rafmacalaba/datause-encoder-data huggingface dataset:
Earlier, we discussed the productivity effects of green/clean investment based on ex-post studies that consider firms from multiple sectors. In this section, we dive deep into green/clean investment effects on production costs in specific sectors, such as manufacturing, power, buildings, and transportation. The indicators used for productivity here differ across sectors and they are not necessarily the TFP considered earlier. For the manufacturing sectors, we still use the TFP as the measure of productivity. Where TFP data is not available or not relevant, we use the cost of energy services because manufacturing sectors, particularly the energy-intensive ones, invest in energy-efficient technologies to reduce their production costs. Private firms invest in energy-efficient devices or processes if the investment is profitable (or it saves overall production costs). Therefore, a reduction in energy costs can be interpreted as a reduction in production costs. For the electricity sector, we look at whether an expansion of renewables sources of electricity generation (hydro, solar, wind) reduces the overall costs of electricity supply from a grid. In the transportation sector, the indicator is cost reduction for transportation services due to clean or alternative vehicles. For the buildings sector, for example, we look at whether the EE measures reduce households’ or businesses’ utility (energy or electricity) bills. However, studies examining the impacts of the adoption of clean energy technologies are limited in the agriculture sector.

# **4.1 Manufacturing sector (excluding transportation and electricity generation sectors)**

Energy is an important input to manufacturing industries. Costs of fuels and electricity constitute a major share in the total cost of production in energy-intensive industries, such as iron and steel, aluminum, cement and glass, pulp and paper, chemicals, and fertilizers. These industries often invest in energy-saving technologies to reduce their energy bills. Some countries have regulations that mandate improvements in EE in industrial establishments. In such a case, Porter’s hypothesis would hold if the investments in EE improvements reduce overall production costs or improve productivity.

Several recent studies provide empirical evidence to support the positive relationship between improvements in EE and productivity (Li et al., 2021a; Huang and Wu, 2021; Haider & Bhat, 2020; Filippini et al., 2020; Unver & Kara, 2019; Arriola-Medellín et al., 2019; Zuberi & Patel, 2019; Xylia et al. 2017; Morrow III et al. 2013; Hasanbeigi et al. 2012). Using detailed firm17
# Box 2: Labor Market Signals **for** Education In Indonesia

The Indonesian economy experienced sharp growth in the 1980s, Manufactured exports underpinned this growth, primarily in low-skill, labor-intensive textiles, garments, footwear, lumber and wood products, and processed foods and agricultural products. The opening of the economy to trade expanded employment opportunities for unskilled and semi-skilled workers, particularly women. The monitoring of labor market signals shows a changing balance of demand for skills by level of education, a change that is not fully captured by manpower requirements forecasts. The labor market signals used include social rates of return, unemployment for 26 to 35 year olds, and median job search time, each monitored by level of schooling primary through university.

|Level of Education||Market Signal<br>for 1988|s|Manpower<br>Requirements|
|---|---|---|---|---|
||Real<br>Rate of<br>Return|Unemp. Age<br> <br>26-35<br>|Median<br>Job Search<br>Time|Repelita V<br>for 1989-93<br>(000's)|
|Primary and under|13%|.7%|1.0 mon.|-5,217|
|Jr. Secondary|13|2.6|1.2|-289|
|Sr. Secondary Gen.|13|3.7|2.0|+779|
|Sr. Secondary Voc.|10|2.0|2.0|+491|
|Academy<br>|12|6.5|2.0|+50|
|University|6|7.8|2.0|+457|

The observation of trends in the real rates of return from 1982 to 1988, not pictured here, reveal a sharp drop in the returns to a university education from approximately 11 to 6 percent. The returns to other levels of schooling also fell, but remained above the average return to physical capital of 9.1 percent. Unemployment rates for university graduates rose during the decade reinforcing the trend observed in the returns to a university education. The reduction of public sector employment as the economy adjusted during the decade to the decline of oil revenues reduced employment opportunities for many university graduates.

The forecast of manpower requirements for the 5-year planning period of Repelita V projects a large shortage of primary graduates, a smaller shortage of junior secondary graduates, and a surplus of graduates above this level, particularly among senior general secondary graduates. Labor market signalling, focused on real rates of return, provides a different picture of emerging skills demand in Indonesia. It calls for a more balanced allocation of spending on primary, junior secondary, and senior general secondary education than does the manpower requirements forecast, which fails to anticipate the strong demand for general secondary graduates. Both approaches provide support for a reduction of spending on higher education.

