rafmacalaba/datause-encoder-data huggingface dataset:
# **3.4 Incorporation of carbon tax**

A carbon tax is levied on the price of fossil fuels to cause a reduction of carbon dioxide emissions emanating from the use of fossil fuels. The carbon tax is modeled as an excise tax that adds to the unit price of the fossil fuels in proportion to their carbon contents. We consider an upstream implementation of the carbon tax whereby no user can escape its payment. The tax is already included in the prices paid by all users of the fossil energy good. This suggests that all firms pay the carbon tax. The government collects revenue from the carbon tax that may be recycled somehow in the economy. We will discuss in the next section different options to recycle carbon tax revenues considered in this study.

# **3.5 Data**

One of the main challenges to analyze policy impacts on the informal economy is data. Despite the large informal economy, developing countries do not systematically collect data regarding the informal economy. Even if they conduct surveys occasionally, the results are not made available to the general public. A CGE analysis addressing the informality requires a social accounting matrix that explicitly accounts for the informal sector. Fortunately, the National Statistical Office (NSO) developed an input-output (I-O) table for the year 2013 that captures the informal sector based on surveys conducted by the same office. However, the data have not been published. We worked with the NSO to prepare a SAM explicitly identifying the informal sectors where possible. The original I-O table available from NSO has 107 production sectors and the same number of commodities. A production sector is divided into two categories – formal and informal. Based on the objective of our study, we developed a SAM with 18 sectors and 18 commodities by aggregating and disaggregating the original I-O table as required. Each sector in the SAM has two types of producers – formal and informal. It is not possible to distinguish the formal and informal categories as the data is not available to track the distinction. Moreover, it is not necessary to have the formal and informal categories in the consumption side. However, a household provides two types of labor – formal (those working in the formal sectors) and informal (those working in the informal sector). The data for the various components of the SAM are provided in Appendix A.

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linked to economic inequity have become one of the most dominant economic challenges of the coming decade. In the context of growing polarization of electoral processes around the world, this realization is now shared by a diverse set of actors, including academics in search of new and more inclusive growth models,<sup>10</sup> the World Economic Forum (WEF), and business leaders. The WEF has indeed used its global business councils to stimulate new thinking on how to redress the problem of non-inclusive growth before it undermines many aspects of the global system.<sup>11</sup>

# **3. Gender-Enhanced Growth Diagnostics: Theoretical Insights**

It is helpful to introduce gender distortions within a consistent growth framework.<sup>12</sup> Using this framework we evaluate the efficacy of approaches currently on the table to motivate our approach. We find that many of the existing approaches to dealing with gender-based inequality have proven to be unsatisfactory when operationalized. To better assess their efficacy, we find it useful to categorize them in some broad fashion, realizing that in doing so we may be unfairly limiting their aspirations. Nevertheless, there is merit in distinguishing among the various alternatives using the utility maximizing framework of Hausman, Rodrik and Velasco (2005).

If _u_ is the welfare of the average individual in a country, maximizing utility from consumption subject to standard resource constraints and the pre-existing distortions in the economy results in the following Lagrangian: where c is consumption, τ is a set of distortions in the economy, μks(τ) and μkp(τ) are the net marginal valuations of activity k by society (s) and by private agents (p), which are functions of the distortions τ, but also depend on consumption, labor supply, assets and other variables (these are omitted for the sake of simplicity). Finally, λ is a set of Lagrange multipliers that are all greater or equal to 0. From the first-order conditions we obtain that the gain in welfare from reducing distortion τi in activity _i_ is given as: The first term on the right-hand side of expression (2) is the direct effect of a small change in distortion τi, which we assume is strictly positive. A small reduction in this distortion increases aggregate welfare _u_ by an amount given by the multiplier associated with this constraint, λi. The multipliers differ in size, indicating that not all constraints are equally binding (Rodrik, 2010). The larger the multiplier is, the

> 10 See Kanbur and Stiglitz (2016), for example.

> 11 See, for instance, Guriev, Leipziger, and Ostry (2017, 2018).

