rafmacalaba/datause-encoder-data huggingface dataset:
# **II. EGYPT AND THE GLOBAL ECONOMIC CRISIS**

The previous section showed that the recent boom period (2005-2008) was driven by external demand and investment. The increased export revenues, the surge in foreign direct investment and augmented remittances from abroad all led to the investment boom and high growth rates. Unfortunately, with the global financial crisis the main external sources of growth were expected to drop off sharply, leading to a considerable slowdown since the mid-2008. This section concentrates on the impact of the crisis on economic growth and analyzes the policy responses to it.

## **A. THE IMPACT OF THE CRISIS ON GDP AND EMPLOYMENT**

## **_1) The impact on economic growth_**

The impact of the global economic crisis on Egypt’s growth was not as large as originally expected. The crisis reduced growth from 7 percent to 4.7 percent in FY09. All components of demand have been affected with lower net exports, consumption growth and investment. However, this is a better than expected outcome since early projections had forecasted FY09 economic growth between 2 and 4 percent.<sup>8</sup> This slowdown was also significantly less than in the developed economies or in most other emerging markets.

The better than expected performance can be explained by five factors. First, because of a lack of integration with the world financial system, Egypt’s traditional banking sector had little direct exposure to the toxic assets in developed economies. Second, a positive – though declining – current account balance since 2005 (around 2 percent of GDP). Egypt’s external needs were low in a context of global financial imbalances. Third, the fiscal stance and high nominal GDP growth led to significant declines in the public debt ratios in the years preceding the crisis. Hence there was room for policy maneuver, as Egypt had cushions that permitted assimilating the shock. Fourth, the limited role of private investment (around 15 percent of GDP in 2008) compared to the rest of the world, minimized the impact of the shock on overall investment, which suffered the bulk of the crisis impact. Also, the confidence crisis had a marginal impact on consumption growth which remained positive (around 5 percent). In the developed economies, for instance, business investment fell by 15 to 20 percent, while consumption only fell by 1 to 5 percent. Fifth, the response to the crisis was

> 8 Other estimates include those of Abou-Aly, 2008 (4 percent in FY09), the IMF, 2009 (3.6 and 3 percent in FY09 and FY10 respectively), and the Ministry of Economic Development, 2009 (4.4 and 4.0 percent in FY09 and FY10 respectively).

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While this seems attractive, a significant problem with this approach is that a reliable GNI deflator is needed for all countries. Another is that the choice of base year may affect the country classification in undesirable ways.

In our view, the results from this simple examination tend to point to a return to the US GDP (or GNI) deflator as the preferred adjustment factor for the thresholds. Over the period reviewed, it has been relatively stable, which was one of the key issues for making the change to the SDR basket. It also avoids the difficulty of interpretation: the use of the SDR deflator means, effectively, that the thresholds are maintained in SDR units rather than US dollars but then converted to US dollars for communication purposes. It would also eliminate the need for the use of the SDR deflator in the Atlas method, which is another source of confusion; instead, exchange rates would be adjusted using the ratio of local inflation rates to US inflation. In any case, since this change would impact the calculation of thresholds and GNI per capita estimates used for World Bank Group operational purposes, this proposal will need to be further discussed.

# **7. Redefining the thresholds, and other approaches**

If the main purpose of an analytic classification is to provide a mechanism for grouping and aggregating countries for comparison purposes, then a scheme based purely on the ranking of countries is a simple and attractive option. An obvious candidate is to divide countries into four quartiles, based on their relative GNI per capita estimates. This has many advantages: it is simple to understand; it can be constructed easily from the GNI per capita estimates used for the Bank’s operational decision-making; the number of each countries in each group is stable, by design; it does not require the selection of thresholds or procedures to update them each year; and it is more difficult to make use of the classification for “non-analytic” purposes, such as aid allocation.

One major disadvantage is that it requires estimates for all countries, or at least a “range” estimate, since any missing data will affect the position of other countries in the ranking. However, even in the current system, range estimates are made for all countries, so this drawback is relatively easy to accommodate. A second problem is that the classification would be relatively volatile; changes in growth rates of one country compared with another may result in reclassification of either country. A solution to this would to use a “buffer”: for example, 28
**<u>TABLE 3.1:  Infrastructure access by sector</u>**

