rafmacalaba/data-use-mentions-extended huggingface dataset:
of taxes, it also eliminates the cascading effects caused by exemptions, which were accounted for around 10 percent of the total GST collections in the baseline. Overall, this reform has only small poverty and distributional effects, increasing inequality by only 0.1 points as the increase in direct prices is offset by the decrease in prices when producers of previously GSTexempt goods and services can now claim GST on their inputs. This reform could raise an additional 5 percent of indirect taxes (about JOD 120 million), which can be used to fund progressive expenditures to compensate for the small increase in poverty (0.2 points) or to close the fiscal gap. In reality, purchases of goods at informal locations which do not charge GST mean that the impact on poverty and inequality would be even less, although so too may be the impact on revenues.<sup>68</sup> Preserving the zero-rates for some items may additionally provide relief for low-income households, but despite a potentially modest poverty mitigation effect, the fiscal cost could be great.

Finally, we assess reforms to social assistance which are currently being implemented. The current Takaful expansion is not included in the baseline of this study as it began after 2018. Also, since the programmed started after the HEIS data was collected, there is no information about beneficiaries in the survey. Thus,

> 66 This analysis is based on self-reported consumption of electricity from HEIS. This does differ from administrative NEPCO data.

> 67 Leaving STT rates (for mobile phones, sodas, alcohol and tobacco) unchanged.

> 68 This is because poor households tend to make more of their purchases in the informal non-tax paying sector than richer households (Bachas _et al._ 2020).
- Akehurst, G., Simarro, E. and Mas-Tur, A. (2012). Women entrepreneurship in small service firms: motivations, barriers and performance. _The Service Industries Journal_ , 32(15), 2489-2505.

- Amankwah, A., Castaing, P., Owoo, NS.,  Palacios-Lopez, A. (2024). Labor Market Participation and Employment Choice in Ghana: Do Individual Personality Traits and Gender Role Attitudes Matter?. Policy Research Working Papers, 10664, World Bank.

AHIES (2022) Ghana Annual Household Income and Expenditure Survey- Quarterly Labor Force report. Ghana Statistical Service (2022) <u>https://www.statsghana.gov.gh/gssmain/fileUpload/pressrelease/AHIES%202022%20Q1 %20and%20Q2%20Labor%20Force%20Report.pdf</u>

- Amoakohene MI. (2004). Violence against women in Ghana: a look at women's perceptions and review of policy and social responses. _Social Science and Medicine_ , 59(11), 2373–85.

- Amu, JN (2000). The role of women in the Ghanaian Economy, Accra, Ghana. [library.fes.de/pdffiles/bueros/ghana/02990.pdf].

- Anambane, G., and Adom, K. (2019). Assessing the role of culture in female entrepreneurship in contemporary Sub-Saharan society: insights from Nabadam district of Ghana. _Journal of Developmental Entrepreneurship_ , 23(3). https://doi. org/10.1142/S1084946718500176.

- Aterido, R. and Hallward-Driemeier, M. (2011). Whose Business is it Anyway? _Small Business Economics_ , 37(4), 443-464.

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- Bardasi, E., Sabarwal, S.
# **1 Introduction**

Curbing fossil fuel consumption is essential to keep global warming within levels that will still allow societies to function (Intergovernmental Panel on Climate Change, 2018). Yet, governments are reluctant to take significant measures to reduce emissions, including decisive taxes on carbon. This resistance is partly due to the concern that an increase in the price of energy could harm domestic producers by increasing the cost of a key production input (Rentschler et al., 2017). The argument is particularly salient in industrializing countries where fuel consumption is often subsidized.<sup>1</sup> Mexico and Indonesia are cases in point. In 2013-14 both countries set out to reverse years of sizable fossil fuel subsidies. The Mexican government proposed an ambitious carbon tax on fossil fuel consumption in 2013. However, during the approval process, industrial and social groups successfully lobbied the Mexican Congress to reduce the level of the proposed tax on the ground that it would harm the competitiveness of domestic industries and raise inflation (Arlinghaus and Van Dender, 2017).<sup>2</sup> Similarly, the Indonesian government removed much of the long-standing fuel subsidy from the national budget in 2015 so that by 2016 the subsidy had declined to 2.2% of central government’s expenditures (from 13.5% in 2014).<sup>3</sup> However concerns similar to those of the Mexican case eventually led the government to reinstate part of the subsidy, which by 2018 accounted for 4.4% of central government’s expenditures.<sup>4</sup> This paper uses data from these two countries to provide novel evidence that increases in energy prices do not necessarily worsen the performance of manufacturing firms. The key innovation of the analysis is testing for the impact of energy price variations separately for electricity and fuels, the two main types of energy

