rafmacalaba/datause-encoder-data huggingface dataset:
Although there is a rental market of sorts for irrigated land (paddy fields), the village model does not include a land market. When analyzing the village trade in land we found all four households groups to be net renters of land. This is due to a bias in the surveyed sample of households, which excludes households that have migrated from the village. These households are renting out land for a price below the productive value of the land. This difference can be interpreted as an insurance premium the households are willing to pay in order to maintain access to their land, which is collectively owned, in case they need to return to the village. The households remaining in the village thus get an indirect transfer of money from the migration of entire households, through having to pay less than the productive value for land rented in. Analysis of the migration of entire households is beyond the scope of our model. We therefore fix the supply of land at the level observed in the SAM, effectively removing the land market from the model. Taking again the example of the unlinked households with no draught power, the return earned on irrigated land endowment contributes 17.2 percent to its income, broken down into a shadow rent component of 12.4 percent and the above shadow rent component of 4.8 percent. This above shadow rental income results from renting-in land at a price below its marginal production value from migrant households. Non-irrigated land contributes another 7.3 percent to the household income.

A last remark on the village SAM pertains to the lack of data for modeling capital flows in the village. The SAM shows that the household group most involved in migration is a net supplier of capital to the other three groups of households. Although the survey contains some data on the conditions such funds are loaned, insufficient information is available to model a village-level capital market. We therefore assume that the household group lacking draught power but having an outside link spends a fixed share of its income on within-village shadow price for each household nontradable and each household group. These shadow prices are used in constructing the SAM and in calibrating the village equilibrium model.

15
positive patent applications at time t, to assess the intensive margin. To quantify the extensive margin, we replace _yij,t_ in equation (1) with a dummy variable that equals 1 if firm _i_ of sector _j_ filed a patent at time.<sup>21</sup> As only a limited number of firms applied for triadic patents after being sanctioned, we focus our analysis on total patents, high-tech patents and international patent applications.

Table A.8: Post-Sanction Innovation Performance among Sanctioned Firms

||(1)<br>**Paten**<br>**(Inten**|(2)<br>**t Applic**<br>**sive Ma**|(3)<br>**ation**<br>**rgin))**|(4)<br>**Pate**<br>**(Exte**|(5)<br>**nt Appli**<br>**nsive Ma**|(6)<br>**cation**<br>**rgin))**|
|---|---|---|---|---|---|---|
|Treatment_×_Post|-0.127***|-0.239|-0.176***|-0.025**|-0.050**|-0.040***|
||(0.027)|(0.147)|(0.059)|(0.011)|(0.023)|(0.008)|
|No. Obs|5,442|2,040|3,690|6,334|6,334|6,334|
|R-squared|0.653|0.516|0.610|0.242|0.487|0.287|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|Yes|Yes|Yes|

Note: We estimate the coefficient using OLS. Column (1) to (3) uses a set of firms with positive patent filing in each category. Robust standard errors clustered at the 2-digit ISIC level are in parentheses. ***, **, and * indicate significance at levels 1 percent, 5 percent, and 10 percent, respectively.

In the third robustness check, we focus only on firms that already had patents before 2013 and repeat our baseline regression 1 on this sample. Our analysis focuses only on firms that were sanctioned after 2013. The results from our benchmark regression may conflate the treatment effects on the number of firms that start patenting with the impact on the number of patent applications filed by these firms. Therefore, in this robustness check, we isolate and estimate the treatment effect solely on the number of patent applications filed by each firm.

> 21We use linear probability model to estimate the extensive margin in our regression, which includes firm fixed effects and sector-year fixed effect. Using non-linear regression models like logit or probit in this context could lead to incidental parameter problems. Moreover, our primary interest is the marginal effects of sanctions, and the LPM provides reliable estimates for these effects (Wooldridge 2010).

56
- 5 -

electricity demand. In practice, however, the price structure is often complex and reflects various policy considerations, such as affordability of the poor and promotion of energyintensive industries. Therefore, the impacts of raised tariff may not be straightforward. An increasing block tariff mechanism is, for instance, a common practice to encourage largevolume customers to use electricity wisely, while keeping the prices for the poor relatively low. However, this discriminatory pricing may adversely affect the households’ incentives to install a proper meter or falsify metering readings; people do not want to be applied to higher rates. Hence, the net impacts may remain debatable.

