, no effects at all are associated with shallow, unambitious policy choices, which is what one would expect from a “placebo test.” We also show that PTA provisions are particularly effective for financial services, business services and the exchange of intellectual property rights. These are typically regulation-intensive types of services that stand to benefit from the aforementioned disciplines in services PTAs. These results are derived under a demanding fixed effects structure and with three-way clustered standard errors, including dyadic country-pair effects that mitigate against potential endogeneity concerns.

44
unintended consequences for financial systems of EMDEs. As one example, new risk weights, differences in risk measurement of parent banks and their subsidiaries, and stricter capital requirements may exacerbate the deleveraging process and disproportionately affect lending operations in EMDEs. However, as the implementation process is still ongoing, full impact assessments are not yet feasible.<sup>27</sup>

- _Higher and better capital:_ From the capital side, higher regulatory capital requirements induce banks to boost and enhance capital for each unit of risk-weighted assets. Basel III― implemented under the Capital Requirements Directive IV in Europe―dictates that banks achieve significantly higher capital levels coming into force in 2013 and be fully phased in by 2019. The latest available estimate of the aggregate capital shortfall is €478.9 billion which is based on December 2011 data and assumes full Basel III implementation per that date.<sup>28</sup>

- _Capital charges for market risk:_ Basel 2.5 has brought higher capital charges for market risks in banks’ trading operations, which may make these assets less attractive investment opportunities and could trigger de-risking and deleveraging of trading books.

- _Liquidity and funding requirements:_ From the liquidity side, banks are required to withstand sustained liquidity drains and seek more stable sources of funding. However, European banks still have an aggregate shortfall of liquid assets of €1.17 trillion and require an additional €1.4 trillion in stable funding.<sup>29</sup> Together, these liquidity requirements might induce banks to reduce assets, particularly long-term assets, if the necessary (longer-term) funding sources are not available or too costly as has been the case during recent funding stress conditions.

<!-- Start of picture text --> IV.3 Banks have been meeting stricter regulatory IV.4 ... which has directly affected credit standards,<br>requirements by both shedding risk-weighted assets particularly for larger firms<br>and raising capital...<br
else equal. Despite the similar coefficients on key determinants of job loss, the ranking of factors by the magnitude of their average impact differs for the two countries (see Figure 6). In particular, in Georgia, the unexplained determinants of sales contributed positively to firms’ employment levels, and the greatest negative impact on average came through infection risk, whereas the largest adverse impact was through unexplained declines in sales for Jordan. However, infection risk did not have a separate augmenting effect on job losses in Georgia, a finding that aligns with Georgian high-frequency phone survey data showing very few workers claiming to have stopped working in order to 19
0 i (2) ii ii ii ss RR tt ii where represents our measures of firm performance, YY = αα + ββ∙s ssPPs ss_i PPiissi ii + γγZZ + δδ∙DDs + ρρ∙DDi + ττ + εε is a dummy variable that is equal to one for firms that were not registered when they began operating. is a vector of firm-level YY ssPPs ss_i PPiissi ii control variables. We exploit the richness of the enterprise survey data by including a large set of control ZZ variables that the literature has shown to be correlated to firm performance.<sup>13</sup> We control for firm size and age, which numerous authors have shown to be correlated with firm productivity (Biesebroeck 2005 and Haltiwanger, Jarmin, and Miranda 2013). We also proxy for human capital within the firm by controlling for the manager’s years of experience in the sector and whether the firm has formal training programs in place for its employees, which should be associated with higher human capital (Kinda, Plane, and Véganzonès-Varoudakis 2015). Physical capital is measured by whether or not the firm purchased fixed assets in the past year. A firm’s outward orientation is measured by whether it has foreign participation in ownership and whether it is engaged in exporting activities—research has shown that such factors can expose firms to a more competitive environment and to improved technologies, with associated impacts on productivity (Dimelis and Louri 2002; López 2005; Bernard et al. 2007; Guadalupe, Kuzmina, and Thomas 2012). We control for whether or not a firm has a website—a proxy for internet and telecommunication services that may be associated with increased productivity (Clarke, Qiang, and Xu 2015). Access to finance, which has shown to be associated with firm performance (Rajan and Zingales 1998; Gatti and Love 2008), is proxied by whether a firm has a checking or savings account. Finally, we control for two factors of the business environment that may negatively affect firm performance
2 The data in this paper come principally from a 1987 questionnaire inquiry to the 100 developing countries having populations over one million. At the time of this writing, responses were in from 65 countries, with most nonresponses concentrated in the countries with the least programmatic activity, chiefly in Africa and the Middle East. A large amo'int of secondary data has been used to supplement the survey, including information from a historical series created by Nortman (1985 and prior yecrs), beginning in 1969 and containing data through 1983. Nortman's data have never been computerized or aggregated into a si wle time series; we have done that and have combined it with another h cal series for sterilization information (Ross et al., 1985). Our qu:-stionnaire updates both series for 1984 onward and adds a few additional variables.

