rafmacalaba/datause-encoder-data huggingface dataset:
19 Insofar as strengthening the capacity of local governments to raise resources and finance the provision of public services to their residents effectively is a key issue in development, we believe that this study has ramifications well beyond the immediate context of water use in Costa Rica and represents a potentially important advance for the study of urban governance and government capacity in the developing world.

# **References**

Aguilar, C. (2014). Context of Integrated Urban Water Management in Desamparados, Costa Rica. Manuscript.

Akhmouch, A. (2012). _Water Governance in Latin America and the Caribbean: A Multi-Level Approach_ (No. 2012/4). OECD Publishing.

Allcott, H. (2011). Social norms and energy conservation. _Journal of Public Economics_ , _95_ (9), 1082-1095.

Allcott, H., & Mullainathan, S. (2010). Behavioral science and energy policy. _Science_ , _327_ (5970), 12041205.

Bernedo, M., Ferraro, P.J., & Price, M.K. (2014). The Persistent Impacts of Norm-Based Messaging and Their Implications for Water Conservation. _Journal of Consumer Policy_ , 37 (3), 437-452.

Brown, Z., Johnstone, N., Haščič, I., Vong, L., & Barascud, F. (2013). Testing the effect of defaults on the thermostat settings of OECD employees. _Energy Economics_ , _39_ , 128-134.

Chapman, G. B., Li, M., Colby, H., & Yoon, H. (2010). Opting in vs opting out of influenza vaccination. _The Journal of the American Medical Association_ , _304_ (1), 43-44.

Chetty, R., Looney, A., & Kroft, K. (2009). Salience and Taxation: Theory and Evidence. _The American Economic Review, 99_ (4), 1145-1177.

Datta, S. and Mullainathan, S. 2014. “Behavioral Design for Development”. Review of Income and Wealth 60(1):7-35.

El Tiempo (2014). Seis embalses están en la peor crisis en cinco años: Municipios de Cundinamarca se enfrentan a racionamiento de agua. Retrieved from http://www.eltiempo.com/estilo-devida/ciencia/seis-embalses-estan-en-la-peor-crisis-en-cinco-anos/13992695 Ferraro, P. J., & Miranda, J. J. (2013). Heterogeneous treatment effects and mechanisms in informationbased environmental policies: Evidence from a large-scale field experiment. _Resource and Energy Economics_ , _35_ (3), 356-379.

Ferraro, P. J., Miranda, J. J., & Price, M.K. (2011). The Persistence of Treatment Effects with Norm-Based Policy Instruments: Evidence from a Randomized Environmental Policy Experiment. _The American Economic Review_ , _101_ (3): 318-22.
Table 3: Fixed-effects regression results for employment elastiticy of growth: labor market

|Variables|ECA|WE|PECA|PWE|EU10|BalTur|CIS|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Hours regulations||||||||
|Output<br>Coeff.|0.255|0.351|0.088|0.093|0.465|0.502|0.103|
|t-stat.|4.3|5.0|4.1|1.7|5.7|1.8|1.8|
|Other variable<br>Coeff.|0.001|0.001|0.000|-0.001|0.000|0.014|0.003|
|t-stat.|0.5|0.5|0.3|-0.5|-0.2|1.0|1.1|
|N|232|196|746|136|100|63|76|
|R2|0.36|0.53|0.25|0.87|0.63|0.37|0.60|
|Hiring regulations||||||||
|Output<br>Coeff.|0.252|0.356|0.087|0.092|0.476|0.451|0.104|
|t-stat.|4.4|5.1|4.1|1.7|6.1|1.6|1.8|
|Other variable<br>Coeff.|0.003|0.000|0.000|-0.006|0.004|0.002|0.001|
|t-stat.|1.7|0.0|-0.5|-1.2|2.3|0.3|0.2|
|N|242|196|767|134|110|63|76|
|R2|0.37|0.53|0.25|0.88|0.64|0.36|0.59|
|Size of shadow economy||||||||
|Output<br>Coeff.|0.103|0.426|0.012|0.043|0.134|-0.481|0.148|
|t-stat.|1.3|5.5|0.7|1.3|0.9|-1.7|2.0|
|Other variable<br>Coeff.|-0.001|-0.005|-0.001|-0.010|-0.010|-0.051|0.000|
|t-stat.|-0.2|-1.7|-1.7|-10.0|-1.5|-2.7|0.1|
|N|223|171|842|172|89|45|89|
|R2|0.24|0.66|0.28|0.77|0.46|0.36|0.49|
|Informality||||||||
|Output<br>Coeff.|0.080|0.368|0.032|0.064|0.168|-0.143|0.104|
|t-stat.|1.9|5.5|2.3|2.2|2.2|-0.7|2.2|
|Other variable<br>Coeff.|0.033|1.183|0.037|-0.136|-0.488|1.862|0.030|
|t-stat.|0.4|4.2|1.0|-1.5|-2.1|1.0|0.4|
|N|301|265|1213|261|134|50|117|
|R2<br>Note: The dependent varia<br>parameter estimate of out<br>fixed-effects.|0.22<br>ble is em<br>put grow|0.56<br>ploymen<br>th. All re|0.22<br>t growth.<br>gressions|0.51<br>The outp<br>also inclu|0.38<br>ut coeffic<br>de countr|0.35<br>ient is the<br>y and year|0.43<br>|