|**Access**|**Urban**||**B**|**y settlemen**|**t type**||**By poverty**|**level**|
|---|---|---|---|---|---|---|---|---|
||**N**|**ALL**|**N**|**Formal**|**Informal**|**N**|**Non-Poo**|**r**<br>**Poor**|
|**Electricity**|||||||||
|_Nominal_|14,202|76%|14,202|81%a|65%a|14,030|82%a|70%a|
|_Effective_|14,202|75%|14,202|80%|65%a|14,030|81%|69%a|
|_Quality-adjusted_|14,202|46%|14,202|54%a|29%a|14,030|52%a|39%a|
|**Water**|||||||||
|_Nominal_|14,202|58%|14,202|69%a|34%a|14,031|66%a|49%a|
|_Effective_|14,202|55%|14,202|67%a|30%a|14,031|63%a|47%a|
|_Quality-adjusted_|14,202|28%|14,202|36%a|11%a|14,031|33%a|23%a|
|**Toilet**|||||||||
|_Nominal_|14,199|67%|14,199|77%a|46%a|14,027|71%a|63%a|
|_Effective_|14,199|48%|14,199|59%a|25%a|14,027|55%a|41%a|
|_Quality-adjusted_|14,199|34%|14,199|43%a|15%a|14,027|42%a|26%a|
|**Sewage Disposal**|||||||||
|_Nominal_|13,715|46%|13,715|55%a|27%a|13,546|56%a|36%a|
|_Effective_|13,715|42%|13,715|51%a|24%a|13,546|52%a|33%a|
|_Quality-adjusted_|13,715|41%|13,715|50%a|23%a|13,546|50%a|32%a|
|**Access Roads**|||||||||
|_Nominal_|14,203|41%|14,203|45%a|33%a|14,031|48%a|35%a|
|_Effective_|14,203|33%|14,203|38%a|24%a|14,031|39%a|27%a|
|_Quality-adjusted_|14,203|24%|14,203|29%a|13%a|14,031|31%a|17%a|
|**Public Transport**|||||||||
|_Nominal_|14,202|93%|14,202|92%|94%b|14,030|94%|91%|
|_Effective_|14,204|52%|14,204|54%|48%a|14,031|57%|46%|
|**Garbage Collection**|||||||||
|_Nominal_|14,196|48%|14,196|55%a|34%a|14,024|55%a|41%a|
|_Effective_|14,196|38%|14,196|43%a|28%a|14,024|44%a|32%a|
|**Street Lights**|||||||||
|_Nominal_|14,198|46%|14,198|47%d|45%d|14,028|51%a|42%a|
|_Effective_|14,197|32%|14,197|33%a|29%a|14,027|36%a|28%a|
|**Phone**|||||||||
|_Nominal_|14,166|90%|14,166|90%|90%|13,998|93%a|87%a|
|_Effective_|14,166|90%|14,166|90%|90%|13,998|93%a|87%a|

Notes:

1. Statistical significance is denoted ‘a’ :(sig. at <=1%); ‘b” (sig. at <=5%); “c” (sig. <=10%)

2. For phones, there was no measurable difference between nominal and effective access. A very small percentage of landlines were nonworking, but these households also owned a working mobile phone.

However, as discussed earlier, nominal access data can be misleading as they do not accurately reflect either the functionality of service, or whether the service is used. Figure 3.1 depicts both nominal and effective levels of access, showing the significant disparity that can occur between variables. For example, we find that while 93% of households are located within a 20-minute walk of public transport facilities (or nominal access), only 52% of households have a member who regularly uses these facilities. This suggests that while there has been considerable investment, mostly by the private sector, in expanding the public transport network, only about a half of households need to use it. It illustrates not only the potential pitfalls of relying solely on nominal access data as a measure of service coverage, but 9
minimizes the standard error of a target variable while explicitly accounting for the stratified clustered sample design.<sup>16</sup> The standard Neyman allocation rule provides an optimal allocation of PSUs when the sample follows a stratified random sample design (i.e. squared root of the design effect of _DEFTh_ =1 for each stratum _h_ ), and when finite sample correction is zero (i.e. fh=0). For stratified clustered samples, the standard Neyman allocation requires modification to achieve an optimal allocation. The modified Neyman allocation rule is given by the following expression: where _h_ is a sub-stratum or stratum within a district; ��∗ is the optimal sample allocation in sub-stratum _h_ ; _n_ is the total number of households in a district; ��� is the population share in each sub-stratum _h_ ; ��� is the standard deviation of the target variable of interest; DEFT is the square root of the design effect of the target variable of interest; and �1 ���� is a finite sample correction factor. For the sample design of the HIES 2016, we assume finite sample correction is zero.<sup>17</sup> All parameters required to estimate the optimal allocation of PSUs across sub-strata are estimated based on the HIES 2010 data. Since the cluster size in the HIES 2016/17 is identical to the cluster size used in the HIES 2010 (20 households), the intra-cluster correlation coefficients used to estimate the _DEFTs_ can be computed directly from the HIES 2010 data.<sup>18</sup> After deriving the modified Neyman allocation to produce reliable poverty statistics at the district level, the following five additional adjustments are made to achieve the other two goals

> 16 The Neyman allocation rule is derived from an optimization problem consisting in minimizing the variance of a target variable subject to a pre-determined fixed sample size. The full derivation of the modified Neyman allocation rule used for the allocation of PSUs in the HIES 2016/17 is presented in Annex 2.

