rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6.** Commercial users’ views on the quality of infrastructure services, <u>by country income group</u>

||Electricity<br>(2001-02)|Water<br>(2001-<br>02)|Sanitation<br>(2001-02)|Telecoms<br>(2001-02)|Roads<br>(2000)|Railroads<br>(2001-<br>02)|Ports<br>(2001-<br>02)|Airports<br>(2001-02)|
|---|---|---|---|---|---|---|---|---|
|Income<br>group|_Quality of_|_services_|_Quality of_<br>_water pollution_<br>_regulation_|_Quality of_<br>_telephone_<br>_infrastructure_||_Quality of_|_services_||
|Low|2.6<br>(9)|4.0<br>(27)|2.3<br>(9|3.4<br>(9)|3.4<br>(27)|2.7<br>(9)|2.6<br>(9)|3.6<br>(9)|
|Lower<br>middle|4.2<br>(25)|4.8<br>(24)|3.1<br>(25)|4.9<br>(25)|4.2<br>(24)|2.6<br>(25)|3.5<br>(25)|4.2<br>(25)|
|Upper<br>middle|5.1<br>(20)|5.0<br>(18)|4.0<br>(20)|5.6<br>(20)|4.1<br>(18)|2.9<br>(26)|3.8<br>(20)|4.5<br>(20)|

Note: Ratings are on a scale of 1 to 7, with 7 indicating the highest quality. Figures in parentheses indicate the number of countries for which data are available.

Source: World Economic Forum 2003; World Bank 2003.

The evidence suggests that:

- Perceived quality is higher in higher-income countries in all sectors (except roads, and there the difference is minor).

- Even in upper-middle-income countries, perceptions are lower than best practice, which is rated at 7 on the scale used in the World Economic Forum (2003).

- Water and telecoms are generally perceived to be the highest-quality service. These favorable perceptions reflect the bias introduced by indicators collected from the business community. For instance, water may not be a major issue for many businesses, many of which have their own supplies and do not depend on urban utilities. But many households, urban and rural, suffer from poor-quality water supplies.

# **4. How was the infrastructure sector financed in the 1990s?**

Infrastructure is financed from three main sources: the public sector, ODA and the private sector. According to DFID calculations, during the 1990s, government or public utilities financed 70% of actual total infrastructure spending from own resources—which included cost recovery and subsidies—while official development assistance (ODA) 16
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that there is no theoretical foundation behind the model, even though well fitted to actual trade data (Deardorff, 1984).<sup>11</sup> The third calculates the preference margin to make a comparative analysis of the export revenues of beneficiary countries with an estimate of the revenues that might have been earned without the preferential scheme (e.g., Alexandraki and Lankes, 2004).<sup>12</sup> The approach is easy to apply, but it is in danger of overestimating the preferential effect because the competitive international market prices is the lowest possible and may not be achievable, particularly to developing countries, due to incomplete information and lack of channels.<sup>13</sup> Finally, the fourth approach to assess the impact of trade preferences—in particular preferences being eroded—on exports is based on a computational general equilibrium (CGE) model, such as the Global Trade Analysis Project (GTAP) model (e.g., Nordås, 2004; Mlachila and Yang, 2004).<sup>1415</sup> As well known, however, this approach is vulnerable to the “black-box” criticism; the confidence in CGE is invisible and remains open to argument.

11 From the more general perspective, a gravity regression tends to show that the bilateral GSP extended from the North to developing countries have a stronger positive effect on trade than a multilateral trade agreement (Rose, 2002). This does not mean that all multilateral trade institutions are ineffective. More precisely, Rose’s evidence indicates that countries belonging to the GATT/WTO do not have significantly different trade patterns than non-member countries. Paiva (2005) finds that some regional free trade agreements (FTA) are also effective in enhancing trade between the partners involved.

12 Alexandraki and Lankes (2004), focusing on banana and sugar preferences, estimate the average preference margin for 76 middle-income countries at about 5 percent. The countries eligible for the bananas protocol include Belize, Côte d’Ivoire, Dominica, Dominican Republic, St. Lucia, St. Vincent and the Grenadines, and Suriname. On the other hand, the major beneficiaries from the sugar protocol are Mauritius, Fiji, Guyana, and Barbados.

> 13 Subject to this risk, the following section _will_ calculate the preference margins for major beef and veal exporters.

