rafmacalaba/datause-encoder-data huggingface dataset:
- _15 -_

## m. **CHARGiNG** FOR **RoADS** - **NDEcr MUTODS**

# A. Indirect Charging via Vehicle Ownershi Purchase Tax and Annual License Fees

13. Methods of charging for roadway usage can be indirect or direct (see Table 1). The most direct way of charging for road use is via electronic road use charging. The indirect methods involve charging for a surrogate of usage such as vehicle ownership. The primary instruments are acquisition/purchase taxes for vehicles, such as import duties and first registration taxes. High purchase taxes and annual license fees are often used as means of reducing the size of the vehicle fleet. They have been used quite effectively in places like Korea and Hong Kong by raising the ownership costs and dampening the rate of acquisition of private cars (Armstrong-Wright, 1986, p. 126). Hefty first registration taxes (close to 100% of the landed price) for private automobiles have been instrumental in keeping a lid on the total vehicle fleet, in contrast to low purchase taxes (around 15% of the cost-insurance-freight value) for commercial vehicles (Hau, 1989). Indeed, the resultant decrease in the aggregate number of cars means that there would be an underutilization of the road network in uncongested and rural areas, whereas the existing vehicle fleet (notably the taxi fleet) would tend to be used much more intensively in urban areas. Nevertheless, the heavy taxation via first registration taxes and annual icense fees has been regarded as administratively simple and effective albeit nonselective. While high taxes on a final consumption good (such as the private car) and low taxes on an intermediate input (such as commuting) are generally considered socially desirable, high annual license fees and purchase taxes are still sledgehammer approaches to the problem of congestion because they do not address the issue of efficient road utilization.

# B. Indirect Charging via Vehicle Usage

# 1) By Amount of Use: Fuel Tax and Tax on Tires and Vehicle Parts

14. A better method of charging for congestion indirectly is to aim for vehicle usage by the amount and the place of usage. The first instrumen **t** o consider is the ubiquitous fuel tax. Since
sectors underlying Figure 5. Moreover, the averages in Figures 5 and 6 are calculated over a different number of sub-sectors. Thus, it is possible that for some countries in Figure 6 a low productivity sub-sector influences unweighted and weighted averages of KLEMS labor productivity, while the same low productivity sub-sector is ‘buried’ under a more aggregate sub-sector in Figure 5 and thus does not influence unweighted and weighted averages of WIIW labor productivity.

**_Figure 6:  Average KLEMS Labor Productivity in the Services Sector_**

<!-- Start of picture text --> Panel A. Unweighted Average Labor Productivity Panel B. Weighted Average Labor Productivity<br>Hungary Hungary<br>Slovenia Slovenia<br>Czech Rep. Slovak Rep.<br>Poland Czech Rep.<br>Slovak Rep. Poland<br>Estonia Lithuania<br>Lithuania Estonia<br>Latvia Latvia<br>0 20 40 60 80 100 120 140 160 0 50 100 150 200 250 300 350<br>Thousands of PPP Euros per Employee Thousands of PPP Euros per Employee<br>1997-2000 2001-2004 1997-2000 2001-2004<br><!-- End of picture text --> Source: KLEMS database 2007.

Note: The averages of labor productivity are taken across 21 NACE 2-digit sub-sectors listed in Appendix Table A.1.

Value added in real estate activities is very high during 1997-2004 as a result of a real estate boom in the ECA region while employment in that sub-sector is relatively low. Hence, labor productivity in that sub-sector is artificially high, particularly in Hungary, while this does not necessarily represent high efficiency. Hence, we show in Figure 7 unweighted and weighted averages of KLEMS labor productivity excluding real estate activities as these provide a more realistic representation of the average efficiency of services in EU-8 countries. In this figure the productivity divide between EU-5 countries and Baltic countries is less clear, although Hungary still exhibits the highest average productivity. In fact, Estonia ranks higher than the Czech Republic, Slovenia, and the Slovak Republic in terms of unweighted average KLEMS labor productivity and above the Slovak Republic and Slovenia in terms of weighted average KLEMS labor productivity. However, the figure shows that both unweighted and weighted averages of 15
the weights. The main data source for Eslava et al. (2004) is the Colombian Annual Manufacturers Survey (AMS) by DANE (National Statistical Institute). I discuss further details about the productivity estimates below in Section 5.2.1.

The import demand elasticity measure is based on the structural estimates in Kee, Nicita and Olarreaga (2004), which I combine with the GDP data from the World Development Indicators (WDI), and import data from COMTRADE. The import demand elasticities are available only at the 3-digit ISIC level.<sup>42</sup> In order to obtain the TFP measure of the upstream industries, I employ the input-output tables provided at the 3-digit ISIC level by Nicita and Olarreaga (2001), which were compiled from version 4 of the Global Trade Analysis Project (GTAP) database. Excluding the inputs being used from the own sector, the upstream measure is based on a combination of TFPs of the remaining input sectors as weighted by their share of usage.