14
<!-- Start of picture text --> ‘ }*te<br>= br’<br>HEE Complete information<br>HE Without marital<br>education, with labor or J :<br>HE Without labor only<br>Without marital or<br>education, without labor : x<br>Not in GMD - - ‘<br>Developed<br>No data<br>IBRD 43503 | FEBRUARY 2018<br><!-- End of picture text -->
historic migration rates and the 1997 state GDP per capita.<sup>9</sup> Migration itself may have resulted in greater development, and so by controlling for these variables, we may be removing some of the effects of migration. Therefore we present results both with and without these controls when we examine the impact of migration on the principal health outcomes.

Column 3 of Table 2 shows that after adding these state-level controls, the 1924 state level migration rate still remains a strong instrument, while none of the state controls are individually significant. This provides us with some confidence in the validity of our instrument.

# **3.2 Estimation Methods**

Standard two-stage least squares (2SLS) estimation can then be used to estimate the impact of migration on birthweight using the historic migration rate as an instrument. However infant mortality is a binary outcome, which raises additional estimation choices. Our baseline approach is to use maximum-likelihood estimation of Amemiya’s generalized least squares estimator (see Newey, 1987)<sup>10</sup> , which can then be compared to the results obtained under standard probit estimation when we don’t use an instrument. This reduces to a bivariate probit model when our measure of migration is the binary indicator of whether the household has a migrant member or not. We will refer to this as the IV-probit method. This method will result in predicted outcomes which lie between 0 and 1, but relies on joint normality assumptions which may not apply in practice. As an alternative, we also present results from 2SLS estimation. Angrist (1991) provides conditions under which linear instrumental variables estimation will consistently estimate average treatment effects in the case of binary endogenous variable. While these conditions are unlikely to hold exactly, Angrist uses Monte Carlo evidence to argue that they may hold approximately, and that the 2SLS technique can perform well in practice.

> 9 State GDP per capita are obtained from the INEGI website, <u>www.inegi.gob.mx,</u> _Sistema de Cuentas Nacionales de Mexico_ , accessed November 9, 2004.

> 10 This is estimated using STATA’s divprobit add-in package.

- 10 -
|**Country**|**Intervention**|**Method**|**Test**|**Effect**<br>**Sizes**|**Reference**|
|---|---|---|---|---|---|
|Philippines|Reading materials, teacher training to<br>incorporate reading into curriculum|RCT|**Filipino test**|**0.09**|Tan, Lane and Lassibille,<br>1999|
|Philippines|The Dropout Intervention programme||**Reading**|**0.06**|Abeberese, Kumler and<br>Linden, 2011|
|South<br>Africa|English and Operacy programme (EOP):<br>suggestopic pedagogy|RCT|**English test**|**0.6**|Mouton, 1995|
|Uanda|Reading to Learn (RtL): teacher<br>rearednessschool leadershi|RCT|Oral literacy exam|0.12|Lucas et al 2014|
|g|pp, p,<br>classroom learning environments||Oral literacy exam|0.13|. ,|
||||**English word recognition**|**‐0.1**||
|Ud|Northern Uganda Literacy Project<br>NULP h ii d|RCT|**EGRA test**|0.08|Ki d Th 2015|
|gana|(): teacer tranng an parent<br>engagement; Gov treatment arm||Oral English score|‐0.06|erwn an ornton ()|
||||**Writing test score**|**‐0.09**||
||||**EGRA test**|**0.3**||
|Uanda|Northern Uganda Literacy Project<br>(NULP): teacher trainin and arent|RCT|Oral English score|0.09|Kerwin and Thornton (2015)|
|g|g p<br>engagement;NGO treatment arm||**Writing test score**|0.19||
||||**English word recognition**|‐0.14||