> 12 See Leipziger and Ingram (2017).

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**Figure 5. Urban Rental in Studied Countries by Housing Starters and Young Households**

<!-- Start of picture text --> 35.0<br>30.0<br>25.0<br>Poland<br>Lithuania<br>20.0<br>Serbia<br>Russia<br>15.0<br>Armenia<br>Romania<br>10.0<br>5.0<br>0.0<br>Total Housing Starters Young Households<br><!-- End of picture text --> _<u>Source:</u>_ Household <u>surveys (Table 1</u> _<u>)</u>_

# _Public Rental Sector_

In stark contrast to the figures on private rental, the data show that the capacity of the public rental sector to absorb young starter households is highly impaired in large parts of the region (Table 6). The comparison between Poland and Russia is telling: in Poland, with an actively managed public and public-private housing system (discussed below), starter households are proportionally over-represented in public rental housing and proportionally represented in private rental housing. In Russia, in contrast, starters are both significantly under-represented in public rental housing and vastly over-represented in privately rented housing. Similarly, the public rental sector no longer is significant as a source of housing for young households in Lithuania, Romania, and Serbia.<sup>51</sup> The private rental sector has turned into a significant source of supply for the young - and the most mobile households, while the public rental sector has lost at least part of its policy functionality for such groups. Has it retained its functionality in the income dimension?

> 51  In Serbia the survey found no starter households in the public rental sector.

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**Table 4: Decomposition of Changes in Growth Rates from 1986-90 to 1991-93 - Investment Excluded**

|Country|Actual<br>change in<br>|Predicted<br>change in<br>|Contribut<br>g<br>|ion to predicted<br>rowth rates fro<br>|change in<br>m<br>|Regression<br>residuals|
|---|---|---|---|---|---|---|
||rowth|rowth|Five reform|Time effect<sup>2</sup>|Other||
||g<br>rates|g<br>rates|<br>variables'||variables||
|Argentina|7.464|8.818|9.036|-1.246|1.029|-1.354|
|Bolivia|1.561|1.384|1.55|-1.246|1.081|0.177|
|Brazil|-0.252|-2.246|-1.222|-1.246|0.222|1.994|
|Chile|0.901|-1.584|0.722|-1.246|-1.059|2.485|
|Colombia|-0.049|-0.023|0.52|-1.246|0.704|-0.026|
|Costa Rica|0.923|-1.076|-0.075|-1.246|0.246|1.999|
|Ecuador|1.584|0.512|0.465|-1.246|1.293|1.072|
|Guatemala|1.197|0.563|1.292|-1.246|0.518|0.635|
|Honduras|1.141|0.01|0.538|-1.246|0.719|1.13|
|Mexico|1.116|2.161|2.801|-1.246|0.607|-1.045|
|Panama|9.178|1.471|0.607|-1.246|2.11|7.707**|
|Peru|4.504|9.103|8.75|-1.246|1.599|4.598*|
|Paraguay|-0.92|-0.049|0.639|-1.246|0.559|-0.871|
|El Salvador|1.714|1.766|2.795|-1.246|0.218|-0.052|
|Uruguay|0.255|-1.54|0.521|-1.246|-0.814|1.795|
|Venezuela|2.261|0.544|1.994|-1.246|-0.203|1.716|
|**Regional**<br>**Average**|2.036|1.238|1.933|-1.246|0.552|0.798|

**1** Set of reform variables does not include Investment /GDP

2 The time effect corresponding to the last penrod is equal to the overall constant plus this penrod's dummy coefficient.

* Statistically different from zero at the 0.10 level of significance on a one-tail test.

****** Statistically different from zero at the 0.05 level of significance on a one-tail test.

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Once the adjusted performance indicators are derived I compare them across countries in two ways. Statistical tests are performed to see what countries stand out as relatively greater or lesser successes in each dimension of economic performance, and then their chosen policies (also adjusted for the economic environment) are examined for similarities. Chisquare statistics from contingency tables and Spearman correlation analysis are also used to identify common rankings of countries by their countryspecific economic performance. These comparisons of rankings are nonparametric but permit an identification of common elements in the policyperformance nexus across countries.

hta.

