rafmacalaba/datause-encoder-data huggingface dataset:
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# m. **ROAD** USER COSTS

## A. **Road Damage Externality (RDE)**

As discussed earlier, the analysis of the road damage externality depends heavily on the maintenance strategy adopted by the road authority: whether the timing of the periodic overlay is triggered by the road condition or whether it is fixed.

In the first case, Newbery's method allows for an easy calculation of the road damage externality. When traffic is the sole source of road deterioration, when traffic flows are constant, and when the age distribution of roads is uniform, additional traffic has no effect (on average) on the operating costs of other vehicles. In this case, the only externality associated with road damage is the advancement of the resurfacing date. This externality can be measured by the cost of the overlay divided by the number of vehicles that will reduce the road to a state requiring its repair. In this case it is sufficient to know the cost of the periodic overlay and the marginal effect of a vehicle on road condition to measure the externality costs.

The effect of an additional standard axle load depends on the standard of the road. The heavier the traffic (both in terms of its volume and loading) the more efficient it is to build a stronger (and more costly) road. The higher the standard of the road, the lower the damage caused by each passing axle. Hence, the volume of traffic and the marginal damage are negatively correlated.

Our first estimate of the RDE is based on Newbery's method, allowing for road deterioration due to weather and for growth of traffic. Following Newbery, this calculation ignores the effect of road deterioration on vehicle operating costs.<sup>2</sup> Table 9 is based on Newbery's estimates of RDE. The computations are based on a traffic growth rate of 7 percent, a pavement deterioration rate (associated with weather) of 2 percent, and a discount rate of 12 perceni. As the table indicates, road user costs range from 1.76 to 0.36 C per ESAL-km and decline sharply as the pavement loading (and pavement strength) increase.

The overlay costs of paved road in Tunisia ($20,000-50,000) are very similar to those prevailing in Ghana, so Newbery's findings for paved roads can be easily adapted by using the Ghanaian loading distribution. In Ghana, the volume of heavy vehicles is lower due to the lower traffic volumes, but in the total loading this factor is more than offset by the heavier axle loads. In Table 9 two estimates of the average axle load are used: the first (based on Bhandari, 1990) of 0.9 ESAL per vehicle, and the second (based on SWKP, 1988a) 2 Newbery has shown that even if one allows for traffic growth the externality associated with increased vehicle operating costs is only of negligible importance (about USCO.02-0.06 per ESAL-km).
based on the Chilean products dataset are similar to those obtained for a U.S. products dataset by Bernard et al. (2006b) and an Indian products dataset by Goldberg et al. (2008).<sup>5</sup> Table 1 shows average coefficients of variation in unit values for selected 4-digit industries. The statistics show a substantial degree of heterogeneity in unit values across plants and point to some interesting differences across industries. Industries with homogeneous products and thus less scope for quality differences such as cement or petroleum refineries are characterized by low average coefficients of variation. However, industries where quality is expected to play a more important role such as electrical machinery, motorcycles, and professional equipment are characterized by higher coefficients of variation.

# **2.2. Transport Costs**

Our measure of transport costs is based on detailed information provided by the Latin American Integration Association (ALADI) on freight costs excluding insurance costs and the free on board customs value (fob) of Chilean imports for each 8-digit Harmonized System (HS) code, exporting country, and year from 1997 to 2003. First, we compute for 8-digit HS code _i_ from exporting country _c_ in year _t_ freight rates as the ratio of freight costs ( _freightict_ ) to the fob value of imports ( _fobict )_ : _TCict_  _freightict_ / _fobict_ . Second, we aggregate these freight rates from the 8-digit HS code, exporting country, and year level to the 4-digit ISIC (revision 2) and year level using (i) a concordance between 8-digit HS and 4-digit ISIC codes and (ii) weights given by Chile’s 8-digit HS fob imports from each exporting country and year as a ratio to Chile’s total imports in the corresponding 4-digit ISIC code in that year. Appendix 2 provides more details on the construction of the freight costs measure hereafter referred to as ‘transport costs measure’.

