rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 2-1 Tax revenues as a share of GDP are higher than the sample average at 20.7 percent**

<!-- Start of picture text --> International comparability of tax revenue<br>40%<br>West Bank and Gaza<br>35%<br>(2017), 20.7%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Country (CEQ assessment year of survey)<br>Tax revenue as % of GDP (ranked lowest to highest) Iraq (2018) Mexico (2012) Pakistan(2018) Guatemala (2011) Sri Lanka (2009) Ethiopia Indonesia (2012) Ecuador (2011) Paraguay (2014) Costa Rica (2010) Ghana (2012) Honduras (2011) Burkina Faso (2014) Russia (2010) El Salvador (2011) Colombia (2014) Peru (2009) Peru (2011) Chile (2013) Armenia (2011) Lesotho (2017) China (2014) El Salvador (2015) Tunisia (2010) South Africa (2015) Argentina (2012) Turkey (2014) Georgia (2013) United States (2016) Romania (2016) Bolivia (2009) South Africa (2010) Namibia (2016) Tajikistan (2015)<br>Dominican Republic (2013) West Bank and Gaza (2017)<br><!-- End of picture text --> Source: CEQ Data Center, Government Finance Statistics Manual West Bank and Gaza

**West Bank and Gaza’s social expenditure as a share of GDP is also a high 14.1 percent.** This compares to the average among both lower middle-income countries (9.4 percent) and upper middle-income countries (11.5 percent) in the sample of countries included in CEQ Institute’s Data Center.<sup>31</sup> It is also high compared to the average of 10.3 percent of all countries in the CEQ data center sample (indicated by the dashed red line in Figure 2-2).

> 31 CEQ Data Center on Fiscal Redistribution (https://commitmentoequity.org/datacenter) accessed June 2023.

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take various forms, such as aligning the definition of units, reference period, population, variables, classification, measurement error, missing data, and derivation of variables. For example, in the case of statistical matching between household budget surveys, we harmonize the reference period by annualizing variables whenever possible and appropriate. Additionally, we harmonize the categorization of variables to ensure they are classified in a consistent manner across different surveys. The harmonization process is facilitated by the fact that both the Household Budget Survey (HBS) and the European Union Statistics on Income and Living Conditions (EU-SILC) share a similar definition of a household. This definition, as described by Serafino and Tonkin (2017), states that a household comprises individuals who live together in the same dwelling and share meals or jointly provide living conditions. Essentially, a household refers to a group of people residing in the same place, with a degree of interdependence in their daily living arrangements, such as cooking and sharing meals together.

**We next identify potential matching variables and harmonize them with each other.** Table A.2 in Appendix 1 gives an overview of the variables that are included both in the HBS 2019 and the EU-SILC 2020. We mark those variables that we include in the set of potential matching variables in green. We also depict the respective categorization of these variables. As there are some important differences in the categorization of most of the variables, we harmonize them with each other. Next, we aggregate all indicators at the household level. Based on our assessment of potential matching variables, we harmonize the variables as follows:

- Harmonize HBS regions to EU-SILC regions (BG3 and BG4)

- Create a dummy variable for urbanization, both in HBS and EU-SILC

- Create a categorical income variable of 20 categories, the lowest category representing those households with the lowest income

- Generate household characteristics based on individual-level characteristics, which are as follows:

- Household with at least one child (<15); with at least one pensioner; with at least one elderly

- (+64); female-headed household; a household with at least one unemployed

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Second, the spatial variation is extremely important for planning climate resilient infrastructure as exposure of infrastructures to natural hazards is highly location‐sensitive. To demonstrate this, we select WWTPs and superimpose changing flooding hazard maps for return period > 50, varying RCP and time period for model MPI‐ESM‐LR (Figure 5). This model represents the medium range of all models, which is a reasonable scenario of flooding extent in the future periods concerned.

We observe that by 2035, flood hazards for RCP 4.5 are concentrated along the Yangtze River (Figure 5a), in southern Hubei, northern Hunan, central Anhui, northern Jiangxi and Shanghai. Provinces such as Guangxi, Guizhou and Ningxia face wide‐spread increasing flood hazard whereas Shaanxi (north), Shandong (northwest), Qinghai (central‐north), Heilongjiang (east), Tibet (central) experience regional specific flood hazard. The WWTPs located in these areas, if not designed to an appropriate level, will face increase flooding hazards. By 2055, flooding hazard escalate in large parts of Tibet and Xinjiang, Inner Mongolia. The WWTPs located in these regions, not previously exposed to flooding hazards at return period 50, now face increasing risks. For RCP8.5, concentrations of increasing flooding probability are similar to RCP4.5 during the period 2016‐2035, along the Yangtze River. Notable differences are in regions such as northern Heilongjiang, Jilin and northern Inner Mongolia, which suffer increasing flood hazard whereas they do not in RCP4.5. By 2055, hazards become widespread with very few areas left intact.

