rafmacalaba/datause-encoder-data huggingface dataset:
(2006) find that cost/risk of production disruption, other priorities not related to energy consumption, information search costs related to energy efficient appliances/devices, higher opportunity costs of investment, lack of sub-metering and split incentives with energy service companies all are barriers to adopt increased energy efficiency. Sardianou (2008) investigates the determinants of industrial decision-making with respect to energy efficiency investments in Greece through a survey of 779 industrial firms around 2005 followed by an empirical analysis using a Probit model. A majority (62%) of firms surveyed reported they did not consider energy saving a first priority although 52% of the sample reported energy saving as a decision criterion when installing new machines or buildings. Fifty-six percent of the sample reported that they would develop an energy conservation policy if a competitor industry had implemented relevant actions; while 70% of firms indicated they were not aware of existing new technologies. Based on a sample of 2,848 German commercial and services sector firms that were surveyed during the 1990s, Schleich (2009) econometrically assesses the relevance of various types of barriers to energy efficiency at the sectoral level and across fifteen sub-sectors. The analysis suggests the lack of information and priority-setting of upper management, who often do not consider energy efficiency as a strategic priority as the main barrier to energy efficiency improvement.

Historically, energy consumption has remained inefficient in Ukraine due to ageing infrastructure and prolonged consumption subsidies (Ogaranko and Hubacek, 2013) These developments have strengthened calls for Ukraine to increase its clean energy base and improve its energy efficiency. The government has indeed enacted several policies to promote the adoption of clean and energy-efficient technologies (Trypolska, 2012) – however, the adoption of energy-efficient technologies remains slow. While barriers to clean energy adoption and options to improve investment have been examined by OECD (2012), to our knowledge, the same analysis has not been done for energy efficiency despite its strategic importance. It is therefore important to investigate the barriers to adoption of energy efficient technologies in the country.

This study aims to empirically examine the energy efficiency barriers in Ukraine in the commercial and industrial establishments. Specifically, the study attempts to examine key questions related to energy efficiency barriers including: 3
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Following the definition of the shock period, we move on to explain what is meant by “exposure.” While widely accepted that children under the age of 2 are greatly influenced by undernutrition, less attention has been focused on nutritional shocks to intrauterine fetuses. Barker (1997) clarifies the link between intrauterine undernutrition and negative health outcomes. He specifically focuses on undernutrition during pregnancy, suggesting that maternal health during the fetal stage strongly influences the growth and development potential for children later in life. Shrimpton et al. (2001) mainly emphasized the influence of negative intrauterine nutritional statuses on weight outcomes. More recent research has also demonstrated the strong negative effects of fetal undernutrition on children’s height measurements (Victora et al., 2010).

Nutritional studies have identified an early development window, from the point of conception to around the child’s second birthday, which is the primary determinant of children’s nutritional status (Victora et al., 2008). Our intrauterine period is defined as 8 months before the child was born, and the early development window as the child’s intrauterine period plus his/her first two years of life. We use the early development window concept to differentiate children exposed to the shock versus those who were not exposed to the shock during this critical stage of growth. This means that at the start of the shock period in August 2001, children born in September 1999 had one month of exposure to the shock within their early development window. Since the shock lasted until March 2003, children had up to 19 potential months of exposure to the shock within their early development window. As the maize prices receded to more typical levels after March 2003, children born between March and October 2003 only experienced the shock intrauterinally, while those born after October 2003 were not exposed to it.<sup>22</sup> Given what is meant by the shock period and the child exposure to the shock, our identification strategy identifies the causal effect of household tobacco adoption on child stunting by estimating a TSLS regression using only the sample children that were not exposed to the shock during their early development window, and differencing out the resulting coefficient of interest from the coefficient that is obtained from a TSLS regression using only the sample children that were exposed to the shock during their early development window.<sup>23</sup> The common unobserved factors

> policies to avert similar crises in the future.

