rafmacalaba/datause-encoder-data huggingface dataset:
the endogeneity of the equivalence scales complicates the comparison of poverty trends across household types. Indeed, the real values of poverty lines do not grow at the same rate for all households, because the equivalence scale evolves over time.

# **Section 3: Poverty figures over the period 2015 - 2017/18**

# **3.1 Poverty figures in 2015**

We start with some descriptive poverty statistics in 2015.

As shown in Table 1, the national poverty rate estimated in 2015 is 24.8 percent. Table 1 shows that, in 2015, the poor lived more than proportionally in rural areas and in Upper Egypt, where per capita total expenditures are lower.

_Table 1: Spatial distribution of poverty in Egypt in 2015 (total expenditures)._

|Statistic|National|Metropol.|Lower<br>Urban|Lower<br>Rural|Upper<br>Urban|Upper<br>Rural|Borders|
|---|---|---|---|---|---|---|---|
|% of population*|100.0%|6.4%|7.7%|29.8%|14.3%|38.6%|3.2%|
|Per capita total<br>expenditures*|8599|12088|9884|8075|8479|5745|9177|
|Poverty rate (total<br>exp.)*|24.8%|11.0%|9.6%|17.1%|23.9%|54.8%|15.5%|

**Source** : *Authors’ calculations based on HIECS 2015. **CAPMAS (2019).

Table 2 complements these insights by providing a more complete picture of Egypt’s poor in 2015. Poverty affects 29 percent of children, 25 percent of adults, and 17 percent of the elderly. Hence, children are more affected by poverty than adults. On average, poor households have a larger size (5.6 members) than non-poor households (3.9 members). The former also have more dependents (2.7 dependents) than the latter (1.6 dependents). Poor households are more often headed by a person with low education than non-poor households. Poor households are more likely to have a head who is working. Finally, poor households are less likely to own welfare-relevant assets, such as private cars, washing machines, or smartphones. This unequal distribution of welfare-relevant assets is not surprising but confirms that the poverty measurement methodology adequately identifies households with fewer assets.

_Table 2: Profile of the poor in 2015 (total expenditure)_

|Profiles among poor households<br>(total expenditures)|Poor<br>household|Non-poor<br>household|
|---|---|---|
|Fraction<br>of<br>poor<br>among<br>Children<br>Elderly<br>Men<br>Women|29%<br>17%<br>26%<br>24%||
|Average HH size|5.60|3.93|
|Average number of dependents|2.67|1.64|
|Fraction of poor HHs whose head has low education|95%|81%|
|Fraction of poor HHs whose head is not currently working|25%|31%|

6
# **Appendix Table 7: The Kitagawa-Oaxaca-Blinder Decomposition of Business Revenues by** **<u>Urban/Rural</u>**

||(1)|(2)<br>Urban|(3)|(4)|(5)<br>Rural|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|Overall|Endow.|Coeff.|Overall|Endow.|Coeff.|
|Male|12.380***|||11.494***|||
||(0.182)|||(0.116)|||
|Female|11.681***|||10.410***|||
||(0.071)|||(0.087)|||
|Raw Difference|0.699***|||1.085***|||
||(0.195)|||(0.145)|||
|Difference due to|0.892***|||0.570***|||
|Endowments|(0.335)|||(0.187)|||
|Difference due to|-0.193|||0.515**|||
|Coefficients|(0.383)|||(0.251)|||
|Individual Age||0.037|-4.282||0.129|-6.695*|
|||(0.046)|(3.367)||(0.080)|(3.628)|
|Individual Age Squared||-0.033|2.153||-0.130*|3.619**|
|||(0.036)|(1.744)||(0.074)|(1.778)|
|Number of Adults in HH||0.009|-0.527||-0.000|0.316|
|||(0.010)|(0.327)||(0.002)|(0.248)|
|Number of Children Aged 0-5||-0.003|0.207||-0.021|0.255|
|In HH||(0.011)|(0.166)||(0.020)|(0.188)|
|Individual owns a Financial||0.118***|0.189||0.305***|0.062|
|Account||(0.045)|(0.189)||(0.063)|(0.079)|
|Individual is Married||-0.001|0.378||0.010|0.918|
|||(0.007)|(0.401)||(0.017)|(0.723)|
|Individual is Divorced/Married||0.016|0.014||-0.044|0.042|
|/Separated||(0.026)|(0.044)||(0.035)|(0.052)|
|Individual Completed||-0.013|-0.009||0.001|0.002|
|Secondary Schooling||(0.052)|(0.065)||(0.001)|(0.002)|
|Individual has a Vehicle||0.063|-0.033||0.048|-0.038|
|(bicycle, motorcycle, car)||(0.043)|(0.239)||(0.060)|(0.299)|
|Business has Electricity||0.224***|0.098**||0.024**|0.006|
|||(0.061)|(0.047)||(0.011)|(0.009)|
|Business has Running Water||-0.001|0.005||0.004|-0.001|
|||(0.061)|(0.043)||(0.003)|(0.001)|
|Business has a Phone||0.007|-0.810||0.049|-0.264|
|||(0.006)|(0.521)||(0.033)|(0.481)|
|Business Maintains||-0.217**|-0.271***||0.010*|0.012|
|Accounting Records||(0.091)|(0.098)||(0.005)|(0.013)|
|Business is registered||0.135|0.126**||0.004|0.003|
|||(0.119)|(0.059)||(0.004)|(0.004)|