> 17 The finite sample correction factor must be taken into account when the sample size corresponds to a large percentage of the size of the population of interest. This condition rarely holds in practice.

> 18 The performance of the sample is directly affected by the decision to fix _ex-ante_ the cluster size in each PSU to 20 households. This follows the decision of the BBS to follow field data collection protocols closely aligned with those used in the three previous rounds of the HIES collected in 2000, 2005, and 2010. This paper does not assess the performance of the sample under the selection of different cluster sizes.

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**Table A6** Impact of Boys Treatment Among Enrolled

||(1)|(2)|(3)|
|---|---|---|---|
||**Boys**<br>**Enrolled**|**Endline**<br>**Control Mean**|**Observations**|
|**A. Female Data**||||
|IPV often index||||
|treatment x post|-0.288<br>(0.184)|0.000|3,662|
|IPV in last year index||||
|treatment x post|-0.312**<br>(0.144)|-0.000|3,662|
|Sexual activity index<br>treatment x post|-0.297***<br>(0.060)|-0.000|3,662|
|**B. Male Data**||||
|Violence attitudes index<br>treatment x post|-0.442***|-0.000|1,542|
||(0.141)|(0.086)||
|IPV often index||||
|treatment x post|0.020|0.000|1,542|
||(0.091)|(0.087)||
|IPV in last year index||||
|treatment x post|0.088|0.000|1,542|
||(0.166)|(0.153)||
|Sexual activity index||||
|treatment x post|-0.097<br>(0.096)|-0.000<br>(0.071)|1,542|

_Notes._ The table presents estimates of _β_ 1 in column 1 from estimating equation 1 in Panel A and equation 2 in Panel B. Column 2 presents the outcome mean at endline in ELA only communities among females (males) who were not assigned to the _Goal_ treatment (whose connected female was not assigned to the _Goal_ treatment), and column 3 shows the number of observations. See Tables 2 and 3 for outcome definitions and control variables. Standard errors, clustered at the club level, are presented in parentheses below coefficients estimates in column 1. .01 ***, .05 **, .10 *. _Source._ Panel A: Female baseline and endline data, balanced panel. Panel B: Male baseline and endline data, balanced panel. The sample in the _Boys_ treatment arm is restricted to females with males in their network who enrolled in _Boys_ treatment in Panel A and to males who enrolled in _Boys_ treatment in Panel B.

65
# LESSONS FROM TAX REFORMs AN OVERVIEW

The World Bank's Tax Reform Project la an attsiipt to document and analyze the experience with tax reform in ten developing countries. _'he_ purpose of this project has been to try and obtai.: a better appreciation of how developing countries may improve the performance of their tax systems. Towards this end, each country study has delved into the questions of what motivated these tax reforms, what they were intended to accomplish, how they were implemented and how successful they have been in achieving their objectives. The basic thrust of this paper is inductive; that is, it attempts to discern empirical regularities and see whether there are some general lessons that can be extracted from a comparison of the experience with tax reform in different countries.

The countries encompassed by this project are Turkey, Indonesia, Korea, Bolivia, Colombia, Mexico, Jamaica, Morocco, Malawi and Zimbabwe. All of these countries have undertaken a significant tax reform within the last zwo decades. They can be divided into two different groups. Colombia, Indonesia, Mexico, Jamaica, Korea and Turkey are distinguished from the others by the fact that they have completed at least one fairly recent major reform whose effects can be cbserved and analyzed. More resources have been committed to studying tile effects of tax reform in this group than in the other group of countries. The second group, including Morocco, Malawi, Zimbabwe and Bolivia, consists of countries that are currently in the process of reforming their tax systems. Within the latter group, Bolivia has made more progress than the others in laying down the foundations of a new tax system but important administrative reforms remain unfinished in that country. Due to the contemporary nature of this tax
“correct and consistent use” rather than “acquisition.” They and others note that educating users about the advantages of improved biomass cookstoves and promoting them are typically insufficient. Instead, stoves need to be truly appropriate and offer net benefits in the long-run (e.g. Jeuland and Pattanayak, 2012; Mobarak et al., 2012). Outside the ICS literature, using evidence from a large energy efficiency program in the United States, Alcott and Rogers (2014) analyze the long and short run effects of providing social comparison information to energy users. They find that the intervention quickly increases energy efficiency behaviors, but these effects decay once the intervention is removed. They note that this “backsliding” has important effects on the costeffectiveness of such interventions.