> 22The estimation results are robust to longer and shorter definitions of the price shock group. For a more detailed look at the price trends in Malawian staples food in general, and maize in particular see Minot (2010). 23We later test the robustness of our results by separating the shock exposed group into (i) children with a year or less of exposure and (ii) children with more than 1 year of exposure.

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_ANNEX 5 ESTIMATING THE FINANCIAL IMPLICATIONS OF TEACHER OVERSUPPLY_ The teacher oversupply issue in Indonesia presents several forms of inefficiency, one of them being the oversupply of teachers. Addressing the supply issue could result on significant potential savings. The table below shows an estimate of the potential savings. The calculations are based on the following method and assumptions:

- Only public school teachers are included, since this is the area government is financing and has control over.  Student data used in the calculations is also only for public schools.

- Because 22 percent of public junior secondary teachers and 25 percent of public senior secondary teachers are part-time, an adjustment is made so that two part-time teachers is equivalent to be one full-time teacher. (The number of part-time teachers in private schools is much higher, at 55 percent and 63 percent for junior and senior respectively).

- It is assumed that the average teacher salary is 17 million per year for primary teachers and 18 million for junior and senior secondary teachers.

- The proposed policy was applied on a school-by-school basis on a survey sample so that a realistic estimate of the number of required teachers and level of teacher oversupply by school is determined. The resulting figures were then applied nationally, with weighting applied to schools by size.

- The effective STR is used in the calculations. There is a difference between proposed STR and effective STR. For example, the primary STR used is 30:1, but when applying the formula of each school getting at least 4 teachers for each school and a target STR of 30:1, the effective STR is actually 26:1.  This is because (1) a school with, say, 40 students, will still get 4 teachers and have an STR of 10:1 and (2) the additional teacher allocation is rounded up, so a school with, say, 160 students will get 6 teachers, for an STR of 27:1.

The allocation formula for Junior Secondary and Senior Secondary teachers is complex because it currently requires that teachers be assigned to classes for specific subjects. For the purposes of analysis, an STR is applied with what would be more in line with other countries than Indonesia’s currently low STRs of 14:1 for Junior Secondary and 12:1 for Senior Secondary.

In the following table, column A reports the actual supply of teachers whereas column B shows what the STR could be if the proposed new entitlement formula is used instead. In the latter scenario, 22.8 percent fewer teachers are required (or 19.4 percent taking part-time into account). This would amount to a salary savings of approximately 6.7 trillion Rupiah. Taking part-time teachers into account (assuming 2 part-time = 1 full-time), the amount is reduced to 5.6 trillion Rupiah, which is still approximately 10 percent of the total education budget. This represents significant potential savings and would become even more significant with the impact of the new teacher law, where teachers who become certified will receive an allowance equivalent to their base salary (see the Teacher Salaries, Incentives, and Education Quality section four).

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The prices received for cotton oil give a different picture, however. SN‐ CITEC received CFAf 13,400 per jc and 10,850 per jc during 2007/08 and 2008/09 while the smaller companies received CFAf 12,200 per jc and 10,500 per jc during these two seasons, implying premiums of only 10 percent and 3 percent.<sup>**25**</sup> One would have expected SN‐CITEC to command an even larger premium for oil, given its advantages mentioned above as well as the high quality oil it produces.

There are two mutually reinforcing explanations for the company’s low oil premium. First, quality is not adequately priced by the consumers and, second, the edible oil market is highly integrated in Burkina Faso, in the sense that im‐ ported oils dictate the price (in the same way that the edible oil markets are inte‐ grated at the world market level, as the discussion in the main text established). This has important implications because it shoes that large cotton seed processing operations using advanced technology, while efficient from a tech‐ nological perspective, may not be economically profitable in the SSA context.