# A xology

Since considerable confusion exists in defining cost measures, we begin with a basic typology. The distinctions made here are observed throughout the paper.

First, payments are made to: (a) acceptors, (b) providers, and (c) recruiters, all focused on the act of accepting a method (usually sterilization). These payments may be in cash or in kind and are usually given immediately upon acceptance. They vary in amount, and on this variation rests much controversy. A small payment merely replaces out-of-pocket costs and thereby removes a barrier, whereas a large payment can be a strong inducement to an act that would otherwise not have occurred
# 8. **Comparison with Monitoring Results for India**

A recent monitoring study for Indian households (World Bank, 2002; Balakrishnan, et al., 2002; Parikh, et al., 2001) has provided useful comparative information about indoor pollution levels and their determinants. Table 16 displays average pollutant concentrations by room, stove location and fuel type for the two countries. The Indian data are reported for respirable suspended particulate matter (RSPM), defined as the fraction of inhaled aerosols capable of penetrating the alveolar (gas-exchange) regions of the adult lung. The authors report that ratios of RSPM to PM10 varied from 0.57 to 0.73 in their samples, with a mean of .61.

Comparison of Tables 16a (India) and 16b (Bangladesh) indicates both similarities and differences. The India results for gas fuels are almost identical to the Bangladesh results when the latter are multiplied by .61 (the RSPM/PM10 ratio). The Indian ambient reading is also within the same range as the Bangladesh urban reading. However, the results for solid (biomass) fuels are quite different. For kitchen areas, the India concentrations are all much higher than their RSPM-adjusted counterparts for Bangladesh. The India concentrations for living areas are also much higher than the Bangladesh concentrations, although the differences are not as extreme.

Both studies have used regression analysis to explore the determinants of indoor air pollution, and both have come to generally-similar conclusions: Fuel choice, cooking location and other ventilation factors all play significant roles. However, the India study finds that fuel choice is the dominant factor, while our results for Bangladesh suggest that ventilation factors are collectively more significant. In the India study, pollution intensity is highest for dung, followed by woodfuels, kerosene and gas. Our estimated means in 32
Econometric theory and practice suggest that Model B is the better model for several reasons. Firstly, the presence of structural breaks in data generating processes must be allowed for in the specification or else one can have problems with the reliability of the estimators and the distortions in diagnostic tests. Secondly, the magnitude of the 1994 CFA devaluation of 50% suggests that it is a fundamental explanatory variable to explain shifts in the real effective exchange rate. Finally, the R-squared in Model 2 is significantly better than the R-squared in Model 1. As a result, the key findings from Model 2 are the following:

- The movement of the real effective exchange rate in UEMOA and CEMAC cannot be

- fully explained by a vector of fundamentals.

- The 1994 devaluation is the key factor which explains the movement in the REER in

- both zones from 1970 to 2004.

- An increase in openness as a result of trade liberalization in the UEMOA zone has

- resulted in an appreciation of the REER. A 3% gain in economic openness results in a 1% increase in the REER in UEMOA, while in CEMAC there is no relationship.

- There is no evidence that terms of trade are cointegrated with the real exchange rate.