> 1Coady et al. (2015) estimate that energy subsidies would account for between 13% and 18%
%|-713|-2.5|19|83|-420|-22|
|1986-1990|162|8.3%|-421|-2.6|14|33|-157|-11|
|1991-1995|118|6.0%|-308|-2.6|7|14|-92|-13|
|1996-2000|176|8.8%|-456|-2.6|12|50|-338|-28|
|2000-2002|56|7.2%|-145|-2.6|4|12|-73|-18|

_1/ Period classification is based on the beginning of crisis episodes_ _2/ Total degree is the sum of standardizes scores exceeding the threshold_ _3/ Crisis (episode) average is calculated as total degree divided by the number of quarters (episode) Source: Author_ <!-- Start of picture text --> Fig. 6: Number of different crisis types in crisis episodes Fig. 7: Average crisis degree in each episode<br>(cumulative)<br>90 60<br>80<br>50<br>70<br>60 40<br>50<br>30<br>40<br>30 20<br>20<br>10<br>10<br>0 0<br>80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02<br>Source: Author Source: Author<br><!-- End of picture text -->

# **3. Crisis Transmission Mechanism**

The crisis identification exercise suggests that the relationship between crisis types becomes closer in these days than the past. Existing crisis studies mainly deal with
changes in trade and foreign investment ratios, and non-agricultural growth rates.

Third, we test robustness of results to splitting up the sample by region and by time periods. The former is implemented to test whether any particular region might be driving the result, while the latter is done to test whether the effects of food prices were systematically different in the 2000s, a period of heightened international price volatility. We also extensively tested for interactions between changes in food prices and other structural indicators of interest (such as indicators of the size of agriculture in total GDP or total employment and the magnitude of initial poverty rates), to see whether there is observable heterogeneity in the effects of food price changes on national poverty (results available upon request).

Lastly, we also investigate whether changes in food prices influence agricultural production and input use and unskilled wages. For the agricultural production regressions, we adopt the same form as equation (1) and its IV-GMM analog, but we replace poverty rates with various measures of agricultural output or input use. We also exploit the availability of monthly data on rural and urban wages in Bangladesh to test whether rice prices differentially influence rural and urban labor markets, using both auto-distributed lag (ADL) models and vector error correction models (VECM). These results are reported in section V.

IV. ESTIMATING THE NET POVERTY IMPACTS OF INCREASES IN FOOD PRICES This section presents some core results on the associations between changes in poverty headcounts and changes in real food prices, followed by a description of their robustness to different methodological variations.

8
in low‐income countries (LICs), respectively (Sumner 2012).

Moreover, it is now well understood that poverty is not limited to income poverty; it also includes deprivations in basic services such as health, education, and standard of living, hunger, illiteracy, social exclusion, loss of sense of belonging and identity, and disempowerment, often reflected in truncated core capabilities to flourish and protect oneself and others. Poverty encompassing multiple deprivations is known as multidimensional poverty (UNDP, 1990) and captured in the Multidimensional Poverty Index (MPI) (UNDP, 2010). In 2014, this index, encompassing 108 developing countries (31 LICs, 67 MICs, and 10 HICs) corresponding to 78% of the world’s populations, reported 1.6 billion people as multidimensional poor (Alkire et al. 2014). This means poor in dimensions of education, health, and living standards. This assessment, however, does not include China and Argentina, and none of the >30 high‐income OECD countries, hence missing poverty in countries with significant affluence. Even so, this number is higher than the current 1.2 billion of people living on <$1.25/day, understood as extreme poor (World Bank 2014a). The MPI also captures intensity of poverty as degree of deprivation (e.g. not having electricity, not having improved sanitation), as well as destitution as an extreme expression of poverty. In 2014, roughly half of all multidimensionally poor people fell into this category of destitution. Based on 2005/06 data, close to 350 million destitute people live in India (a lower MIC) alone or 28.5% of the country’s total population (Alkire et al. 2014). In 2010, calculated from the same data set, India’s poorest eight largest states together shared more people in MPI than the 26 poorest countries in Africa together (ibid). The challenges for fair and timely adaptation to climatic changes
), “Regional Integration in Asia: the Role of Infrastructure.” In Pan-Asian Integration: Linking East and South Asia, ed. J.Rancois, P. B. Rana and G. Wignaraja, 439-486, Houndmills, U.K. and New York: Palgrave Macmillan.

Goswami, A., A. Mattoo, and S. Saez (2012), “Exporting Services: A Developing Country Perspective”, The World Bank.

<u>Hoekman, B. (2006), Trade in Services at 25: Theory, Policy and Evidence, World Bank,</u> mimeo.

Kalinova,B., A. Palerm and S.Thomsen (2010) “OECD’S Restrictiveness Index: 2010 Update, OECD Working Papers on International Investment No. 2010/3, OECD Investment Division, www.oecd.org/daf/investment/workingpapers Magiera, S. (2011a) “Indonesia’s Investment Negative List: An Evaluation for Selected Services Sectors”, Bulletin of Indonesian Economic Studies, Vol. 47, No.2, pp. 195-219.

