rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6864

# **Abstract**

The analysis of household energy consumption patterns is critical for evaluating public mechanisms, such as subsidies and social tariffs that aim to provide lower income earners with better access to energy sources. This paper focuses on Mexican households to analyze the relations between their levels of income, consumption of different forms of energy, and the role played by different household characteristics. Using microdata from the Mexican Income Expenditure Surveys, the paper first relate income and energy expenditure to determine the shape of this relation. It then applies OLS and Tobit models to determine how income levels affect energy consumption in relation to other covariates. The results show a positive relation for income deciles and energy consumption and some household characteristics— pointing to differentiated mechanisms for improving energy use.

This paper is a product of the Energy Unit, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at arielyepez@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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# **Table 4.3a: Non-response bias in HFPS**

||||**Ethiopia**|||||**Malawi**|||
|---|---|---|---|---|---|---|---|---|---|---|
||**Sample**|**Co**|**ntacted**|**In**|**terviewed**|**Sample**|**Co**|**ntacted**|**I**|**nterviewed**|
||_Mean_|_Mean_|_Bias due to_<br>_non-_<br>_contact†_<br>_(2) -(1)_|_Mean_|_Bias due to_<br>_non-interview†_<br>_(4) - (2)_|_Mean_|_Mean_|_Bias due to_<br>_non-_<br>_contact†_<br>_(7) -(6)_|_Mean_|_Bias due to_<br>_non-interview†_<br>_(9) - (7)_|
|**Characteristic**|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|**Sample size(number of households)**|_5,372_|_3,353_||_3,247_||_2,337_|_1,743_||_1,729_||
|Rural|0.588|0.533|-0.055***|0.535|0.002|0.765|0.749|-0.016*|0.749|0.000|
|Average household size|4.484|4.461|-0.023|4.469|0.008|4.990|5.008|0.017|5.026|0.018|
|Dependencyratio|0.915|0.866|-0.049**|0.865|-0.001|0.975|0.933|-0.042***|0.939|0.006|
|Female head(%)|0.262|0.244|-0.017|0.244|-0.001|0.262|0.256|-0.007*|0.250|-0.006|
|Age of head|41.962|41.042|-0.92**|41.049|0.007|44.911|45.234|0.324|45.246|0.012|
|**_Religion of head_**|||||||||||
|Orthodox|0.485|0.522|0.038|0.528|0.005||||||
|Muslim|0.284|0.266|-0.017|0.268|0.002|0.828|0.840|0.012|0.838|-0.002|
|Christian|0.217|0.201|-0.016|0.194|-0.007|0.133|0.123|-0.011|0.124|0.001|
|**_Marital status of head_**|||||||||||
|Married|0.740|0.742|0.002|0.741|0.000|0.744|0.746|0.002|0.751|0.004|
|Formerlymarried|0.175|0.170|-0.005|0.171|0.000|0.232|0.228|-0.004|0.228|0.000|
|Never married|0.085|0.088|0.003|0.088|0.000|0.023|0.024|0.001|0.020|-0.005|
|**_Education level of head_**|||||||||||
|Head Literate|0.586|0.640|0.054***|0.643|0.003|0.944|0.953|0.008***|0.952|-0.001|
|None|0.456|0.397|-0.059***|0.395|-0.002|0.591|0.545|-0.046***|0.547|0.002|
|Primary|0.310|0.322|0.012|0.324|0.002|0.251|0.260|0.009*|0.262|0.002|
|Secondary|0.129|0.151|0.023***|0.153|0.001|0.111|0.136|0.025***|0.138|0.001|
|Tertiary|0.105|0.129|0.024***|0.128|-0.001|0.047|0.059|0.012*|0.054|-0.005|
|**_Working status of head_**|||||||||||
|Any|0.724|0.722|-0.002|0.723|0.001|0.829|0.842|0.012|0.841|-0.001|
|Wage|0.165|0.194|0.029***|0.192|-0.002|0.198|0.228|0.03***|0.225|-0.003|
|Agriculture|0.492|0.449|-0.043***|0.450|0.001|0.445|0.435|-0.010|0.439|0.004|
|Nonfarm enterprise|0.136|0.147|0.011*|0.147|0.000|0.214|0.219|0.005|0.217|-0.002|
|Ganyu||||||0.205|0.185|-0.02**|0.187|0.002|

- †The results of an adjusted Wald test comparing weighted means are presented (*** p<0.01, ** p<0.05, * p<0.10).