> 5 For example, the average shares of the most important product, the second most important product, and so on, in total sales of Chilean multi-product plants are strikingly similar to those of U.S. and Indian multi-product plants.

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conditions were enforced; because the program‘s target was to alleviate poverty, beneficiaries included poor families even when the household-head or someone else in the household had some labor activity. In addition, as the federal government provided the funds while municipalities were responsible for program implementation –local governments had little incentive to promote formal jobs and reduce beneficiaries or to enforce requirements strictly.

The dataset consisted of two short rotating panels from the national household survey (Encuesta Permanente de Hogares Continua –EPHC) covering 70% of Argentinean urban populations in 28 large urban areas and representing 60% of the population. Individuals are interviewed in two consecutive rounds, left for two other rounds and followed again in the following 2 quarters (rounds). The variables of interest are whether a specific individual transferred to formal employment in period t to t+1 and whether the individual is a beneficiary of PJH in t. The authors employ a difference in difference estimation to study whether program beneficiaries are more reluctant to seek/accept formal jobs than otherwise equal counterparts, particularly when the relative value of the transfer with respect to formal wages dropped.

They find some evidence on the informality bias of the PJH in the period when the value of the transfer was relatively high compared to wages in the formal labor market. The effect of the distortion vanished when earnings in registered jobs went substantially up. The results, however, are not robust to all the specifications.

Camacho et al. (2009) measure the effect of a subsidized regime (SR) for health insurance for the poor in Colombia on informality. The period of study is 1992-05. SR was introduced in 1993 and informality increased steadily in the period 1996-2002.

The SR is designed to reach the poor and the unemployed. The SR is financed with a one percent transfer from the formal sector program, CR, and with local and central government funds. The package is less comprehensive than the CR, but unlike the CR, covers every member in an eligible household regardless of their relationship to the household head. Coverage for any additional family member other than spouse and direct dependents in the CR has an additional cost. Thus 10
continue to rely on less productive farming techniques that continue to deplete soils. At the same time, the results suggest that fertilizer markets are not all together missing in rural Ethiopia, and that government actions to close knowledge gaps and lower transportation costs can increase fertilizer use among farmers.

With this as background, the rest of the paper is organized as follows. The next section provides additional background information about Ethiopian agriculture and related studies of fertilizer demand. The following section develops the empirical model. The next sections present the descriptive statistics followed by the empirical results. The last section concludes.

# **Background**

Agriculture is the mainstay of the Ethiopian economy with its hefty contribution of about half of the GDP. Yet its growth rate over the past four decades had been quite low. Per capita agricultural output in 2003 is not so much different from its level in 1961. This is coupled with the dominance of smallholder farmers with land holding size of 0.96 hectare per household, and yield of 1,167 kg to 2,122 kg per hectare for the main cereal crops (CSA, 2008). As a consequence, few farming households produce significant surpluses to market and many are unable to consistently feed themselves.<sup>3</sup> Yet results from research stations and pilot programs suggest that the economic returns to fertilizer, when combined with improved seeds and better farming techniques can be high (Bekele and Höfner, 1993; Howard et al. 2003). This has prompted the Government of Ethiopia to emphasize agriculture in its economic growth strategy, Agriculture Development Led Industrialization (ADLI). And in particular, the government and donors have supported a number of productivity-enhancing efforts, such as the provision of extension services, the introduction of high yield variety seeds and the timely provision of chemical fertilizers.<sup>4</sup> Between 1991 and 1995, fertilizer use and intensity in Ethiopia rose dramatically from 110,000 metric tonnes (21 kg/ha) to 300,000 (35 kg/ha) in 1999, levels that compare favorably with many countries in Africa (World Bank, 2006). Nevertheless, in subsequent years, fertilizer consumption and intensification fluctuated considerably and intensification has only recently resumed a steady upward trend. Consequently, like many countries in Africa, the penetration of productivity enhancing inputs such as chemical fertilizer, improved seeds, and pesticides is very low. As a proportion of the total crop area at the national level, around 45% is treated with chemical fertilizer (with the average application of 81 kg per hectare) and only 15.2 % is treated with by pesticides. Only 3.5% of the land is planted with improved seeds and extension agents only reach 11% of total cropland (CSA, 2008).

