rafmacalaba/datause-encoder-data huggingface dataset:
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## CHAPTER V

# METHODOLOGY OF MATCHING PROCEDURES: THE PROBLEM _AND_ A PROPOSED SHORT CUT

The criteria for selection of the particular "representative" commodity items on which quantity and price comparisons are ultimately based is a central issue in this kind of study. This chapter describes alternative methods and the matching procedure we adopted.

# Earlier contributions

Many of the methodological problems of intercountry comparisons using the "industry-of-origin" approach were adumbrated by Rostas (1948), and more fully elaborated by Paige and Bombach (1959). Their contributions to solving problems of measurement have already been discussed in chapters I and III. They added large appendices to their studies, in which the actual calculations are presented industry by industry, and are more fully transparent than most other studies of this kind. However, with regard to the matching problem even Rostas and Paige and Bombach do not present a systematic procedure. Their presentation has an ad hoc quality, with no general presentation of the matching issue and feasible options for tackling it. In other studies dealing with international comparisons from the product side hardly any relevant information is given on how the matching problem was dealt with.

Below we develop a number of criteria for a systematic matching procedure which is also economical in terms of time and effort. It may also be helpful to national census statisticians in considering whether their existing product specifications and aggregations can be improved (within the limits of confidentiality, which in some cases is the origin of the comparability problem),

# Product Comparability

Before discussing three possible approaches to matching, we consider the general problem of "product comparability". Time series collected for index purposes (e.g. consumer price indices) for a particular country are, for the most part, based on exact matching. The statistics record, at regular intervals, the price of an identical product, sold in the same condition at the same point in the production chain. For example, food prices generally relate to particular brands of processed food, sold in specified quantities in particular stores (a 10 ounce can of a name brand of baked beans sold in such and such a supermarket at a specified location). Of course it will not always be possible to make exact matches if, for example, the selected outlet closes down or the manufacturer discontinues the particular brand or modifies it in some crucial way, but in general it is probable that exact-matching is the rule rather than the exception for price comparisons within a country.
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<!-- Start of picture text --> food, bev. & tobacco<br>textiles<br>clothing<br>leather<br>wood<br>paper & printing<br>coke<br>chemicals<br>pharma<br>rubber & plastics<br>non-metalic minerals<br>basic metals<br>fabricated metals<br>computers<br>electerical equip.<br>machinery<br>motor vehicle & o. transport<br>furniture<br>other manuf.&repair<br>-.3 -.2 -.1 0 .1<br>% change in M empl AC % change in F empl AC<br><!-- End of picture text --> Source: Authors’ own elaboration using the EIFBS. Weights are used. Gender gap is the difference of the number of male and female employees before COVID (BC) and after COVID (AC).

Other heterogenous effects pertain to firm behavior which captures the extent to which good managerial practices, innovation, the adoption of advanced technologies and worker training have weighed in various ways on the gender gap. These behavioral traits have provided an opportunity for firms to adapt their business models and show greater resilience in coping with the COVID-19 crisis in Egypt (El-Haddad and Zaki 2023a). But do they also positively reflect on the gender gap post-COVID?

**Table 2: Distribution of employment by firm technology adoption and** **<u>worker training</u>**

||Total<br>male<br>empl. BC|Total<br>female<br>empl. BC|Total<br>empl. BC|Total<br>male<br>empl. AC|Total<br>female<br>empl. AC|Total<br>empl. AC|Gender<br>Gap BC|Gender<br>Gap AC|% change in<br>gender gap<br>(AC)|
|---|---|---|---|---|---|---|---|---|---|
|Technology||||||||||
|No|30418|7146|37565|26524|6340|32864|23272|20184|-13.3%|
||80.98%|19.02%||80.71%|19.29%|||||
|Yes|157568|36180|193748|153547|35374|188921|121388|118173|-2.6%|
||81.3%|18.7%||81.3%|18.7%|||||
|Training||||||||||
|No|11258|2205|13462|9352|1914|11266|9053|7438|-17.8%|
||84%|16%||83%|17%|||||
|Yes|143241|34032|177274|138023|32745|170767|109209|105278|-3.6%|
||80.8%|19.2%||80.8%|19.2%|||||

Source: Authors’ calculations using the EIFBS. Weights are used. Gender gap is the difference of the number of male and female employees before COVID (BC) and after COVID (AC).