- This can be partly explained by the fact that the aggregate regional data used in the analysis may obscure the differing terms of trade shocks that affect different countries. � No evidence was found of Dutch disease as a result of increased aid inflows, negating the theory that suggests that large aid flows cause real exchange rate appreciation.

In the second stage of the Engle-Granger test, the error terms from the two cointegrated regressions were tested for unit roots, and they were found to exhibit stationarity using both ADF and other criterion. The ADF statistics for UEMOA and CEAMC were -5.39 and -5.22 above the critical values of -4.32 established by McKinnon (1991) for 5% level of significance. It is important to note that the tendency of OLS to produce stationary residuals means
2018) (ii) data on unemployment rate and government expenditures are taken either from Eurostat (1999 and 2009), from the Milanovic's Household Expenditure and Income Dataset for Transition Economies (HEIDE) data (1992), or from the World Development Indicators. Finally, (iii), poverty is defined as the percentage of people living below $10 a day in 2005 PPP. The variable is calculated on income data using PovCalNet and the World Development Indicators data set.

As part of the contextual variables we also include a measure of the intergenerational elasticity of education from Narayan and Van der Weide (2018). This measure is intended to proxy for the level of _fairness_ in the society. The higher the elasticity, the larger is the correlation between parent and children’s education, implying a lower degree of educational mobility and, thus, a less fair process of income generation. The elasticity is estimated for each 10-year birth cohort from the 1930s to the 1980s in each country. We use three versions of this elasticity. The first one is a country-year variable, which is the population-weighted average of the elasticity of the cohorts which are 20 to 39 years old in each given country and year. The second and third versions are country-cohort specific: the first version varies across countries and birth cohorts but not across years and the second is estimated separately for each gender. These variables are only available for the subset of individuals born in 1930 or after.

11
br>|*<br>Human and non-human<br>resource quality and quantity<br><sup>*</sup><br>In administrative activities<br>*<br>Recruitment Career paths,<br>|0<br>Adequacy of procedures for<br>different internal activities and<br>taxpayer related activities<br>including:<br>||
|Service<br>delivery:<br>Efficiency|*<br>Revenue to cost indicators<br>*<br>Time indicators<br>Taxpayer survey<br> <br>|promotion and transfer<br>policies<br>Wages, bonuses and pay<br>determination<br>*<br>Training facilities<br>*<br>Automation<br>*<br>Budget size of administration<br>*<br>Financial, personnel,<br>equipment and activity<br>management<br>*<br>Feasibility and extent of<br>outsourcing and privatization|Compliance, valuation,<br>taxpayer identification, tax<br>audit, taxpayer education,<br>taxpayer services, tax<br>collection, tax sanctions, tax<br>appeals, border inspection and<br>clearance procedures, special<br>customs procedures,<br>warehousing, preshipment<br>formalities, post clearance<br>checks, information exchange<br>and sharing arrangements||

7
their debt burden indicators deteriorate significantly. On the contrary, the sluggish recovery in Europe and slower growth in China affects only a minority group of countries (7 out of 24), in particular those whose main export market is the Euro Zone.

# **REFERENCES**

- IMF (2014). Regional Economic Outlook, Sub-Saharan Africa, World Economic and Financial Surveys, International Monetary Fund (IMF), Washington D.C. (October).

- Merotto, Dino, Tihomir Stucka, and Mark R. Thomas (2015), “African Debt since HIPC: How Clean is the Slate?” MFM WP Series Number 2, The World Bank (March).

- Painchaud, François and Tihomir Stučka (2011), “Stress testing in the Debt Sustainability Framework (DSF) for Low-Income Countries”, mimeo (May).

- Tyson, Judith (2015). “Sub-Saharan Africa International Sovereign Bonds”.  Overseas Development Institute.

- UNCTAD (2014). “World Investment Report 2014”. UNCTAD, Geneva.

- World Bank (2015). Migration and Remittances Team website. <u>Migrationandremittances@worldbank.org.</u>

- World Bank (2015). Private Participation in Infrastructure Database. http://ppi.worldbank.org/index.aspx.

- World Bank and IMF (2012). “Revisiting the Debt Sustainability Framework for Low-Income Countries”.

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