I have assembled data for 75 countries from the 1989 World Tables and World Debt Tables of the World Bank; the characteristics and precise definitions of these data and countries are given in the appendix. The measures of economic performance examined in this study include the growth rate in real output (YGR), the inflation rate (DINF), the current account/GNP ratio (CAR) and the domestic investment/GNP ratio (IR). These are not exhaustive, but reflect performance along a number of dimensions important to the developing country. YGR and DINF are indicators of internal balance: success in structural adjustment occurs when resources are being allocated efficiently and with little excess demand. CAR measures performance in attaining external balance; structural adjustment has most recently required adjustment to limited access to international credit, and this variable measures relative success in that. IR is a measure of intertemporal balance: even though policies and external events may
can be raised have been relaxed. Limits on capital outflows by individuals and firms have been liberalized as well.

As a result of the steady relaxation of capital inflows and outflows, India has rapidly become more financially integrated, Figure 4. It is currently one of the largest recipients of private capital flows in the emerging world, and on average attracts about 8 percent of private capital flows and loans to the emerging countries (excluding China, which attracts almost a third of total private capital flows to all emerging markets).<sup>11</sup>

**Figure 4: Allocation of Private Capital Flows (equity, bonds and loans) across Emerging Markets, 2014 (% of flows to all emerging markets excluding China)**

<!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>% of Total Flows 0<br>MontenegroLao P.D.R.GeorgiaSenegalMarshall IslandsSerbiaTunisiaMacedonia, FYRC662GDP@IFSCôte d'IvoireEl SalvadorGhanaUkraineJordanCosta RicaZambiaGuatemalaKuwaitTrinidad and TobagoEthiopiaParaguayEgyptJamaicaOmanCroatiaVietnamSri LankaAzerbaijanDominican RepublicBahrainBulgariaPanamaUruguayArgentinaKenyaEcuadorNigeriaMoroccoQatarHungaryPakistanMacao SARPhilippinesSaudi ArabiaKazakhstanThailandRomaniaPeruSouth AfricaRussiaPolandColombiaMalaysiaChileIndonesiaTurkeyUnited Arab EmiratesIndiaMexicoBrazil<br><!-- End of picture text --> Source: Global Financial Stability Report, IMF, 2015 Capital inflows to India are comprised of FDI, portfolio debt and equity flows, corporate borrowing abroad (called external commercial borrowings or ECB), and deposits held by Indian diaspora in the Indian banks (known as non-resident Indian deposits or NRI deposits). As a result of continued capital inflows, the stock of external liabilities has increased rapidly, far outpacing the size of the economy. The balance of liabilities has tilted in favor of equity liabilities over time, the share of which has increased from about one-fifth in the mid-1990s to about half in recent years, Figure 5.

> 11 Though among the largest in absolute terms, India does not rank as high for capital inflows in proportion to GDP.

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preferably over a longer time-period and changes in specific characteristics of guarantee schemes.

Looking forward, we hope that this first overview based on a survey of PCG funds motivates more research. First, more theory is needed to better understand which organizational, pricing and risk management features can help maximize the impact of PCG funds, while minimizing the risk. Second, more empirical research on specific funds and how they have developed over time can help understand which features have worked best in practice. Such research, however, requires time-series data and preferably relies on loan- and borrower-level data. Finally, more research is needed to do a proper cost-benefit analysis of PCG funds compared to other SME government interventions. Even if one agrees with the hypothesis that subsidies are justified to foster the access of SMEs to external finance, it is still important to understand, which intervention is the most cost-effective to do so.