The variable definitions and sources are presented more in detail in the appendix (Section A.3). In Table 4, I provide the summary statistics for all the variables I use in the estimations.

# 5.2.1 Productivity Estimates

The productivity estimates come from Eslava, Haltiwanger, Kugler and Kugler (2004). They estimate total factor productivity (TFP) as the residual from the following production function for each firm i = 1, ..., N and period t = 1982, ..., 1998 where Kit, Lit, Eit, and Iit denote capital, labor (total employment hours), energy consumption, and materials, respectively. An important concern in such an estimation is the simultaneity bias; that is, productivity shocks may be correlated with the inputs. They correct for this bias by considering a measure of downstream demand as an instrument for inputs along with regional government expenditures and input prices. A great advantage of this dataset is that it involves plant level input prices which have not been available to the other researchers in the field requiring them to use non-parametric estimation techniques.<sup>43</sup> Furthermore, the output measures commonly

> 42See the appendix for a discussion on how the import demand elasticity is computed (Section A.2).

> 43The methodology in these studies was developed by Olley and Pakes (1996), and advanced by Levinsohn and Petrin (2003). They employ investment or intermediate inputs to control for the correlation between the input levels and the unobserved firm-level productivity shocks.

20
9 the impacts of agricultural domestic and trade policies, distinguishing them from the impacts of other merchandise trade policies. A second distinction is that we examine inequality as well as poverty. Third, we focus on the effects of current policies, i.e. of full (not partial) global liberalization, whereas the Hertel and Winters study focuses mainly on the multilateral partial reform proposals that were on the table as of 2005. The country case studies examine not just multilateral trade reform but also unilateral reforms that individual developing countries might implement. The effects of unilateral action are compared with what full liberalization abroad would generate, so as to be able to assess the relative importance domestically for each nation of own-country policies as distinct from those of other countries (over which the country has influence only indirectly via trade negotiations).

The national CGE models are able on their own to estimate the effects of unilateral reform of agricultural or all merchandise trade-distorting policies. For the national modeler to estimate the effects of other countries’ policies, however, requires input from a global model. The World Bank’s LINKAGE model is chosen here for that purpose. It too is calibrated to 2004, based on Version 7 of the GTAP global protection database (Narayanan and Walmsley 2008) apart from the replacing of its applied agricultural tariffs for developing countries with the more comprehensive set of national price distortion estimates from Valenzuela and Anderson (2008).

There are various ways of transmitting the results derived from a global CGE model such as LINKAGE to a single-country CGE model. Like Hertel and Winters (2006), we adopt the approach developed by Horridge and Zhai (2006). For imports, Horridge and Zhai propose the use of border price changes from the global model’s simulation of rest-of-world liberalization (that is, without the focus developing country). For the focus developing country’s exports, the shift in its export demand curve following liberalization in the rest of the world is given in percentage changes by x=(1/σ).q where x is the percentage vertical shift
preference, educational attainment for abilities, household income for family circumstances, and number of children for fertility. The proximity of these measures may vary across contexts and, even so, it may be difficult to accurately measure these proxies. We argue that using an individual fixed effect in longitudinal data that follow the same individual over time is a better approach, since we are not necessarily interested in estimating the effect of, say, fecundity on work participation; instead, we are interested in accounting for unobserved individual dispositions toward work. However, it is important to note that we can only control for time-invariant individual characteristics and preferences. We need to assume, for example, that fertility, career, and family preferences do not change over time.

We can estimate: y��� �α�β TK�� ⋅Eligible�� �γ TK�� �δ Eligible�� �μ� �φ� �θ� �ψ Xijt �ε��� �3� where y���, TK��, Eligible��, μ�, φ�, and Xijt are as described earlier. The inclusion of an individual fixed effect, θ�, helps account for important omitted variables such as career and family preferences, which thereby minimizes the estimates’ biases. It may also potentially relax the identifying assumption in the DDD setting. In Section 4.2, we have to assume that the employment gaps of _different_ mothers with and without any preschool-aged child in high- and low-growth districts are not systematically different from one another. In DDD-FE, we compare withinmothers and we assume that in the absence of differences in preschool access, the employment gap of the same mother with and without any preschool-aged child would have been the same. In other words, as the child ages from 2 to 3 and becomes eligible for preschools, if the mother is exposed to the same preschool density, then the difference in her likelihood to work (when the child was of age 2 and when the child was of age 3) would not be systematically different. Note that since it is a special case of the DDD setup, the internal validity of the DDD strategy naturally lends itself to the DDD-FE strategy. Figure 5 and 6 similarly serve as justifications to the common trends assumption in DDD-FE.

