rafmacalaba/datause-encoder-data huggingface dataset:
sector employment; and between four percent and 16 percent of total employment, including public sector and non-governmental organizations (NGOs) employment(Figure 8).<sup>15</sup> Obviously the situation varies considerably from one Arab country to the next. While most nonoil economies are dominated by SMEs, Gulf Cooperation Council (GCC) countries tend to be dominated more by larger corporations (a fact related to the importance of oil and gas).<sup>16</sup> In nonGCC countries. SMEs are estimated to employ as much as 67 percent of the labor force informally (Loayza and Wada 2010).<sup>17</sup> Naturally, it is likely that these numbers would increase in countries with large informal sectors.

**_Source:_** Government databases, and World Development Indicators.

**_Source:_** World Bank Enterprise Surveys (2006-2009).

SMEs are not only major contributor to employment but they also help in achieving inclusive growth through addressing gender disparities. The firms’ surveys and the workers module show that increasing the number of woman entrepreneurs could lead to a rise in the percentage of women in the workforce in the Arab region. This is mainly due to women-owned firms hiring more women as opposed to male-owned firms because of various factors including cultural traditions and work environment (Figure 10). Women workers report that there is less potential for harassment in SMEs that are women-owned, as well as in large firms irrespective of the gender of ownership, as in the latter there are in general clear institutional set up, transparent HR rules, labor protection and human rights policies.

> 15It must be noted that in healthy economies, SMEs account for the great majority of jobs and this is clearly not the case in MENA. However, SMEs are still important because of their potential for rapid and sustained growth rather than for their current role in employment because it is not as high as it would be in more a healthy economy or can be in the region as a whole.

> 16 The majority of enterprises are microenterprises, rather than SMEs in many MENA countries, including Egypt, Morocco, Lebanon and Yemen. In Saudi Arabia, SMEs are the majority, while the United Arab Emirates enterprise sector is more balanced between microenterprises and SMEs.

> 17It is worth noting that employment in SMEs is likely to be significantly under-counted in official records due to imprecise records on the informal sector, mainly due to the under-reporting of employment for tax reasons, or enterprises not being registered. Accordingly, the number would rise particularly in countries with large informal sectors. However, it is estimated that only six percent of GCC labor is estimated to be informal.

13
# **4.1 School autonomy in budget planning and management**

The school autonomy in budget planning and management indicator has three sub-indicators:

- a. Level of autonomy in the management of the school’s operational budget

- b. Managerial autonomy of schools in managing non-teaching and teaching salaries

- c. Autonomy of principals to raise additional funds on top of what the school receives from the central and local governments

A _Latent_ indicator means that the school may not manage any money because the Ministry of Education or the local government sends goods directly to the school and pays for all services directly, without any intervention by the school. At the opposite end, an _Advanced_ indicator means that school administrators may receive a transfer that is deposited into the school’s bank account, disburse payments to teaching and non-teaching staff; pay suppliers of goods and services, and manage the school flow of funds.

In 2012, education funding in Thailand amounted to 441.5 million Baht ($13.6 million), or 18.7 percent of the government’s budget (Bureau of the Budget 2013). Of this amount, 341 million Baht were spent in basic education, and the rest in university education and other educationrelated expenditures. Funding is decentralized to the Educational Service Area (ESA), which may not correspond with geographical boundaries. Each ESA oversees the block transfers of operational funds to its schools. School principals have autonomy over the use of operational block transfers. Table 3 shows the results of benchmarking policy intent and policy implementation using the SABER rubric. A small panel of informants assessed policy intent, while the implementation score averages out the scores of 227 school principals.

**Table 2:  Thailand: Policy Intent and Policy Implementation Scores for Indicator of Budget Autonomy**

|**Sub indicator**|**Intent**<br>**Score**|**Implementation**<br>**Score**|**Intent**|**Implementation**|
|---|---|---|---|---|
|**1.1 Management of the**<br>**operational budget.**School<br>principals manage the operational<br>budget under ESAguidelines|**3.0**|**3.3**|Established<br>|Established<br>|
|**1.2 Management of non-**<br>**teaching staff & teacher**<br>**salaries.**ESA delegates managerial<br>authorityto schoolprincipals.|**1.0**|**2.8**|Latent<br>|Emerging<br>|
|**1.3 Raising additional funds for**<br>**the school.**School principals raise<br>additional funds from parents and the<br>private sector.|**3.0**|**3.0**|Established<br>|Established<br>|
|**Overall**|**2.3**|**3.0**|Emerging<br>|Established<br>|

Source: World Bank 2013 and school survey data. The differences between the intent and implementation scores are statistically significant at the 0.001 level except for indicator 1.3, where the difference is not statistically significant.