Mattoo, A., R. Rathindran, A. Subramanian (2006), “Measuring Services Trade Liberalization and Its Impact on Economic Growth: An Illustration”, Journal of Economic Integration, Vol. 21, No. 1, pp. 64-98.

Nielson, J. and D. Taglioni (2004), “Services Trade Liberalisation: Identifying Opportunities and Gains”, OECD Trade Policy Working Paper 1.

19
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<!-- Start of picture text --> a, 0 5 10 km<br>a ———<br>?<br>nay i |<br>gr EERE Be<br>=-non GHeeomomSSSE8@ See| S88zl<br>BE SSE88 SSeeee<br>a SESE 8 See Bu<br>‘2 itl EEEEE BE 8 [|<br>a | traf BEBOBOSs<br>San 8 SSSR Oe<br>EES SSESERES |_|<br>eT eee<br>Ge SSeceoes a<br>f Population density: Landscan<br>...... 2012 (
Note: The figure reports histogram of estimated AuROC values for each sector. Estimation and classification are performed over the in-sample 2015-19 period.

The specification using trade RCA data selects a larger number of significant variables than in the case of FDI. Table 3 reports the results for variables with statistically significant estimated coefficients (at the 10% significance level). We find a very strong overlap between dimensions associated with attractiveness to FDI and successful trade performance: 26 of 33 variables are common to both specifications. This is expected given the strong complementarities between investment and trade, noting also that FDI and trade can move in opposite direction and be substitute (the market-seeking argument). The overlap suggests strong complementarities in policies dominate over policies that may for instance favor infant industry type approaches (limiting trade to attract more domestic based production).

Differences between the RCA specification and earlier results for FDI are that network infrastructure (ICT, road transport) and financing variables become significant. There may be several reasons for this result. First it may be more complicated to internalize infrastructure services on an international basis (as opposed to more domestic focused in the case of FDI), and infrastructure services are key to external connectivity. Second, trade success englobes not only large players but also smaller firms who may be more reliant on external supply of key inputs.

Another expected result is that trade policies take more importance. The model picks an additional trade policy related variable in that belonging to an FTA is significantly associated with export success.

The sign of total natural resource rents changes direction when compared to FDI (but not mineral, coal or natural gas rents). In the context of trade it is generally admitted that natural resources abundance can lead to Dutch Disease type effect, an anti-export bias. At the same time resource windfall may grow the size of the domestic market and thus play a positive role in attracting market seeking FDIs. This would explain the change in sign between the two specifications.

22
outcomes in areas such as female education, child mortality, fertility, and governance (Branisa et al., 2013).

In this study, the focus is magnified even further by exploring legal institutions that discriminate on the basis of gender. Such institutions promote gender inequality before the law and are easily identifiable with concrete implications for gender inequality outcomes. This has some support in the literature. The presence of non-discrimination clauses in hiring has been found to be related to positive women’s labor force participation in the formal private sector (Amin and Islam, 2015). Similarly restrictions to women’s rights to inheritance and property as well as impediments to opening a bank account or freely pursuing a profession are found to be strongly associated with large gender gaps in female labor force participation (Gonzales et al., 2015). The primary goal of the paper is to construct a simple composite measure to illuminate the legal disparities faced by women and how it is linked to their wellbeing.<sup>1</sup> By and large the composite measure is a summation of the number of legal disparities faced by married and unmarried women. The components of this measure are based on 7 areas – Accessing institutions, Using property, Getting a job, Providing incentives to work, Going to court, Building credit, and Protecting women from violence. This measure is then used to elucidate the degree of legal gender disparities as well as the composition of these disparities across the world, including both developed and developing economies. The study then explores how well the measure correlates with other gender inequality indices and gender inequality outcomes.

> 1 This composite measure builds on a measure used by World Bank’s _Women, Business and the Law_ (WBL) as a tool to capture overall legal disparities faced by women, and is entirely based on the data collected by WBL.

3
**Figure 7 – Land Cover Classes and Distance to City Center ‐ Dar es Salaam**

<!-- Start of picture text --> Dar es Salaam Land cover c. 2011 Dar es Salaam Land cover c. 2011<br>(Percent) (Total)<br>100% 250<br>80% 200<br>60% 150<br>40% 100<br>20% 50<br>0% 0<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br>Distance from CBD (kilometers) Distance from CBD (kilometers)<br>Residential (Regular) Residential (Irregular) Residential (Regular) Residential (Irregular)<br>Commercial/Industrial Commercial/Industrial<br>Percent Area in sq kms Total Area in sq kms<br><!-- End of picture text --> Source: These charts have been calculated using land cover classification layers derived from high resolution satellite imagery (<1m) circa 2011/2012.