Decreasing flood hazard, on the other hand, is less in extent in general. For RCP 4.5, most are concentrated in Hebei, eastern Sichuan and central Jiangsu, eastern Inner Mongolia. By 2055, the overall number of areas facing decreasing probability of flooding declines. For RCP 8.5, it is similar to RCP 4.5.

Not only understanding the location of WWTP is important, it is also crucial to know what the protection standards are. In some cases, areas along the Yangtze River no longer face increasing hazard (compare Figure 5a and 5c). However, this does not mean that the WWTPs are safe. If they are designed at a return period lower than 50, for example, at 30, it could still be facing increasing flood hazards.

14
|**Table A3: Co**|**mparison of po**|**pulatio**|**n in samp**<br>**Benchm**|**le and res**<br>**ark year**|**t of Afric**|**a**<br>|
|---|---|---|---|---|---|---|
|||||||**Total**|
|||**1993**|**1998**|**2003**|**2008**||
|GDP per capita (2011|All|2269|2327|2446|3075|2347|
|<br>PPP)|Sample|2542|2527|2720|3456|2596|
||Out of sample|1637|1795|1750|1793|1727|
|Share middle/high|All|10|10|10|9|10|
|<br>income countries (%)|Sample|10|12|10|12|11|
||Out of sample|9|3|8|1|7|
||All|27|27|27|27|27|
|Fragile (%)|Sample|12|8|13|28|11|
||Out of sample|60|74|60|23|65|
||All|42|42|42|42|42|
|Resource rich (%)|Sample|35|32|38|51|35|
||Out of sample|58|68|52|12|59|
|Aid per capita|All|1849|1302|2233|2632|1795|
|<br>(current USD)|Sample|1551|1274|1538|2530|1454|
|<br>_Notes: Population wei_<br>_benchmark years 1993_|Out of sample<br>_ghted. The last co_<br>_to 2008._|2514<br>_lumn is t_|1372<br>_he (unweig_|3932<br>_hted) aver_|2953<br>_age over t_|2606<br>_he_|

29
9 goods. A reason for this differential impact, which is consistent with our findings presented in Section III, is precisely that services have a lower import content than goods and manufacturing exports.

Unless otherwise specified, we will focus on manufacturing exports in the rest of the paper. The reason is threefold. First, manufacturing is the sector that has been mostly associated to the growth in cross-border supply chains that is at the heart of our explanation for the decline of the REER elasticity. Second, we can exploit the information on GVC integration at the sectoral and sub-sectoral level from the TiVA database in the analysis of next section. Finally, focusing on a narrower sector and (as done in Section III) on subsectors is also a way of addressing the endogeneity problem of aggregate data, as export growth of narrower sectors is less likely to directly impact the value of the exchange rate.

# **C.   Discussion: Why Has the REER Elasticity Declined?**

One possible concern is that the lower elasticity identified in this paper is exclusively driven by the global financial crisis. To have a better understanding of exactly when the sensitivity started decreasing for (manufacturing) exports, we run rolling regressions for the same specification using 7- year windows.<sup>8</sup> Figure 2 shows that the REER elasticity has gradually decreased since mid-1990s and remained almost flat since early 2000s. Importantly, the decline in REER elasticity of exports pre-dates the global financial crisis. The weak external demand associated to the crisis is, therefore, a factor among others at play. What can these factors be?

While not exhaustive or exclusive, we see four types of explanations for the lower REER elasticity of exports. First, the lower responsiveness of exports to real exchange rate changes may reflect the fact that trade growth has slowed down. Trade growth expanded in the 1990s as production fragmented internationally into GVCs and decreased in the 2000s as this process decelerated

> 8 For robustness, we also estimate regressions with smaller (5 and 6 years) and larger (8 years) windows and find similar results.
to innovate. Private equity is not necessarily a silver bullet for innovation, as it may entail agency costs such as managerial myopia. Managers may underinvest in long-term projects, because of a lack of proper monitoring from market shareholders and increased pressure to meet short-term goals. Going public has been associated with less novel innovation since it is perceived as being riskier by managers (Bernstein 2015). Managers also try to protect their companies from losing market value (being undervalued) due to costly and risky innovation projects that are not understood properly by the market, in the fear of becoming targets to hostile takeovers.