> 3 Demeke et al (1998) noted that 790 kg of grain is needed to meet the minimum calorie requirement assuming 156 kg / person/year grain requirement for a household of five.

> 4 See Byerlee et al. (2007) for a discussion of government policy aimed at promoting fertilizer use in Ethiopia.

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unrelated to inherent measured village attributes, suggesting a strong random component to election timing.

Although observables don’t influence election timing, there must be village unobservables affecting timing, so that it is not entirely imposed from the outside. While reforms mandated local elections, in many villages formal elections were delayed, with elites remaining in control. Formal elections must be certified and approved by the subdistrict ( _kecamatan_ ) government. Given the mandate to hold elections, what affected the timing of elections after early 2006? Interviews with local election official suggested many factors that are exogenous to specific villages: subdistrict priorities (in a time of massive aid delivery) and capacities to monitor and authorize elections. In our village survey, we asked the key cited reasons for what prompted a post-tsunami election, allowing for multiple factors, although mostly one factor was cited. First are pressure from the subdistrict or district government (55 of 150 cases) and village head died in the tsunami (35 cases). We thought complaints from villagers about the aid delivery process would be important, but that appears to be incorrect. In only four (of 150) cases did villages report that aid disputes of any type prompted the election; and only six report that aid allocations issues were the main election issue. Another set of about 25 villages stated incompetence, forced to resign, and been head too long. There are also 61 villages, which say that the head (or any replacement) had simply completed his term, but this seems to be a more polite phrasing of “been head too long”.<sup>9</sup> These last types of

> 9. We have some information on dates of pre-tsunami elections, with a wave of about 65 elections in 2000– 03 after national democratization in 1999. It seems that these were informal elections. Indeed, if they were formal with the proscribed time between elections, they should have had new elections in 2005–09. The overlap between this 65 and the 61 citing term completed is only 25.

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# **1.2. Financial Sector Landscape of Sri Lanka**

Figure 1. Classification of financial sector players in Sri Lanka <!-- Start of picture text --> Financial Service Providers<br>Formal Semi - Formal Informal<br>Licensed Commercial Banks<br>Licensed Specialized Banks<br>Co-operative Societies<br>Licensed Finance Companies Rotating Savings & Credit<br>Micro Finance Institutions<br>Specialized Leasing Companies Association<br>Community Based Organizations<br>Licensed Microfinance Institutions Money Lenders<br>State-programs (e.g. Samurdhi,<br>Authorized Money Brokering Friends & Relatives<br>Divineguma)<br>Companies<br>Authorized Primary Dealers<br><!-- End of picture text --> Source: Updated by authors based on Kelegama et al. (2014) and Tilakaratna (2016).

As seen in Figure 1, Sri Lanka has a diverse financial sector which includes formal,<sup>9</sup> semi-formal, and informal financial service providers. There are currently 26 licensed commercial banks (including 13 foreign banks) and 7 licensed specialized banks (CBSL, 2018). Physical access points include over 6,000 bank branches/outlets and 5,000 ATMs island-wide. As of March 2019, bank branch density of Sri Lanka was at 16 branches per 100,000 adults,<sup>10</sup> nearly double the South Asian regional average of 9.4 per 100,000 adults.<sup>11</sup>

# **2. Comparative Analysis of the State of Financial Inclusion in Sri Lanka**

When analyzing financial inclusion, it is important to look beyond basic metrics such as ownership of a transaction account. It is the usage of such accounts and other financial services which is more critical. Sri Lanka has made noteworthy advancements in terms of basic financial inclusion metrics. For example, the number of adults with accounts at formal financial institutions stands at 74 percent, ahead of the regional average in South Asia of 68 percent (Global Findex 2017). However, usage of transaction accounts has room to improve, with 26 percent of adults having

> 9 Formal financial services are regulated by the financial sector regulators (Central Bank of Sri Lanka covering banking, finance and leasing companies, Insurance Regulatory Commission of Sri Lanka covering insurance, and Securities and Exchange Commission of Sri Lanka covering capital markets ).