14 Nordås (2004) shows that China and India will come to dominate world trade in textiles and clothing, and that other countries will severely suffer from preferences erosion. For Sub-Saharan Africa, the U.S. preferences under the African Growth and Opportunity Act (AGOA) are considered insufficient for African countries to maintain the market shares on U.S. apparel imports (USITC, 2004).

15 Mlachila and Yang (2004) find that the phase out of textiles and clothing quotas would substantially reduce the Bangladeshi textile and garment exports, threatening the country’s balance of payments and employment. It is attributed to the fact that the country has neglected improving its fundamental competitiveness in the textile industry for years.
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Liberia, the study used the instrumental variable spatial autoregressive model (Drukker et al. 2011), which allows the above-mentioned empirical issues to be addressed simultaneously. Policy implications will be developed from the estimation results: Where should investments be concentrated, in the primary city or other urban areas? What kind of transport connectivity affects firm location? The remaining sections are organized as follows: Section II provides an overview of recent developments of formal businesses and road networks in Liberia. Section III develops an empirical methodology and describes our data. Section IV presents main estimation results and discusses some policy implications. Section V concludes.

# **II. CURRENT ROAD NETWORK AND FIRM LOCATION IN LIBERIA**

Liberia is one of the poorest countries in the world and has a population of approximately 4.4 million. Gross domestic product per capita was US$457 in 2014.<sup>1</sup> Many people live below the poverty line, depending on subsistence farming without access to markets. According to the national census, the national poverty rate was estimated to be 54.1 percent in 2008 (LISGIS 2009). There is significant urban–rural inequality. Monrovia has a poverty rate of 31.6 percent, but poverty is generally more prevalent in the inland north central region (72 percent), such as Lofa County, which was most severely affected by the recent Ebola outbreak.

Agriculture is an important sector, employing approximately half of the labor force. A significant number of people work in the informal sector, which accounts for nearly 60 percent of the total labor force (table 1). The industry and service sectors, especially in the formal sector, are thin. A recent labor force survey showed that approximately 17,000 people are employed in the manufacturing sector and 343,000 in the service sector.

1 According to _World Development Indicators_ .
comprehensive survey distinguishing the consumption patterns of different income groups. If the necessary data is available, it is possible to use an additional excel file that has been developed for TRIST to do the analysis. The tool calculates the changes in consumer expenditure across groupings in response to a given trade reform assuming that consumption patterns do not change. It is once again important to notice that this analysis provides only a short-term and a partial view on the issue. There are important economy wide effects of trade reform that the tool cannot take into account.

We again consider the duty free island scenario for Mauritius for which a comprehensive consumer expenditure survey is available. Table 9 shows how different income groups are affected by the reform. The resulting falls in consumer expenditure for different income groups and for constant consumption baskets range between -1.0 and -1.5 percent. Given that Mauritian tariffs are already very low to start with, it is not surprising that the reform does not produce larger expenditure changes on average. It is, however, interesting that changes in expenditure differ between income groups although we consider a reform that drives tariffs to zero across all product groups. This further emphasizes the importance of differentiating carefully between different population groupings when analyzing the impact of trade reforms. In conjunction with the techniques outlined above to analyze the impact of trade reforms on sectoral employment and production, the researcher can draw an informative picture of how different population groups, rather than solely the country as a whole, are affected by the reforms in the short-term.<sup>31</sup> These analyses can form part of a more extensive investigation of the welfare and poverty impact of trade reform.

**Table 9: Expenditure changes in response to tariff reform to duty free island in Mauritius**

|Income Groups|Old Expenditure|New Expenditure|% Change in Expenditure|
|---|---|---|---|
|All Classes|9568|9442|‐1.3%|
|Less than 2,000|2780|2752|‐1.0%|
|2,000 to <5,000|3598|3556|‐1.2%|
|5,000 to <7,500|5443|5374|‐1.3%|
|7,500 to <10,000|6967|6877|‐1.3%|
|10,000 to <15,000|8941|8819|‐1.4%|
|15,000 to <20,000|11772|11617|‐1.3%|
|20,000 to <30,000|14911|14694|‐1.5%|
|30,000+|22202|21932|‐1.2%|

1) In Mauritian Rupees 2) All tariffs are driven to zero 3) Elasticities: Exporter Substitution (1.5), Domestic Substitution (1.5), Demand (0.5)

# _Trade diversion_

A preferential liberalization will reduce prices and increase imports from the beneficiary country. But at the same time it will displace imports from other (potentially more efficient) sources. Thus, a preferential liberalization affects not only the overall price level of the goods affected but also the relative price of goods from different sources.

