rafmacalaba/datause-encoder-data huggingface dataset:
**_Energy demand models for policy_** **_<u>formulation</u>_**

models group consumers into a few representative agents to represent the “millions of decisions made by millions of individuals,” and provide relatively stylized descriptions of their decision making; (ii) incomplete coverage of social and environmental impacts; [I can see why this would be a problem in normative modeling, but why does this cause forecasts to miss the mark?] (iii) lack of adequate technological detail; and (iv) unrealistic economic assumptions such as fully employed and efficiently allocated resources, rational individuals, optimizing firms and perfectly functioning markets.

Simultaneously, the importance of developing countries in the world energy scene has grown significantly in the past thirty years. Although energy models have considered developing countries, the basic assumption was that they follow the same features of industrialized countries but with a different time lag (Urban et al 2007). However, this has not turned out to be true. For example, China has sustained a high level of economic growth for decades and has emerged as a major global player. Such global players have now changed the focus of energy sector development from the developed countries to developing countries. At the same time, within these fast growing economies as well as in many lower income countries, access to clean and affordable energy remains a major development issue.

A number of surveys of energy demand models have appeared previously, including, among others, those by Hartman (1979), Bohi (1981), Bohi and Zimmerman (1984), Craig et al (2002), Worrel et al (2004), Wirl and Szirucsek (1990). These studies, however, focus mainly on developed countries. Similarly, Bhatia (1987), Shukla (1995), Pandey (2002), Urban et al (2007) have focused on energy modeling from a developing country perspective. In addition, Dahl (1991, 1994a, 1994b), Cooper (2003), Dahl and Sterner (1991), and Espey (1998) provided energy, particularly oil demand elasticity surveys. As important as they are, most of these reviews have focused on: (i) a single approach -- econometric or end-use; (ii) demand analysis by mainly focusing on the elasticities of demand and their variability among or across studies; and (iii) comparison of the forecast with actual demand in developed country contexts.

9
(patch, gum, inhaler, or nasal spray).<sup>6</sup> The average client made a deposit every two weeks and ended up committing 550 pesos ($11 USD) by the end of the six-month contract period. 550 pesos is about 20% of monthly income<sup>7</sup> and roughly equal to the average outof-pocket expense for about 6 months’ worth of cigarettes incurred by CARES clients at baseline.

Our results suggest that CARES helps smokers quit. Smokers randomly _offered_ CARES were an estimated 3.3 to 5.8 percentage points more likely to pass the 6-month urine test than the control group, and 3.5 to 5.7 percentage points more likely to pass the 12-month urine test than the control group. Using the random assignment of whether CARES was _offered_ to estimate treatment effects-- specifically, intention-to-treat effects- generates results that are free of bias (e.g., from an omitted variable such as the strength of desire to stop smoking). We then estimate the effects of CARES _usage_ -- treatment-onthe-treated effects-- by using the randomly assigned offer to instrument for usage. These estimates indicate increases of 31 to 53 percentage points in the likelihood of 12-month test passage. The voluntary takeup decision means that one must be careful in thinking through whether these treatment-on-the-treated estimates will generalize to the full population of smokers. We discuss this issue in the Conclusion after detailing the design and results of the present study.

The 12-month results are more interesting for several reasons. The 6-month test date was scheduled up to 4 weeks in advance, and the test could be passed by abstaining from smoking for as little as a few days before the test date.8 The 12-month tests, in contrast, were “surprise” tests with only a day or two gap between test solicitation and administration. The 12-month results also lacked any incentives for fraud, since all commitment contract money had been returned or forfeited at 6 months, and hence there was no financial consequence tied to the 12-month test result. Lastly, practical reasons required that subject compensation for taking the 6-month test vary across treatment arms

> 6 Seventeen percent of smokers U.S. smokers reported using nicotine replacement medication during the last 12 months in a nationally representative 2001 phone survey (Bansal et al. 2004). In the only study  we know of from the Philippines, only six percent of a sample of relatively heavy smokers who had already decided to quit had ever used any form of nicotine replacement therapy in past smoking cessation attempts (Tipones and Fernandez 2006).

> 7 Income is very roughly estimated from marketer observations of subject appearance and work activity. 8 Possibilities of gaming the 6-month test aside, the public health literature finds that even short-term abstention or failed quit attempts increase the probability of quitting eventually.

