rafmacalaba/datause-encoder-data huggingface dataset:
projects at different levels of abatement are calculated.<sup>19</sup> In terms of average cost of mitigation, the relative attractiveness of different types of projects appears to be similar to that resulting from the estimation of net abatement cost as reported in table 5. The structures of average cost curves for different project types are similar to those as depicted in figures 4 and 5.

We also estimate the project costs in Brazil, China, and India separately. Relative attractiveness of different types of project within each of these countries, as well as the comparative advantage of these countries for different types of projects appear to be similar to that of the full model (i.e., model IV in table 7) with little exceptions.

# **5 Conclusions and Policy Implications**

In this paper, we look at the cost of producing emission reduction credits under the Clean Development Mechanism using project data. We control for the duration of the projects, the type of technology used in the project and the year in which the project began. In our preferred (full) model, we employ a complete set of fixed effects associated for each host country and for each technology type, but we also estimate versions of the model that use broader classifications of projects and regional dummies rather than hostcountry dummies. We repeat the analysis for China, India and Brazil, countries that host a large number of CDM projects, and for specific types of projects. Many of the projects generate CERs and simultaneously create additional power-generating capacity, so we consider a separable cost function based on an explicit disentanglement of costs and revenue. We also consider an alternative joint cost functions that is consistent with inseparable costs.

In general, we find no evidence of increasing returns to scale. At mean levels, all calculated elasticities were less than one, although constant returns to scale could not be ruled out in some cases. We found significant variation in scale effects by type of projects; for example, H/PFCs & N2O reduction

> 19 For the electricity generating CDM projects, total project costs vary with the volume of emissions abatement and electricity which are joint outputs. For the purpose of calculating total costs of those projects at different levels of emissions abatement, total electricity output is regressed on total abatement without an intercept term. The coefficient estimate is used to calculate electricity outputs at different levels of emissions abatement and used in project cost calculation accordingly.

23
GDP, assets managed by pension funds increased by 1.85 times, from 38 percent in 1996 to 71 percent in 2005. Since their inception in 1981 and 2005, pension funds grew at an average annual rate of 28 percent in GDP terms. Furthermore, pension funds held around ten percent of equity market capitalization (which corresponds to around 28 percent of free-float), 60 percent of outstanding domestic public sector bonds, and 30 percent of corporate bonds’ capitalization in 2004. Figure 1 shows the evolution of pension system holdings as a share of GDP.

As assets under management expanded, the industry consolidated. The number of PFAs operating in Chile decreased by two-thirds while the number of pension funds doubled. The number of PFAs decreased from 15 to six due to a series of mergers and acquisitions that took place mostly in the late 1990s. Since the number of pension funds in the market has been proportional to the number of PFAs, the number of pension funds increased from 15 (one per PFA) to 30 (five per PFA) from July 1996 to December 2005.

# **3. Data and Turnover Statistics**

The data used in this paper come from Chile’s Superintendency of Pensions ( _Superintendencia de Pensiones, SP_ ) and consist of a panel of all the portfolio investments of PFAs in operation, for each of their funds, during the period July 1996 to December 2005 at a monthly frequency, including information on returns. In other words, the dataset has information on the price and quantity for every security held by fund per unit of time. We define a fund as a pair PFA/fund type (e.g., Fund C of PFA _Aporta_ configures a single fund). After cleaning the dataset, we use 3,869,290 observations, representing all domestic fixed income securities and domestic equity held during each month by at least one fund.

13
# Table A.6: Psychological and sexual violence

|Full sample<br>(1)|Rural<br>(2)|Urban<br>(3)|
|---|---|---|
|**Panel A: Psychological violence index**|||
|Fraction of female leaders<br>0.138|0.270|-0.092|
|(0.164)|(0.189)|(0.229)|
|Observations<br>62,122|43,981|18,141|
|Outcome mean<br>0.002|0.011|-0.019|
|**Panel B: Sexual violence index**|||
|Fraction of female leaders<br>0.014|0.098|-0.028|
|(0.182)|(0.208)|(0.262)|
|Observations<br>62,122|43,981|18,141|
|Outcome mean<br>0.003|0.019|-0.037|
|State fxed efects<br>x|x|x|
|District characteristics<br>x|x|x|
|Individual characteristics<br>x|x|x|

_Notes:_ Data are from the 2015-16 National Family Health Survey (NFHS) of India. The table presents 2SLS estimates using the fraction of female leaders in a district in close elections as an instrument. The dependent variables are psychological violence index in Panel A and sexual violence index in Panel B. Column (1) presents estimates for the full sample of women, while columns (2) and (3) restrict the samples to those living in rural and urban areas, respectively. We define close elections as those in which the victory margin is less that 3 percent of votes. All regressions include state fixed effects and control for the share of seats in the district that had close elections between women and men. Individual controls include age, years of schooling, and indicator variables for living in a rural area, religion, and caste. District-level controls include the share of female population, the share of urban population, and the share of SC-ST population in the district, and the female and male literacy rates. Robust standard errors are clustered at the district level. ***, **, and * denote significance at the 1, 5, and 10 percent levels.