Source: W.W. McMahon, Boediono, and Abas Gozali, "Market Signals and Labor Market Analysis: A New View of Manpower Supplies and Demands in Indonesia, Table 10, (mimeo).

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other than with their current client/ employer. The experience of having previously changed jobs, including potentially involuntarily, may perhaps have persuaded workers about the value of having unemployment insurance in the event of a job loss more than those who have not yet experienced employer/ client mobility.

Table. 3a. Likelihood of Choosing Job A with alternate unemployment insurance coverage offers/ earnings, by Income level

|||Income<br>||
|---|---|---|---|
||Low Income|Med Income|High Income|
|Payment of RM 800 per month for 6 months, 0.5% lower income|0.205***|0.172***|0.205***|
||[0.0433]|[0.0394]|[0.0460]|
|Payment of RM 800 per month for 6 months, 0.2% lower income|0.151***|0.143***|0.198***|
||[0.0389]|[0.0401]|[0.0399]|
|Payment of RM 1200 per month for 6 months, 0.5% lower income|0.156***|0.105**|0.174***|
||[0.0416]|[0.0332]|[0.0397]|
|Payment of RM 1200 per month for 6 months, 0.2% lower income|0.109***|0.187***|0.247***|
||[0.0326]|[0.0411]|[0.0494]|
|N|343|347|348|

_Standard errors in brackets * p<0.05, ** p<0.01, *** p<0.001_ Table 3b. Likelihood of Choosing Job A with alternate unemployment insurance coverage offers/ earnings, by Access to Retirement Savings Income

||EPF Co<br>|verage<br>|Pvt. Retirem<br>|ent Savings<br>|
|---|---|---|---|---|
||No|Yes|No|Yes|
|Payment of RM 800 per month for 6|0.174<sup>***</sup>|0.250<sup>***</sup>|0.204<sup>***</sup>|0.179<sup>***</sup>|
|months, 0.5% lower income|(0.0272)|(0.0545)|(0.0339)|(0.0356)|
|Payment of RM 800 per month for 6|0.132<sup>***</sup>|0.244<sup>***</sup>|0.143<sup>***</sup>|0.191<sup>***</sup>|
|months, 0.2% lower income|(0.0251)|(0.0478)|(0.0304)|(0.0345)|
|Payment of RM 1200 per month for 6|0.085<sup>***</sup>|0.318<sup>***</sup>|0.099<sup>***</sup>|0.202<sup>***</sup>|
|months, 0.5% lower income|(0.0203)|(0.0578)|(0.0253)|(0.0378)|
|Payment of RM 1200 per month for 6|0.138<sup>***</sup>|0.297<sup>***</sup>|0.137<sup>***</sup>|0.242<sup>***</sup>|
|months, 0.2% lower income|(0.0247)|(0.0575)|(0.0272)|(0.0433)|
|N|762|276|577|461|

12
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where is binary being 1 when > 1 and 0 otherwise, the average proximity of j (see equation 6), jj,jj,tt+TT _t_ and _t+T_ indicate begin and end year of the period and jj,jj,tt+1 **Z** is a vector of control xx AASS ωω variables (discussed below).<sup>10</sup> The regression only considers activities where the country initially does not have a comparative advantage in (i.e. < 1). The _β2_ coefficient is expected to be positive as a higher initial AS level for an activity is likely to increase the possibility that it AASSjj,jj,tt develops into a new specialization, that is, that it passes the threshold of AS=1. The main coefficient of interest is _β1._ If it is positive, it indicates that a new activity which is more proximate to the current specialization basket of a country has a higher likelihood of developing into a new specialization.

# _4.3 Empirical results_

The results of the baseline regression are reported in Table 3, with initial specialization and average proximity lagged with five years.<sup>11</sup> Country-year and industry-year dummies were added to control for any time-varying country or industry characteristics. In the baseline regression, a positive and significant coefficient on average proximity is found (column 1). A one standard deviation increase in average proximity of a new activity increases the probability of specializing in this activity by 6.1 percentage points in the full sample of economies. This positive and significant relation is found for both advanced and developing economies, with a stronger relation for the latter group (3.3 and 6.3 percentage points respectively, see columns 2 and 3).

