rafmacalaba/datause-encoder-data huggingface dataset:
- 40 -

||(0.0019)|(0.0012)|(0.0009)|(0.0013)|(0.0014)|
|---|---|---|---|---|---|
|ln_AirConditioner_|0.0080 ***|0.0094 ***|0.0128 ***|0.0131 ***|0.0116 ***|
||(0.0028)|(0.0019)|(0.0013)|(0.0020)|(0.0023)|
|ln_Water Boiler_|0.0031|–0.0003|–0.0019 **|–0.0009|0.0010|
||(0.0020)|(0.0013)|(0.0009)|(0.0013)|(0.0015)|
|ln_Computer_|0.0003|0.0048 ***|0.0028 **|0.0051 ***|0.0058 ***|
||(0.0025)|(0.0017)|(0.0012)|(0.0018)|(0.0021)|
|ln_PowerOutages_|–0.0042 **|–0.0019 *|–0.0015 *|–0.0024 **|–0.0024 *|
||(0.0018)|(0.0012)|(0.0008)|(0.0012)|(0.0013)|
|D(_Urban_)|0.229 ***|0.164 ***|0.198 ***|0.245 ***|0.228 ***|
||(0.034)|(0.022)|(0.016)|(0.022)|(0.024)|
|Constant|5.057 ***|5.352 ***|6.040 ***|5.519 ***|6.313 ***|
||(0.670)|(0.416)|(0.289)|(0.419)|(0.465)|
|Obs.|4678|4678|4678|4678|4678|
|Pseudo R-squared|0.315|0.281|0.268|0.264|0.273|
|Number of district dummies|35|35|35|35|35|

1/ In the two-stage quantile regression, _I_ , ln _MP_ and ln _D_ are replaced with the predicted values from the 1st stage regressions of the IV estimator. Note: The dependent variable is the log of observed electricity consumption (kWh). The standard errors are shown in parentheses. *, ** and *** indicate the 10%, 5% and 1% significance levels, respectively.

# **VIII. CONCLUSION**

The paper examined residential electricity demand in Albania, with light cast on the potential impacts of metering and climatic conditions. Given volatile international energy prices and expected global warming, it is becoming increasingly important to manage the demand for electricity or energy in general. To achieve the successful demand side management, metering is essential because it is difficult to design an efficient pricing mechanism without knowing customers are actually consuming energy.

In Albania, metering has long been an alleged concern, including malfunctioning meters, falsification of meter readings and illegal connections. The country’s electricity transmission and distribution losses have been substantial until recently. This is mostly attributable to these nontechnical inefficiencies and partly to technical losses. In addition, Albania is considered to be one of the most vulnerable countries to external energy shocks and climatic conditions, because the country is dependent more than 95 percent on hydropower for domestic electricity generation. In the future, less precipitation is expected and more
Table 18: Program Impact on nutritional status of school age children

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||<br>|6-15years old<br>|BMI-for-ag <br> <br>|e(z-scores)<br> <br>|6-12years old<br>|<br>|weigh<br> <br>|t-for-age(z-s<br>6-10years ol<br>|cores)<br>d<br>|
||All|Boys|Girls|All|Boys|Girls|All|Boys|Girls|
|School Meals|0.022|0.003|-0.024|0.082|0.166|-0.011|0.225|0.235|0.1|
||[0.088]|[0.135]|[0.121]|[0.118]|[0.189]|[0.167]|[0.112]**|[0.172]|[0.160]|
|Dry Rations|-0.124|n.a.|-0.106|-0.152|n.a.|-0.172|0.153|n.a.|0.087|
||[0.110]|n.a.|[0.123]|[0.153]|n.a.|[0.174]|[0.121]|n.a.|[0.165]|
|Follow up|0.276|0.341|0.247|0.273|0.286|0.295|0.03|-0.041|0.11|
||[0.062]***|[0.096]***|[0.086]***|[0.084]***|[0.135]**|[0.122]**|[0.080]|[0.123]|[0.115]|
|Girl|0.075|||0.102|||0.135|||
||[0.049]|||[0.071]|||[0.057]**|||
|Constant|-1.161<br>[0.064]***|-1.169<br>[0.098]***|-1.153<br>[0.080]***|-1.197<br>[0.075]***|-1.158<br>[0.108]***|-1.124<br>[0.087]***|-2.308<br>[0.062]***|-2.328<br>[0.087]***|-2.232<br>[0.085]***|
|Observations|5290|2073|3217|3269|1277|1992|3884|1800|2084|
|R-squared|0.520|0.610|0.610|0.590|0.660|0.670|0.63|0.71|0.72|

# Standard errors in brackets

* Significant at 10%; ** significant at 5%, *** significant at 1%

Dependent variables are z-scores of BMI (columns 1-6) and z-scores of weight for age (columns 7-9). Regressions also control for child age, gender and household fixed effects (not reported).