11
Historically, on many maritime routes liner shipping companies were allowed to cooperate on prices, capacities or schedules (“liner conferences”). Conferences thus are a particular form of institutionalized cartels and owe their existence to the fact that some countries exempt shipping lines from competition law. Since the 1990s, however, the influence of price-fixing agreements has decreased sharply: although 150 conferences operated in the world in 2001, less than 30 survived in 2010 (CI Online, 2010; OECD, 2002), mainly as a result of legislative changes such as the Ocean Shipping Reform Act (OSRA) in the US and the repeal of the block exemption for liner shipping conferences by European countries (Regulation 4056/86). Among the 118 routes in our sample, a carrier agreement is active on 37 routes (detailed list provided in Annex Table A.4).

# _Barriers to trade in mode 3: commercial presence_

The impact of investment restrictions in the shipping sector has not been studied in the literature. Data unavailability may have been the main reason but it also seems the case that the complementarity between cross-border trade in shipping services and commercial presence has not been fully appreciated. In tramp shipping, tankers or dry bulk carriers are chartered by a single customer and so the transaction can easily be arranged by phone or via internet. In contrast, in the liner shipping segment a company needs hundreds or even thousands of customers to fill a container ship or general cargo vessel (Bertho, 2013). It is much more difficult to manage ten thousand boxes pertaining to ten thousand customers than ten thousand tonnes of crude oil pertaining to one customer. The development of a network of offices by establishing a commercial presence can greatly facilitate the administration and organization of vessels’ calls as well as the management of cargo. Second, international transport increasingly takes the form of “door-to-door” or multimodal delivery. It is therefore important for maritime companies to establish a commercial presence abroad in order to have their own inland transport facilities or to develop partnerships with local transportation firms to facilitate the hinterland leg from the port to the final delivery point.

To obtain data on such mode 3 restrictions, we draw on the Services Trade Restrictions Database (Borchert, Gootiiz and Mattoo 2014) which provides detailed information on the incidence of policy measures in a number of services sectors, including maritime shipping. Table 1 provides an overview of individual policy measures applied to liner shipping. These measures either affect market entry, post-entry operations, or the regulatory environment.

6
<!-- Start of picture text --> Figure 1.3: Sub-Saharan Africa remained poor in a multi-polar world<br>1,400,000,000<br>1,200,000,000<br>1,000,000,000<br>Pov. headcount - China<br>800,000,000<br>Pov. headcount - Low&Middle<br>600,000,000<br>Inc w/o China<br>400,000,000 Pov. headcount - Sub-Saharan<br>Africa - developing<br>200,000,000<br>-<br>1981 1984 1987 1990 1993 1996 1999 2002 2005<br>Source: WDI 2011<br><!-- End of picture text --> In 1980, per capita incomes in China were about one-third and in India they were about 50 percent of the developing countries in Sub-Saharan Africa (Table 1.1). And, China had more than 4 times and India more than two times the number of poor people (less than $1.25 a day (PPP 2005 dollars)). By 2005, an eightfold increase in per capita income reduced China‘s poverty rate to 16 percent compared to 85 percent in 1980. The threefold increase India‘s income levels had less impressive results. However, in SubSaharan Africa, there was virtually no change in the poverty rate. It declined from 53 percent in 1980 to only 51 percent in 2005. Unambiguously, Sub-Saharan Africa had slipped from being more prosperous than China and India in the 1980s to being the poorest region in the world.