33
|||(0.007)|(0.024)||(0.030)|(0.040)|
|---|---|---|---|---|---|---|
|Business operates machines||-0.025|-0.008||0.036|0.005|
|||(0.029)|(0.094)||(0.029)|(0.045)|
|Number of Men Employees||-0.083|-0.037||0.106|0.017|
|||(0.158)|(0.026)||(0.087)|(0.018)|
|Number of Women Employees||0.003|0.036||-0.010|-0.048***|
|||(0.015)|(0.044)||(0.017)|(0.011)|
|Number of Days HH Male||0.824**|-0.124||0.678***|0.066|
|Member Worked||(0.356)|(0.147)||(0.146)|(0.054)|
|Number of Days HH Female||-0.696***|-0.023||-0.406***|0.012|
|Member Worked||(0.190)|(0.098)||(0.073)|(0.078)|
|Business is in Food Sector||-0.082|-0.192||-0.061*|-0.152|
|||(0.055)|(0.194)||(0.036)|(0.101)|
|Business is in Clothing||-0.010|-0.106||0.011|-0.109|
|Sector||(0.015)|(0.147)||(0.010)|(0.090)|
|Business is in Construction||-0.094*|0.070||-0.033|-0.098**|
|Sector||(0.055)|(0.127)||(0.023)|(0.045)|
|Constant|||13.541*|||-1.333|
||||(8.028)|||(2.540)|
|Observations|989|989|989|3,657|3,657|3,657|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1, The reference dummy is the service sector 49
Argentina.<sup>22</sup> Although the average and median elasticity of high-income countries is higher than one, the lower range is less than one, overlapping with the upper range of the developing countries. The reasons are many: the boundary is ad hoc, and the income range for high income is wide; at the lower range are new entrants, such as former communist countries like Latvia and Poland, and island and Latin American countries like Barbados, Bermuda, and Chile. If we restrict the group to early OECD countries, the lower value is greater than 1.10 for both elasticities.

Table 3: Descriptive Statistics for developing and high-income countries

||<br>**Arming**|<br>**ton (σ)**|<br>**CET**|<br>**(Ω)**|
|---|---|---|---|---|
||**Developing**<br>**countries**|**High-income**<br>**countries**|**Developing**<br>**countries**|**High-income**<br>**countries**|
|**Average**|0.71|1.42|0.59|1.46|
|**Median**|0.74|1.39|0.59|1.33|
|**Standard deviation**|0.16|0.42|0.19|0.50|
|**Range**|0.32-0.99|0.78-2.49|0.22-0.96|0.74-2.87|

Source: Authors' calculations.

Our results are consistent with recent findings of lower elasticities for macro or aggregate elasticities than those of micro elasticity studies of specific sectors or commodities. Because a cointegration equation corresponds to a long-term equilibrium, the results are comparable to the range of 1.75 to 2.25 for the long-term elasticity estimates of Boehm, Levchenko, and Pandalai-Nayar (2023), especially for the high-income group. Past results that do not correct for nonstationarity issues in time-series estimation will also be spuriously high. The survey and analysis by Bajzik et al. (2020), including many past studies at sectoral levels, show a broader range in magnitude. Plausible explanations mentioned before include aggregated imports, exports, and domestic goods are composites of many goods; developing countries are less diversified, and the composition of these goods tends to differ; and the external balance of payments constraint at the macro level could limit substitution possibilities. Finally, this study provides countryspecific estimates of trade elasticities for many countries, 128 developing countries and 63 high-income countries. Not only were estimates for developing countries lacking in the literature, but estimates of the export supply side were also lacking.

# V. Conclusion

This paper tries to fill a lacuna in the literature. While the Armington elasticity has been estimated at the sectoral level for a number of (mostly) developed countries, the equivalent elasticity in the widely used aggregate, 1-2-3 model of developing countries has typically been assumed. We provide empirical estimates of the import and export elasticities of the 1-2-3 model for 191 countries. Using data from 1970-2019 and the Vector Error Correction model as the dominant technique, we derive robust estimates that also square with intuition. Elasticities for high-income countries are generally greater than one, averaging around 1.4, while those of lower-income countries are below one, averaging around 0.65. Not only do these estimates confirm

> 22 Argentina became high-income in 2017.

18
on the files accompanying the Enterprise Performance Statement and the Balance Sheet Statement, we have created a comprehensive profile for every firm which includes the industry (KVED/NACE) and territory codes, as well as exporting status in every year which were used as controls. The industry codes were used to assign manufacturing firms into one of eleven sub-industries. In every sub-industry, we cut off the top 1 percentile of the sample (measured by employment, capital and output) to exclude outliers.