41
# **Appendix S3. Additional results on supply and wage responses**

**Figure S3.1 International prices and production indices in major countries and regions, 2000-2012**

<!-- Start of picture text --> 200 110 120 200<br>sub‐Saharan Africa Central Asia<br>105 110<br>150 150<br>100 100<br>100 100<br>95 90<br>50 50<br>90 80<br>0 85 70 0<br>Prices (right axis) Quantity (left axis) Quantity (left axis) Prices (right axis)<br>130 200 130 200<br>South America South Asia<br>120 120<br>150 150<br>110 110<br>100 100<br>100 100<br>50 50<br>90 90<br>80 0 80 0<br>Quantity (left axis) Prices (right axis) Quantity (left axis) Prices (right axis)<br>120 200 120 200<br>South‐East Asia China<br>110 110<br>150 150<br>100 100<br>100 100<br>90 90<br>50 50<br>80 80<br>70 0 70 0<br>Quantity (left axis) Prices (right axis) Quantity (left axis) Prices (right axis)<br>120 200 150 200<br>Nigeria India<br>110 130<br>150 150<br>100 110<br>100 100<br>90 90<br>50 50<br>80 70<br>70 0 50 0<br>Production Prices Production Prices<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> 33
policies that allow people to locate closer to their jobs and find ways to make public transit options more attractive to all residents. Coordinated land use and transport policies are an important step in this direction. Policies that put emphasis on the revival of areas in and around public transport stations could contribute to changing this perception.

Further, an analysis considering the heterogeneity of household types suggests clear differences in preferences across neighborhoods. Given the lack of information on household income, we use education and job informality as proxies for income levels in the areas analyzed. Descriptive statistics show that areas with high-educated households or neighborhoods with a low proportion of informal workers face higher housing prices (evaluated at the mean), suggesting socioeconomic segregation within the city. Moreover, these households not only live in high-value dwellings, but also are better located in terms of accessibility to employment subcenters and proximity to public transport. These results are also evidence of the existence of spatial segregation in the city. Disadvantaged households are segregated, not only because of their economic conditions, but also because they are located farther away from economic centers, with less access to employment opportunities. Although the estimations suggest homogeneous effects in terms of the negative perception of public transport, households living in areas with lower average education levels and higher informality value less being closer to formal employment subcenters than households in areas with higher average education and lower job informality levels. This analysis opens a series of questions regarding segregation and accessibility to jobs that points at an important area for future research.

# **References**

<mark>Adair, A., McGreal, S., Smyth, A., Cooper, J., and Ryley, T. (2000). House prices and accessibility: The testing of relationships within the Belfast urban area.</mark> _<mark>Housing studies</mark>_ <mark>,</mark> _<mark>15</mark>_ <mark>(5), 699-716, doi: https://doi.org/10.1080/02673030050134565</mark> <mark>Agostini, C.A., and Palmucci, G.A. (2008). The Anticipated Capitalisation Effect of a New Metro Line on Housing Prices.</mark> _<mark>Fiscal studies</mark>_ <mark>,</mark> _<mark>29</mark>_ <mark>(2), 233-256, doi: 10.1111/j.14755890.2008.00074.x.</mark>

> <mark>Ahlfeldt, G. (2011). If Alonso was right: modeling accessibility and explaining the residential land gradient.</mark> _<mark>Journal of Regional Science</mark>_ <mark>,</mark> _<mark>51</mark>_ <mark>(2), 318-338</mark> , doi: 10.1111/j.14679787.2010.00694.x.

29
HFPS. In Burkina Faso, Ethiopia, Malawi, and Nigeria response rates are lower (sometimes substantially) for rural and agricultural households, suggesting potential for nonresponse bias in these segments. However, as for the case of coverage bias, there are post-survey adjustments (described in Section 3.3) that can be implemented to at least partially correct for nonresponse bias.