> 10 Annual Report CBSL 2018.

> 11 World Development Indicators, 2015.

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choice of sample or variables. Moreover, they pass all the commonly applied tests for misspecification, including tests for omitted variables and heteroskedasticity.

The general lack of statistically significant results probably reflects the extremely noisy nature of the GRDP data and the fact that, in the interests of avoiding problems with endogeneity, we have only used information about the initial conditions in each period as our explanatory variables. Since our panel contains a rich source of information about how both GRDP and explanatory variables change over time, we now turn to estimations that better exploit the panel nature of the data.

# **Fixed-effect panel regressions**

One possible problem with the above model is the potential presence of unobserved heterogeneity in the dataset. In other words, there may be factors specific to each district that determine the growth rate but that are not included within our dataset. If these factors are fixed over time, this unobserved heterogeneity can be removed using fixed-effects panel estimation. Thus, it is possible to obtain a less biased estimate of some of the parameters of key interest.

The disadvantage of fixed-effect panel estimation, of course, is that by removing factors that are fixed over time it automatically excludes all the geographical determinants of growth. Furthermore, some variables have very little variation over time and so one must not rely too heavily on variables where the estimation results may be driven by spurious changes in explanatory variables. We therefore arrive at the parsimonious model shown in Table 7.

**Table 7: Fixed effects panel regression of annual per capita GRDP growth**

||**-1**<br>**1993-97**|**-2**<br>**2001-05**|**-3**<br>**1993-2003**|
|---|---|---|---|
|Ln per capita real GRDP, initial year|-0.374|-0.403|-0.210|
||(0.026)***|(0.026)***|(0.015)***|
|Weighted average growth of neighboring districts during the period|0.375|0.001|0.368|
||(0.030)***|(0.025)|(0.023)***|
|Share people ever/being in junior secondary school per total population|-0.240|0.445|-0.100|
||(0.187)|(0.106)***|(0.113)|
|Labor force, initial year|0.116|0.083|0.022|
||(0.032)***|(0.019)***|(0.009)**|
|Gini coefficient of sectoral employment|0.028|-0.038|0.031|
||(0.096)|(0.042)|(0.052)|
|Constant|3.876|5.163|2.708|
||(0.476)***|(0.441)***|(0.241)***|
|Observations|1130|1163|2048|
|Number of groups|240|297|250|
|d|0.42|0.23|0.25|

R-squared Robust standard errors in parentheses. * significant at 5%; ** significant at 1%. Note: Growth rates for 1993-97 and 1993-2003 are based on constant 1993 prices; growth rate for 2001-05 is based on constant 2000 prices.

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|**Country**|**1970s and 1980s**|**1990s**|**2000s**|
|---|---|---|---|
||| 1999: free health care cards for the poor<br>distributed, but scheme met with limited<br>success.||

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<!-- Start of picture text --> aICD<br>AFRICA INFRASTRUCTURE<br>COUNTRY DIAGNOSTIC<br><!-- End of picture text --> aICD AFRICA INFRASTRUCTURE COUNTRY DIAGNOSTIC <!-- Start of picture text --> &EME. DE 5,ON<br>iyLer, "<br>ze &,<br><5 S<br>SWorreco me<br><!-- End of picture text --> <!-- Start of picture text --> , '<br>j yo<br>African Union<br><!-- End of picture text --> <!-- Start of picture text --> AGENCEfelfel<br>DE DEVELOPPEMENT<br><!-- End of picture text --> AGENCEfelfel DE DEVELOPPEMENT <!-- Start of picture text --> + ICLa<br><!-- End of picture text --> <!-- Start of picture text --> eco<br><!-- End of picture text --> <!-- Start of picture text --> ge FOR Ap,<br>Ors,<br>= oe<br>s m\' %<br>%\ 2 Hi<br>NEPAD<br><!-- End of picture text -->
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