3
Table 3: Number of Municipalities, by 1997 Education Spending as % of Revenue and 1997 Transfers as % of Revenue, Showing How Crowd-Out of Local Revenue with FUNDEF Revenue Was Limited

||||Tran|sfers as % of|Revenue|||
|---|---|---|---|---|---|---|---|
|||_<_75 %|(75-85] %|(85-90] %|(90-95] %|(95-100]|%<br>Total|
|_Panel A: All Municip_|_alities_|||||||
||_>_32.5%|53|86|87|168|270|664|
||(30, 32.5]%|58|106|90|123|182|559|
|Education Spending|(27.5, 30]%|122|186|140|180|235|863|
|as % of Revenue|(25, 27.5]%|172|245|175|229|269|1,090|
||25%|597|544|263|248|214|1,866|
||Total|1,002|1,167|755|948|1,170|5,042|
|_Panel B: Predicted N_|_et Benefciarie_|_s of FUN_|_DEF_|||||
||_>_32.5%|42|56|65|125|245|533|
||(30, 32.5]%|30|63|54|89|153|389|
|Education Spending|(27.5, 30]%|70|101|65|87|162|485|
|as % of Revenue|(25, 27.5]%|75|112|77|106|165|535|
||25%|303|214|95|99|130|841|
||Total|520|546|356|506|855|2,783|

_Notes:_ 25% of revenue is the imposed minimum expenditure on education, and thus the lowest expenditure category. Predicted net beneficiaries are municipalities that, as of 1997, would receive more from FUNDEF than they were required to pay in if the rules of FUNDEF were applied to 1997 enrollment and revenue levels.

_Sources:_ Author’s calculations based on data from Tesouro Nacional (1997).

Table 4: Correlation Matrix, Measures of Income and Inequality

||(1)|(2)|(3)|
|---|---|---|---|
|(1) Average income per capita, 10,000s of 2005 Reais|1.0|||
|(2) Median income per capita in 2000, 10,000s of 2005 Reais|0.72|1.0||
|(3) Gini coefcient in 2000|-0.04|0.01|1.0|

_Source:_ IBGE (1995-2008).

42
associated with a job loss rate of only 1.0 percent. Although the modeled and surveymeasured estimates are not as close as they were for Jordan, our prediction model does a reasonable job of matching the aggregate survey-measured job losses of 7.8 percent for that wave.

_Table 5-6 Georgia: Job Loss Projections PFPS Jobs as a Percent of PreCOVID Levels_

||As of Wave 1|Cumulative<br>Wave 2|
|---|---|---|
|Modeled Job Losses: Percent of PFPS<br>jobs lost (relative to Dec 2019) as<br>projected|19.0|5.9|
|_Of which_: Due to Permanent closures|0.0|1.0|
|Survey-measured job losses: Percent of<br>PFPS Jobs Lost(relative to Dec 2019)|18.1|7.8|

Source: Authors’ calculations.

Notes: The post-COVID survey is not directly comparable to the projected jobs lost as it does not contain enough variables to separate PFPS workers.

It appears from the CFUWBES data that Georgian formal private firms were better able to remain in

|_Table 5-5 Georgia: Selected Potential Jobs at Risk Influ_<br>_CFUWBES Waves Rounds 1 and 2_|_ence Variables,_<br>Wave 1|_Means of_<br>Wave 2|
|---|---|---|
|Temporarily closed due to COVID (weeks of closure )*|5.7|2.3|
|Infection risk (score) (0-300)|162.4|162.4|
|Percentage of firm workers working remotely<br>(percentage)|3.8|2.9|
|Sales change (percent change year-on-year, same<br>month in 2019)|-53.8|-28.2|
|Change in export share of sales (percentage difference<br>from 2019 ES)|-0.7|-1.2|
|Supply of goods and materials has decreased (1=yes)|0.61|0.45|
|Started/increased online or delivery business activities<br>(1=yes)*|0.21|0.17|
|Faced financial issues (1=yes)*|0.81|0.73|
|Received other policy support (1=yes)*|0.19|0.19|
|Received wage subsidies (1=yes)*|0.08|0.07|
|Interaction between whether had export sales in 2018<br>and trade shock|-5.1|-5.1|
|Source: CFUWBES.|||
|Notes: See note to table 5-2.|||

operation, boost sales, and re-absorb workers even under crisis conditions than their Jordanian counterparts. Between waves 1 and 2, they reported an additional 2.3 weeks of COVID-related closures on average and a lower share of workers was working remotely than before (2.9 percent). Moreover, the percentage of firms receiving policy support remained flat. However, there was a