**Table 1.1: Emergence of the new growth poles have led to a huge reduction in global poverty but not in the poorest region of the world**

||China|India|SSA|
|---|---|---|---|
|1980 - Incomeper capita,PPP(constant 2005 International dollars)|$524|$895|$1789|
|2005 - Incomeper capita, ,PPP(constant 2005 International dollars)|$4100|$2300|$1759|
|1980: % ofpopulation below$1.25/day|85%|65%|53%|
|2005: % ofpopulation below$1.25/day|16%|41%|51%|
|1980: Population belowpovertyline|835,062,177|462,737,493|212,499,751|
|2005: Population belowpovertyline|207,552,224|455,784,361|384,212,419|
|_Source: World Development Indicators, 2011_.||||

# **Section 1.2: Structural transformation and catch-up**

The path to prosperity in the developed countries was marked by a process of structural transformation reflected in the structure of employment and value-added in primary, secondary and tertiary industries. Long term economic trends from the pre-industrial stage of development in a large number of developed countries confirm that at the end of each episode of catch-up, the fast grower‘s economy had a structure which was closer to that of a developed as opposed to a low-income country (Syrquin 1988, page 206; Chenery 1979, page xvi; Abramowitz 1983, page 6
|**Tabl**|**e 4: Selected R**|**egressions Res**<br>**Po**|**ults Based on **<br>**oled Binary P**|**Pooled Data**<br>**overty Proxi**<br>_S_|**es**<br>_Hhld_||
|---|---|---|---|---|---|---|
|||_Experiment vs._<br>_Standard_||_a_<br>|_me ouseo_<br>_Experiment vs._<br>_Standard_|_s:_<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**Model**|1|2|3|1|2|3|
|**Controls**|NO|YES|YES|NO|YES|YES|
|**Interviewer Fixed Effects**|NO|NO|YES|NO|NO|YES|
|Experiment|0.027***|0.023***|0.022***|0.027***|0.023***|0.022***|
||(0.005)|(0.005)|(0.005)|(0.004)|(0.004)|(0.004)|
|**Observations**|197,513|197,443|197,443|107,080|107,010|107,010|
||**Po**|**oled Ordered **|**Categorical P**|**overty Proxie**|**s (3 Categori**|**es)**|
|Experiment|0.015*|0.020**|0.021***|0.014**|0.018**|0.016**|
||(0.009)|(0.008)|(0.008)|(0.007)|(0.007)|(0.007)|
|**Observations**|8,465|8,462|8,462|4,590|4,587|4,587|
||**Po**|**oled Ordered **|**Categorical P**|**overty Proxie**|**s (4 Categori**|**es)**|
|Experiment|-0.009***|-0.006*|-0.006*|-0.003|-0.003|-0.003|
||(0.003)|(0.003)|(0.003)|(0.002)|(0.002)|(0.002)|
|**Observations**|8,465|8,462|8,462|4,589|4,586|4,586|
||**Po**|**oled Ordered **|**Categorical P**|**overty Proxie**|**s (6 Categori**|**es)**|
|Experiment|0.031***|0.032***|0.031***|0.025**|0.023**|0.023**|
||(0.011)|(0.011)|(0.012)|(0.010)|(0.011)|(0.011)|
|**Observations**|8,466|8,463|8,463|4,590|4,587|4,587|
||**Poo**|**led Ordered C**|**ategorical P**|**overty Proxie**|**s (11 Categori**|**es)**|
|Experiment|0.002|0.002|0.002|-0.004|-0.004|-0.004|
||(0.003)|(0.003)|(0.003)|(0.002)|(0.002)|(0.002)|
|**Observations**<br>**Note:**Experiment is equal to|2,798<br>1 if the house|2,797<br>hold was subje|2,797<br>ct to the exp|1,514<br>eriment quest|1,513<br>ionnaire treat|1,513<br>ment, and 0|

**Note:** Experiment is equal to 1 if the household was subject to the experiment questionnaire treatment, and 0 otherwise. The estimations use the pooled (binary or ordered categorical) data at the household level; The results for the pooled binary and pooled ordered categorical poverty proxies originate from multivariate Logit and Ordered Logit regressions, respectively. While multivariate Logit regression results are marginal effects, the multivariate Ordered Logit regression results represent marginal effects on the probability of being in the lowest category. The control variables, as specified in Equation 1, are included when noted. The regressions are weighted and take into account clustering and stratification; The results are robust to varying the set of control variables. ***/**/* indicate statistical significance at the 1/5/10 percent level.