_Figure 3: HFPS Response Rates_ <!-- Start of picture text --> Overall Rural Urban Agricultural Non-agricultural<br>BURK I NA F A S O E T H IOP IA MA LA W I NIGE RIA UGA NDA<br>93 94 92 94 92<br>83 81 83<br>79 76 74 73 73 76 78 72<br>70<br>67 65<br>60 63 59 62<br>52<br>50<br><!-- End of picture text --> _Source: Authors’ calculation based on HFPS data._

# _Sampling methods for phone surveys_

When considering the sampling approach to adopt for a survey, there are substantial trade-offs to each in terms of representativeness and methods available for correcting bias. Among the three common approaches (i.e., recontact, RDD, or telecom list-based), recontact surveys that use phone numbers from previous in-person, representative surveys may be considered the preferred sampling method and has also been the most commonly used approach in low- and middle-income settings (Ceballos et al., 2020; Dabalen et al., 2016; Glazerman et al., 2020; Himelein et al., 2020). The main advantage of this approach is that there exists a well-defined (often representative) sample selected for a previous in-person survey and for which there is a profile of characteristics available for the sample units (i.e., households or individuals). This permits a direct assessment of the representativeness of the selected and successfully interviewed phone survey sample and facilitates post-survey adjustments to reduce selection biases (Ambel et al., 2021; Gourlay et al., 2021; Himelein et al., 2020). The existing information is also critical for identifying agricultural households.

Response rates are typically far lower for RDD surveys than recontact surveys, thus requiring more attempts and a larger selected sample to achieve the same number of completed interviews. Furthermore, under RDD, response rates can be expected to worsen if data collection targets agricultural or rural subjects, as they will need to be identified. Effectively, this will add a step to the sample selection process to verify with respondents if they belong to the target population. With contacts from existing 9
- 48 -

represent a market equilibrium, as mentioned above. A variant of the fixed-p case was used by Bradford and Fullerton (1981) who assumed that arbitrate occqrred at the firm level. This implies that the firm faces the same cost of finance from all sources, so: Given the cost of funds to the firm, the rate of return received by savers in various assets, and thus _rj,_ can be calculated.

The fourth arbitrate assumption is that used by Boadway, Bruce and Mintz (1984). It is referred to as the _open economy asswuption_ and seems particularly appropriate for the case of developing countries. The basic assumption is that the cost of debt and equity finance facing a country are determined by international capital markets. More particularly, for debt and after-tax return to fbreign debt-holders is given exogenously. If starred refer to foreign ones, the following international arbitrate conditions must hold: This arbitrate equation, which determines i, assumes that exchange rate movements reflect differences in expected inflation (and are taxed as capital gains).

On the equity financing side, a further assumption is made for data reasons. The rate of return on equity is calculated from observed stock market data in a manner which does not allow one to distinguish the rate of return on retained earnings from that on new share issues. The rate of return on equity paid by firms is therefore assumed to be the same for both. Denoting it by ps , it is given by: The value of **p 8** must satisfy an international arbitrage condition analogous to (30) with p, replacing i. The net return received by household savers, p and a , can then be computed from (31) and used to obtain rn. The value of r paid by firms is simply:
<!-- Start of picture text --> 40<br>o<br>o<br>o<br>°< 20 !<br>so<br>-20<br>Corn Tortilla Sugar Beans Meat Chicken Milk Eggs<br>BE Urban<br>Rural<br>——— 95% Conf. Interval<br>© I I © I I<br>vs | | = | |<br>| | |<br>== | | <~ | |<br>Qo | | Zou | |<br>=||3||<br>_|||| _|| ||<br>| | | |<br>2 | | 2 | |<br>2006 2008 2010 2012 2006 2008 2010 2012<br>©|I©II<br>-||=||<br>|<br>BE | | x | |<br>© || @ | !<br>22 | | ee<br>fs) | | D | |<br>a ec| |( a | |<br>| | | |<br>© | | © | |<br>2006 2008 2010 2012 2006 2008 2010 2012<br><!-- End of picture text -->
We estimate participation at various CO2 prices based on our sample as well as a national rural CO2 supply function for a program that asks rural households to reduce fuelwood collections by 25%. We scale our experimental results using the 2011 estimated national rural population of 4.4 million rural households (CBS, 2012; 2016) and the 1.45 million rural households who are members of CFs (MoFSC, 2017).

