rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: First Stage & ITT Impacts on Primary School Enrollment

||Preschool||Primar|y School||
|---|---|---|---|---|---|
||(1)<br>Enrolled at<br>preschool|(2)<br>Currently<br>enrolled at<br>school|(3)<br>Repeated<br>grade|(4)<br>Appropriate<br>grade for age|(5)<br>Primary<br>school<br>enrollment<br>index|
|Treatment|0.734<sup>_∗∗∗_</sup>|0.060<sup>_∗∗_</sup>|-0.030<sup>_∗∗_</sup>|0.057<sup>_∗∗_</sup>|0.159<sup>_∗∗∗_</sup>|
||(0.020)|(0.026)|(0.015)|(0.026)|(0.052)|
||[0.000]|[0.045]|[0.055]|[0.054]|[0.008]|
|Observations|3764|3760|3742|3760|3680|
|Control mean|0.019|0.633|0.145|0.631|-0.000|

_Notes:_ *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.10. OLS estimates at the target child-level are presented using school outcomes reported by the caregiver at endline. Standard errors clustered at the community level are shown in parentheses. All regressions include district fixed effects. Westfall and Young (1993) _p_ -values are shown in square brackets, where 10,000 replications were used. The primary school enrollment index is constructed over columns (2) to (4) following Kling et al. (2007). Estimates are weighted to give each community an equal weight in the analysis. Covariates are not included in the regressions.

# **6 Main Results**

## **6.1 First Stage: Preschool Enrollment**

The DICIPE reduced the cost of access to preschool education through the construction of preschools and the provision of early childhood services in beneficiary communities. Thus, the first stage of our impact evaluation investigates whether DICIPE caused an increase in preschool enrollment.

Participation in the program was high in the treated communities. As shown in column (1) of Table 2, treated communities saw a 73.4 percentage point increase in the enrollment of the target children at preschool compared to the control group, which had a preschool enrollment rate of only 1.9%. This preschool enrollment rate in the control group is not due to proximity to preschools in treated communities, since none of the ECD centers is located within a reasonable walking distance from the households in the control group.<sup>14</sup> Instead, enrollment in the control group is mainly driven by short-term

> 14On average, households in the control group are located 8.4 kilometers from the nearest ECD center in

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The analysis starts with econometric estimation based on municipality-level panel data using both OLS and IV methods. The IV employs the instrument constructed by Lipscomb et al. (2013) and its estimates suggest that a 1 percentage point increase in electricity access increases the share of services by 1.06-1.14 percentage points; whereas the shares of agriculture and manufacturing decline, respectively, by 0.65-0.7 and 0.42-0.43 percentage points. The theoretical model simulations further support the important role of advances in electricity on the structural transformation process, explaining 17 percent of this process for the Brazilian case during the period 1970-2006. The model, however, predicts that the growing public capital mainly favors the manufacturing sector, which is what the OLS findings imply. Interestingly, the theory is able to rationalize these two different econometric results and supply a clearer view of the effects.

The reason is that the role of electric power infrastructure arises through two channels. The first one, which is what the IV method mainly captures, runs through consumption and investment as a consequence of supply-side forces linked to the output production technologies. These supply-side effects accelerate the reallocation of production inputs towards services. More specifically, in the model, when sectors are complementary, their consumption and investment shares increase with the relative product price. Because manufacturing is the most energy intensive sector, and also the one that experiences the fastest decrease in fixed costs as electricity availability improves, the relative prices of primary and tertiary products grows with advances in electricity access, and hence the reallocation of resources towards those sectors (especially services) speeds up.

The second one, on the other hand, represents a demand-side channel that favors the growth of manufacturing and, to a lesser extent, the one of services, because the creation of infrastructure demands additional investment that comes mainly from the secondary sector. A further increase in the investment effort is allowed by the net reduction in fixed costs that the economy enjoys with electrification. Due to the large impact of investment on manufacturing, it is the second channel that dominates and the share of manufacturing is 5
countries they studied. Regulatory institutions in developing countries are often found to be not independent implying that political interference prevents energy prices from being-cost reflective. Country level corruption on contracts granted to the IPPs also prevented the reforms from producing their intended effects in developing countries such as in Southeast Asia (Henisz and Zelner, 2002). On the other hand, a study by ESMAP (2011) shows that vertical unbundling decreased electricity tariffs by 10% indicating a higher degree of competitiveness in developing countries.