# _2.3 Estimating Reduced CO2 Emissions from ICS_

CO2 emissions from the use of cookstoves depend primarily on the technical specifications of the stoves and assumptions about fuels displaced. Under the Framework Convention on Climate Change non-Annex 1 countries, such as Ethiopia, do not have emissions reductions obligations. The relevant emissions reductions displaced are therefore in Annex 1 developed countries using commercial fuels. In 2012 the executive board of the CDM approved protocols for crediting ICS projects. As discussed in UNFCCC (2012), emissions reductions from ICS should be based on “fossil fuel emission factors of substitution fuels likely to be used by similar users.” Lee et al. (2013) further suggests “... the use of a weighted average value of 81.6, representing a mix of 50% coal, 25% kerosene, and 25% LPG.” In this paper we adopt these norms.

The contribution of fuelwood conservation to reduced forest degradation and therefore fewer carbon emissions depends on the nature of fuelwood harvest (Johnson et al. 2009; Lee et al. 2013). A key parameter is therefore the fraction of woody biomass used “that can be established as non-renewable biomass” (UNFCCC 2012). The degree to which biomass harvests are unsustainable depends primarily on the management regime. If fuelwood is taken under open access regimes, for example, very limited management, including replanting and harvest mitigation, is expected (Ostrom, 1990; Gordon, 1954). Such settings are likely to have close to 100% nonrenewable biomass. Estimating non-renewable biomass with precision is difficult and an important area of current research (Bailis et al., 2015; Johnson et al., 2009; Lee et al., 2013). UNFCCC (2012) has therefore provided default estimates of percent non-renewable biomass for low-income countries and many countries in sub-Saharan Africa have default values above 90%. Perhaps

**8 |** P a g e
**Table A4.** Decomposition results of the emissions changes and contributions of various driving forces **across the globe** over the early period (1980-1990), late period (20052015), and entire period (1980-2015).

|**Period**|**1980-1990**|**2005-2015**|**1980-2015**|
|---|---|---|---|
|**Emissions change (Mt)**||||
|Overall change|1,898.5|4,952.6|14,299.9|
|_EC_|-141.9|67.3|521.4|
|_ECS_|-468.0|-208.0|108.6|
|_EI_|-2,780.1|-5,909.6|-13,716.7|
|_PCG_|3,719.8|8,758.4|20,655.0|
|_P_|1,568.7|2,244.4|6,731.6|
|**Contribution (%)**||||
|Growth rate|13.7|21.3|103.1|
|_EC_|-1.0|0.3|3.8|
|_ECS_|-3.4|-0.9|0.8|
|_EI_|-20.0|-25.4|-98.9|
|_PCG_|26.8|37.7|148.9|
|_P_|11.3|9.7|48.5|

**_Note:_** _EC_ , _ECS_ , _EI_ , _PCG_ , and _P_ refer to emission coefficient, energy mix, energy intensity, income, and population, respectively.

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extract from a group of variables a few orthogonal linear combinations of the variables that capture the common information most successfully. The first principal component of the asset and housing condition indicators is the linear index that captures the largest amount of information, so this study uses that as the measure of wealth. Filmer and Pritchett (2001) demonstrate the construction and internal validity of this index as a measure of wealth.

The assets included in the construction of the index include ownership of mortar, bed, table, chair, fan, air conditioner, radio, CD or cassette player, television, sewing machine, stove, refrigerator, washer, bicycle, motorbike or vehicle, drum, sofa, coffee table, cupboard, lantern, desk, clock, iron, computer, satellite dish, solar, generator and cellphone. The housing condition included in the construction of the index include the material (permanent or not) of which the dwelling, outer walls, roof and floor are made of; the source of lighting (electricity or otherwise) in the house; the source of water (pipe or otherwise); the kind of toilet facility (water closet or otherwise); number of rooms in the house; and number of rooms per capita. Following Filmer and Pritchett (2001) and Dzanku (2015) households within the top 60% of the distribution of the wealth index are classified as non-poor, and those within the bottom 40% are classified as poor. Using the same logic, households with the bottom 16% are classified as ultra-poor.<sup>7</sup>

# **Data and Sample Selection**

The study uses the two-wave Malawi Integrated Household Panel Survey (IHPS) data collected by the collected by the National Statistical Office of Malawi (NSO) with support from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA)

> 7 In order to test the sensitivity of the estimates to the benchmarks used for the asset poverty classifications, we also classified farmers into quintiles of the wealth index. The conclusions from the results based on the quintile classification are similar to the those based on the asset poverty classification. The results for the quintile classifications are available upon request.