Source: Snilstveit et al. (2015) Notes: Tests and effect sizes in bold are included in the calculation of the results in this paper.

32
Table 2: Sample Sizes by Poverty Measure and Survey

|Age in<br>|MonetaryPov|ertyMeasures|||Multi-Dim|ensional PovertyM|easures – Deprivati|ons||
|---|---|---|---|---|---|---|---|---|---|
|completed|National overt|International 19$ a||||||||
|<br>years|py<br>line|.<br>day povertyline|water|sanitation|shelter|information|education|Food|Health|
|1|||GHS1 – 2010/2011:|GHS1 – 2010/2011:|GHS1 – 2010/2011:|||GHS1 – 2010/2011:<br>4,405 (4,205)<br>GHS2 – 2012/2013<br>3,463 (1,075)<br>GHS3 – 2015/2016<br>2,986 (659)<br>GHS -2018/2019:<br>3,256 (649)<br>|GHS1 – 2010/2011:<br>1,548 (462)<br>GHS2 – 2012/2013<br>1,160 (331)<br>GHS3 – 2015/2016<br>Missing<br>GHS -2018/2019:<br>Missing<br>MICS – 2016/2017<br>11,255(4,297)<br>|
|2<br>3<br>4|GHS1 – 2010/2011:<br>13,947<br>GHS2 – 2012/2013:<br>|GHS 1 – 2010/2011:<br>13,947<br>GHS2 – 2012/2013:<br>|<br>13,947 (473)<br>GHS2 – 2012/2013:<br>13,403 (71)<br>GHS3 – 2015/2016:|<br>13,947 (563)<br>GHS2 – 2012/2013:<br>13,403 (135)<br>GHS3 – 2015/2016:|<br>13,947 (61)<br>GHS2 – 2012/2013:<br>13,403 (45)<br>GHS3 – 2015/2016:|||MICS – 2016/2017:<br>28,578 (3,137)|MICS – 2016/2017 age 0-4<br>28,578(17,831)|
|5<br>6<br>7<br>8<br>9|13,403<br>GHS3 – 2015/2016:<br>12,850<br>|13,403<br>GHS3 – 2015/2016:<br>12,850<br>|12,850 (116)<br>GHS4 -2018/2019:<br>13,157 (-)<br>MICS – 2016/2017:<br>89,033 (200)|12,850 (62)<br>GHS -2018/2019:<br>13,157 (-)<br>MICS – 2016/2017:<br>89,033 (248)|12,850 (58)<br>GHS -2018/2019:<br>13,157 (-)<br>MICS – 2016/2017:<br>89,033 (1,619)|GHS1 – 2010/2011:<br>9,542 (-)<br>GHS2 – 2012/2013:|GHS1 – 2010/2011:<br>9,542 (406)<br>GHS2 – 2012/2013:|||
|10<br>11<br>12<br>13||||||9,940 (-)<br>GHS3 – 2015/2016:<br>9,864 (51)<br>GHS -2018/2019:<br>9,901 (-)|9,940 (776)<br>GHS3 – 2015/2016:<br>9,864 (6)<br>GHS -2018/2019:<br>9,901 (-)|||
|14<br>15<br>16<br>17||||||MICS – 2016/2017:<br>33,040 (45)|MICS – 2016/2017:<br>60,455 (9)|||

Source: Authors’ own estimates based on GHS 1 – 2010/2011, GHS2 – 2012/2013, GHS3 – 2015/2016, GHS4 – 2018/2019 and MICS 2016/2017 Note: Numbers represent sample sizes of the respective age samples with numbers in parentheses representing the number of observations with missing information for the respective poverty measure. For the health deprivation, additional completed age categories are marked in blue, indicating that there is data available for individuals in these age groups from the MICS, but for comparison not used in the main analysis of this study. For the additional deprivations sample sizes are as follows: Not-in-school deprivation: Wave 1: 9,542 (454), Wave 2: 9,940 (1,078), Wave 3: 9,864 (6), Wave 4: 9,895 (-); protected water deprivation: Wave 1: 13,947 (377), Wave 2: 13,403 (175), Wave 3: 12,850 (187), Wave 4: 13,148 (-); improved sanitation deprivation: Wave 1: 13,947 (563), Wave 2: 13,403 (135), Wave 3: 12,850 (62), Wave 4: 13,148 (-).