Nagayama (2009) shows that cross-subsidies in electricity pricing declined with the progress in the electricity sector liberalization in Asian developing countries although liberalization models do not necessarily reduce average electricity prices. In Latin America, the impact of liberalization on electricity prices is mixed. The wholesale and retail prices have often risen due to unbundling and privatization in order to assure return in investment expected by private investors.

The effects of reforms on electricity price cost-margin (i.e. the difference between electricity price and cost) and cross subsidy levels can be different between industrial and residential consumers across both developing and developed countries. Participation of IPPs in the generation market and existence of wholesale markets seems to decrease industrial price-cost margin in Latin American countries (Erdogdu, 2011). Likewise, the establishment of wholesale electricity markets and a market regulator had a downward effect on the residential price-cost margins in developing countries. Unbundling an inherent element of the textbook reform model), with privatization, also led to a decreasing effect on residential price-cost margins in the developing countries in the Latin America. These evidence shows that each of the main reform steps can produce diverse impacts on the price-cost margins and cross-subsidy levels across different countries. On the other hand, the overall impacts of reforms on the industrial and residential price of electricity depend on the level of industry restructuring.

The impact of reforms on electricity prices has been less frequently studied on a regional basis and the focus of most research to date has been at the utility level. India provides an interesting case to assess the differences in regional outcomes of reforms considering the different states sharing a common economic and political system. Sen and Jamasb (2012) analyze the impacts of individual reform measures on key economic and power sector variables for different Indian states and showed that the average price of electricity was unaffected by reforms while passing of tariff order in different states as a mechanism to correct price distortions significantly lowered the industrial price of electricity. Tariff order also rationalized electricity pricing by 15
# **2. MOROCCO’S GROWTH MODEL: FEATURES, ACHIEVEMENTS, AND CHALLENGES**

This section begins by characterizing the main features of the growth model that Morocco has pursued in the past decades. It then considers its achievements in terms of economic and social outcomes, and, discusses the growing difficulties that it has encountered in more recent years.

## **2.1 Main Features of the Current Growth Model**

By and large, in recent decades Morocco has pursued a growth model fundamentally based on the expansion of domestic demand, supported by an expansion of consumption (itself fueled by wage increases linked to minimum wage adjustments) and high levels of public investment, mainly through State‐Owned Enterprises. In addition, the increase in public investment contributed (at least initially) to an expansion in private investment through a complementarity effect.<sup>5</sup> During the period 2009‐2017 for instance, household consumption spending has averaged 50 percent of GDP and has increased in real terms by about 3.7 percent per annum, with an average contribution of 2.2 points to GDP growth during that period (Ministry of the Economy and Finance, 2019). For its part, gross investment has averaged 30.4 percent of GDP and has contributed to about 0.4 percent on average to real GDP growth over the same period (Ministry of the Economy and Finance, op. cit.).<sup>6</sup> Between 2001 and 2017, total investment has fluctuated between 25 percent and 38 percent of GDP (Figure 2.1). Morocco’s investment rate remains one of the highest in the world. The role of domestic demand as a key driver of sustained economic growth – and, correspondingly, a relatively small role of net exports – is not unique to Morocco; it is in fact common in the Middle East and North Africa (MENA) region (Figure 2.2).

> 5 In a recent study, the International Monetary Fund (2018, Box 5.1) estimated cross‐country regressions of the determinants of private investment in the Middle East and Central Asia. They find a very strong effect of access to finance, but a negative effect of public investment, which suggests a crowding‐out effect. However, as emphasized in the literature (Agénor, 2012), the complementarity effect should be estimated in terms of the stock of public capital, not the flow of public investment. In that setting, it is possible that the flow of public investment has a negative effect (as estimated by the IMF study) while at the same time the stock of public capital has a positive effect.