> 31 Given that we have information on employment shares across income groups in different sectors.

24
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109(Yi.60), ...-, lOg(.80) We would also like to consider the case in which we have some information as to one of the elements of the country-specific effects, in particular, the country's level of education (equation (10)). The proxy we use for the level of education is taken from Mankiw, Romer, and Weil (1992). This is the percentage of the working-age population that is enrolled in secondary school, a measure that is approximated by the product of the gross secondary enrollment ratio times the fraction of the working age population that is of secondary<sup>school</sup> age (i.e., aged 15 to 19).

Our sample consists of 98 countries (see Appendix B for a list of countries considered). These are the countnes for which data are available and for which oil production is not the primary economic activity. It is well known that standard growth models do not account for economies based on the extraction of natural resources and not on value-added activities. Excluding the countries for which data are not available may create sample selectivity problems given that these countries are frequently the poorest ones. Therefore, we will not presume that the results obtained here can be applied to those very poor economies.

Equations (8) and (10) represent the cases we wish to study. Writing those two equations more explicitly, we have logy1, = 01og(nj'-O0.05;) + 0ogs, + (1 +y)logiy4 5 + (8) ' )
petroleum products of 54.7 percent. In order to correct for this discrepancy, the Altertax tool<sup>11</sup> (Malcolm, 1998) is employed to lower the subsidy rate in the GTAP database. All petroleum products imported into Nigeria are subject to this rate, hence the Altertax simulation is applied to the imported petroleum products tax rates corresponding to each of three Nigerian agents in the model: firms (tfm); government (tgm), and private households (tpm).<sup>12</sup> Each of these taxes implies a wedge between the composite import price (pim(i,r)) and the respective consumption prices for each agent (referred to in the GTAP model as pfm(i,r), ppm(i,s) and pgm(i,r)).

# **3 Scenarios**

The analysis is conducted by simulating two cuts to the fuel subsidy:

- Complete removal of the subsidies on imported petroleum products, and

- Partial removal of subsidies so that petroleum product prices do not increase more than 10 percent of the baseline price of fuel.

This study also analyzes two alternative policies, which could mitigate the impacts on poor households and the domestic economy of the partial subsidy removal:

- Introduction of a subsidy on domestic petroleum product production, with expenses being equal to the former government expenditures on import subsidies, to increase domestic output of that industry;

- Introduction of a government transfer scheme, with expenses being equal to the former government expenditures on import subsidies, targeting rural households, which are poorer on average than urban households. We consider this scenario the preferred scenario for assessing the welfare implications of a subsidy reduction, as the benefits from lower expenditures on the subsidy are directly transferred to households, instead of increased government expenditure such as for the provision of public goods.

The government transfer to poor households is undertaken by first identifying poor households. Six households are identified as poor. All six households are rural and are selected due to their

> 11 The Altertax approach runs a simulation where tax rates are set to their desired value and the updated post-simulation database is saved for subsequent policy experiments. A special closure and parameter file are applied to ensure that the rate-changing simulation has the least possible effect on other cost and sales shares. This closure fixes real regional trade balances, whilst the parameter settings amount to Cobb-Douglas everywhere, which keeps budget shares fixed.

> 12 We refer to the lower case parameters in GTAP, tfm, tgm, and tpm as tax rates, where, in fact, these variables represent changes to the "power" of taxes, a technical definition, which can easily be translated into tax rates. So, we use tax “rates” and “power” of taxes interchangeably, recognizing they are numerically different.