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**Table 6a: Drought Impacts on Child Stunting (OLS with Cluster x Month Fixed Effects)**

|**VARIABLES**|(1)<br>**All**|(2)<br>**Mozambique**|(3)<br>**Malawi**|(4)<br>**Zimbabwe**|(5)<br>**Tanzania**|(6)<br>**Zambia**|
|---|---|---|---|---|---|---|
|Drought: Birth Season|0.07***|0.07***|-0.03|0.11***|0.05***|0.12***|
||[5.43]|[2.64]|[-0.87]|[4.08]|[2.59]|[4.96]|
|Drought: Season after Birth|0.04***|0.02|0.09***|0.04|0.04*|0.05**|
||[3.07]|[0.47]|[2.62]|[1.21]|[1.84]|[2.01]|
|Mother ht between 152 and 160 cm|0.13***|0.13***|0.13***|0.11***|0.14***|0.09***|
||[10.75]|[4.66]|[3.81]|[5.40]|[7.57]|[4.12]|
|Mother ht below 152 cm|0.25***|0.24***|0.29***|0.25***|0.27***|0.21***|
||[17.56]|[7.45]|[7.49]|[7.19]|[12.08]|[7.44]|
|Mother under 19 yrs|0.05***|-0.00|-0.02|0.05|0.08**|0.02|
||[2.72]|[-0.12]|[-0.45]|[1.30]|[2.45]|[0.68]|
|Age:7-24 months|0.19***|0.15***|0.14***|0.18***|0.18***|0.25***|
||[11.98]|[3.95]|[2.63]|[5.82]|[7.33]|[8.35]|
|Age:25-59 months|0.23***|0.19***|0.18***|0.23***|0.24***|0.22***|
||[15.38]|[5.34]|[4.13]|[8.39]|[10.51]|[7.06]|
|No Livestock|0.03***|-0.01|0.02|-0.04*|0.06***|0.07***|
||[2.77]|[-0.37]|[0.58]|[-1.74]|[2.67]|[2.90]|
|Does not treat water|0.03**|0.02|0.00|0.06*|0.03**|0.00|
||[2.18]|[0.44]|[0.00]|[1.77]|[1.98]|[0.14]|
|Mother underweight|0.04**|0.11**|0.06|0.07*|0.01|0.06*|
||[2.19]|[2.43]|[1.11]|[1.71]|[0.36]|[1.78]|
|Mother able to read part of sentence|0.01|0.04|0.01|-0.00|-0.01|0.05|
||[0.70]|[0.78]|[0.20]|[-0.08]|[-0.23]|[1.43]|
|Mother cannot read at all/Unknown|0.01|0.03|0.05|0.04|-0.00|0.01|
||[0.69]|[1.04]|[1.43]|[1.16]|[-0.03]|[0.57]|
|Constant|-0.09**|-0.00|-0.22*|-0.16*|-0.12*|0.02|
||[-2.25]|[-0.01]|[-1.65]|[-1.86]|[-1.95]|[0.26]|
|Observations|28,017|5,002|3,618|4,400|9,358|5,639|
|R-squared|0.46|0.44|0.52|0.47|0.47|0.46|

Notes: *** p<0.01, ** p<0.05, * p<0.1. Robust standard errors are clustered at the DHS cluster level. All regresssions employ cluster x month fixed effects. The weights used are person weights adjusted for the national populations. The first regression is the same as the first regression in table 3. Additional controls are for roof, floor and wall quality, cooking fuel, employment in agriculture, source of water, type of toilet, ownership of radio, tv, bicyles, refridgerators, cars, trucks, birth order, birth interval, gender of child and the gender of the household head. All metrics are based on children being measured below -2 standard deviation thresholds.

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# 6.4. Accounting for Urban Friction

Due to higher traffic, urban centers tend to suffer from congestion problems (referred to as higher friction in the urban center in GIS terms). The average speeds of vehicles in the city are expected to be lower than in intercity corridors and even rural areas assuming all other conditions are equal. HDM-4 can be used to model congestion effects by inputting a reduced speed based on travel time and speeds calculated using Google’s Directions Application Program Interface (API), which takes congestion into account.