The current paper analyzes household data from the Albania’s Living Standards Measurement Studies (LSMS) for 2002–2005. Because there are multiple endogeneity issues, it applies the Nelson-Olsen (1978) two-stage estimator, which models a system of censored and non-censored equations. The remainder of the paper is organized as follows: Section II summarizes the theoretical and practical issues related to metering. Section III discusses the possible effects of climatic conditions on the electricity demand, in particular in the context of Albania. Section IV develops an empirical model and discusses various econometric issues to estimate the electricity demand. Section V describes our data, and Section VI presents the main estimation results. Section VII discusses robustness of the estimation results and some policy implications. Section VIII then concludes.

# **II. METERING IN THEORY AND PRACTICE**

Metering is an essential policy instrument to motivate customers to use electricity wisely. In theory, a simple model can show that people would overconsume electricity in the absence of metering. There is no incentive for overconsuming customers to install a meter, even if the cost of meter installation is small. By contrast, unmetered customers who are consuming less than the norm will have strong incentives to install a meter, if the installation cost is small enough. This self-selective process would end up with the overconsumption situation where unmetered customers would overconsume the utility services. One might think that overconsumption would not occur if the normative amount of consumption, which is applied
# **TABLE A9: Economist Intelligence Unit (EIU)**

## **A9: The Economist Intelligence Unit (EIU)**

http://www.eiu.com The Economist Intelligence Unit is a for-profit organization producing analysis and forecasts of the political, economic and business environment in more than 180 countries. The EIU was founded in 1949 and is based in London. In 1997, the EIU launched two quarterly publications which contain some governance measures: The Country Risk Service, and the Country Forecasts. The assessments in these publications are based on regular contributions from a global network of more than 500 information-gatherers. A panel of regional experts checks the accuracy, consistency and impartiality of these assessments. Our databases utilize data about the individual subcomponents of these country risk ratings, that were made available to us by EIU.

In the table below we list the variables included in each of the governance indicators. In this paper, we use data from 1997, 1998, 2000, and 2002-2005.

|**Table A9: Economist Intelligence Unit(120**|**developed and dev**|**elopin**|**g coun**|**tries)**||||
|---|---|---|---|---|---|---|---|
||2005|2004|2003|2002|2000|1998|1996|
|**Voice and Accountability**||||||||
|Orderly transfers<br>|X|X|X|X|X|X|X|
|Vested interests|X|X|X|X|..|..|X|
|Accountablity of Public Officials|X|X|X|X|..|..|X|
|Human Rights|X|X|X|X|..|..|X|
|Freedom of association|X|X|X|X|..|..|X|
|**Political Stability**||||||||
|Armed conflict|X|X|X|X|X|X|X|
|Violent demonstrations|X|X|X|X|..|..|X|
|Social Unrest|X|X|X|X|X|X|X|
|International tensions / terrorist threat|X|X|X|X|X|X|X|
|**Government Effectiveness**||||||||
|Quality of bureaucracy / institutional effectiveness|X|X|X|X|X|X|X|
|Excessive bureacucracy / red tape|X|X|X|X|X|X|X|
|Government policy (pro business stance)|..|..|..|..|X|X|..|
|**Regulatory Quality**||||||||
|Unfair competitive practices|X|X|X|X|..|..|X|
|Price controls|X|X|X|X|..|..|X|
|Discriminatory tariffs|X|X|X|X|..|..|X|
|Excessive protections|X|X|X|X|..|..|X|
|Discriminatory taxes|X|X|X|X|..|..|X|
|**Rule of Law**||||||||
|Violent crime|X|X|X|X|X|X|X|
|Organized crime|X|X|X|X|X|X|X|
|Fairness of judicial process|X|X|X|X|X|X|X|
|Enforceability of contracts|X|X|X|X|..|..|X|
|Speediness of judicial process|X|X|X|X|..|..|X|
|Confiscation/expropriation|X|X|X|X|..|..|X|
|Intellectual property rights protection|X|X|X|X|..|..|X|
|Private property protection|X|X|X|X|..|..|X|
|**Control of Corruption**||||||||
|Corruption among public officials|X|X|X|X|X|X|X|
|_Country coverage_|_120_|_120_|_115_|_115_|_115_|_115_|_115_|

55
Freeman, & Richard, (1995) “Are Your Wages Set in Beijing?” _The Journal of Economic Perspectives_ , _9_ (3) _,_ 15-32.