11
# Annex 2. OLS model without double-causality correction A. Impact on income and monetary poverty

||_Incom_|_e_||_Poverty_<br>||
|---|---|---|---|---|---|
||_Mean income_|_Gini_|_Food_|_Capabilities_|_Assets_|
|FAIS ∞|−0.222***|0.688***|11.15***|11.88***|10.67***|
||(0.00524)|(0.0978)|(0.262)|(0.262)|(0.229)|
|Own revenues ∞|0.0678***|0.368***|−3.058***|−3.105***|−2.707***|
||(0.00333)|(0.0623)|(0.167)|(0.167)|(0.146)|
|Unconditional revenue ∞|0.0998***|0.285**|−5.344***|−5.729***|−5.038***|
||(0.00639)|(0.119)|(0.320)|(0.320)|(0.279)|
|Conditional revenue ∞|0.0443***|−0.746***|−1.908***|−1.915***|−1.320***|
||(0.00673)|(0.126)|(0.337)|(0.337)|(0.294)|
|Direct workers ‡|0.644***|−1.880|−21.82***|−22.03***|−18.36***|
||(0.0707)|(1.322)|(3.541)|(3.536)|(3.090)|
|Indirect workers ‡|−1.453***|−15.08***|105.5***|95.62***|53.14***|
||(0.218)|(4.073)|(10.91)|(10.90)|(9.521)|
|Productivity per occupied ‡‡|0.0387***|0.449***|−3.011***|−3.182***|−2.879***|
||(0.00489)|(0.0914)|(0.245)|(0.245)|(0.214)|
|Private business assets ‡‡|0.0489***|0.275***|−0.946***|−0.871***|−0.478***|
||(0.00327)|(0.0610)|(0.164)|(0.163)|(0.143)|
|Semiurban|0.00735|1.113***|−2.652***|−2.973***|−2.693***|
||(0.0120)|(0.225)|(0.602)|(0.601)|(0.525)|
|City|−0.0189|1.135***|−0.614|−0.568|0.138|
||(0.0158)|(0.295)|(0.790)|(0.789)|(0.690)|
|Constant|6.682***|29.15***|59.57***|67.42***|81.38***|
||(0.0404)|(0.756)|(2.025)|(2.022)|(1.767)|
|N|6,143|6,143|6,143|6,143|6,143|
|R-2|0.717|0.065|0.644|0.662|0.655|
|Municipalities|2,048|2,048|2,048|2,048|2,048|

_Note:_ The mean income corresponds to the average household income per capita in each municipality in logarithms. Monetary values are expressed in real terms in August 2014. Pooled OLS. Semiurban refers to municipalities with a population between 2,500 and 15,000. City refers to municipalities with more than 15,000 inhabitants.

∞ Fiscal revenue items in the logarithm of the average monthly per capita value three previous years. ‡ Occupied per inhabitant, employees of the economic units or indirect workers, reported in the economic census of the circa year. ‡ ‡ Productivity by the total of occupied and active of the economic units of each municipality. Standard error in parentheses. *** p < .01 ** p < .05 * p < .1 29
The legal form these organizations take is largely driven by national policy, legislative frameworks and history. For example, SPSPs in countries with a tradition of cooperatives in other sectors, such as Kenya, Colombia and Bangladesh tend to be predominantly cooperatives. A large number of SPSPs that are registered as non-profit organizations— such as cooperatives, neighborhood associations, community groups, and societies— provide network services. Most of the SPSPs that are informal are individual entrepreneurs or family businesses. Many water SPSPs are members of SPSP associations (e.g. Kenya, Pakistan, Ghana), which serve a number of functions including sinking common or shared boreholes, purchase bulk water from utilities, and lobby for improvements in terms and conditions of service. For SPSPs operating on a formal basis, operating constraints (e.g., access to finance) are often closely linked to organizational status: for example fully private ventures rely on equity and informal borrowing while non-profit SPSPs may have access to grant funding to augment their own resources; similarly the legal form of the organization may also limit SPSP operations – a study on small scale providers in Kenya noted that the rights of small providers (e.g. to operate a bank account) varied according to the legislation (e.g. Societies Act, Cooperative Act) they operated under.

# **4.2 Technology**

The range of technology used by SPSPs is as varied as the types of SPSPs documented above (see Table 6). Some SPSPs are only engaged in the supply of services, while others are also involved in production. The technology employed may extend upstream from delivering a service to the customer to the means for producing or generating water supply or electricity, in the case of independent SPSPs. Such providers bear additional equipment costs related to generating or producing, as well as for storage or water treatment, as appropriate, compared with dependent SPSPs. SPSPs are known, and often criticized, for applying technological standards that may not conform to national norms. A key reason cited in the literature is the rigid nature and high costs implied by existing norms and the need to match services to the affordability levels. SPSPs are also known to have introduced innovation in improving cost effectiveness of service delivery, however, to date this has not been well documented or studied.