A stream of the literature focuses on the beneficial effects of private markets in the funding of firm innovation. Investors in private markets comprise a wide range of finance types and providers, from crowdfunding and wealthy private investors (i.e., angel investors), to large investment funds such as venture capital, private equity, and private debt. For example, private equity involves the pooling of funds from private investors to be invested in private companies and it entails a medium to long-term investment horizon that is well-suited to finance innovative projects. Moreover, the closer monitoring of a firm operation involves the lowering of agency costs. For instance, patents from leveraged buyout (LBO) firms are more cited, and do not lead to shifts in the nature of research and focus on previously established areas of innovation portfolios. Institutional ownership has been associated with greater innovation and higher R&D expenditures which is less likely to decrease following poor performance, as it may be the case in the initial stages of innovation (see for example Aghion, Van Reenen, and Zingales 2013).

The rest of the paper is organized as follows. Section 2 describes the empirical approach. Section 3 presents the results and robustness tests. Section 4 discusses policy implications and concludes.

# **2 Empirical approach**

In this section, we first describe the data, sample, and variables used in the analysis. We then illustrate the empirical model.

# _2.1 Data and sample_

We gather information from multiple rounds of the World Bank Enterprise Surveys (WB ES) for 104 countries over the period 2009-2018.<sup>2</sup> The WB ES collects information from formal enterprises in the manufacturing and services sectors on a variety of aspects, including access to finance, corruption, infrastructure, crime, competition, and performance measures. We focus on the manufacturing sector

> 2 The Enterprise Survey data is available at the following link: <u>https://www.enterprisesurveys.org/en/enterprisesurveys.</u>

5
that in the 1820s, real wages in the Netherlands were about 70% higher than in the Yangtze valley.<sup>6</sup> Thus, similarity in the economic position of workers across the world, implicitly assumed by Marx and Engels, had a firm basis in the reality of the time. And, as we have seen, this is crucial because it is the similarity in the economic positions which allowed Marx and Engels, as well as later Marxists, to derive the principle of international solidarity of the working class.

We can reach the same conclusion that the main income cleavage was the one between classes, and not between countries, from a slightly different vantage point, if we consider global inequality, that is inequality between world citizens. In a pioneering work, François Bourguignon and Christian Morrisson (2002) have used a collection of their own and other authors’ estimates of income distributions for 33 country-groups and mean incomes from Angus Maddison to construct worldwide income distributions, at approximately twenty-year intervals, for the period 1820-1992. They estimate global inequality, measured by the Gini coefficient, to have been about 53 Gini points in 1850, and to have been composed in almost equal proportions of between-country and within-country inequalities. The former (between-country inequality) refers to that part of inequality that is due to the differences in country mean incomes while the latter (within-country inequality) is that part of total inequality which is due to inequalities existing in each individual country. To fix the ideas, we can call the between-country inequality ―locational‖ because it depends on the differences of mean incomes between various places (countries), and the within-inequality, ―class‖ inequality because it depends on different individuals, living in the same country, having different incomes and belonging to different social groups.<sup>7</sup> Specifically, Bourguignon and Morrisson estimate that the global Gini in 1850, amounting to 53.2 points, can be broken down into 25.9 Gini points (49 percent) due to location, and 27.3 Gini points (51 percent) due to class. Thus, around mid-19<sup>th</sup> century, one-half of inequality between individuals in the world was explained by unequal development of countries and another half by income

> 6 Li and van Zanden ́s results favor the view that appreciable difference in wages existed as early as 1800s  in opposition to other writers (especially  Pomerantz (2001) and to some extent Bairoch, 1997, p. 111)  who believe that  the gaps were  less.

> 7 Between-inequality is inequality that would exist if everybody in every country had the mean income of his/her country, or in other words, if inequality _within_ each nation were zero.

5
<!-- Start of picture text --> 30.0%<br>25.0%<br>20.0%ome10.0% |yeeyeesmT, FN ae<br>:<br>ee0.0% = 2a,Ct ani eS — OOA<br>-5.0% ee|<br>-10.0%<br>sSssess<br>ASSBSS5 853858538383ses ssggeeggeeasz8355383<br>———— core inflation-cale = = headline inflation === Core inflation (CBE)<br><!-- End of picture text -->
elastic supply of unskilled labor. As a result wage levels have remained low, except for high skilled, well-educated workers.