> 6 A study by the World Bank (2018f) suggests that transport and telecommunications are the two components of infrastructure that have contributed the most to growth in Morocco during the period 1985‐2015, with the contribution of electricity being negligible.

4
support the marginal choice results in Table 3. If a climate change increases (decreases) the relative income from a specific farm type, that farm type is more (less) likely to be chosen by farmers. For example, as precipitation increases, the net revenue from rainfed farms increases and the net revenue from irrigated farms drops. In Table 3, we see farmers choose rainfed farms more often and irrigated farms less often. As temperature increases, the net revenue from crop-only rainfed farms falls and so does the probability of choosing this farm type. With warmer temperatures, farmers earn more net revenue from mixed irrigated farms and so farmers shift to this farm type. The only exception where the sign of the change in net revenue is different from the sign of the change in choice probability concerns the temperature effect on crop-only irrigated farms. Warming is predicted to increase the net revenue but reduce the chance this farm type is chosen. It is possible that this odd result is due to the inclusion of Egypt in the sample, where despite the relatively moderate temperatures along the Nile, every farm is irrigated.

# **5. Forecasting Climate Change Impacts and Adaptations**

The analysis in the previous section provides ample evidence that climate affects the choice and conditional incomes of each farm type. As climate change unfolds in the coming century, these choices and incomes from each of these farm types are expected to change across Africa. In this section, we simulate the changes in the probability and conditional income of each farm type for different climate scenarios. Farm type choice changes will hinge on many factors such as economic development, technological change, agricultural policy, and international trade. The current model assumes these factors remain unchanged. We consequently are not predicting what the future will look like but rather just trying to understand the role of climate change. In order to make serious predictions of future outcomes, it is important that future studies take account of changes in these other factors.

We examine a set of climate change scenarios predicted by Atmospheric-Oceanic General Circulation Models to provide a range of estimates that are consistent with the predictions in the most recent IPCC (Intergovernmental Panel on Climate Change) report (IPCC 2007). Specifically, we use the A1 scenarios from the following three models: CCC (Canadian Climate Centre) (Boer et al. 2000), CCSR (Centre for Climate System 10
2 The second page adds results for every 25 years from 2000 to 2150 (as well as rates for the 1985-2000 period). This page provides fewer indices, including only the population by age and sex, the birth, death, net migration, and growth rates, total fertility, life expectancy, and infant mortality.

Most of these numbers are standard demographic indicators, but the infant mortality rate requires some qualification. More properly labeled lqo, it is an index extracte from a Coale-Demeny model life table chosen to provide an appropriate life ..xpectancy. It need not, and often will not, correspond to an observed infanit mortality rate.

# Methods and Data Sources

The key elements in these projections are base-year (mid-1985) total population estimates and age-sex structures, base-period (1985-90) mortality, fertility, and migration rates, and assumed trends in the rates. These elements and additional assumptions that are made will all be described.

The basic methodology is identical to that used last year (see Zachariah and Vu 1988). Those familiar with the previous publication may therefore wish to skip to the summary of results. Data and sources have been updated; an Appendix indicates the sources for all base-year or baseperiod estimates.

# Total RoDulation and a&e-sex comRositiOn

The estimates of total population in mid-1985 come from diverse sources. Where possible, estimates are Bank projections from reasonably complete censuses or official figures from governments with comprehensive population registration. Other- official figures are also used, either from the U.N. PoRulation and Vital Statistics Re_ort or from government publications. When these figures do not pertain to 1985, they are projected forward. Other sources of estimates include the U.N. 1988 revision of world population prospects (forthcoming), the U.N. 1984 assessment (published in 1986), Eurostat (1987), the U.S. Bureau of the Census (1985), and Celade (1987). The specific source for each country is listed in the Appendix.

The 1985 population of each country was distributed by age and sex using a percentage distribution from either the latest census, adjusted for errors in age reporting, or the U.N. 1984 assessment (the 1988 revision was not yet available for this purpose).