Huang, S. (2009). Migrants’ wage cut of 7% ~ 50% in Zhujiang River Delta, experts called for migrants upgrade to technicians. _First Financial Daily, 17 Feb.,_ A16. IMF. (2009). World Economic Outlook 2009: Crisis and Recovery.

Johnson, I., & Batson A. (2009). China's migrants see jobless ranks soar. _The Wall Street Journal_ , _3 Feb.,_ A1.

Kong, S. T., Meng, X., & Zhang, D. (2009). Impact of economic slowdown on migrant workers, Chapter 12 in R. Garnaut, L. Song & W. T. Woo (eds.), _China’s New Place in a World in Crisis Economic, Geopolitical and Environmental Dimensions._ Acton, A.C.T.: ANU E Press.

National Bureau of Statistics of China (NBSC). (2009). <mark>There are 225.42 million migrant workers at the end of 2008.</mark> _<mark>25 March.</mark>_ <mark>(A</mark> vailable at <u>http://www.stats.gov.cn/tjfx/fxbg/t20090325_402547406.htm)</u> Park, A., Cai, F., & Du, Y., (forthcoming). Can China Meet Her Employment Challenges? Chapter in J. Oi, X. Zhou, & S. Rozelle (eds.), _Growing Pains: A Study of China’s Growth and Development_ . Washington, DC: Brookings Press.

Sheng, L., Wang, R., & Yan, F. (2009). The impact of international financial crisis on the employment of rural migrant workers in China. _Chinese Rural Economy,_ (9), 4-14. Wang, D., Zhang, Z., Cheng, J., & Hou, H. (2009). The impact of the international financial crisis on transfer of rural labour force in China’s impoverishes areas. _Chinese Rural Economy,_ (9), 21-27.

Xiao, J. (2008). The biggest printing enterprise is on the verge of bankruptcy in Shaoxing. _City Express (Dushikuaibao), 9 Oct.,_ 11-14.

26
- and third, the use of the differences in attainment profiles as a diagnostic tool.

# _II) Enrollment and drop-out among the poor_

Average attainment can be decomposed into two parts: the fraction of children that ever enroll and, conditional on having enrolled, the grade at which children leave school. We do not distinguish between those who never enrolled and those who may have enrolled but did not complete even one year of schooling, and in the following discussion the two descriptions are used interchangeably.

- _A) Patterns of enrollment and dropout_

There are four patterns of enrollment and drop-out of the poor, which tend to follow regional patterns:

- low ever enrollment and high drop-out (Western/Central Africa)

- low ever enrollment and low drop-out (South Asia)

- high ever enrollment and early high drop-out (Latin America)

- high ever enrollment and late (East Africa) or very late drop-out (East Asia and

# Central Asia/North Africa/Europe).

Table 2 presents the proportion of 15 to 19 year olds from the poorest 40 percent who completed (at least) grade 1, grade 5, primary school, and grade 9. Because the lengths of the cycles (primary versus lower secondary --sometimes together called "basic"-- and upper secondary) differ across countries we show the results both for the comparable number of grades (grade 5) and for the comparable cycle (primary)." Grade 9 was chosen as the highest

> "Presently we are using the UNESCO reporting of the structure of primary and secondary, which may or may not reflect country realities and moreover, may not have been relevant to the situation a decade ago.

19
Bradley and Corwyn 2002; Burchinal et al. 2000; Duncan and Brooks-Gunn 1997; Evans 2004; Hart and Risley 1995; Hoff 2003; Walker et al. 2011; Yeung et al. 2002). We focused on two subdomains from the HOME that were found to be correlated with math and literacy scores among children 7–10 years of age in the reference population: the domain relating to fostering growth and education and the domain relating to the provision of active stimulation (Bradley and Corwyn 2005).<sup>5</sup> The adapted HOME scale was included in the model as an ordinal scale from 0–10, with one point per item (see appendix S2 for complete list of questions).

# III. EMPIRICAL METHODS

The empirical methods described below for the analyses are parametric in nature, and, as such, are correlational. We are careful throughout the analysis in claiming neither causality nor inference about structural parameters in our results.