21
To calibrate the joint distribution of consumption and labor income, I use data from Lakner and Milanovic [2016]. The data set contains surveybased measures of _either_ household consumption or disposable income by decile. The calibration requires the joint distribution of labor income and 1 consumption for computing the term �0<sup>_u′_(</sup><sup>_ci,t_)</sup><sup>_li,t/Ltdi_.Iwillassumethat,</sup> for the bottom 9 deciles, consumption is given by disposable income and that income consists only of labor income ( _ci,t_ = (1 _− τt_ ( _li,t_ )) _wtli,t_ ). The top 10% is assumed to have no labor income and consumes only capital income. Formally, using _q_ ˆ _i_ to denote the estimated consumption or income of decile _i_ , I approximate: Note that ignoring the labor income of the top 10% vastly _understates_ the benefits from additional redistribution. The highest decile likely receives a relatively large share of aggregate labor income, and has the lowest marginal utility of consumption. Thus, instituting transfers from this group to the rest of the population is highly desirable from a welfare perspective. By ignoring the top 10%, the burden of financing the transfers falls only on the bottom 90%, which is less favorable. The assumption that the top decile receives no labor income is thus highly conservative in this context.<sup>2</sup> In addition, if taxes are progressive, then the approximation _li/L ≈ q_ ˆ _i/_<sup>�</sup> _j_<sup>_q_ˆ</sup><sup>_j_understatesthebenefitsfromadditionalredistribution.Underthe</sup> assumption that taxes are progressive, disposable income is more equally distributed than gross labor income. Note that the marginal benefits from

> 2At the same time, including the top 10% under the assumption that _ci,t_ = (1 _− τi,t_ ) _li,t_ may overstate the benefits from redistribution. The reason is that the top decile is likely to receive some capital income, which tends to be highly concentrated among the rich. Consequently, both the consumption level and the level of disposable income likely exceed labor income. The assumption _ci,t_ = (1 _− τi,t_ ) _li,t_ would therefore overstate the extent to which an increase in labor taxes would tax the rich. The most conservative approach is to ignore this group.

11
typically remains about 20 percent or below in all countries and years, except for Mauritania where it rose rapidly to almost 40 percent after 2008.<sup>5</sup>

# **“** **_De facto” FHHs: Demographic and socioeconomic approaches_**

The term “head” is a loaded term carrying strong connotations about household decisionmaking power that has traditionally been given to the oldest-male member (regardless of his breadwinner status). This is certainly an issue in the Arab region, where the traditional patriarchal system may preclude designating the female as “head” in the presence of a disabled adult male or a son (regardless of age), even if the woman is the main income-earner.

A _de facto_ FHH can thus be defined as one where the male head is temporarily or regularly absent, or (if co-resident) is not the main breadwinner (Buvinic and Youssef, 1978; Buvinic _et al._ , 1983; Klasen _et al._ , 2015). _De facto_ headship accounts for the demographic composition of the household, as well as the socioeconomic circumstances determining the respective members’ relative contributions to household resources (Rosenhouse, 1989; Handa, 1994, 1996; Rogers, 1995; Varley, 1996; Buvinic and Gupta, 1997; Fuwa, 2000; Posel, 2001; Budlender, 2008; Grown and Valodia, 2010; Chant, 2010; Rogan, 2013; Klasen, 2015).

A _de facto_ FHH may be more vulnerable to poverty for several reasons. In many societies, the absence of male connections to local economic and social institutions can be debilitating. _De facto_

> 5 One complication in classifying married self-reported FHHs arises where one spouse works overseas and sends home remittances, which is common in the region. If the overseas spouse is the husband, the stay-behind spouse might or might not designate herself as the household head in his absence. This can underestimate poverty among “true” FHHs, where the female head does not rely on others for support, since some of the self-declared female heads or main income-earners are in fact temporary designees while the main income-contributing spouses are overseas. In the surveys for all years, remittances are major sources of income for self-reported FHHs, consisting for example of 68% of the consumption per capita in Egypt, and 37% of the consumption per capita in Jordan (Appendix B, Tables B.1 and B.4, all years columns). However, the surveys lump together domestic and overseas remittances and do not allow any further breakdown or provide information on the amount of overseas remittances. The surveys do not identify the relations between the remitters and the households, which complicates matters as such remittances might be alimony or in-kind support.

9