50
Chong et al.

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-  8  -

supplies of these services tend to be beyond government’s control, there are some indications pointing to a healthy growth of software exports mainly to the EU and U.S. According to one estimate and anecdotal evidence, around 80 percent of Bulgaria’s software output of estimated at around US$ 400 million, or around US$ 340 million, is sold abroad.<sup>6</sup> Since the total value of ‘other services’ sold abroad was US$ 565 million in 2003, it is rather unlikely that this fully accounts for the contribution of the software sector. According to the same source, these exports are expected to grow at around 30 percent per year over the next several years.

# **B** **_._** _Performance: exports of goods in comparative perspective_

Bulgaria stands out among CEEC-10 economies as the worst performer in terms of exports of goods. No other CEEC-10 economy has experienced such a volatile export performance and such deep contraction not only immediately after the collapse of central planning but also several years into transition. Bulgaria’s time profile makes it more comparable to CIS economies rather than to CEEC-8 but only in its performance through early 1990’s. It became comparable to that of CEEC-8 economies experienced during their early stages of transition only 6-7 years later around 1997.

Bulgaria’s overall performance in 1991-95 appeared to be in line with that of other Central European economies. Data in Figure 1 for two countries, Hungary and Romania with total exports (US$ 5.2 billion and US$ 4.9 billion) roughly twice as large as total exports of Bulgaria (US$ 2.3 billion) in 1993, provide vivid illustration of distinctive features of Bulgaria’s export performance.<sup>7</sup> The differences in dynamics were enormous. By 2004 Hungary’s total exports stood at 179 percent of Romania’s exports and 442 percent of Bulgaria’s, while Romania’s at 246 percent of Bulgaria’s exports.

**Figure 1:** _Total exports of goods of Bulgaria, Hungary and Romania in 1993-2003 (in millions of US dollars)_

<!-- Start of picture text --> 35,000<br>30,000<br>25,000 Hungary<br>20,000<br>15,000 Bulgaria<br>10,000<br>Romania<br>5,000<br>0<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> Source: Derived from the IMF Direction of Trade statistics.

While the value of Bulgaria’s exports increased 125 percent between 1991 and 1995 as compared to Hungary’s 56 percent and Romania’s 62 percent, the recovery from a transformational recession turned out to be short-lived. There was a dramatic reversal of fortunes in 1996-99, with total exports in terms of value falling every year during this period. The largest contraction of almost 10 percent was in 1997. While the value of Bulgaria’s total exports in 1999

> 6 As reported in _IntelliNews - Bulgaria Today_ , March 25, 2005 (07:02)

7 Note that the data used in Figure 1 differ from those in Table 1, as they are derived not from the balance of payments statistics but from the IMF direction of trade statistics.
- 13 -

- Aj1 The degree to which SES differences among students relate to subsequent achievenment (attitudes).