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|**Table5. Ch**<br>|**aracterist**|**ic roots, da**|**mping pe**<br>|**riods, perio**<br>|**ds of cyde**|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|France||||Germany||Italy||Japan||UK||
|c.r|i.p|d.p|P.c|**c.r**|d.p|c.r|d.p|c.r|d.p|c.r|d.p|
|0.189||||-0.0115|86.96|-0.0217|46.08|-0.0376|26.595|0.21||
|-0.0205||48.78||-0.176|5.68|-0.1257|7.955|-0.05178|19.312|-0.0417|23.98|
|-0.2049||4.88||-0.4819|2.075|-0.17|5.88|-0.2392|4.18|-0.2968|3.368|
|-0.5||2||-0.5|2|-0.2558|3.908|-0.3088|3.238|-0.4811|2.078|
|-0.512||1.95||-0.51|1.961|<br>_-0.5_|2|-0.42|2.381|_-0.5_|2|
|-0.5922||1.688||-0.56|1.786|-0.50012|1.999|-0.5|2|-0.573|1.745|
|-0.8||1.25||-0.576|1.735|-0.5028|1.989|<br>-0.512|1.953|-0.71|1.408|
|-0.88||1.136||-1.32|0.785|-0.83|1.205|-0.5215|1.917|-0.724|1.38|
|-0.7509|<br>0.2964|1.332|21.194|<br>-1.3315|0.751|-1.07|0.935|-0.58|1.723|-1.79|0.559|
|-0.7509|<br>-0.2964|1.332|21.194|<br>-1.59|0.629|-2.239|0.447|-2.4|0.417|-1.808|0.553|

c.r.= characyteristic root, i.p.= imaginary part, d.p.= damping period, p.c. = period of cycle cases display an unstable root (see table _5)_ while the other three cases present only stable roots. For the three stable country cases -Germany, Italy and Japan- information of some interest can be obtained from sensitivity analysis results (tables 6, 8, 9). First of all it can be noted that adjustments coefficients in the sectoral export shares have a stabilizing effect, i.e. their increase moves the root away from the bifurcation value, in all three cases (the only exception being cc5 in the case of Japan); the same can be said of C6' the adjustment speed in the knowledge accumulation process. One interpretation of this result is that a stronger ability to deliver, which can be considered as a sign of stronger competitiveness, stabilizes the specialization structure. Ambiguous effects on stability (in the sense that an increase in their value stabilizes some roots while destabilizing others) are generated, again in all three cases, by changes in 1 5 and 31,0, respectively the knowledge elasticity in the science based export share and the coefficient of high tech imports in the knowledge accumulation equation,. This effect is also present in the case of Japan for 133, the knowledge elasticity in the scale intensive sector. Considering that, in the case of Germany and Japan, these elasticities are constrained to be the same in more than one sector one interpretation is that a very high knowledge elasticity in knowledge sensitive sectors may, eventually, lead to a change in the specialization structure.

28
# **Table 3: Differences in socio-economic characteristics between ever-widowed and ever-** **<u>divorced women</u>**

||Ever-widowed|Ever-divorced|
|---|---|---|
|Rural area|0.52|0.39***|
|Age|58.68|40.21***|
|Age at first marriage|18.26|18.76|
|Ever been to a French school|0.13|0.37***|
|Total number of children (alive)|4.54|3.42***|
|Log of total household consumption per capita (CFA francs per year)|12.38|12.49*|
|Log of total cell consumption per capita (CFA francs per year)|12.38|12.42|
|Household size|10.73|10.09|
|Household head|0.32|0.17***|
|Cell head(incl. household head)|0.61|0.82***|
|N|585|417|

Note: All characteristics are expressed as shares of the marital status group except for age, number of children, consumption aggregates and household size. Divorced includes separated women. All significance tests are relative to ever-widowed women, where *** p<0.01, ** p<0.05, * p<0.1. Source: Authors’ calculations using PSF1.

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