In terms of firms’ behavioural traits, the employment gap is about five times and twelve times greater in firms that adopted technology and those that provided worker training prior to COVID respectively compared to their counterparts (Table 2). This is not surprising as women tend to be concentrated in low-tech, traditional sectors such as clothing. In the sample, on average female employment accounts for just 3% and 7% of total employment in the relatively high-tech automotive and computer sectors. In terms of changes, the employment gap has considerably declined in non-tech and no-training oriented firms. It declined just over 5 times 9
**Table 2: Coverage of Quantitative Cost-Recovery Analysis undertaken for this Paper**

|**Country/State**|**Year of Data for Pre-**<br>**Reform Analysis**|**Years Covered in the**<br>**Post-Reform Analysis**|**Scope of Utility-level Analysis (Ownership**<sup>**b**</sup>**)**|
|---|---|---|---|
|Colombia|1987|2010-2016|Codensa (Private)|
|Dominican Republic<sup>a</sup>|1987|2010-2015|Edesur (Public)|
|Egypt, Arab Rep.|1987|2011-2016|EEHC (Public)|
|India - Andhra Pradesh|1987|2011-2015|APEPDCL (Public), APSPDCL (Public)|
|India - Odisha|1987|2011-2015|CESCO (Public), WESCO (Public)|
|India - Rajasthan|1987|2011-2015|AVVN (Public), JDVVN (Public), JVVN (Public)|
|Kenya|1987|2010-2016|KPLC (Public)|
|Morocco|1987|2013|ONEE (Public)<sup>c</sup>|
|Pakistan|1987|2013-2016|K-Electric (Private), LESCO (Public)|
|Peru|1987|2010-2016|Hidrandina (Public), Luz del Sur (Private)|
|Philippines|1987|2010-2016|Beneco (Private), Meralco (Private)|
|Senegal|1991|2010-2016|SENELEC (Public)|
|Tajikistan|2003|2010-2016|Barki Tojik (Public)|
|Tanzania|1991|2012-2016|TANESCO (Public)|
|Uganda|1987|2012-2016|UMEME (Private)|
|Ukraine<sup>d</sup>|1994|2012-2017|Dniprooblenergo (Private),<br>Khmelnytskoblenergo (Public)|
|Vietnam|1993|2010-2016|NPC (Public)|

_Notes:_<sup>a</sup> Approximated by data for Edesur as full-country data was not available.<sup>b</sup> Indicates majority ownership. cApproximated by sector-wide data. dC1-C3 cost recovery is approximated by A1-A3 as systematic information on government support to the utilities was not available. _Source:_ World Bank staff.

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Figure IX: Returns to Schooling by Sector <!-- Start of picture text --> .2<br>.1<br>0<br>Non Agriculture<br>-.1 Agr i culture<br>6 7 8 9 10<br>Log GDP p.w.<br>Returns to Schooling<br><!-- End of picture text --> _Notes_ : The figure plots the estimated sector-specific coefficient on years of schooling from a regression with log wage income as dependent variable against log GDP per worker (in PPP terms). Each dot corresponds to a different country ˆ year sample. The estimation is performed on the World Bank I2D2/GLD database, while GDP per worker is taken from version 9.0 of the Penn World Tables (Feenstra et al., 2015).

capital. This would result in skills mismatch, workers’ frustration, and lower returns to human capital investments. In addition, given that agriculture is the sector displaying the largest productivity differences between poor and rich countries (Caselli, 2005), facilitating the reallocation of workers away from this sector through human capital accumulation might support economic growth in the developing world (Figure VII.i).