Individual fixed effects, however, force comparisons within mothers. To leverage variations in preschool availability, we need frequent observations of the same mother across time. Identification, first, comes from variation in age eligibility—we compare a mother’s work participation when her child is aged 2 and not eligible for preschool and the next year when her child is aged 3 and starts to be eligible for preschool. Second, identification comes from variations 14
labor survey data for Egypt to arrive at a larger estimate of the capital share in Egypt, 0.6.

- The annual capital depreciation rate: � = 0.04.  This is the depreciation rate used in the estimation of the capital stock and follows the seminal work of Nehru and Dhareshwar (1993).

- The annual growth rate of the labor force: ��� = 0.025.  This is the average growth rate of the number of workers for the period 2001‐2008, as estimated from Egypt’s national employment statistics.  This represents an update of the estimate presented in Loayza and Honorati (2007).

- The annual average increase in education: ����� ���� = 0.12.  Education is proxied by the average number of schooling years in the adult population, as reported in Said (2008) for Egypt for the period 1980‐2000.  This estimate for the average increase in schooling is similar to that obtained using the Barro and Lee (2001) database for the same period.

- The average annual rate of return to education: � = 0.05.  This is proxied by the average rate of return for each year of schooling, as reported in Herrera (2009) for Egypt for the period 1988‐2006.

- The current (or targeted) level of net foreign liabilities as ratio to GDP: � = 0.2.  This corresponds to the official “international investment position” in average for the period 2001‐2007, as reported by the IMF’s Balance of Payments Statistics.

- ���

- Net income plus transfers from abroad, as ratio to GDP : ����� � = 0.052.  The ��

- numerator of this ratio is equal to the difference between Gross National Disposable Income (GNDI) and Gross Domestic Product (GDP), and the ratio corresponds to the average for the period 2001‐2007.  It is obtained from statistics reported by the World Bank and the IMF.

_Productivity Scenarios._ A key parameter in the simulations presented below is the rate of growth of total factor productivity, ��� . The available estimated rates of TFP growth in Egypt vary according to the period under consideration and the method of estimation (see Herrera 2009, and Loayza and Honorati 2007). They approximately range from ‐1.5% to 2.5%, with the 10
14 The expected government Lransfer will be greater than that associated with the limited guarantee on existing debt (i.e., _V5_ > V, and _V5_ > V1) and may. be less than or greater than the expected value of the benchmark guarantee (VO). A government guarantee on new debt of the existing holding company wiIl lower the government's obligation relative to the benchmark case when the new project has a low variance and is negatively correlated with the existing project.

Equation (26) emphasizes the importance of the entry restriction for the expected amount of direct tax revenue that the goverment must raise in order to compensate the nonentrepreneur for losses produced by the new policy. Even with a blanket guarantee on new as well as old debt of the existing representative holding company, the expected fiscal cost of the guarantee may be less than that associated with allowing the entrepreneur to create a new firn to finance the new project _without_ govemment guarantees while allowing the government to guarantee the old debt of existing firms. Ensuring the debt of both new _and_ old financial finns is, of course, the worst policy for the welfare of the representative entrepreneur and nonentrepreneur.

# 6. Discussion

The standard approach to bank bailouts stresses the problems created by inadequate standards for bank capital, poor supervision by bank examiners, and an unwillingness of regulatory officials to close or recapitalize financial institutions that are in distress. This approach is well represented in books such as Benston _et aL_ (1986), in recent books on banking crises of the 1980s [such as White (1991) and Brock (1992)], and in the World Bank's 1989 _World Development Report_ on financial systems and development. The regulatory problem addressed by the standard approach can in some circumstances be described as one of the "time inconsistency" of bank regulations: bank regulators and politicians cannot credibly precommit themselves not to bailout banks in distress, so banks take actions that lead to financial bailouts. In this view, good bankdng regulation in the form of capital requirements, audidng, and timely closure or recapitalizaton of banks serves to minimize the time consistency problem.

This paper, in contrast to the standard approach, has suggested that financial bailouts may be part of a pareto-improving transfer mechanism that is associated with a growth-creating policy reform. The model developed in the previous sections should not be interpreted as stating that _all_ (or even _most)_ financial
filaments (chapter 54) sector downstream.

Relative to the global product space, the overall pattern of Syrian exports appears to be clustered around products on the periphery, especially in the agricultural and textile categories located in the upper left quadrant of Figure 3. Relative to 2001, Syria has expanded its comparative advantage in those sectors, and currently holds a firm comparative advantage in those areas, as evidenced by the clusters of solid black squares in the figure.<sup>17</sup> Moreover, Syria’s comparative advantage now also appears to include an (albeit limited) extension into the global industrial core. The ongoing structural transformation undergone by the Syrian economy since liberalization in 2000 is, again, corroborated by this alternative representation of the data.