25. Effective schools would be characterized as simultaneously having a high average level of achievement, fji and a weak differentiation effect with regard to SES (i.e., a small value for Pjl). These effects are hypothesized to vary across schools as a function of sector (national public, local public and private) and school-level differences in social composition, material and nonmaterial resources, emphasis on academic achievement (student motivation and effort), orderly environment and local control.

# Sample and Data

26. The data come from the IEA Second International Science Study, conducted in the Philippines in 1983. A two-stage stratified sampling design was used. 2 Stratification was based on thirteen geographical regions oL the Philippines. Within each region, schools were classified into public (government-supported) and private (supported by private funds) schools. Within the public sector, the schools were classified into two further strata: Barangay/Municipal High Schools (referred to in this paper as "local schools") and National, Provincial and City High Schools (referred to in this paper as "government schools"). This gave a total of 39 strata. At the first stage, schools were selected with probability proportional to the number of classes; at the second stage, one intact grade 9 class was chosen by simple random sampling. A total of 269 schools and approximately 10,000 students participated in the study. After cleaning,

> 2 ,locos Region, Cagayan Valley, Central Luzon, Southern Tagalog, Bicol, Western Visayas, Central Visayas, Eastern Visayas, Western Mindanao, Southern Mindanao, Southwestern Mindanao, Manila
# **2. FINCA and the DRC Context**

Even within Africa, which lags other developing regions in financial development (Allen et al., 2014, 2015: Beck and Cull, 2014, 2015), the Democratic Republic of Congo (DRC) has stood out as one of the least banked countries in the region. In 2014, 17% of adults in the DRC had an account with a formal financial institution. In comparison, 34% of adults in Sub-Saharan Africa, 54% of adults in developing economies, and 62% of adults worldwide had formal accounts.<sup>14</sup> Moreover, the 17% figure represents a substantial gain in financial inclusion in the DRC, since the 2011 figure stood at less than 5% (Demirguc-Kunt and Klapper, 2012a,b).

The country’s long history of conflict has no doubt led to institutional and infrastructural deficiencies that impede economic growth, and thus also impede demand for financial services. However, evidence from financial diaries in other developing countries (Collins et al., 2009) suggests strongly that poor clients have many financial needs and, because their incomes are often irregular and emergencies arise, their financial lives are unpredictable. The low financial usage ratios reported above therefore make it highly likely that demands for financial services for large segments of the Congolese population go unmet by its formal financial services providers such as commercial banks.

FINCA DRC is part of FINCA, an international microfinance institution present in 23 countries worldwide, focused on offering financial services and products to small scale businesses and households. Founded in 1985 in Bolivia, FINCA opened its first program in Africa in 1992 in Uganda. In 2009 FINCA introduced branchless banking – ATMs, computerized points-of-sale transactions, and cellular banking – in an effort to expand its outreach while keeping associated costs low. In late 2016, 2016 FINCA had over 643,000 savers in Africa, an average disbursed loan

> 14 The figures for account ownership are from Demirguc-Kunt, Klapper, Singer, and Van Oudheusden (2015).

7
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<!-- Start of picture text --> Polic Reserc<br>Population, Health, and Nutritlon<br><!-- End of picture text --> WPS 1004 This paper- a product of the Population Policy and Advisory Service, Population and<sup>Human Resources</sup> Department- was prepared for a review of effective family planning programs.<sup>Copies of this paper are</sup> available frce from the World Bank, 1818 H Street NW, Washington, DC 20433. Please<sup>contact Otilia</sup> Nadora, room S6-065, extension 31091 (October 1992,23 pages).

In the 1980s, signs that Sub-Saharan Africans would welcome family planning in numbers sufficient to make a difference in fertility rates were scattered and weak. Pessimists cited formidable cultural and socioeconomic barriers; optimists provided resources for pilot projects, coupled with research to document results and to guide expansion and replication.

Among projects with measurable achievements in acceptance of family planning in settings that were less than promising were the Ghana Registered Midwives Project, the Ruhengeri Project in Rwanda, and the Sudan Community-Based Family Health Project. All were associated with the Operations Research Program of Columbia University's Center for Population and Family Health.