Unlike in Uganda and Tanzania, most companies in Burkina Faso did not report electricity problems (in fact, 18 out of 21 managers said that they have not experienced any electricity disruption). Most respondents strongly favored the imposition of high tariffs on imported edible oils as well as export restrictions on oilseeds. The interest rate paid by the companies averaged 13 percent, broadly in line with Tanzania’s rates and lower than Uganda’s. Lastly, when managers were asked about cotton oil’s potential for biofuel use, some responded that they were not aware of such potential, and those that were aware expressed doubts for the profitability of cotton oil as a biofuel feedstock.

Half of the respondents said that the cotton oil they produce is consumed domestically while the remainder said that some of their oil is exported to neigh‐ boring countries, including Mali, Niger, and Senegal. About 12 percent of SN‐ CITEC’s cotton oil is exported, primarily within the region. Burkina Faso’s trade policies on cotton by‐products are less restrictive than Benin’s; in 2005/06 it banned exports of cottonseed, and it also imposed an 18 percent value added tax.

Most of Burkina Faso’s cottonseed is consumed domestically by the seed processing industry. An estimated 1 percent is used unprocessed as animal feed. There have been reports that an export ban on cottonseed was introduced in 2005/06. However, statistics indicate that during this season Burkina Faso ex‐ ported an estimated 81,643 tons, equivalent to 20 percent of the country’s cotton‐ seed output, while similar level of exports were recorded for the subsequent two seasons (Bayoulou 2010). On the import side, it appears that some cottonseed is

> **25** In table D2, SN‐CITEC’s price is reported in tons; it has been converted to 20‐liter container by dividing by 55. A similar conversion was made in table D3 for the smaller companies.

**_Page 54_**
# **3. Data**

This paper uses Pakistan Integrated Household Survey (PIHS) 1998/1999, PIHS2001/2002, and PIHS2005/20006<sup>3</sup> for data analysis. The Pakistan PIHS data include different modules such as household demographic information, household assets module, employment and schooling. The dataset also includes a detailed consumption module, which has been used to examine the relationship between wealth and child activity decisions.

This paper defines child labor as a child who is aged between 10 and 14, and employed. Although different organizations have used different age limits to define child labor, in general, the minimum age of child employment is considered to be 15 years. This is largely based on the minimum age of completion of compulsory schooling. The lower bound of 10 years is used because surveys only ask household members aged 10 years old or above about their employment activities. The definition of “work” is based on the question: “Did (the person) do any work for pay, profit or family gain during last month at least for one hour on any day?” If the household member answers “yes”, he/she is considered to be employed.

Community is defined as the primary sampling units (PSU) provided in the data. Since households are randomly selected within each PSU from lower income, middle income and high income groups, the average at the community level is considered to be a good representative of prevailing conditions in the community.

# **4. Empirical Method**

The empirical estimation is based on the reduced form of equation 1. The empirical methods applied have three notable features.

> 3 A similar survey was also conducted in 2004/2005 but some critical questions in the labor module in 2004/2005 are different from the surveys used in the paper. Therefore, the study can not use PIHS2004/2005.
stronger for MOST than for MAX, (iii) accounts for more than 30% of the differences in productivity between establishments, and (iv) for 50% between agricultural establishments, and (v) the association between establishment productivity and technology sophistication is not weaker in low- than in high-income countries.

We plan to build on the methodological and empirical contributions of this paper in various directions. First, we intend to extend FAT to collect data in more countries and also to create grids for tasks in new sectors that allow us to provide a detailed technological account of more establishments.

Second, the distinct nature of MAX and MOST documented in this paper deserves scrutiny. On the theoretical side, we plan to develop frameworks that connect productivity to the range of technologies used but also to how intensively they are used and that rationalize the observed gap between MAX and MOST. On the empirical front, it seems of first-order importance to assess the relevance of the possible explanations for the gap.

A separate issue that we have overlooked in this paper is the aggregation of technology sophistication across business functions into an establishment-level sophistication index. In this paper, we have taken the reasonable shortcut for a descriptive exercise of constructing establishment-level measures of technology sophistication as the simple average of the function-level sophistication measures. However, developing a theory that rationalizes the variation between the functions of an establishment in technology sophistication and that provides a foundation for the construction of measures of technology sophistication at the establishment level would be a major development.