First, urban areas must be defined. This is done based on three criteria: percentage of built-up land; population; and status as a national or provincial capital. Roads which intersect the urban cluster mask are identified and considered “urban” for the purposes of analyzing urban friction. Travel time and speeds are then calculated for these roads using Google’s Directions API.

An “urban friction coefficient” can be defined to provide a sense of congestion effects: The urban friction coefficient can be interpreted as the percentage speed reduction of the original HDM4 speed estimates. For the urban road links in Colombia, Ecuador, and Peru, the average friction coefficients are 0.50, 0.36, and 0.39 respectively. In other words, there is on average a 50 percent speed reduction in Colombia, 36 percent speed reduction in Ecuador, and 39 percent speed reduction in Peru in the urban centers defined by the study.

For the calculation of road user costs in HDM-4, the speeds derived from Google were inputted directly to the model and calculated as an additional road characteristic for all urban roads. That is, in the case of urban roads, HDM-4 was used to calculate the road user cost associated with an existing road link rather than the road user costs associated with a type of road based on the seven road characteristics.

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parameter, and _ε_ is an error term. Here, we specialize to _y_ = _θ e_ + _a_ + _ε_ , where _θ_ is teacher ability, _e_ is teacher effort, and _a_ is household (student and/or parent) effort. That is, we enrich the specification of labour to allow for (additively separable) teacher and household inputs but abstract from the role of physical capital and school-level productivity.<sup>17</sup> Using a theoretical framework built around this education production function, we show that school management structures can impact student learning outcomes via the following three channels:

1. _Teacher selection_ : schools with high management scores offer compensation packages that select in more able (higher _θ_ ) and more intrinsically motivated (lower effort cost) teacher types.

2. _Teacher incentives_ : schools with high management scores offer compensation packages that extrinsically incentivize, and adopt practices that intrinsically motivate, more effort from any given teacher type that selects in.

3. _Household incentives_ : schools with higher management scores institutionalize a strong work ethic and culture of high achievement among students and encourage greater parental involvement within the school (higher _a_ ).

In Section 3.2, we present a simple model that suffices to make the points above. In Section 3.3 and Section 3.4 we explain, intuitively, how the above selection and incentive effects are driven by people management and operations management. Then, in Section 3.5 we draw together these results and discuss implications for policy. We also briefly comment on how predictions would change in an alternative model featuring ‘low-cost private schools’.

# **3.2 The model**

We focus on a teacher who must decide whether to accept a job offer in her assigned public school, or decline it and apply to a private school or the outside sector.

> 17In principle, parents could play a further role by selecting between schools. Since our PISA data cannot speak to this issue, we leave the analysis of management-induced household selection for future work. Note that we assume management practices change effective labour inputs. In their study of the IT industry, Schivardi and Schmitz [2019] assume that management is an additional input, alongside capital and labour, in an approach that they term “management as a production technology”.

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Bruhn, Miriam, and Inessa Love. 2014. “The Real Impact of Improved Access to Finance: Evidence from Mexico.” _The Journal of Finance_ , 69(3): 1347-1376.

Brune, Lasse, Xavier Giné, Jessica Goldberg, and Dean Yang. 2016. “Facilitating Savings for Agriculture: Field Experimental Evidence from Malawi.” _Economic Development and Cultural Change_ , 64(2): 187-220.

Burgess, Robin, and Rohini Pande. 2005. “Do Rural Banks Matter? Evidence from the Indian Social Banking Experiment.” _American Economic Review_ , 95(3): 780-795.

Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, and Saniya Ansar. 2022. _The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19_ . Washington, DC: World Bank.

Dupas, Pascaline, Sarah Green, Anthony Keats, and Jonathan Robinson. 2016. “Challenges in Banking the Rural Poor: Evidence from Kenya's Western Province.” In _African Successes Vol. III: Modernization and Development_ (NBER Conference Report), edited by Sebastian Edwards, Simon Johnson, and David N. Weil. University of Chicago Press.

Dupas, Pascaline, Dean Karlan, Jonathan Robinson and Diego Ubfal. 2018. “Banking the Unbanked? Evidence from three countries.” _American Economic Journal: Applied Economics_ , 10 (2): 257-297.