# modest improvement in

the share of firms reporting recent supply disruptions, with a decline from 61 to 45 percent. Crucially, sales rebounded between the two waves of the CFUWBES to a year-on-year decline of 28.2 percent, up from a 53.8 percent drop for wave 1. Whether due to greater ex ante resilience, better input and output market linkages, or more flexible labor markets, Georgian formal firms were better able to bounce back and rehire PFPS workers.<sup>44</sup>

> 44 This is despite the fact that Georgia’s overall closures were higher in the period between waves 1 and 2 than Jordan’s and overall stringency indices were similar (See Section 3.3).

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# Table 8

# INSTRUMENTAL VARIABLE ESTIMATES a/

# Dependent variable: Log P

||SITC 6|SITC 7|SITC 8|
|---|---|---|---|
|Intercept|4.056<br>(.958)|1.541<br>(.142)|3.701<br>(.856)|
|Investigation|-/.484<br>(.260)|-.075<br>(.034)|.191<br>(.088)|
|Dutyk/|.353<br>(.180)|-.012<br>(.017)|.030<br>(.038)|
|Log w*|.129<br>(.203)|.673<br>(.031)|.195<br>(.190)|
||.33|.66|.61|
|N|192|480|312|

a/ All equations include time and intercept dummies. ~/ Instruments include lagged changes in price and Log w*, lrelw, the lagged share of the sector in total imports, log w*, and time and industry dummies.
43 how far reform or program affects the overall UHC index. Van de Poel et al. (2014), for example, find that maternity care voucher schemes in Cambodia increased attended deliveries by about 10 percentage points and antenatal visits by around 3 percentage points. On the (we think, reasonable) assumption that the program had no effects on other indicators in our UHC index, and using plausible values for these other indicators, the voucher program might have increased Cambodia’s UHC score from 32 to 33. The index thus allows the impacts of specific programs to be seen in broader perspective. Incorporating the costs of reforms into the picture, one could also start to get at the cost-effectiveness of different reforms in terms of UHC. For example, in the example above, could other equally costly reforms in Cambodia have produced a larger increase in the UHC index?

There is a second approach that might prove fruitful in linking the UHC index to policy, namely to see what the correlates are at country level of high levels of and/or changes in the UHC index. For example, Wagstaff et al. (2015) apply the index to the countries of Latin America and find a correlation between the UHC index and the degree of integration of a country’s health system: the countries with fully or nearly fully integrated systems do better than the countries with semi-integrated systems, which, in turn, do better than the countries that have yet to start integrating their health systems. One would, of course, like to move beyond correlations; it may be, for example, that the countries of Latin America with the more integrated health systems also have relatively high per capita incomes. In principle, one could assemble a country-level panel dataset, and use to it to tease 43
# **3.2.2 How they spend their money**

Table 3 looks into the composition of household expenditure per month. The average total household expenditure per month is approximately BDT 8,700 (equivalent to US$109), of which about 59 percent is spent on food consumption. This is within the range found in Banerjee and Duflo (2007), which, based on a 13-country sample, states that food typically represents from 56 to 78 percent of consumption among rural poor households. About 35 percent of the food expenditure is on cereals, a food staple in Bangladesh, and only a small share is spent on quality protein such as meat and dairy products. About 85 percent of the sample households report that they are concerned with food shortages, and about 82 percent indeed experienced insufficient food intake over the last 12 months. The distribution of expenditure is similar when per capita consumption is considered.