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<!-- Start of picture text --> High Income Low and Middle Income<br>N<br>N,<br>2005 2010 2015 20202005 2010 2015 2020<br>year<br>— (mean) log Infrastructure _——— (mean) log GDP<br><!-- End of picture text --> <!-- Start of picture text --> East Asia & Pacific Europe & Central Asia Latin America & Caribbean<br>wo<br>i)<br>Middle East & North Africa South Asia Sub-Saharan Africa<br>lo<br>° AAV POV fF<br>iQ<br>s,sa Ss ie<br>2005 2010 2015 20202005 2010 2015 20202005 2010 2015 2020<br>year<br>— (mean) log Infrastructure |.———— (mean) log GDP<br><!-- End of picture text -->
substantially in South Africa, from some 30 percent ten years ago reflecting the success to date of the policy of widening access to the electricity network. Nevertheless, South Africa’s level of overall access remains significantly behind—by about a third—its own income group of upper middle-income countries (87 percent). A related (and corroborating) statistic focusing on households indicates that about 65 percent of all households in South Africa report having access to electricity compared to 23 percent for the SSA region and 74 percent for the upper middle-income countries.

# **Affordability--Pricing**

South African average end-user prices (US cents/kwh) are some of the cheapest in the world at 3 cents for residential and 2 cents for non-residential customers. By comparison, these user prices in upper middle income countries are, on average, about 9 and 7 cents, respectively, while SSA rates are 6 and 5 cents. The world average is 9 cents for residential customers and 6 cents for non-residential customers. In South Africa, the low user prices do not seem to reflect obvious underpricing, as is the case in many developing countries with the attendant quasi-fiscal losses. Instead, it is a consequence of the low cost structure of what is considered a well-run state electricity company (Eskom) and the fact that part of the capital stock has already been depreciated as there has been little investment in the sector over the past two decades; hence, the fixed cost component of the electricity cost/price in South Africa is very low.

**Figure 29  Electricity transmission and distribution losses (% of total output)**

<!-- Start of picture text --> 19<br>20<br>15<br>14 14<br>15<br>10<br>8<br>6<br>5<br>0<br>South Africa Upper MiddleIncome SSA Middle Income High Income OECD World<br><!-- End of picture text -->

# **Technical Performance**

Measured by the percentage of aggregate transmission and distribution losses, technical efficiency of the electricity sector is strong. Electric power transmission and 49
# **Exchange Rates**

Specific tariffs are typically specified in euros, but the value of imports are in US dollars. We use the average annual interbank exchange rates to convert euros to US dollars.<sup>12</sup>

# **III. Methodology: Calculation of Most Favored Nation (MFN) Tariff Rates Ad Valorem Tariffs**

Most tariffs in the Russian Federation are simple ad valorem tariffs. For these tariff lines, the MFN tariff is relatively straightforward, as we do not have to calculate the ad valorem equivalent of the specific tariff.

# **Combined Ad Valorem and Specific Tariffs**

Calculation of the tariff structure of the Russian Federation is complicated by the fact that for a large number of tariff lines there is a potential specific tariff component. The number of tariff lines with a potential specific tariff component has gradually grown over time, from 1609 in 2001 to 2020 in the year 2011. Most of these tariff lines with a potential specific tariff component used a ―combined‖ tariff structure.<sup>13</sup> For products with the combined tariff structure, the maximum of the ad valorem or specific tariff is the tariff that applies.

# **Tariffs for the Years 2001-2010**

Define _v hjt_ and _q hjt_ as the value and quantity, respectively, of imports of tariff line _h_ from country _j_ in year _t._ As mentioned above, we have these data for 2001 through 2010 and the first half of 2011. Since we are interested in the MFN tariff, we exclude imports from the Commonwealth of Independent States (CIS) countries, because CIS imports enter tariff-free for the most part.<sup>14</sup> Suppose further that are ad valorem and specific parts of the tariff applied to the tariff line _h_ at time _t,_ t=2001-2011.

> 12 Our source for exchange rates is: http://www.oanda.com/currency/historical-rates/.

> 13 Footwear is an exception where the sum of the specific and ad valorem tariffs is applied and there are a limited number of tariff lines where only specific tariffs apply.