20
# **V.   ESTIMATES OF EPA TRADE AND FISCAL REVENUE EFFECTS: METHODOLOGY AND LITERATURE**

There is an abundant literature analyzing the potential impact of regional trade agreements, of which an EPA is one example. Recent studies have analyzed the likely implications of EPA implementation for ECOWAS or Sub-Saharan Africa from various perspectives, in particular the opportunities and challenges from a development perspective (United Nations Economic Commission for Africa, 2004; Hinkle and Schiff, 2004; Hinkle and Newfarmer, 2005), the trade and budget effects using a partial equilibrium model and/or a general equilibrium model (CAPE, 2002; Busse et al., 2004; UNECA, 2004), and the dynamic gains using a general equilibrium model (UNECA, 2004).

While a general equilibrium model would be desirable to adequately capture the interactions between sectors and elasticities of substitutions, and to simulate dynamic effects in EPA, data availability and practical experience have induced many economists to use the partial equilibrium model as a second-best option.<sup>14</sup> Much of the earlier analysis of the effects of trade agreements relies on Viner’s (1950) discussion of trade creation and trade diversion (Robinson et al., 2003). A partial equilibrium model can help estimate trade creation, trade diversion, and welfare effects, as well as fiscal revenue implications of trade policy changes.

We assess the likely impact of EPA tariff changes on trade and the subsequent fiscal revenue changes, using a partial equilibrium model developed by Verdoorn, (1960), following Busse, Borrmann and Grossmann (2004). It is an import demand function which has also been used in previous studies with some variations (Testa, 1997; Laird and Yeats, 1986; UN- ECA, 2004; Koranchelian, 2006).

Verdoorn’s model, which is described in the Annex, is based on the normal assumptions of partial equilibrium analysis. Also, it is assumed that goods imported from different countries are imperfect substitutes ( “the Armington assumption”) and that the supply response to the price reduction, as a result of tariff reduction or elimination, will allow the EU producers and exporters to meet any demand arising in the importing countries as a result of price reduction. That is, EU export supplies are perfectly elastic.

**Trade creation** captures the trade expanding aspects of liberalization that leads to the displacement of inefficient producers in a given preferential trading area. It is assumed that there is full transmission of price changes when tariff or non-tariff distortions are reduced or eliminated (Laird and Yeats, 1986; Ben Hammouda et al., 2004). That is, the domestic price of goods imported would change with the change in an ad valorem tariff. However, the price might

> 14 In some cases, a general equilibrium analysis can be usefully complemented with a partial equilibrium analysis as in the UN 2004 study on EU-ECOWAS EPA.

9
<!-- Start of picture text --> Percentageof ninis, 15 to 18<br>oo HE Compete Secondary<br>= HE Incomplete Secondary<br>Bs ME Incomplete Primary<br>HE) Newer Attended<br>1390) «(1555 eno 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Percentageof ninis, 19 to 24<br>HE Complete Higher<br>=, HE Incomplete Higher<br>= HE Complete Secondary<br>Es Complete Primany<br>MG Incomplete Primary<br>HE) Newer Attended<br>1390 «(1995 ed 2005 2010<br><!-- End of picture text -->
This paper analyzes the impact of a cash transfer program on cognitive development outcomes in early childhood in rural areas of Nicaragua. The program, known as _Atención a Crisis_ , makes sizeable payments, equivalent to about 15 percent of per capita expenditures for the average recipient household. Households eligible for the program were randomly assigned to treatment and control groups. A follow-up survey collected data on both groups approximately 9 months after the treatment group started receiving transfers. Random assignment allows us to estimate program effects with very few assumptions.

There are a variety of reasons why one might expect a program like _Atención a Crisis_ to have a positive effect on cognitive development in early childhood. Children in better-off households appear to have higher levels of cognitive development than those in poorer households in developing countries.<sup>1</sup> These associations may not be causal—rather they may reflect a correlation between parental wealth, parental genetic endowments, and child cognitive development. However, if cash transfers like those made by _Atención a Crisis_ allow households to spend more on nutritious foods, early stimulation, or health care, this may result in improvements in the cognitive development of children.

There are other features of the _Atención a Crisis_ program that could result in improvements in cognitive development. In order to remain eligible for _Atención a Crisis_ transfers, parents in the program were meant to ensure that school-aged children enroll in school and attend regularly, and take preschool-aged children for regular visits to health centers, where they are weighed, and receive vaccinations, micronutrients, or food supplements, as necessary.<sup>2</sup> In this sense, the program was similar in nature to other conditional cash transfer programs, such as the much-studied PROGRESA program in Mexico. The education and health conditions attached to the transfers imply changes in relative prices, which could result in shifts in expenditures towards human capital investments.