_Magnitude and Evolution of the Socioeconomic Gradients_ The primary objective of our analysis is to document the magnitude of the association between child development outcomes and disparities in SES and to document its evolution over time. We have a multidimensional set of domains (indexed by �� with data on cognition (visual spatial processing, working memory, and reasoning), language, and executive function spanning the preschool years when children were 3–6 years old to the school years when children are seven and older. For disparities in SES, household wealth quintiles from when children were 0–3 years are used<sup>6</sup> .

In a parametric framework, ��� gradients are estimated by OLS using the following specification (Todd and Wolpin 2007): where ��� is the adjusted z-score (internally standardized) obtained for child i on the � developmental domain j at time t. �� is the magnitude of the gradient for _k_ -th quintile ���, expressed as the size of the difference in mean z-scores obtained by wealth quintile ��� relative to the bottom quintile ( _k_ =1, the excluded category). The error term includes unobserved child and household characteristics clustered at the village level, which is our primary sample unit. The base specification is performed on the pooled sample and includes demographics (age and gender) and the full set of wealth quintile dummies (and urban and regional dummies). The evolution of the gap over time is modeled by adding a full set of interactions between quintile indicators and age � dummies, thereby allowing �� to flexibly vary with age.

The robustness of the results is assessed by controlling for a large set of childhood and parental characteristics ���. First, the longitudinal dimension of the panel is exploited and lagged child

> 5. Items were adapted to what was appropriate for a mostly poor and rural Malagasy context based on input from a local clinical psychologist.

> 6. Results are robust to using wealth rankings using the contemporaneous wealth indicators. These results are not presented but are available upon request.
point occurs at 58 percent of urbanization. Finally, higher diesel prices is associated with lower per capita carbon emissions. The coefficients associated with gasoline prices are of the expected sign but are not statistically significant. Because gasoline prices are highly correlated with diesel prices, including the diesel price in the regression could absorb most of the explanatory power.

In the above analysis, we do not control for the existence of transportation policies aimed at reducing transport-related emissions. As a robustness check, we collect data from IEA transportation policy database and control for the existence of various transportation policies in the estimation. We use the principal component analysis method to create one comprehensive transport decarbonization policy variable that summarizes several transportation policies: fuel efficiency standards, tailpipe emissions regulations, driving restrictions, promotion of electric vehicles, promotion of biofuels, R&D and vehicle emission standards. Including transportation policy variable in the regression reduces the sample size by half due to limited data availability for many developing countries. The results are reported in Table 3. The existence of transportation policies has a strong impact, sharply decreasing per capita emissions. The coefficients of the other variables have the expected sign, but the statistical significance reduces due to the smaller sample.

# **6. Conclusions**

Emissions from the transportation sector – which includes travel of people and goods by road, rail, aircraft, and marine vessels; and the pipeline transportation of many fuels – represented more than 24 percent of global CO2 emissions in 2018. Worldwide, emissions from transportation are increasing faster than those from any other sector. Decarbonization of the transport sector therefore plays a crucial role in achieving the Paris Agreement target of limiting global temperature rise to 2 degrees Celsius – and preferably to below 1.5 degree Celsius – compared to pre-industrial levels.

This paper examines the status of the decoupling of transport-generated carbon emissions and economic growth of 145 countries. In addition, it studies the patterns and drivers of transport-related CO2 emissions of 145 countries during the period from 1990 to 2018. We observe considerable differences in the decoupling status of countries according to their income levels. Most high-income countries are in the relative decoupling states while most of the countries with low incomes are in no decoupling states. This implies that there is considerable room for breaking the linkage between economic growth and carbon emissions in low- and middle-income countries.

15
the Institute of Epidemiology, Disease Control and Research and the Directorate General of Health Services websites, respectively.

# **4. Stylized Facts**

# _4.1 Electricity Consumption over Time_

**Figure 1:** Electricity Consumption over Time

- a. Monthly, July 1993 to June 2013                b. Daily, February 2010 to October 2021

<!-- Start of picture text --> Mega kilo-watt hours Megawatts<br>15000<br>4000<br>3000<br>10000<br>2000<br>5000<br>1000<br>0 0<br><!-- End of picture text --> Figure 1, panel a, shows the monthly electricity consumption in Bangladesh from July 1993 until June 2013, and panel b shows the daily electricity consumption from February 2010 until October 2021. A few things are clearly noticeable from the figure: first, electricity consumption is increasing over time; second, there is seasonality in the data; and third, electricity consumption varies strongly from day to day. Part of the daily variation may be explained by measurement or reporting issues and may not actually reflect variation in electricity consumption. Electricity consumption can hence be at best a noisy measure of economic activity. The variation is much larger in some divisions compared with others. For example, it tends to be lowest in Dhaka and highest in Rajshahi.