On a monthly basis, households regularly use the following non-food items: fuel, beauty and hygiene products, transportation, and others. The expenditure on these items is about 13 percent of the total household expenditure. Lastly, the non-food annual spending category includes health care, education, housing, and leisure; this category makes up more than one-quarter of the total household expenditure, with housing representing over 30 percent of non-food annual expenditure. Because the majority of households own their house in rural Bangladesh despite the prevalence of poverty, housing expenditures mostly derive from maintenance and repair costs instead of renting expenses.<sup>14</sup>

> 14 This is related to the dismal living conditions. Most houses have tin roofs and mud floors, making them vulnerable to adverse weather conditions which the country is prone to. On average, houses are 300 square feet, and the average number of rooms is less than two for more than six household members. According to UN-Habitat (2006), a dwelling is considered to be insufficient living space if each room is shared by more than three individuals. Although about one-third of the surveyed households report that they have electricity services connected to the house, cooking typically relies on wood or straw burning, and the majority use pit or kancha toilette for sanitary facilities without running water or electricity.

12
Balkan countries, low-income Commonwealth of Independent States and middle-income Commonwealth of Independent States regions. The survey questionnaire was administered to a nationally representative sample of 1000 persons within each country. After collecting some basic household-level information, one person aged 18 years or older was selected at random within each household to answer detailed questions on education and labor, current activities, and attitudes and values. There is also a retrospective section where respondents answered some objective questions regarding their general live events, economic condition, nature of work and migration.

In this section we provide some descriptive evidence on the overall satisfaction with life for people in these transition countries. In figure 3.1, a fractional polynomial curve plots the share of each nationally representative population who agrees or strongly agrees with the statement “All things considered, I am satisfied with my life now” against the per capita GNI of the respective countries. The degree of fit in fractional polynomial projection is 0.48 as opposed to the linear projection of 0.1. This U shaped curve replicates other findings about the relationship between income and happiness (Easterlin, 1974).

# **[Figure 3.1 is about here]**

The LiTS questionnaire asks “What would be the minimum amount of money that this household would need to make ends meet at the end of each month?” We use this information as a proxy for household income level, a standard method in the literature (Blanchflower, et al., 2004; Luttmer, 2005). We replicate the above graph (figure 3.1) replacing per capita GNI with average monthly household consumption expenditure. This is shown as Figure 3.2, which yields a similar U-shaped relationship (with a 0.26 degree of fit) showing richer or poorer countries have higher share of people with life satisfaction. In both of these graphs we find countries from EU region on the north-east corner (rich countries) and countries from low-income Commonwealth of Independent 9
# Characterization of Ambient Air Quality in Selected Urban Areas in Uganda: A Low-Cost Approach<sup>1</sup>

Deo Okure<sup>2</sup> , Engineer Bainomugisha<sup>3</sup> , Nancy Lozano-Gracia<sup>4</sup> and Maria Edisa Soppelsa<sup>5</sup>

**_Key Words:_** Urban Air Pollution Uganda, Low-cost Air Quality Monitoring in Uganda, mobile air pollution monitoring, Spatiotemporal Variations.

**JEL** : Q01, Q53, R00,

> 1 This work is part of the Pollution Management and Environmental Health program (PMEH) supported under TF0A3415. The PMEH program focuses on providing technical assistance to these countries, facilitating pollution management, promoting public awareness, and helping them reduce the negative effects of pollution on health. 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.

> 2 AirQo, Makerere University - info@airqo.net

> 3 AirQo, Makerere University - info@airqo.net

> 4 World Bank - Urban, Disaster Risk Management, Resilience and Land Global Practice

> 5 World Bank - Urban, Disaster Risk Management, Resilience and Land Global Practice
Economic Parameters of Deforestation 28 ers the profit maximizing level of effort in managed forests; fef) the timber harvest as a function of the effort in managed forests; - _e(k,t)_ the profit function for managed forests is 7TQc,t) (eI'pQc,t) - eSw - cr.