The _Atención a Crisis_ program also included a social marketing campaign—beneficiaries were told that transfers were intended to improve the diversity and nutrient content of children’s diets and to buy school materials. The social marketing of the program may have transmitted knowledge about child-rearing

> 1 References include Paxson and Schady (2007, 2008) on Ecuador; Halpern et al. (1996) on Brazil; Ghuman et al. (2005) on the Philippines; see also Schady (2006) for a discussion.

> 2 As we discuss below, there was an implementation glitch whereby compliance with the health condition was not monitored by the program.

2
will be 7 countries with poverty greater than 30%. These are: Demcratic Republic of Congo 73.9%, Haiti 58.2%, Burundi 54.5%, Comoros 37%, Nigeria 33.3%, Congo Brazzaville 33%, and Benin 33%.<sup>3</sup> In brief, relying on growth alone, virtually no scenario gets us to the goal of no country left behind. Other kinds of policy interventions will be necessary. I shall discuss some of these later.

The third criticism pertains to the use of a money metric measure of poverty, instead of a multidimensional measure. The latter would take into account not just income, but also health, nutrition, education and other critical dimensions of what constitute the minimal requirements of the good life (Stiglitz, Sen and Fitoussi 2010; Alkire and Foster 2011; Pattanaik, Reddy and Xu 2012; Ray and Mishra 2012).

This is an important criticism; however, before discussing it, it is important to remove one potential misunderstanding. The goals are ends to pursue but if, in addition, they are viewed as a statement on what the World Bank will not pursue—namely, anything that is not a mission goal will not be studied and deemed worthy of pursuit—that would be a mistake. That is not what the statement of mission goals is meant to do. It is right to expect that the Bank Group will work with countries to promote the individual components of the typical constituents of multidimensional poverty reduction.

Keeping the above clarification in mind, which hopefully will temper some of the criticisms, it is worth pointing out that the use of a money metric measure is not without some advantages. To see this, note first that there is no universal set of _all_ constituents of the good life. One can always think of additional dimensions to describe a decent standard of living. Hence, wherever one draws the line, there will be criticism of why an _even more_ multidimensional measure was not used.

> 3 The numbers quoted here and elsewhere, whenever they appear without a source citation, are based on estimates by the World Bank’s Poverty and Inequality group in the Research Department.

7
the inertia. Moreover, our model does not take into account the widespread shortage of foreign exchange in Ethiopia, and thus one of the key transmission channels between global and domestic prices is non-existent in the current context. It is thus unclear whether the government or private sector can import food grains in large quantities even if the differences in cereal prices between domestic and foreign markets are considerable. Finally, it is important to point out that inflationary expectations, which explain a large part of Ethiopia’s recent inflation, are not randomly generated. Expectations are based on fundamentals of the economy and market conditions and policymakers can influence expectations with appropriate policies. These caveats should be kept in mind when interpreting our results.

The reminder of the paper is organized as follows: Section 2 gives a short description of Ethiopia’s recent macroeconomic performance and then outlines the popular hypothesis of Ethiopia’s inflation trajectory. Section 3 provides the theoretical framework and the formulation of empirical models, and discusses how various hypotheses are tested. Section 4 describes and analyzes the money market, foreign sector, and agricultural market, with the purpose of formulating explanatory variables for the inflation model. Section 5 develops the final error correction models, while Section 6 illustrates how various scenarios might affect future inflation in Ethiopia. Section 7 discusses major findings and concludes.

# **2. Inflation in Ethiopia: Background and Hypotheses**

This section first gives a brief description of the Ethiopian economy and its inflation experience. It then outlines some of the hypotheses put forward to explain the historically unprecedented inflation process.

# _2.1 Economic Performance and Inflation_

Ethiopia is one of Africa’s largest countries with an estimated 77 million people in 2008. According to government data, about 38 percent of the population lived below the official poverty line in 2005, but it is likely that a larger proportion experiences extended periods of poverty due to shocks (Bigsten and Shimeles, 2008). Evidence on the welfare impacts of high food inflation on the rural population is somewhat inconclusive, but there is evidence of a significant negative impact on the urban population (Loening and Oseni, 2008).

5