<!-- Start of picture text --> (a) 2001 (b) 2007<br><!-- End of picture text --> Figure 3: Syrian exports in global product space, 2008. Products are classified according to 4-digit SITC product lines. Black squares indicate products exported with comparative advantage. While Syria exports some products in the global industrial core, the majority of RCA products are located in the upper-left periphery comprising agriculture and garments. Network visualization produced with Cytoscape (Shannon _et al._ 2003).

Textiles, more generally, are also one of the most dynamic sectors in terms of Syrian exports, having switched from nonspecialization to specialization.<sup>18</sup> This contrasts 17The product space representation for Syria for earlier years (1985 and 2000) are available online, and accessible via http://www.chidalgo.com/productspace/country.htm.

18A fuller discussion of the sectors which display the greatest changes in RCA is relegated to the annex.

16
of country systems should therefore be greater in general for multilateral than for bilateral donors.

Among multilaterals, a further distinction can be made between the MDBs (multilateral development banks including the World Bank, IMF and regional development banks) and other multilateral donors including the UN system and European Commission (EC). The MDBs have a “country-led business model” that aims to align operations in support of recipient countries‟ national development strategies (World Bank, 2011). The non-MDB multilaterals are a heterogeneous group, but for multiple reasons we expect their use of country systems to fall short of the MDBs‟. In the EC, “responsibility rests in the hands of serving politicians from member states,” so its decisions are less apolitical than other multilateral agencies that “have genuinely delegated their management to an executive board” (Martens et al., 2002: 47). National representatives in EC foreign aid decision-making committees devote considerable effort to pursuing opportunities for their own nation‟s aid contractors (Martens et al., 2002: 193). The various UN agencies are treated as a single donor in the PDMS, with their data collected and reported by the UNDP.<sup>11</sup> Some UN agencies specialize in humanitarian aid, which is not included in the PDMS. Other UN agencies provide mostly technical assistance, which rarely uses country PFM systems.

Among bilateral donors, the distinction between OECD-DAC donors and non-DAC donors may be important.<sup>12</sup> Use of country systems is expected to be higher for DAC donors, as the DAC is the leading advocate of Paris Declaration objectives including increased use of

> 11 Some bilaterals also have multiple aid agencies but the survey collects and reports data only at the national level, e.g. for  the USA and not separately for USAID, the MCC, etc.

> 12 Non-DAC bilaterals in the sample include only Turkey and Korea.  The latter is now a DAC member, but it was not during the 2005-2010 period covered by the surveys.

12
By the time of the 2000 population census, progress on enrolling children in elementary school was significant, with 72 and 71 percent of boys and girls who were then age six, respectively, enrolled in school. Thus, between 1990 and 2000 there was considerable expansion of rural schools and success in reducing the age at which children started school to six, but most of the children in the RCRE sample used in the analysis of this paper were born earlier. The youngest students in the RCRE sample were of age to finish middle school in 2002 and decide about whether to enter high school or not in 2003, and these students would have entered first grade by 1994. Only 18 percent of our sample entered elementary school between 1991 and 1994, while the other 82 percent started primary school in 1990 or before.

Evidence on the timing of efforts to meet goals for lower enrollment ages suggests that the change was not smooth between 1990 and 2000. Efforts to lower enrollment rates were pursued in urban areas first, and then much of the focus on rural school enrollment ages occurred after the mid-1990s with other efforts to expand services in rural and more remote areas. Given evidence from the CCAP surveys, the Gansu Study of Families and Children, and enrollment rates at age six and seven from the 1990 census, there appears to be ample evidence to support our assumption that for most children in our sample, the first year of primary school enrollment was at age seven.

There will be some variation around this starting age, and as suggested by one of our critical readers, it is important to think about both how it will affect our estimates and the robustness checks that are worth performing. For identification, it is most important that our instrument for migrant employment is uncorrelated with unobserved differences in ability. First, phase in of the compulsory education law suggests that much variation in starting age is at the community level, and this is evident in the data. This dimension of variation is unrelated to student ability. Second, students starting school at age six or age eight likely make decisions about entering high school one year before or one year after the date we assume in this paper. We know whether the decision to enroll in another year of schooling was made, and being off by a year will mean that the predicted increase in migrant share from the village will not be perfectly mapped into the year of the decision. The quartic in years-since-IDs is a smooth function and we do not see extremely sharp jumps in the predicted migrant share variable from one year to the next, so it is unlikely that the instrument is systematically related to within village variation in unobserved ability, or that it will introduce bias to our analyses.

A second assumption on the duration of primary school is important for assigning the year of potential high school enrollment. In some parts of rural China, primary school lasted for five years for much of the period, whereas other regions achieved the six-year mandate much 50