# **Nairobi**

The landlocked city of Nairobi is home to more than 3.14 million people, making it the second largest city by population in the African Great Lakes Region, after Dar es Salaam.<sup>35</sup> This centrally located capital is a major hub for commerce, transport and regional development, generating roughly 45% of the nation’s GDP and employing 25% of the nation’s work force. Despite its economic strength, inequality among its inhabitants has grown at a much higher rate than the rest of the country. The UN‐Habitat estimates that roughly 60% of its population lives in slum households, and 78% of those slum household have no running water.<sup>36</sup> Nairobi is also home to the largest urban slum in Africa, Kibera, which
When comparing across these groups, the five In-between Cities follow a very similar pattern of employment access per population, despite very different geographies, sizes and processes of economic development. They only begin to show differentiation when reaching the highest levels of accessibility, which serve only the top (in access terms) 30% of their populations. This may suggest, in comparison to the other two types of cities, that in fact the public transit system is relatively poor at affecting spatial mismatch for a large proportion of the population, and follows closely the existing residential and job patterns rather than being able to disrupt.

In terms of population distribution, while not universal, several cities show a distinct access hollow’, where the densest populated districts cluster around, but not at, areas of maximum employment access. As accessibility is calculated for all employment opportunities, for locations with multiple clusters, and even multiple parallel transit routes (as in the case of Conakry) the best access levels may fall not on, but between them, in locations with good access to multiple transport routes and multiple employment centers, highlighting currently low-density areas with high development potential.

This is the first study applying a similar methodology across different cities and based on similar, if not identical, data, to produce comparable accessibility metrics in data-poor environments. As such, it allows an analysis of patterns and trends in spatial data, establishing both a relevant benchmarking process and the integration of individual geographic circumstances. This data set can be used, and further built on, to understand the interactions between distributions of jobs, people and transport and mapping out the dynamics and challenges of urban agglomeration in Africa. It can potentially serve as a component for transport planning, transit-oriented development, land use planning, and investments in increasing labor markets and employment opportunities in African cities.

Specifically, this initial benchmarking opens up a number of avenues for further policy oriented research and operational engagements. First, the simulation of counterfactual scenarios, by comparison to the baseline, can provide some orders
% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>2014 2015 2016 2017 2018 2019 2020 2021 2014 2015 2016 2017 2018 2019 2020 2021 2014 2015 2016 2017 2018 2019 2020 2021<br>Micro (1-10 employees) Small (11-50 employees) Micro (1-10 employees) Small (11-50 employees) Micro (1-10 employees) Small (11-50 employees)<br>Medium (51-100 employees) Large (>100 employees) Medium (51-100 employees) Large (>100 employees) Medium (51-100 employees) Large (>100 employees)<br>Figure 7. Share of formal firms by Figure 8. Share of real value Figure 9. Share of labor of formal<br>overall sector added of formal firms by overall firms by overall sector<br>sector (constant 2010 prices)<br>100% 100% 100%<br>90% 90% 90%<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>2014 2015 2016 2017 2018 2019 2020 2021 2014 2015 2016 2017 2018 2019 2020 2021 2014 2015 2016 2017 2018 2019 2020 2021<br>Agriculture, forestry, and fishing Mining and quarrying Agriculture, forestry, and fishing Mining and quarrying Agriculture, forestry, and fishing Mining and quarrying<br>Rest of industry Non-industrial services Rest of industry Non-industrial services Rest of industry Non-industrial services<br>Source: World Bank and ZRA staff calculations based on CIT database<
Set<br>(N = 2,845 Projects)|
|---|---|---|
|FY95|221|213|
|FY96|228|221|
|FY97|213|204|
|FY98|252|243|
|FY99|244|226|
|FY00|205|194|
|FY01|205|181|
|FY02|192|181|
|FY03|208|186|
|FY04|210|192|
|FY05|228|198|
|FY06|220|200|
|FY07|192|164|
|FY08|186|145|
|FY09|147|97|
||**Analytical Data Set as a**<br>**share of WB IPF Lending**|**90%**|

41
-41-

The last term in both equations, the inverse of the coverage ratios, can be derived from table 3.5.

The results for the "adjusted" gross value of output comparison between Brazil and the USA and Mexico and the USA are presented in table 3.7 and 3.8 respectively. The figures for the countries in their "own" currencies are taken from the industrial censuses, i.e. Censo Industrial for Brazil, Resumen General for Mexico (except the adjustments for indirect taxes and subsidies for malt beverages, tobacco and petroleum refining, and the value added figure for petroleum refining, which we derived from Sistema de Cuentas Nacionales de Mexico) and Annual Survey of Manufactures 1975-1976 for the USA in 1975. The estimates for the USA at Brazilian and Mexican unit values are derived from formula (3.7a), and the estimates for Brazil and Mexico in US dollars from formula (3.7b).