12
- 28 -

Table 4: Panel Data Regressions (5-Year **Time** Interval)

# Dependent variable: 5-year average growth rate of income

|Method ofEtimate|Pooled OLS|Fixed-effects|Random-effects|
|---|---|---|---|
|_St/Ssf_|0.165<br>(0.020)|0.147<br>(0.016)|0.155<br>_(0.015)_|
|_ShiSh_|0.024<br>(0.019)|0.051<br>(0.020)|0.030<br>(0.016)|
|_N/N_|0.715<br>(0.271)|1.207<br>(0.533)|0.833<br>(0.312)|
|C<br>_2_|0.026<br>*(0.007)||0.023<br>(0.080)|
||0.400|0.609|0.498|
|_R2_|0.385|0.335|0.147|
|Countries:|49|49|49|
|Observations:|125|125|125<br>|
|Test of Restrictions:||F(48,73)=3.29|X<sup>2</sup>(3)=5.52|

Note: Standard errors are in parenthesis.

Table 4 presents the regression results using 5-year time interval data. The regressions in Table 4 use the basic model without added variables, corresponding to these in Table 2. The first apparent result in Table 4 is that the R<sup>2</sup> 's are improved greatly compared to the corresponding results in Table 2 or even the larger-variable regressions in Table 3. The second thing to notice is that how once again similar the estimates from the three regressions are.

The most important result in Table 4 regressions is that the estimated coefficients on _SfISf and Sh ISh_ are not only positive and very significant, they are also much larger in magnitude than those estimated with annual data.
that, “Income inequality does not appear to affect population health directly. Poverty, however, is correlated with intra-urban health inequity and poverty does influence mortality—especially infant and child mortality—through several channels.<sup>21</sup> Poverty and inequality together may lead to the erosion of social capital in urban areas and aside from eroding community support systems, could be responsible for increasing crime and violence against persons and property.” (Demombynes and Ozler 2005; Wilkinson 2005). As Deaton (2003a, p 151) notes, the social environment and an individual’s position in the social hierarchy does matter for the perception of health. His views are supported by a study of 22 European countries which found that people lower ranked on the social scale and with limited education had lower self-rated health (von dem Knesebeck, Verde, and Dragano 2006). This might be traced to the erosion of social capital. When income inequality diminishes the closeness and quality of social relations, social capital starts to fray, and the health conditions of the poor deteriorate (Miller and others 2006; Wilkinson 2005).<sup>22</sup>

# **Clean Water and Sanitation make Healthy Cities**

Rising incomes pull people out of poverty and higher incomes make possible the pooling of capital to finance urban infrastructure. In particular, infrastructure which provides urban residents with clean water, efficient sanitation, and solid waste disposal services has proven to be a highly significant determinant of urban health outcomes. This was the case in nineteenth century Britain, in the United States during the early twentieth century, and in developing countries during the latter part of the twentieth century. The stylized fact which emerges form this experience is that, _Rising per capita urban incomes can set the stage for investment in water and sanitation infrastructure so as to provide the broad mass of the urban population with access to affordably priced and reliable services. Furthermore, investment in urban infrastructure was and is a key driver of growth and employment_ . Although many factors contribute to improvements in urban

> 21 See Waelkens and Greindl (2001, pp 24-27) and Wagstaff (2002).

> 22 A study by Miller and others (2006) finds a positive relationship between community-level social capital and good health.  A similar study exploring the effect of neighborhood effect on health finds that having wealthier neighbors can improve health for young black males. This is equivalent in fact to a 40% increase in their income (Miller and Paxson forthcoming).  See also Demombynes and Ozler (2005) on crime and inequality in South Africa.