> 10 See Hausmann and Klinger (2007) for a comparable regression setup.

> 11 Note that the period is 7 years for the initial period 2000-2007 and 5 years for the periods after using overlapping periods 2007-2012, 2008-2013, ..., 2013-2018. This is because ADB MRIOTs are not available for the years 2001-2006. Robustness of the results to alternative lags and non-overlapping periods is considered below.

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Analyses of Bolsa Escola in Brazil and PROGRESA in Mexico found that UCTs would have little, if any, impact on school enrollment – implying that almost all of the impacts of these programs were due to the schooling condition (Bourguignon, Ferreira, and Leite 2003; Todd and Wolpin 2006). In the Malawi experiment, on the other hand, we find a modest impact on enrollment in the UCT arm, and this effect is 43% as large as the CCT arm. Our study thus confirms that conditions attached to cash transfer programs are effective in increasing enrollment, but the size of this effect is likely to be smaller than suggested by earlier studies, at least for poorer countries like Malawi.

Not only is school enrollment significantly improved in the CCT arm over the UCT arm, but the evidence presented shows that CCTs are more cost-effective in raising enrollment than UCTs in this context. To achieve the same enrollment gain obtained from a $5/month total transfer in the CCT arm, a transfer of more than $10 to the parents in the UCT arm is needed. This difference is much larger than the additional cost of administering a CCT program – possibly by an order of magnitude.<sup>48</sup> Furthermore, the average number of payments in the CCT arm was approximately 14.1 (out of a possible total of 20 over two years), compared with 17.9 in the UCT arm, implying that the actual amount of transfers made per person was 19% lower in the CCT arm over the two-year intervention. Savings of this magnitude due to non-compliance with the schooling condition would more than make up for the additional administrative cost of monitoring in most programs.

While CCTs were more cost-effective than UCTs in increasing school enrollment and attendance, they had no effect on reducing the likelihood of teenage pregnancies or marriages. The offer of a CCT appears to have been ineffective in dissuading those with a high propensity to drop out of school from getting married and starting childbearing, especially among girls sixteen or older

> 48 World Bank (2009) cites Grosh et al. (2008) to report that the median administrative cost is 8% of total program costs in ten CCT programs for which administrative cost data were compiled by that study. Given that monitoring compliance with the schooling condition is only part of the administrative costs, the additional cost would be a few cents on the dollar. However, monitoring and enforcement of conditionality in many of these programs may not have been as thorough as they were in our experiment, which could reduce the relative effectiveness of CCTs compared with UCTs.

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8

# **3. A Survey of Empirical Cost Frontiers in the Water Sector**<sup>**5**</sup>

As suggested in section 2, in practice, the costs of regulated public utilities depend on a variety of factors in addition to output levels and input prices. This section reviews what earlier studies reveal about the relative importance of these additional factors The first published paper is by Stewart (1993). In a report prepared for the UK water regulator, OFWAT, he estimated a cost function for the UK water sector. Stewart describes the three stages of the water business: water production (i.e. extraction from natural sources), water treatment and water distribution. Costs reflect the costs associated with each one of these stages. In general, these costs can in turn be decomposed into three types themselves: operation, maintenance and the return to capital. Stewart focuses on operational costs only. As explanatory variables, he considers: the size of the distribution network, the volume of water sold (these are in fact his two main variables), the volume of water put through the distribution network, the number of properties rented, the volume of water sold to non-residential users. In addition, Stewart considers also some control variables. This requires the identification of the various stages of the production of water services: production, various treatments, distribution and collection. Each may involve different types of treatment, distribution and collection's technology, which have to be modelled somehow since they have consequences for cost levels. For instance, treatment costs will differ with the sources of raw water. "Raw" water can be extracted from two sources: underground and surface sources. It is important to distinguish because often ground water (also not always) requires less treatment than surface water and these influents of. In addition, Stewart considers as control

> _5_ From a methodological viewpoints some of the empirical studies done in the UK for Gas (Waddams-Price and Weyman-Jones, 1996) and Electricity (Bums and Weyman-Jones, 1994) are quite useful and relevant. Relying on the Malmquist approach, they separate between the frontier shifts effects of privatization (technological gains) and the catch-up effects reflecting the improvement for a given technology. These studies have however been criticized for not taking into account properly the effects of external effects (hence the relevance of control variables).