In Ghana, midwives in private practice were trained and given other support to initiate family planning services.

In the Sudan, rural catchment areas and work assignments of rural primary health care personnel were changed to introduce famnily planning and strengthen other child survival services.

Positive results were evident from quantitative measures of service delivery and, in Rwanda and the Sudan, from an increase in contraceptive prevalence in the project areas. Other criteria for success included improved management skills, motivation for replicating successful programmatic elements, and potential for continuity.

Questions rermain as to why attitudes changed, when contraceptive use for family limitation will be practiced widely, and how applicable the experiences reported here are to other locations. These projects do not provide the answers. They do, nonetheless, support an optimistic view for the future of family planning in Sub-Saharan Africa.

In Rwanda, rural community development volunteers added family planning to their educational activities.

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regular channels. In contrast, Hardy and McCasland (2016) use the data from the NAP experiment in Ghana and find no displacement among firms in our sample. Although they do not examine a typical apprenticeship program, Alfonsi et al. (2017) compare the effectiveness of an on-the-job training program with that of a formal vocational training program in Kampala, Uganda. They find that both forms of training improve labor market outcomes, but individuals assigned to formal vocational training had greater earnings growth because of their acquisition of “transferable skills” rather than “firm-specific skills,” highlighting the importance of skills acquisition. Alfonsi et al. (2017) also find evidence that firms are labor constrained. This is consistent with the findings of Hardy and McCasland (2016) for our study context. Rather than focus on firm outcomes, we use the randomized match between trainers and apprentices to identify the characteristics of training providers (firms) that deliver the best outcomes for youth. This novel exercise provides us with a better understanding of the production function for apprenticeship training. To our knowledge, our study is the first to explore this dimension in the context of training programs. This exercise is important, as it can help governments improve the effectiveness of training by introducing a more selective recruitment process for trainers.

Our findings are more pessimistic than those of studies of apprenticeships in European contexts (for example, Acemoglu and Pischke (1998); Fersterer, Pischke, and WinterEbmer (2008). These studies have generally found positive effects of the apprenticeship training on individual participants and have also highlighted the potential for firms to benefit from providing such training. Observational studies of apprenticeship training in Ghana have argued that returns to training are greatest for individuals with low levels of education and are not significantly different from zero for those with more than six years of schooling (Monk, Sandefur, & Teal, 2008; Teal, 2016). Our results are consistent with this notion, since the average duration of schooling in our sample exceeded seven years. Further, Frazer (2006) argues that apprenticeships essentially teach apprentices to their trainer’s business. This is consistent with our that replicate findings showing the treatment shifts individuals out of wage work and into self-employment. Although these shifts are associated with lower earnings, there could be offsetting nonmonetary benefits from self-employment. This possibility is discussed extensively in the U.S. literature on entrepreneurship (Hamilton, 2000; Hyytinen, Ilmakunnas, & Toivanen, 2013). Given the lack of wage jobs in the Ghanaian context, many individuals likely enter selfemployment involuntarily. This could potentially reduce the nonmonetary benefits from self-employment.

5
Policy Research Working Paper 5603

# **Abstract**

Trade policy reforms in recent decades have sharply reduced the distortions that were harming agriculture in developing countries, yet global trade in farm products continues to be far more distorted than trade in nonfarm goods. Those distortions reduce some forms of poverty and inequality but worsen others, so the net effects are unclear without empirical modeling. This paper summarizes a series of new economy-wide global and national empirical studies that focus on the net effects of the remaining distortions to world merchandise trade on poverty and inequality globally and in various developing countries. The global LINKAGE model results suggest that removing those remaining distortions would reduce international inequality, largely by boosting net farm incomes and raising real wages for unskilled workers in developing countries, and would reduce the number of poor people worldwide by 3 percent. The analysis based on the Global Trade Analysis Project model for a sample of 15 countries, and nine stand-alone national case studies, all point to larger reductions in poverty, especially if only the non-poor are subjected to increased income taxation to compensate for the loss of trade tax revenue.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at wmartin1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team