# **3.3 Technology Adoption**

**Human capital is a key driver of both the invention and the adoption of new technologies (Figure VII.c)** . One reason for this is that understanding new technology developments and adapting to changes in the production process require advanced skills, which are typically acquired through education and experience (Nelson and Phelps, 1965). The literature has offered several pieces of evidence in support of this idea. For example, Ciccone and Papaioannou (2009) show that countries with higher levels of education in 1980 saw faster growth in human-capital-intensive industries (relative to other industries) in the following decades, a period characterized by the ICT revolution and the rapid introduction of skilled labor augmenting technologies. Consistently with this idea, Maloney and Caicedo (2022) show that countries with a larger share of engineers in 1900 are characterized by faster technology adoption historically and higher levels of income per capita today. At a more micro level, Foster and

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(from the west) and Afar (from the east). The conflict escalated when neighboring Eritrean forces (from the north) joined the alliance led by the federal Ethiopian army. Banking, telephone, electricity, transport, and other basic services were suspended in most parts of the region immediately after the war broke out. Most of Tigray was directly affected by the war and quickly became inaccessible to humanitarian assistance. After weeks of intense fighting, the federal and allied forces took control of large swaths of Tigray, including the regional capital Mekelle at the end of November 2020, which continued until June 2021. After capturing the regional capital Mekelle, the federal government installed a provisional regional government in Tigray and some public services such as telecommunication and electricity services were restored, especially in those areas in full control of the federal army. Some humanitarian assistance resumed after the capture of the regional capital but most of these services discontinued after June 2021 when the federal army and the provisional regional government left the capital.

Internal displacements from several parts of Tigray swelled camps set up for internally displaced people (IDPs) in several towns, including the regional capital, Mekelle. While effects of the war remained largely unreported because of the restricted access to internet and telephone services, widespread disruptions, lootings, and civilian massacres were later confirmed, including by an investigation led by the Office of the United Nations High Commissioner for Human Rights (OHCHR).<sup>10</sup> The war has thus far, in addition to deaths and displacements, caused disruptions in livelihoods, loss of economic activity, and incomes to those exposed to it.

The conflict moved southwards into neighboring Amhara and Afar regions when the Ethiopian federal army and allied forces left Tigray in June 2021. As a result, the war subsided in some parts of Tigray but spilled over to neighboring Amhara and Afar regions.<sup>11</sup> The expansion of the war into neighboring Amhara and Afar regions have caused further economic and welfare damages in those regions. A recent assessment by the World Food Programme shows that “hunger has more than doubled” in the Amhara region during the immediate five months since the war expanded to the region (WFP, 2022). Thus far, the HFPS data cover the period until May 2021, forcing us to focus on impacts of the first phase of the civil conflict, which was confined to the Tigray region.

> 10 https://www.ohchr.org/Documents/Countries/ET/OHCHR-EHRC-Tigray-Report.pdf

> 11 However, Tigray remained without communication, electricity and banking services and a dire humanitarian crisis continues to unfold (WFP, 2022).

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# **Figure B1: Coal**

<!-- Start of picture text --> Log scale<br>6<br>Log Price Permanent component<br>5<br>4<br>3<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br><!-- End of picture text -->

**Source** : Authors’ calculations

<!-- Start of picture text --> Log scale<br>1.0<br>Medium-term cycle Transitory component<br>0.5<br>0.0<br>-0.5<br>-1.0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 42 —_
were involuntary their settlement pattern in Turkish border provinces can be considered quasi-experimental. It then compares changes in outcomes in the border regions with camps with those for a control group of regions (in eastern Anatolia).<sup>9</sup> Like this paper, they find displacement in the informal sector, some formal job creation, and negative impacts concentrated among women and the low educated. It finds no impact on wages (even for the displaced groups). There are three important ways in which this paper complements and goes beyond their work. First, rather than take a difference-in-difference approach, where identification comes from places close to the border compared to those further away, we instrument for refugee flows and control for distance from the border. Plausibly both approaches are valid, depending on how important endogenous refugee location choices were in the years 2012, 2013 and 2014.<sup>10</sup> In that case, our work is complementary, tracing out the initial impact of refugees in camps and what happens subsequently as they diffuse throughout Turkey. Second, Ceritoglu et al., as is typical in this literature, estimate the combined impact of the arrival of refugees, the construction of camps and provision of aid and any other border related shocks (such as changes in trade patterns due to war in Syria). In contrast, as discussed, we provide evidence for a well-defined labor supply shock. Finally, we are able to explain both Ceritoglu et al. and this paper’s puzzling finding that average wages among displaced groups of natives do not decline. We show that this is the result of refugee-induced changes in the composition of Turkish employment. Once these are accounted for, we estimate that marginal products do actually fall considerably in the informal sector and for women.