# Mortality level and trend

The base-period mortality levels in the projections are represented by life expectancies obtained, sometimes indirectly, from a variety of sources. Most commonly, figures are based on official or other estimates appearing in Population and Vital Statistics ReRort or government publications. Other figures are taken from the U.N. 1988 revision or the
from Table A1, attrition in our Vietnam data has very little to do with observed household and commune characteristics.

To test attrition bias further, we regress outcome variables of the first round survey on the exogenous X-variables (age, sex and education of household head, maximum education of household’s adult males and females, community variables and so on), attrition dummy and attrition dummy interacted with X-variables. Since our purpose is to determine whether coefficients of the explanatory variables differ for those households that were lost from those that were re-interviewed, we performed a joint significant test (F-test) of the attrition dummy and its interaction variables. From the results we see that at 5 percent level, only the coefficients of boys’ schooling variables are significantly different for those households that were lost from those that were re-interviewed (Appendix Table A2). That is, out of 8 outcome variables, attrition bias barely matters only for 2 of them. Thus we conclude that overall, attrition bias is not an issue for this study.

# **4. The demand for rural electrification**

Overall, household electrification rose by about 50% in survey areas between the two surveys – from 26.1 percent to 79.7 percent (Table 2). Project communes experienced the highest growth in electrification (from 49 households to 476, an almost ten-fold growth), followed by non-project communes (from 77 households to 229, a three times growth). So we see that rural electrification in survey areas was both extensive and intensive, both in project and non-project areas. We would investigate the general impacts of grid electrification first and then examine if there is any difference between the intervention effects of the World Bank project and entities external to the World Bank project.

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Figure 3: Long run marginal costs are downward sloping since fuel efficiency is increasing in vehicle kilometres. A fuel efficiency standard raises demand for vkm.

<!-- Start of picture text --> Demand for vkm<br>SRMC<br>LRMC<br>Vkm/year<br>x* x* (k)<br><!-- End of picture text --> _A fuel efficiency standard and the rebound effect_ A fuel efficiency standard, assuming it is binding, takes the choice of fuel efficiency out of the hands of the consumer<sup>18</sup> . Thus, we may study its effect on the shorter-term utilization decision. Differentiating (8) w. r. t. _k_ , the capital that raises fuel efficiency, we have

> 18 Zachariades (2006) provides an account of developments in fuel efficiency in Europe and the USA, including the role of the Corporate Average Fuel Efficiency standards.  The effectiveness of the fuel efficency standards in reducing the average consumption in the US was jeopardized by the lax standards for large, heavy vehicles (SUVs), which zoomed in popularity over more than 20 years, to take 50% of the market.  Even this effect could probably have been prevented, or at least limited, if the good principle of averaging fuel economy performance across car models in the enforcement of standards had applied also across the boundary between SUVs and others.