Labor is poor people’s most abundant asset and it accounts for the majority of their total income. Nonetheless, the poor are constrained in their labor use in a number of ways: lack of jobs, low wages, and wage discrimination especially for women.

A poverty analysis for Haiti reveals that many workers are poor despite full-time work.<sup>7</sup> The challenge of creating employment is therefore to increase worker productivity and tighten the labor market for competitive wages to lift the employee’s household out of poverty.

This section addresses determinants of employment, workers’ wages, and producers’ income. Employment is analyzed using a probit model. The wage determination process is analyzed using the quantile regression methodology (Section 2). This methodology characterizes the distribution of labor income in more detail than traditional OLS and two stage least squares (2SLS) regressions, as it makes it possible to break down the wage determination process across the entire wage distribution. Producer incomes are analyzed using an augmented production function model allowing for effects of farmer characteristics as well as farm production and infrastructure characteristics.

Port-au-Prince, despite the paucity of jobs, attracts many people from rural areas. Even the remote prospect of regular work draws an estimated 100,000 people each year from the countryside. This migration has led to an explosion of house building that has created work for construction laborers, but for the most part they join the estimated one million Haitians working in the informal sector that covers a multitude of occupations from self-employed traders and artisans to casual laborers. The most common activities are the reselling of minute quantities of everyday goods and basic services. Markets and streets are full of people attempting to make a living by selling items such as used clothing, fruit and vegetables, chewing gum, pens, soap, etc. Others run roadside microenterprises that, for example, repair broken-down machinery of all kinds and break rocks for use in house or road construction.

Formal sector jobs are few in Haiti. Ninety percent of the formal economy jobs that exist in Haiti are located in Port-au-Prince. Approximately half of the formal sector jobs are in the public sector, in particular in education, health, and justice. Other formal jobs are in state-owned enterprises such as telephone and electricity companies, the police force, and the tax and custom services. The rest of those with formal employment work in the small private sector, mainly in assembly factories, banking, commerce, and transportation. Since 1995, the one area of employment that has increased is the provision of private security, reflecting concerns about increased crime.

> 7 See Verner (2007).

7
# **Figure 12. Domestic revenue mobilization in small states and other EMDEs**

_Small states rely much more on grants and other non-tax revenues than other EMDEs but there is scope to mobilize additional tax revenues, particularly in commodity exporters, where tax effort lags behind that of peers. Additional tax revenues could be generated by better harnessing technology to support tax collection and, in some cases, by increasing value-added tax rates._ <!-- Start of picture text --> A. Composition of total revenues, 2011-22 B. Tax effort<br>Percent Tax Grants Other Score EMDEs excl. small states<br>100 1.0 Advanced economies<br>0.9<br>80<br>0.8<br>60<br>0.7<br>40<br>0.6<br>20<br>0.5<br>Small states Small states: Small states:<br>0<br>Small states EMDEs excl. small Commodity Commodity<br>states exporters importers<br>C. Share of electronic tax filing, 2021 D. VAT rates, 2024<br>Percent Small states Percent<br>Small states<br>EMDEs excl. small states 25<br>100 EMDEs excl. small states<br>20<br>80<br>15<br>60<br>40 10<br>20 5<br>0 0<br>Corporate Personal VAT<br>income<br>MDV BHR PLW SUR BHS KIR LCA GUY ATG CPV SWZ FJI GRD MUS STP SYC VUT VCT BRB MNE<br><!-- End of picture text --> _Sources_ : Ernst and Young (database); GFS (database); International Monetary Fund; ISORA (database), McNabb, Danquah, and Tagem (2021); PWC (database); World Bank.

_Note_ : ATG = Antigua and Barbuda; BHR = Bahrain; BHS = The Bahamas; BRB = Barbados; CPV = Cabo Verde; EMDEs = emerging market and developing economies; FJI = Fiji; GRD = Grenada; GUY = Guyana; KIR = Kiribati; LCA = St. Lucia; MDV = Maldives; MNE = Montenegro; MUS = Mauritius; PLW = Palau; STP = São Tomé and Príncipe; SUR = Suriname; SWZ = Eswatini; SYC = Seychelles; VCT = St. Vincent and the Grenadines; VAT = Value-added tax; VUT = Vanuatu. A. Panel shows average composition of total revenue by major component in 34 small states and 97 other EMDEs for the period 2011-22.

B. Tax effort reflects the difference between actual and potential tax revenue. Tax potential estimates are latest available for each country based on the true random effects method reported in McNabb, Danquah, and Tagem (2021). Sample includes 14 small states, 8 small states commodity exporters, 6 small states commodity importers, 35 advanced economies, and 98 EMDEs excluding small states. C. Panel shows share of tax returns filed electronically, for different tax types. Based on a sample of up to 18 small states and 57 other EMDEs for 2021.