The paper proceeds as follows. Section 2 provides background on the Syrian refugees in Turkey and descriptive statistics. Section 3 outlines our empirical strategy and Section 4 presents the results. Extensive placebo tests and robustness checks are presented in Section 5. Section 6 concludes.

> 9 Note that our main results are highly robust to the same, more homogeneous, group of subregions, as well other variations in the subregions used for analysis.

> 10 UNHCR and AFAD data suggest that the share of refugees outside camps was around 15 percent in 2012, 40 percent in 2013 and 85 percent in 2014. Also note that in 2014 AFAD dramatically improved their counting of refugees outside camps, hence the 2012 and 2013 shares are likely significantly underestimated.

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Electricity use is projected to grow at 4.4% during 2015-20 and at 3.0% during 2020-30. The change in the structure of generation sources is given in Table 2 and graphed in Figure 3; we see that electricity use grows faster than coal due to the rise of renewables and nuclear energy – coal-fired electricity grows at 2.7% during 2015-20, nuclear at 12%, wind at 11%. Solar starts from a very tiny base but rises rapidly, at 40% per year. In the 2020-30 period, renewable growth decelerates sharply but is still faster than coal, with the exception of hydro growth which falls to 0.4% per year.

As a result of the shift towards cleaner fuels driven by the assumed improvements in energy efficiency and the endogenous changes in prices in the base case, CO2 emissions, including those from cement manufacture, only grows at 2.6% per year during 2015-20. That is, the CO2 intensity falls faster than energy intensity; by 2030 it is 58.7% lower than the 2005 level (compared to the NDC target of a 60-65% reduction). CO2 emissions rise steadily from 8,300 million tons in 2010 to 12,800 million in 2030, thereafter we project a stabilization around 13 billion tons for at least the next 10 years. Coal use rises to 4.1 billion tons in 2020 and then plateaus at about 4.6 billion beginning in 2030.<sup>9</sup>

# **4. Simulations of Carbon Pricing Scenarios**

In our base case the CO2 intensity falls by 58.7% in 2030 relative to 2015. We consider carbon price trajectories that can reduce CO2 intensity by 60 or 65% by 2030, as in the NDC. To that end, we consider flat rates that apply for all years, and prices that start low and rise every year so as to achieve the same cumulative emission reductions. We also consider two alternative approaches for recycling the carbon revenue to the economy: (i) recycle the carbon revenue by cutting existing taxes and (ii) rebating it lump-sum to households.

These policy scenarios are defined in Table 3. In the R1CUT scenario, we find through trial-and-error that a carbon price starting at 1.6 yuan/ton of CO2 in 2015,<sup>10</sup> and rising gradually to 26 yuan in 2030, hits the 60% reduction goal.<sup>11</sup> In this scenario, the carbon revenues collected

> 9 Our energy projection is based on IEA (2014). In the “Current Policies” scenario of IEA, China’s primary energy use is projected to grow at 2.9% during 2012-20 and 1.9% for 2020-30, and project coal use to grow at 2.1% (201220) and at 1.3% (2020-30).

> 10 All yuan units are measured in 2010 yuan, the base year of the model.

> 11 We should digress here to clarify what is meant by a statement like “a carbon price of  ¥10 per ton in each year of the projection.” In an economy with no TFP growth and little capital accumulation, absolute (and thus relative) commodity prices will differ little from the base year. In this case there is little ambiguity about what ¥10 in each year means. With rapid growth in output and incomes per capita, the price of commodities relative to the price of labor is

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