Dupas, Pascaline, and Jonathan Robinson. 2013. “Savings Constraints and Microenterprise Development: Evidence from a Field Experiment in Kenya.” _American Economic Journal: Applied Economics_ , 5(1): 163–192.

Hernandez, Emilio. 2019. _Agent Networks at the Last Mile_ . Technical Guide. Washington, D.C.: CGAP Jack, William, Adam Ray, and Tavneet Suri. 2013. “Money Management by Households and Firms in Kenya.” _American Economic Review: Papers and Proceedings_ , 103(3): 1-8.

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Lee, Jean, Jonathan Morduch, Saravana Ravindran, Abu Shonchoy, and Hassan Zaman. 2021. “Poverty and Migration in the Digital Age: Experimental Evidence 19
middle-income (LMI) countries (Figure 1). More worryingly, the country has fallen in the rankings over the past five years. Another ranking provided by the United Nations Department of Economic and Social Affairs (UNDESA) provides a similar conclusion when evaluating the country’s e- government: Djibouti is ranked 187<sup>th</sup> of 193 countries in 2016, and its ranking has dropped since 2010.<sup>4</sup> Finally, GSMA estimates that the unique mobile subscription penetration<sup>5</sup> covers less than 30 percent of the total population in Djibouti in 2018 – lagging the average of Sub-Saharan Africa (SSA) of 45%, and that of Middle East and North Africa (MENA) of 64 percent (Figure 2). A lack of competition to drive service delivery, productivity, innovation and lower prices could be one reason for the poor performance in the sector.

**Figure 1: Djibouti has dropped in major ICT world rankings**

<!-- Start of picture text --> 4<br>Sudan<br><!-- End of picture text --> Source: ITU (2017) & UNDESA (2017) Notes: MENA countries in blue, SSA countries in orange. Djibouti’s 2016 rank shown as a green diamond.

> 4 The recent launch of the e-government site by the _Agence Nationale des Systèmes d'Information de l'État_ is not likely to be captured in this ranking.

> 5 The mobile connection penetration refers to a single individual that has subscribed to a mobile service and that person can hold multiple mobile connections (i.e. SIM cards). If one individual actively uses two SIM connections, that person will be counted by the industry as two mobile connections although he or she represents a single mobile subscriber. As it is common for African mobile subscribers to own several SIM cards because of difference in quality or pricing between networks, referring to unique mobile subscribers allows to have a more precise picture of the mobile penetration in a country and to perform more accurate cross-country comparisons.

3
between 2 percent and 3.4 percent, compared to 5-6 percent for foreign currency denominated securities issued on international markets.<sup>4</sup>

**Table 2: Characteristics of public debt, percent of total public debt: 2008-10**

||2008|2009|2010|
|---|---|---|---|
|_Share of debt by type of creditor_||||
|Multilateral|20.4|25.4|28.8|
|Bilateral|0.3|0.2|0.2|
|Private banks and other|79.3|74.4|71.0|
|_Share of debt by currency_||||
|Domestic|59.7|47.1|45.3|
|Foreign|40.3|52.9|54.7|
|_Share of debt by maturity_||||
|short term|49.3|37.8|35.2|
|medium term|10.7|19.9|23.4|
|Long term|40.1|42.4|41.4|
|Average remaining maturity of debt (years)|3.0|3.7|4.0|
|domestic|1.4|2.0|2.1|
|External|5.5|6.5|7.0|
|_Share of debt by interest rare_||||
|Fixed|31.3|42.4|52.0|
|Floating|68.7|57.6|48.0|

_Source: Romanian Ministry of Public Finance (2011), “Report on Public Debt Management”, Bucharest, http://www.mfinante.ro/rapoarteMFP.html?pagina=domenii_ Recourse to multilateral loans has brought about some positive movements in the characteristics of public debt (table 2). The maturity structure of public debt has been lengthened, with short-

> 4 The Government issued a 5 year Eurobond at a yield of 5.17 percent in March 2010.

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logical upgrading in response to fuel price is reduced - as well as for foreign plants, which are more likely to use technology closer to the frontier (Blackman and Wu, 1999; Guadalupe et al., 2012; Brucal et al., 2018).