D. Panel shows standard VAT rates as of March 2024. EMDEs excluding small states include 81 economies.

43
_Table 6: Shapley-Owen-Shorrocks decomposition of the poverty trend from 2015 to 2017/18 between the effect of change in total expenditures and the effect of change in poverty lines_

|Region|Poverty<br>rate 2015|Δ poverty rate<br>(2017/18-<br>2015)|Δ<br>total<br>expenditure<br>effect|Δ poverty<br>line<br>effect|Total<br>expenditures<br>2015|Total<br>expenditures<br>2017/18<br>(deflated)|
|---|---|---|---|---|---|---|
|Metropolitan|11.0%|14.3%|14.4%|-0.2%|12,088|9,626|
|Lower Urban|9.6%|3.0%|6.8%|-3.8%|9,884|8,560|
|Lower Rural|17.1%|8.2%|14.6%|-6.4%|8,075|6,886|
|Upper Urban|23.9%|4.3%|7.7%|-3.4%|8,479|7,507|
|Upper Rural|54.8%|-3.4%|-0.1%|-3.3%|5,745|5,817|
|Borders|15.5%|25.2%|36.9%|-11.7%|9,177|5,924|
|National|24.8%|6.2%|9.9%|-3.7%|8,599|7,366|

**Source** : Authors’ calculations based on HIECS 2015 and 2017/18 and Consumer Price Index (general number) 2015 to 2018, CAPMAS.

_Table 7: Evolution of the real price of a calorie from 2015 to 2017/18_

|Region|Price of calorie in<br>2015|Price of calorie in<br>2017/18<br>(2015 prices)|General CPI<br>(2015<br>to<br>2017/18)|
|---|---|---|---|
|Metropolitan|0.00393|0.00388|1.59|
|Lower Urban|0.00369|0.00349|1.65|
|Lower Rural|0.00362|0.00342|1.66|
|Upper Urban|0.00376|0.00366|1.61|
|Upper Rural|0.00375|0.00366|1.63|
|Border|0.00407|0.00364|1.65|

**Source** : Authors’ calculations based on HIECS 2015 and 2017/18 and Consumer Price Index (general number) 2015 to 2018, CAPMAS.

The evolution of poverty was heterogeneous across different regions. Putting aside the Border regions, where the smaller sample size provides a less reliable picture, Table 6 shows that the largest increase in poverty took place in the Metropolitan area, where the poverty rate doubled. This is indeed the region in which total expenditures per capita underwent the largest decrease, from about 12,000 Egyptian pounds in 2015 to about 9,600 Egyptian pounds in 2017/18. As a result, poverty increased from 11 percent in 2015 to 25 percent in 2017/18. Poverty also increased in all other regions (although to a smaller extent), except in Upper Rural Egypt. The latter region is the only one where real total expenditure per capita did not decrease over the period. In fact, the poverty rate decreased by 3.3 percentage points in Upper Rural Egypt. However, the decomposition reveals that this reduction is almost entirely due to the reduction in the real poverty lines, and thus does not signal an increase in real consumption in that region. Upper Rural Egypt remained by far the region with the highest poverty rate in 2017/18.

10
**Table 4**

**Summary Indicators of the Relationship between Domestic and External Prices**

||**U.S. Gulf**|**South Africa**|**Nairobi**|
|---|---|---|---|
|**Conventional statistics, equation (1)**||||
|Median_R_<sup>_2_</sup>|0.69|0.60|0.78|
|Lowest_R_<sup>_2_</sup>|0.59|0.50|0.64|
|Highest_R_<sup>_2_</sup>|0.78|0.72|0.88|
|Absolute deviation of_β1_from unity (%)|24|12|3|
|**Stationarity statistics of equation (1) (count o**|**ut of 18)**|||
|ADF <5%|15|8|17|
|PP <5%|15|12|15|
|ADF <1%|1|0|14|
|PP <1%|3|4|14|
|**Stationarity statistics of equation (2) (count o**|**ut of 18)**|||
|ADF <5%|6|14|17|
|PP <5%|14|13|18|
|ADF <1%|0|2|15|
|PP <1%|0|4|13|

**Notes:** This table summarizes conventional and stationarity statistics reported in tables B2 (U.S. Gulf), B3 (South Africa), and B4 (Nairobi), reported in Appendix B.

27