Land owners maximize the present value of their land by choice of logging time, and effort in logging as well as forestry: subject to the condition max LPV > 0. If max LPV • 0, logging would not take place within finite time. The profit maximizing logging time, and levels of effort are functions of all parameters, determined by the following first order conditions: <!-- Start of picture text --> LI'V = 8P =, 124(t) }-rz'(tk) -txt'¶1 = 0<br>LPVs = -PkY= = e p(t=) '=_ -0<br><!-- End of picture text --> The only price change considered is a price level change (). Most of the results from the model with constant effort hold in this model as well i.e. an increase in the discount rate increases logging of unmanaged forests if the latter is sufficiently profitable, and an increase in the costs of either logging or managing forests increases the area of -zunmanaged forest. The deraton of the comparative stcs results is provided in the AppendiT and cntains tie following result for the margin of unMranaged forests and agriculture:
# Counting people exposed to, vulnerable to, or at high risk from climate shocks – A methodology

Doan, Miki Khanh r Ruth Hill r Stephane Hallegatte r Paul Corral r Ben Brunckhorst r Minh Nguyen r Samuel Freije-Rodriguez r Esther Naikal<sup>1</sup> Keywords: Risk, Exposure, Vulnerability, Resilience, Climate change, Development JEL: Q54, C81, D69

> 1 Author order randomized using the American Economics Association author randomization tool.
likely to hit profits. Finally, it is worth noting that the findings of the interaction term are similar in direction in both ‘low growth’ and ‘low income’ regions.

Table 8: Regressions on interaction of lagging region with business environment indicator on firm performance Panel A: Italy and Spain

||Sales<br>Growth|Employment<br>Growth|Investment<br>Rate|ROA|Productivity<br>Growth|
|---|---|---|---|---|---|
|SDB‐DTF (inv)|‐1.586***|‐0.851**|‐10.009**|‐0.162|‐0.017**|
||[‐2.737]|[‐2.469]|[‐2.407]|[‐0.467]|[‐2.091]|
|SDB‐DTF (inv) X Lagging|‐1.091|‐0.128|‐8.304|‐2.670***|0.004|
||[‐0.823]|[‐0.164]|[‐0.945]|[‐3.856]|[0.202]|
|Lagging|0.729|‐0.185|0.641|3.124***|‐0.013|
||[0.406]|[‐0.173]|[0.056]|[3.321]|[‐0.528]|
|SDB‐DTF (inv) + SDB‐DTF (inv) X Lagging|‐2.677|‐0.979|‐18.313|‐2.832|‐0.013|
|F test|5.58|2.19|6.35|25.78|0.65|
|P‐value|0.02|0.14|0.01|0.00|0.42|

Panel B: Poland and Romania

||Sales<br>Growth|Employment<br>Growth|Investment<br>Rate|ROA|Productivity<br>Growth|
|---|---|---|---|---|---|
|SDB‐DTF (inv)|‐1.294|0.117|2.478|‐1.682**|‐0.009|
||[‐1.567]|[0.663]|[0.471]|[‐2.107]|[‐0.596]|
|SDB‐DTF (inv) X Lagging|‐2.305|‐0.177|‐34.804***|‐3.595**|‐0.013|
||[‐1.121]|[‐0.213]|[‐3.082]|[‐2.197]|[‐0.421]|
|Lagging|2.913|0.427|36.936***|4.736**|0.015|
||[1.153]|[0.382]|[2.617]|[2.437]|[0.443]|
|SDB‐DTF (inv) + SDB‐DTF (inv) X Lagging|‐3.599|‐0.06|‐32.326|‐5.277|‐0.022|
|F test|2.93|0.01|7.54|9.77|0.47|
|P‐value|0.09|0.94|0.01|0.00|0.49|

_Note: This table reports the estimates of the firm performance model. SDB‐DTF (inv) is the SDB distance to frontier (inverse); Lagging is the lagging region dummy. SDB‐DTF (inv) X Lagging is the interacted variable. All models include fixed effects for: country, year, and sector; firm controls include: (log) age, (log) size, (log) total assets, assets per employee; and financial leverage. The F‐test and P‐value in the thre last line referest to the test of joint significance of SBD and SBD X lagging coefficients._

# 7. Conclusions and further research

This paper aims to provide an exploratory assessment of the relationship between the business environment and firm performance in European regions, with an emphasis on understanding if and how business environment constraints may be a factor holding back investment, employment, and growth in lagging regions. The analysis, exploiting unique firm‐level data from the _Orbis_ data set along with regional business environment data from the World Bank’s _Subnational Doing Business_ reports, provides strong support for the relationship between the business environment and several aspects of firm performance. The paper also shows that this relationship matters somewhat more for lagging regions, not only because the business environment is rather worse in these regions, but also because the impact of the business environment on firm performance is stronger.

17