Unlike for fuels, we do not find a positive impact of electricity prices on plants’ performance. This result is consistent with the fact that electricity prices do not incentivize productivity-enhancing capital replacement, because electricity powers technologies that are already on the efficiency frontier. Hence plants have less room to improve the capital vintage as a result of electricity price movements. In the absence of the capital replacement channel, rising electricity prices turn out to have a negative impact on plants’ performance and profitability, in line with previous literature (Abeberese, 2017; Marin and Vona, 2017). In fact this effect is more muted in Indonesia than Mexico, which supports the hypothesis that Indonesia’s manufacturing industry may use electricity-powered capital less efficiently than Mexico’s.

One advantage of the Indonesian and Mexican manufacturing data is that they include detailed information on outputs and inputs for each plant over time. However only the Indonesian data includes value and quantity of energy consumed, which allows estimating plant-specific unit prices for electricity and fuels. The Mexican data includes only the total plant-level expenditures on electricity and on other fuels. To estimate plant-level energy prices, we combine various sources of data. For electricity we combine monthly data on regional electricity tariffs by firm size with plant-specific sales, electricity expenditures and monthly production; to construct fuel prices we match sector-specific fuels’ consumption with monthly fuel price data and plant-specific monthly production data. A challenge with these energy prices - particularly for Indonesia - is that they are endogenous with respect to plant-level outcomes. This problem is less severe for Mexico as the plant-level data are constructed using national, sectoral and regional data. To address these endogeneity concerns we instrument energy prices in both countries using energy prices of neighboring plants for Indonesia and, in the case of Mexico, average energy prices of plants from the same sector and state (excluding the firm analyzed in each case).

Despite the spite of the differences in the data, the instrumental variables employed and in the structure of the two economies, the result that an increase 3
**Table 4. Relative learning mobility controlling for career emphasis for teachers**

||**Qua**|**ntitative reaso**|**Depende**<br>**ning**|**nt variable:**<br>**Nativ**|**Z-scores in Sa**<br>**e language (Sp**|**ber PRO**<br>**anish)**|**Forei**|**gn language (**|**English)**|
|---|---|---|---|---|---|---|---|---|---|
|**Variable**|**Ordinary**<br>**Least**<br>**Squares**|**Non-**<br>**parametric**<br>**matching**|**Instrumental**<br>**Variables**|**Ordinary**<br>**Least**<br>**Squares**|**Non-**<br>**parametric**<br>**matching**|**Instrumental**<br>**Variables**|**Ordinary**<br>**Least**<br>**Squares**|**Non-**<br>**parametric**<br>**matching**|<br>**Instrumental**<br>**Variables**|
|**Th**|-0.4636***|-0.3810***|-1.2405***|-0.1441***|-0.1010***|-0.9698***|-0.0560***|-0.0003|-1.3436***|
|**eacer**|(0.0161)|(0.0333)|(0.2353)|(0.0166)|(0.0316)|(0.2188)|(0.0184)|(0.0312)|(0.2209)|
|**Saber 11 scores in**||||||||||
|Quantitative reasoning|0.5032***<br>(0.0047)|0.4380***<br>(0.0120)|0.5317***<br>(0.0107)|||||||
|Native language||||0.4966***<br>(0.0043)|0.4715***<br>(0.0108)|0.4944***<br>(0.0112)||||
|Foreign language|||||||0.6226***<br>(0.0055)|0.6009***<br>(0.0136)|0.6392***<br>(0.0102)|
|**Teacher * Saber 11 scores in**||||||||||
|Quantitative reasoning|-0.1983***<br>(0.0190)|-0.0905**<br>(0.0399)|-1.1270***<br>(0.2633)|||||||
|Native language||||0.0398***<br>(0.0155)|0.0587<br>(0.0390)|-0.1560<br>(0.1882)||||
|Foreign language|||||||-0.0456<br>(0.0300)|0.0463<br>(0.0518)|-0.9986***<br>(0.2633)|
|**Teacher program has emphasis in**|0.4183***|0.5551***|0.8530***|0.2612***|0.2989***|0.9865***|1.6177***|1.4955***|2.6941***|
|**the assessed subject in Saber**|(0.0330)|(0.0573)|(0.2306)|(0.0307)|(0.0630)|(0.2125)|(0.0488)|(0.0829)|(0.2299)|
|**PRO**||||||||||

**(** **_Continues on next page_ )**

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