Finally, we are interested in potential carbon supply shifters. We therefore run OLS regressions of marginal WTA values on key household and community variables. We emphasize that the cross-section nature of our data does not allow us to convincingly infer causality, because it is possible that omitted variables simultaneously affect choice experiment responses and key household and community variables. Nevertheless, the experimental nature of our dependent variable virtually rules out reverse causality, though perhaps not all possible confounders and we do not seek to infer causality. We merely would like to know which respondent characteristics, technologies previously adopted, assets and perceptions of REDD+ are associated with higher WTA. Correlations are therefore sufficient for our purposes.

# **3. Results**

We begin our discussion with a brief overview of descriptive statistics, followed by estimates of household participation in programs to reduce fuelwood collections and then present national level supply functions for CO2 reductions from a program that requires all households to reduce fuelwood collections by 25%. We do this for CF members and those outside the CF program and then consider the costs of carbon sequestration due to open grazing closures in non-CF communities.<sup>12</sup> Finally, we present our OLS analysis of carbon supply shifters.

> 12 In the appendix, for the assumed normally distributed models, we trim 12 of 650 observations for non-CFs and 2 of 650 for CFs that have WTA estimates that are above $1000 and below -$1000 for 1% (average 24 kg/year) reductions in

21
# **Credit Ratings**

Even if VFs are not driven by profit, they may seek to maintain a good credit rating – if only to keep open the option of being able to borrow (to on-lend) should the need arise. VFs are rated as A, AA, or AAA, by the development banks (such as the BAAC), although some VFs either do not know their credit rating, or have not been rated.

It is useful to ask what might drive credit ratings; we do this with a multinomial logit model, applied only to rated VFs, and where the omitted category is “A”. The most important results are summarized in Table 22, which reports a version of the model where only the statistically significant variables are retained. The results of estimating separate models for VFs that on-lend, and those that do not, using all the available variables, are shown in Appendix 1; the endogenous switching model is not warranted here, as the coefficients on the inverse Mills ratios in the second-state equations are not statistically significant.

**<mark>Table 22.  Estimation Results of Multinomial Logit Model of the Determinants of Credit Ratings</mark>**

|**Determinants of Credit Ratings **|rating=AA||rating=AAA|
|---|---|---|---|
|Supply: Fund Capital||||
|VF requires members to save|0.083||0.274|
||(0.189)||(0.189)|
|Proportion who have accounting experience|4.973**||3.524*|
||(2.119)||(2.106)|
|Accounts are computerized (yes=1)|0.103||0.396*|
||(0.208)||(0.207)|
|Demand: Village Size and Organization||||
|Village has a market (yes=1)|-0.196||-0.395**|
||(0.195)||(0.195)|
|Demand: Household Characteristics||||
|Household assets (excluding land)|-0.150||-0.294*|
||(0.154)||(0.155)|
|Loan terms and conditions||||
|VF borrows to on-lend to members|0.796***<br>(0.274)||0.961***<br>(0.273)|
|Memo items:||||
|Number of observations||1,919||
|p-value for significance||0.00||

Source: Village Fund Survey 2010 and SES 2009. Robust standard errors in parentheses. Stepwise regression uses p=0.2 cutoff for dropping variables. For fuller regression, see Appendix 1. * p<0.05, ** p<0.01, *** p<0.001. Omitted rating is “A”.