11
public utility networks. In many developing countries, the problem of deficient electricity supply is further exacerbated by financing constraints that prevent smoothing these fixed costs (Steinbuks, 2012). Electrification can lead to higher or lower operating costs. On the one hand, a lower need for supporting equipment like self-generating electricity machines will contribute to reduce them (Foster and Steinbuks, 2009, Steinbuks and Foster, 2010). On the other, improvements in electricity access are conducive to the technological progress that generates increasing returns, such as e.g., adoption of electric irrigation pumps in India (Rud, 2012), which contributes to raise those costs.

The quantitative analysis in our study is based on Brazilian data for the period 1970-2007, although the model simulations cover a slightly shorter time interval, 1970-2006, because we lose one observation when forming predictions. These data indicate that our analytical framework is well suited to examining the influence of electrification on structural transformation in Brazil. Unlike many developing countries, Brazil offers comparable data across sectors on gross value added, employment levels, and size and number of firms. The advances in electrification and the interconnection process in Brazil started as early as the 1960s, when high voltage transmission lines connected the Southeast states of São Paulo, Rio de Janeiro and Minas Gerais. The process accompanied the rapid pace of industrialization observed during the 1960s and 1970s, which required significant infrastructure investments. During 1970-2006, the value added shares of agriculture and manufacturing declined and that of services increased. As expected, these patterns are replicated by the sectoral employment shares. As regards firm size, the manufacturing sector has, on average, the largest firms, followed by the services sector, and the agriculture sector that has the smallest firms. The average establishment size remains relatively stable in the agriculture and services sectors and exhibits cyclical behavior in the manufacturing sector.<sup>4</sup>

> 4The evidence on the relationship between a country’s income per capita level and average firm size is not conclusive. For example, using data on the manufacturing sector, Alfaro et al. (2009) find that firm size decreases with the level of income across nations, and Laincz and Peretto (2006) report no trend in average firm employment in the U.S. More recent papers, such as Poschke (2017) and Bento and Restuccia (2017), on the contrary, document that average establishment size is positively correlated with GDP per capita in the cross-section of countries.

4
increase in VAT, rather than through a more significant increase in the price of one commodity (electricity).<sup>6</sup>

**Figure 7:** Impacts of expansion of wind power on economic welfare (% change from the baseline)

<!-- Start of picture text --> ‐0.07<br>‐0.08<br>Subsidy<br>‐0.09<br>Mandate<br>‐0.10<br>2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br>Change from BAU [%]<br><!-- End of picture text -->

# **3.3 Impacts on GDP and sectoral value added**

Figure 8 presents impacts of 10% wind penetration to sectoral value added and GDP.<sup>7</sup> The results vary significantly across the sectors. Moreover the sectoral impacts are much different between subsidy and mandate cases. A mandate to promote wind power expansion would reduce sectoral value added of energy sectors, most notably electricity and oil and gas. This is because the mandate makes electricity expensive thereby reducing its demand. In response electricity generation to meet the demand also drops. Fuel demand for electricity also decreases thereby lowering their production and thus value added. Although a production subsidy to wind power does not raise electricity price, it would still have regressive impacts in certain sectors. This is because, the wind power production subsidy is financed through an increase in value added tax in all other sectors. The value added tax would have negative implications in outputs of sectors which produce relatively higher value added per unit of

> 6 This finding recalls the general point from public finance theory that for a given level of total expenditure, deadweight loss is higher if the expenditure is financed with significant increases in taxes on a small number of goods versus smaller per-unit taxes on a larger number of goods (taking into account also the relative elasticities of demand of the various goods).  See Parry (2003) for a discussion of this in the context of greenhouse gas mitigation instruments.

> 7 Like in any CGE study, we have presented the impacts as percentage change from the baseline. However, if any reader is interested to know the actual dollar value of the impacts, they are available upon request to authors.

12
and uses of finance, especially the role of the stock market as a source of finance? What about the mix between internal and external sources of finance and the mix between capital expenditures and other uses of funds?

The central issue regarding finance for the firm is its composition between intemal and external sources. While retained eamings and depreciation are the main components of intemal finance, debt and equity are the two components of extemal finance. Cash flows are defined as the sum of retained earnings and depreciation. Throughout this paper, the terms cash flows and internal finance are used interchangeably.