As the measure of output, we used net sales after excise taxes from the Financial Results Statement. The Balance Sheet Statement is the source of the capital measure for which we used the end-of-year value of the tangible assets. For the production function estimation we used investments in tangible assets which come from the Enterprise Performance Statement. The same statement is also a source for our employment variable. It is measured as the “yearaveraged number of enlisted employees”, which is a rough estimate of the full time equivalent of labor used. The material costs come from the same statement in 2001-2004, whereas since 2005 they have been available from a separate Sectoral Expenditures Statement. The statement provides detailed information about the firm’s expenditures on purchases from 22 manufacturing sectors and 15 service sectors. Data from this statement were used to construct an individual firm-specific index of services liberalization as we explain in the Appendix. All variables were deflated by the appropriate price deflators available from the National Statistical Office. The descriptive statistics for the sample are presented in Table 2.

# **4.2 Methodology**

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# Figure 2 _: Mandate of business reform committees_

<!-- Start of picture text --> Improve competitiveness globally by improving the<br>business regulatory framework<br>Improve inter-agency coordination on business<br>regulatory matters<br>Provide a platform for public-private dialogue<br>Improve the business environment for domestic<br>companies<br>Propose and draft new business regulation<br>Undertake or supervise the undertaking of Regulatory<br>Impact Analysis (RIA)<br>0 10 20 30 40 50 60 70 80<br>count<br><!-- End of picture text --> _Source: Reform Committee data_ _Note: Most common mandates of business reform committees for economies. Figure based on 107 economies with a functioning business reform committee._ The majority of economies with a functioning reform committee state that their mandate is to improve competitiveness globally by improving the business regulatory/legislative framework, going beyond the business environment for domestic companies (Figure 2). The second most common reason is to improve the inter-agency coordination on business regulatory/legislative matters, followed by an opportunity to provide a platform for public-private dialogue. The least number of reform committees indicate that their focus is undertaking or supervising Regulatory Impact Analysis (RIA) to ensure that the newly proposed regulatory reforms meet quality standards. One possible explanation of this finding could be that they have separate bodies that work on this matter; another could be that it is simply not their priority.

# **4.2. Organizational structure and operational framework**

One of the main elements of reform committees is its leadership, who sets priorities and decides where the committee is housed vis-à-vis the country’s centers of power (Figure 3). In close to 50 percent of economies with a functioning reform committee, the priorities are set at the level of a Ministry, followed by the Prime Minister’s office. Europe and Central Asia is the region where most reform committees have their priorities set by the highest government body (i.e., the Prime Minister / President). This is the case for Azerbaijan, Belarus, Kyrgyz Republic, and others. Second to Europe and Central Asia is Sub-Saharan Africa, followed by Latin America and the Caribbean. For example, the President of Togo, through an executive order, established _Cellule Climat des Affaires (CCA)_ (Box 2) _._ _Box 2: Togo’s Business Climate Unit as an example of a business reform committee placed under the Head of State_ The Republic of Togo established the Business Climate Unit (La Cellule Climat des Affaires, or CCA) following a presidential decree on September 29, 2017. This committee, placed under the direct supervision of the Head of State, aims at overseeing the implementation of reforms improving the business climate in Togo, and particularly the reforms expected to improve the economy’s ranking on any reports from <u>domestic or global institutions. The Business Climate Unit has administrative and financial</u> 10
are more likely to achieve employment after graduation. Specifically, the sample demonstrates that attendance at SMK increases the probability of achieving employment by 7 percent. This effect seems to be most prominent for the sample of male students, with the sample of female students not demonstrating the same effect to a significant degree (Table 2).

# **Table 3: Probit estimates of EMPLOYED in 2000 (IFLS3, n=683, 350 & 333), without censoring correction**

||Without cen<br>|soring correctio|n<br>||||
|---|---|---|---|---|---|---|
||All||Female||Male||
|Independen|Probit|Marginal|Probit|Marginal|Probit|Marginal|
|t variables|Coefficient|effect|Coefficient|effect|Coefficient|effect|
|RURAL|-0.014|-0.005|0.006|0.002|-0.038|-0.015|
||(0.107)|(0.042)|(0.150)|(0.058)|(0.154)|(0.061)|
|FEMALE|0.062|0.024|||||
||(0.102)|(0.040)|||||
|AGE|0.269***|0.106***|0.201***|0.079***|0.345***|0.136***|
||(0.039)|(0.015)|(0.055)|(0.021)|(0.059)|(0.023)|
|MARRIED|-0.953***|-0.318***|-1.116***|-<br>0.360***|0.040|0.016|
||(0.198)|(0.050)|(0.232)|(0.054)|(0.476)|(0.189)|
|SMK|0.184*|0.072*|0.118|0.046|0.256*|0.101*|
||(0.101)|(0.040)|(0.142)|(0.056)|(0.146)|(0.057)|
|Intercept|-5.481***||-4.044***||-7.022***||
||(0.777)||(1.059)||(1.170)||
|R<sup>2</sup>|0.07|0.07|0.07|0.07|0.09|0.09|

Standard errors in parentheses.

As mentioned earlier, this approach is prone to generate biased coefficient estimates due to the exclusion of those participating in tertiary education. The panel data allows us to correct this, as information regarding the schooling and employment status of the entire sample 12
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