Village Funds that borrow to on-lend have higher credit ratings. The effect is strong and also unsurprising, although the direction of causality is unclear: VFs may borrow because their ratings are good enough, but borrowing VFs are also likely to be careful to maintain good credit ratings. The credit ratings are stronger for 34
||(0.805)|(0.945)|(1.231)|(1.601)|(0.822)|(1.316)|
|---|---|---|---|---|---|---|
|_PTA ij_|1.733*|2.081**|2.438**|3.656***|2.759***|2.466***|
||(1.001)|(0.818)|(1.104)|(1.055)|(0.762)|(0.925)|
|_BIT*Distance ij_|0.00025**|-4.95e-06|0.00025**|0.00017|0.00028***|0.0003**|
||(0.000105)|(0.000101)|(0.000105)|(0.000118)|(9.70e-05)|(0.000123)|
|_PTA*Distance ij_|-0.00021|-0.00033***|-3.80e-05|-0.000536**|5.01e-05|-0.000268**|
||(0.000175)|(0.000121)|(0.000125)|(0.000227)|(0.000117)|(0.000114)|
|_BIT ij*Pol stabilityj_|-0.481|-1.188**|||||
||(0.331)|(0.556)|||||
|_PTA ij*Pol stabilityj_|0.126|0.272|||||
||(0.417)|(0.359)|||||
|_BIT ij*Inst qualityj_|||0.365|-4.197**|||
||||(1.796)|(1.828)|||
|_PTA ij*Inst qualityj_|||-3.094|-0.174|||
||||(2.054)|(2.434)|||
|_BIT ij*Rolj_|||||-0.0587|-0.500***|
||||||(0.0691)|(0.116)|
|_PTA ij*Rolj_|||||-0.292***|-0.0349|
||||||(0.101)|(0.0827)|
|Constant|-13.41***|-11.74***|-12.68***|-22.08***|-14.10***|-17.00***|
||(2.343)|(3.533)|(3.056)|(4.811)|(2.420)|(3.374)|
|Home FE|Y|Y|Y|Y|Y|Y|
|Sector FE|Y|Y|Y|Y|Y|Y|
|Observations|2,508|2,508|1,852|1,852|2,508|2,508|

Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 We then explore whether our main results, namely that the effects of IEAs on FDI depend on the distance between countries, holds in the context of South-South and South-North investment flows. We begin by including two three-way interactions ( _BIT*Dist*SS_ and _PTA*Dist*SS_ ). The results, in Table 9, show that while the former is statistically significant, the latter is not. Thus, emerging market firms pay more attention to BITs when investing in other developing countries than when investing in advanced nations, as shown by the results in Table 7. In addition, they tend to demand greater institutional commitments -- in the form of BITs, for example—the further are these developing country destinations. This is confirmed when we divide the sample into developing and developed destination countries, or South-North and South-South investments. As the results from models 19 and 20 show, the interaction of _BIT*Distance_ continues to be positive and significant when restricting the sample to S-S investments. By contrast, none of the interaction or main effects are significant for the N-S sample.<sup>19</sup>

> 19 The hurdle-negative binomial regressions for N-S did not converge. When we run two separate logit and negative binomial regressions, none of the interaction or main effects are significant for N-S.

23
countries and globally and might substantially undermine the effectiveness of environmental policies. In this regard, studying the relationship between informality and both CO2 and non-CO2 emissions can be critical for understanding whether informal sectors in developing countries are, indeed, a major obstacle in pursuing the climate agenda from the perspectives of international finance institutions and governments in developing countries, as well as for informing environmental policies and their design. In fact, this study directly relates to the United Nations Sustainable Development Goals (SDG) 11 on Sustainable Cities and Communities and SDG 13 on Climate Action. At the same time, it is important to take into account, that in addition to large informal sectors, developing countries also have poor environmental regulations and enforcement compared to more advanced economies, which could further augment these effects.

To be able to explain the estimation results, first, it is important to understand the characteristics of informal sectors in developing countries. This paper uses informal employment as a measure of informality and defines it as the share of self-employed and non-paid workers in total employment in each sector and country. While informal employment includes all those (i) who work for the formal sector but are informal as they do not pay taxes and do not receive employment benefits and (ii) who work for informal unregistered firms; the self-employed and non-paid workers account for a large share of total informal workers in many developing countries. The informal workers use no or very low capital in producing goods or providing services especially when compared to formal firms that use capital-intensive production technologies with high levels of CO2 emissions. Hence, the relationship between informality and emissions could be expected to be either positive, if the effects from non-compliance with environmental regulations in countries with large informal sectors prevail, or negative, if the effect of low emissions from using very low capital exceeds the non-compliance effects. Other effects come into play when non-CO2 emissions are considered.

Overall, the findings using informal employment shares in developing countries illustrate that emissions per value added in the informal sector are higher than in the formal sector when aggregate regressions are conducted. The sector-level regressions showing that larger informal employment is associated with lower emissions only in manufacturing and other services imply that emissions per value added can be larger or lower in these sectors depending on the differences in labor productivity between the formal and informal firms. Instead, for all other sectors, the non-compliance of informal workers with environmental regulations leads to higher emissions. The latter also holds for non-CO2 emissions when conducting country-level regressions and this positive relationship between informality measured in 20