# **Stock market contribution**

As pointed out by Mayer (1988), there are two sources of information for studying aggregate corporate financing patterns in different countries. The first is national flow-of-funds statements that record flows between different sectors of an economy and between domestic and overseas residents. The second source is company accounts that are constructed on an individual firm basis but are often aggregated or extrapolated to industry or economy levels.

Both sources have their advantages and disadvantages. In theory, flow-of-funds statistics provide comprehensive coverage of transactions between sectors. Company accounts are only available for a sample, often quite small, of a country's corporate sector. However, the data that are employed in company accounts are usually more reliable than flow-of-funds. In particular, flow-of-funds are constructed from a variety of different sources that are rarely consistent. As a result, statistical adjustments are required to reconcile entries.<sup>3</sup> 3 See also Corbett and Jenkinson (1994) for a comparative discussion of using flow-of-funds and company accounts.

2
Uhlenbruck, K, P. Rodriguez, J. Doh and L.Eden. 2006. The Impact of Corruption on Entry Strategy: Evidence from Telecommunication Projects in Emerging Economies, _Organization Science._ 17 (3): 402–414 Wells, L.T. and R. Ahmed 2007. _Making Foreign Investment Safe: Property Rights and National Sovereignty._ Oxford University Press.

World Bank 2006b, _Doing Business in 2006: Creating Jobs._ The World Bank. Washington D.C.

24
# Table E9. Average Cost of Own Water Supply by Size of Own Water Production, Nigeria

# (Naira per Gallon)

|Own Water P<br>(1000 ga|roduction1Fixed<br>IVariable ITotal<br>Llons)<br>ICost r<br>1Cost F<br>Icost<br>i|
|---|---|
|----------------------|+<br>+<br>++<br>1|
||...........<br>...............................<br> <br>................<br>|
|IFIVE<br>IALL<br>ISTATESI|MEAN<br>0.06031<br>0.32971<br>0.39001<br>I.....................+.......--<br>-<br>-.--.........<br>I|
|I<br>I<br>|IN<br>**1,**<br>73.00001<br>73.oool<br>73.00001<br>I|
|1|**-**<br>**-**<br> <br>**-**<br> <br>|
|<br>I<br>I1-<br>_9_<br> <br>|**..**<br>**..**<br>...<br>. <br>_91WEAN_<br>1<br>0.29601<br>2.40641<br>2.70241<br>|
|I<br>1<br>---|<br>+<br>+<br>-<br>I|
|<br> <br>_<br>I||<br> <br> <br><br>IN<br>1<br>6.00001<br>6.00001<br>6.0000:|
|*****<br>**I**|I.................<br>+.......................<br>|
|1100-<br>|IMEAN<br>1<br>0.08431<br>0.24021<br>0.32451<br>|
|I499|1-----<br>+--------- +--|
|<br>|IN<br>1<br>19.00001<br>19.00001<br>19.00001<br>|
|I<br>I|.+<br>-<br>**+.**<br>**-**<br>. . .-.-.<br>...........<br>.-<br>-|
|1500-<br>999|IMEAN<br>1<br>0.04561<br>0.31311<br>0.3588,<br>I<br>m----------+-----------<br>**---**|
|<br>**I**<br>**I**|<br>..<br>IN<br>|<br>10.00001<br>10.00001<br>10.00001|
|11000-<br>|INEAN<br>0.0244|<br>0.08661<br>0.11101<br> <br>|
|14999|1-------------<br>I<br>|
||M IN<br>I<br>20.00001<br>20.00001<br>20.00001|
|*<br>15000-|IMEAN<br>|<br>0.0079,<br>0.0248,<br>0.03261|
|*<br>19999|1----------<br>-+-*-------<br>-<br>---<br>**-I**|
|<br> <br>I<br>I|.<br> <br> <br><br>IN<br>I<br>7.00001<br>7.00001<br>7.00001|
|110000-<br>|IMEAN<br>1<br>0.00231<br>0.00281<br>0.00511|
|lover|-.--.----<br>- ..<br>.<br>.<br>.|
|1|IN<br>11.00001<br>11.00001<br>11.0000;|

!) AnnK ualized capital value of boreholes and accessories.

g/ IncLude fuel, maintenance, parts, and labor.