In this paper, we have intentionally avoided studying the range of business functions conducted in establishments (i.e. the horizontal dimension of the grid). This topic, along with developing models that rationalize the relationship between technology sophistication and the limits of the establishment and its specialization from a task perspective, warrants separate attention.

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# **Appendix A. Results by income quintiles**

_Figure A1: Mean change in market vs. disposable incomes by income groups (in percent)_ <!-- Start of picture text --> AT BE BG CY CZ<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>DE DK EE EL ES<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>FI FR HR HU IE<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>IT LT LU LV MT<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>NL PL PT RO SE<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>SI SK<br>1 2 3 4 5 1 2 3 4 5<br>Market (Scenario A vs. D) Disposable (Scenario C vs. D) Disposable (Scenario A vs. D)<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br><!-- End of picture text --> Note: Income groups defined based on Scenario D. Source: Own calculations based on EUROMOD I4.0+.

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<!-- Start of picture text -->
[|<br>NN _|<br>|<br>im<br>— — —— — —e—<br>35%<br>30%<br>25% TM 2001<br>20% = 2006 —_<br>15%<br>10%<br>5%<br>0%<br>ao as _— i] —) — oO<br>pa foe a Q vo wv o<br>&£ &5s sR BoB 5 sg<br>c ome) ae me = n c 2<br>° et v 2. = = oO<br>=oa Sm- @ get au 8= fo) ==ow<br>ef £° £5 3 @ 25<br>8 S& 22 2 #4 3<br>& “a z<br>8 =<br><!-- End of picture text -->
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**Figure 11: IDEB Public Secondary School Score in Bahia, Ceará and Pernambuco (0-10), 2005-2013**

**Figure 12: IDEB for Public Secondary School Score in Minas Gerais and Rio de Janeiro (0-10), 2005-2013**

<!-- Start of picture text --> PE: PE: Performance Bonus Schools MG: Performance MG:– RJ: Performance Bonus<br>PPT Agreements 2 stage<br>1 stage<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>Year Year<br>Bahia Ceará Minas Gerais Rio de Janeiro<br>Northeast Pernambuco Southeast<br>4 4<br>3.5 3.5<br>3 3<br>IDEB Secondary School IDEB Secondary School<br>2.5 2.5<br><!-- End of picture text --> _Source_ : INEP 2013.

_Source_ : INEP 2013.

In the northeast, Pernambuco shows improvements in the IDEB scores since 2009. These gains differ from the flat trend for the Northeast region. Several states in the region recorded no change or even reductions in their scores, as in the case of Bahia and Ceará.

In the southeast, Minas Gerais showed steady improvement in the IDEB grade for secondary education between 2005 and 2011 but retroceded in 2013. The introduction of performance agreements is associated with the implementation of school-level performance agreements. While the state remains above the regional average, the gap is rapidly closing because Rio de Janeiro and Espiritu Santo recorded large improvements.

Similarly, in the case of Rio de Janeiro, there is a clear discontinuity that is concomitant to the introduction of the results agreements and bonus system for school employees in 2010. After showing no change in three rounds of IDEB, the state recorded a sizable increase of 14 percent in 2011 and 12.5 percent in 2013. As a result, Rio de Janeiro rose from the 23<sup>rd</sup> position in the national ranking of IDEB 2009 to 5<sup>th</sup> place in 2013. The state achieved one of Brazil’s most remarkable turn-arounds in the education sector.

# _Before-and-After Comparison of Outcomes_

The comparison of the average measures corresponding to the periods of time before and after the introduction of performance agreements and variable pay shows that in several cases there are significant differences in outcomes after the implementation of the intervention (Table 3). The before-and-after design is a quasi-experimental model that compares two groups of observations, before and after time points. The state-years preceding the program are averaged to estimate the counterfactual and the later ones to measure results.

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