17
**Table S3: Robustness checks on OLS specifications in Table 1**

||Base<br>estimation<br>(Column 2,<br>Table 1)|Including<br>GDP/capita<br>interactions|Including<br>exporter<br>populations<br>interactions|Including<br>route<br>distance<br>interactions|Including<br>border<br>effect<br>interaction|Capital/ labor<br>ratio<br>Including R<br>countrytim|<br>Mean wages<br>ybczynski effec<br>e trends x indus|Non-prod.<br>share<br>t controls of<br>try quintiles|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||The depe|ndent variable|is Δ log bila|teral exports|on the intensi|ve margin by|exporter-import|er-industry|
|ΔLog country-industry labor|0.573***|0.938***|0.578***|0.269**|0.602***|0.465***|0.332**|0.313**|
|productivity|(0.185)|(0.219)|(0.126)|(0.125)|(0.186)|(0.129)|(0.135)|(0.132)|
|x Second quartile of trait||-0.582**|0.272|0.330**|||||
|indicated in column header||(0.283)|(0.284)|(0.139)|||||
|x Third quartile of trait||-0.972***|-0.368|0.570**|||||
|indicated in column header||(0.271)|(0.248)|(0.231)|||||
|x Highest quartile of trait||-0.734***|0.060|0.268|||||
|indicated in column header||(0.234)|(0.269)|(0.183)|||||
|x Bordering economies|||||-0.581***<br>(0.177)||||
|Effect at first quartile||0.938|0.578|0.269|||||
|Effect at second quartile||0.356|0.850|0.599|||||
|Effect at third quartile||-0.034|0.210|0.839|||||
|Effect at highest quartile||0.204|0.638|0.536|||||
|Value at second quartile start||8,431|8,852,235|2,319|||||
|Value at third quartile start||14,765|29,900,000|5,596|||||
|Value at highest quartile start||19,024|57,100,000|9,184|||||
|Observations|103,839|103,839|103,839|103,839|103,839|103,839|103,839|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author's analysis based on data described in text. Notes: See Table 1. Interactions in Column 4 use Great Circle distances between capital cities. To give a feel for these demarcations, the distances from Beijing, China, to the capitals of Bangladesh, United Arab Emirates, and Spain are 3029 km., 5967 km., and 9229 km., respectively. Columns 6-8 test for the Rybczynski effect within manufacturing. Industries are grouped into quintiles by their US capital-labor ratios, mean wage rates, and non-production worker wage bill shares. Table S1b lists industry groupings. Linear time trends for each country by industry quintile are included in the estimation.
|Coal (including coal gas)<sup>a</sup>|17.5|7-47|
|---|---|---|
|<br>Kerosene|43|35-55|
|Liquefied Petroleum Gas (LPG)|45.5|42-70|
|<br>Natural gas|38 (MJ/m<sup>3</sup>)|54-60|
|<br>Electricity||75|

_Note:_<sup>~~a~~</sup> Values reported for coal cookstoves vary from as low as 7% for unprocessed coal (coal power) metal vented stoves to 47% for honeycomb coal briquette improved stoves used in China (Zhang et al. 2000). _Sources_ : Zhang et al. (2000), Smith et al. (2000), O'Sullivan and Barnes (2007), MacCarty et al. (2008), Berrueta et al. (2008), Jetter and Kariher (2009), WHO (2010), Grieshop et al. (2011), World Bank (2011b), Barnes et al. (2005, 2012), Maes and Verbist (2012), Bansal et al. (2013), Raman et al. (2013) and GACC (2014).

# **4. Factors Affecting Household Cooking Fuels and Cookstoves Choice**

Understanding of key determinants of household cooking energy consumption and cookstoves is important for the design and implementation of effective policies to enhance access to clean cooking. Note that availability and affordability of cooking energy sources and cooking technologies vary widely within and across the countries. In addition, households often use a combination of fuels (fuel stacking) and they do not necessarily switch to more efficient or higher quality fuels for cooking. In the literature, a wide range of factors are mentioned that influence each household's choice of energy types and cookstoves. These include socioeconomic, availability of fuels, cultural, environmental, cookstove characteristics and government policies (Leach, 1992; Masera et al., 2000). Although these factors are presented in isolation in the following sections, they are closely interrelated to each other and they are not prioritized on the basis of their relevance.

# 4.1 Socio-economic factors

A number of studies have shown several socio-economic factors, such as income, education, size and age of the households, time spent at home, and ownership, age and type of dwellings, influence household cooking fuel and cookstove choices. For instance, as per capita income increases, households tend to switch to cleaner and more efficient fuels for cooking; a large number of studies have provided empirical evidence of this.<sup>8</sup> For example, studies by

> 8 See Hosier and Dowd (1987), Leach (1988), Barnes and Qian (1992), Alam et al. (1998), Barnes and Floor (1999), UNDP (2000), Victor et al. (2002), Johansson and Goldemberg (2004), Leiwen and O’Neill (2003), Pachauri (2004), Gupta and Köhlin (2006), Ouedraogo (2006), Wuyuan et al. (2008), Pachauri and Jiang (2008),  Barnes et al. (2011) and Lee L. Y-T. (2013).

11