> 14 In addition to the CIS agreement and the Customs Union agreement with Kazakhstan and Belarus, Russia has bilateral free trade agreements with most of the CIS countries. Despite these agreements there are exceptions to the principle of tariff free access to the Russian market by CIS exporters, but these exceptions are reportedly not common.

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9 schooling begins _<u>a</u>_ <u>, and the probabilities for schooling</u> _πs_ ( _·_ ) and work _πe_ ( _·_ ).

We obtain the population growth rates _gb_ and the mortality functions _δ_ ( _·_ ) from United Nations (2019). We use the medium variant forecasts over the 2020 _−_ 2050 period, assuming that ̄ _a_ = 100. Thus, _δ_ ( _a_ ) = 1 for _a ≥_ 100.<sup>16</sup> We assume schooling starts at age _<u>a</u>_ = 6. Agents enter the work force after age _aw_ = 18 unless they are still in school. We then set the schooling transition probabilities _πs_ ( _·_ ) so as to exactly match the schooling distribution for the relevant country group, based on data from the I2D2 database, the World Bank database of more 1,000 household surveys that we use to estimate the returns to education and experience (see below).

We make the following assumptions regarding the probability of working _πe_ ( _·_ ). We assume that agents may start work once they complete schooling, or reach 18 years of age, whichever is later. At that point, a share _lp_ of agents participate in the labor force. Those that participate face youth unemployment with probability _uy_ each year until they are 24. Above 24 they face unemployment each period with probability _u_ , until they retire at age _R_ = 65. We assume unemployment and participation are equal across schooling and age groups. We measure _lp_ , _u_ and _uy_ for each group using data from World Bank (2020b). Data are based on population-weighted means.

Now we turn to the selection of the returns to schooling and experience. We model _rs_ ( _·_ ) and _re_ ( _·_ ) as step functions so as to capture salient features of the data with few parameters, in order to be able to estimate them consistently. Specifically, we assume that schooling returns _rs_ ( _·_ ) have two values _<u>rs</u>_<sup>and ̄</sup><sup>_rs_such that:</sup> where _s >_ 13 supposes that the agent has completed college.

Experience returns _re_ ( _s, p_ ) have three values: The assumption that _re_ ( _s, p_ ) = 0 after a certain point reflects the finding in the 16Of course some people do exceed 100 years of life, but this is a very small proportion of the population in all countries. See Web Data Appx. _A._ 1 for these distributions and for details of their calculation.
that supply-side constraints do not matter in rural areas. For example, we find that rural firms located in areas where access to credit is a problem that tends to have much lower TFP. However, it seems that from a policy perspective the returns to alleviating supply-side constraints in rural areas may be limited if demand-side problems persist.

Promoting market towns might help facilitate geographic targeting of supply side interventions, such as investments in roads, and could simultaneously boost local demand. The abysmal dynamic performance of non-farm firms in rural towns reminds us, however, that these towns themselves may need to be better integrated with the economy to enable the dynamic gains to market integration to materialize.

# **6 References**

- Ackerberg, D., Benkard, Lanier, C. Berry, S. and Pakes, A. (forthcoming) “Econometric Tools for Analyzing Market Outcomes” _Forthcoming chapter in Handbook of Econometrics_ , Vol.  6.

- Audretsch, D. (1995) “Innovation, growth and survival” _International Journal of Industrial Organisation_ 13:441–457.

- Ayyagari, M., Beck, T. and Demirguc-Kunt, A. (2003) "Small and medium enterprises across the globe: a new database" _World Bank Policy Research Working Paper_ No. 3127.

- Baptist, S. (2008) “Skills and Technology: will South Korea turn into Ghana” _Mimeo, Oxford University._

- Bardasi, E. and Getahun, A. (2007). “Gender and Entrepreneurship in Ethiopia.” _Background paper to the  Ethiopia Investment Climate Assessment._ Washington DC: World Bank. Processed.

- Barret, C. Reardon, T. and Webb, P. (2001) “Non-farm Income Diversification and Household Livelihood Strategies in Rural Africa: Concepts, Dynamics and Policy Implications” _Food Policy_ 26(4): 315-331

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