Map 1 shows the total electricity consumption per capita in the eight divisions before the COVID-19 pandemic in 2019. There is strong variation in electricity consumption per capita across Bangladesh. Dhaka has the largest per capita consumption, followed by Khulna, Chattogram, and Mymensingh. These divisions are more developed than the others, showing that the level of development is positively correlated with electricity consumption per capita.

5
**N** umber of observations 148 19 148

_Note_ :<sup>1</sup> Used for re‐randomization;<sup>2</sup> used for stratification.

_Source:_ Household data set 2011.

If we look at the small group of non‐compliers, who declared their kit to be sold, lost or stolen, we see that they are generally poorer than complying households: They have more children, own less land, have less cows and goats, and have less radios and cell phones.

**Table 3. Balance of Outcome Related Characteristic between Treatment and Control Group (Baseline Values)**

||Tre|atment<br>‐|Control|_t_‐test/chi‐2‐<br>test<br>|
|---|---|---|---|---|
||total<br>(SD)|non<br>compliant<br>(SD)|total<br>(SD)|(total treated<br>vs. control<br>_p_‐values)|
|Lighting hours, categorized (%)<sup>2</sup>|||||
|No lamps or candles|19|26|19||
|Less or equal 3h/day|51|42|51||
|More than 3h/day|30|32|30|1.000|
|Lighting hours per day, continuous<sup>1</sup>|3.1|2.7|3.2|.910|
|Usage of hand‐crafted LED<sup>1</sup>(%)|37|26|35|.628|
|Usage of mobile LED<sup>1</sup>(%)|4|5|3|.520|
|Consumption of candles<sup>1</sup>(pieces per month)|1.25|2.32|1.76|.356|
|Usage of wick lamps (%)|49|47|47|.727|
|Usage of no artificial lighting (%)|12|16|11|.715|
|Consumption of kerosene for lighting<sup>1</sup>(in liter per month)|.46|.35|.54|.372|
|Radio ownership<sup>2</sup>(%)|64|32|64|1.000|
|Mobile phone ownership<sup>2</sup>(%)|36|32|36|1.000|
|Number of mobilephones<sup>1</sup>|.49|.21|.47|.876|
|**N**umber of observations|148|19|148||

_Note:_<sup>1</sup> used for re‐randomization;<sup>2</sup> used for stratification.

_Source:_ Household data set 2011.
compare these predictions against poverty rates calculated directly from the census sample. Our goal is to examine the accuracy of the poverty estimates and to assess whether the confidence intervals produced by the ELL method are correct. We also explore in some depth how well, and under what conditions, the income regression model – estimated at the state level – performs in capturing spatial correlation among small areas.

The paper is organized as follows. Section 2 summarizes the ELL methodology. Section 3 describes the data. Section 4 presents a set of descriptive statistics. Section 5 discusses the validation exercise and its results and section 6 concludes.

# **II. The ELL Method**

The ELL approach analyzes household survey data to impute consumption/income into the population census in order to generate small area welfare measures. ELL (2002, 2003), and Demombynes et al (2007) describe the methodology in detail while Tarozzi and Deaton (2007) provide a useful discussion of the method’s underlying assumptions. The basic idea is straightforward. We estimate welfare measures based on a household per-capita measure of income or consumption expenditure, _yi._ A model of _yi_ is estimated using household survey data, with the set of explanatory variables restricted to those that are also found in, and strictly comparable to, the population census.

We regress the logarithm of _yi_ on a set of household-level demographic, occupational and educational variables, as well as variables at the enumeration area level or some other level of aggregation above the household calculated on the basis of unit record census data (or drawn from some ancillary database): ln _yi_ = **x** _i_ β + _ui_ , (1) where β is a vector of _k_ parameters and _ui_ is a disturbance term satisfying _E[ui|xi] = 0_ .

5