rafmacalaba/datause-encoder-data huggingface dataset:
# Table 8. Subsidies to renewables financed by carbon taxes (scenario RRsub).

|||2015|||203|0||
|---|---|---|---|---|---|---|---|
||Base case|RRsub|R1CUT|Base case|RRsub|R1CUT|R2CUT|
|Variable|values|% change f|rom base|<br>values|% cha|nge from|base|
|GDP (billion ¥2010)|58,956|-0.018|-0.004|126,235|-0.53|<br>-0.11|<br>-0.74|
|Consumption (bil ¥2010)|25,290|<br>-0.014|-0.003|67,264|-0.45|<br>-0.11|<br>-0.71|
|Investment (bil ¥2010)|25,374|-0.020|-0.007|43,747|-0.61|<br>-0.13|<br>-0.86|
|Government consumption (bil¥)|6,973|-0.023|0.00|12,785|-0.40|<br>0.00|<br>0.00|
|Fossil energy use (mil tons of sce)|4,005|-0.41|-0.21|5,958|-5.8|<br>-2.6|<br>-13.1|
|Coal use (million tons)|3,666|-0.63|-0.29|4,610|-10.1|<br>-4.1|<br>-20.0|
|Oil use (million tons)|537|-0.03|-0.04|835|-1.1|<br>-0.7|<br>-4.0|
|Gas use (billion cubic meters)|166,354|-0.29|-0.09|381,163|-6.0|<br>-1.8|<br>-9.4|
|Electricity (billion kWh)|5,554|<br>-0.09|-0.11|8,951|-1.9|<br>-1.5|<br>-7.9|
|Electricity price||-0.18|0.10||-2.2|<br>1.4|<br>7.9|
|CO2 emissions(mil tons)|9,819|-0.48|-0.23|<br>12,797|-7.9|<br>-3.3|<br>-16.0|
|Cumulative CO2 (2015-2030)|||<br>|184,840|-2.1|<br> <br>-1.9|<br> <br>-9.8|
|Carbon taxyuan/ton CO2||¥2.8|¥1.6||¥58|<br>¥26|<br>¥157|
|Electricity generation|share o|f total gene|ration|sha|re of total|generatio|n|
|Coal|0.714|0.709|0.714|0.646|0.584|<br>0.638|<br>0.602|
|Gas|0.027|0.027|0.027|0.051|0.046|<br>0.052|<br>0.054|
|Nuclear|0.034|<br>0.035|0.034|0.076|0.092|<br>0.078|<br>0.087|
|Hydro|0.162|0.166|0.163|0.127|0.145|<br>0.130|<br>0.145|
|Other|0.024|<br>0.024|0.024|0.030|0.038|<br>0.031|<br>0.036|
|Wind|0.034|0.035|0.034|0.056|0.075|<br>0.057|<br>0.061|
|Solar|0.003|0.004|0.003|0.015|0.020|0.015|0.016|

In order to get an idea of the economic impacts of this push away from coal and towards non-fossil fuels in the IEA Current Policies Scenario, we conducted an alternative set of simulations with a “no coal reduction” (NCR) scenario and a “CPS targets” scenario. In the NCR case we allow the coal share to fall from the first year 2010 to 2016 as in the base case, but then maintain close to that share out to 2030. The share of hydro in the base case is falling due to the projected difficulty in finding more hydro resources, and we maintain this in NCR. Note that even with the falling share the absolute production of hydro power is rising over time. The ‐26‐
degree of democracy relate to contemporary income inequality.

My findings show that democracy appears to be non-monotonically related to income inequality, and that it also appears to be non-monotonically related to the level of and dispersion in educational attainment. In particular, long periods of high-quality democratic institutions can lead to substantial decreases in inequality on income and education. My interpretation of these results is: It seems plausible to regard democracy as an important institutional factor in the development of education. Political elites in a democracy have electoral incentives to improve the quality of life for the least advantaged; incentives that are present to a much smaller degree in authoritarian systems. By changing the education output, political regimes map democracy onto long-run changes in the distribution of human capital and then onto contemporary changes in income inequality.

One of the concrete implications of these results is that if democratic institutions contribute to secular-historical changes in policy outcomes, then it is inappropriate to judge the results of institutional reforms on the basis of immediate policy gains. The immediate effects of institutional change are often negative since such change introduces uncertainties and information costs in the short run. Positive changes are likely to take longer to materialize, since they depend upon the establishment of a new equilibrium. (Gerring et al., 2005.) It is unrealistic to expect such reforms to show instantaneous results.

Many important points remain to be considered, I focus on a few important ones. First, what would happen if we consider younger cohorts by having access to recent household survey data –those born after the third democratic wave (Mainwaring and Hagopian, 2005). These cohorts should show larger democracy stocks. Using such data would allow us to further test the validity of the results herein. Second, I did not spend much time analyzing the non-monotonic relationship between the stock of democracy and inequality from the theoretical perspective. It is precise to rethink Acemoglu and Robinson’s theory of the political Kuznets curve from the inter-generational perspective, introducing the role of political change (Person and Tabellini, 2009). However, this is a task that I defer to future work.

# **References**

> Acemoglu, D. (2008). Oligarchic vs. democratic societies. _Journal of the European Economic Association_ , 6(1):1–44.

20
2015; Mbuvi et al., 2012). Many observers would argue that the same holds for Latin American water regulatory agencies for instance.

|||**T**|**able 2: Summar**|**y of data set**|||
|---|---|---|---|---|---|---|
||**Countries with an**<br>**autonomous water**<br>**regulatory agency**|**Countries**<br>**with PPP**|**Countries with**<br>**both an**<br>**autonomous**<br>**regulator and**|**Countries with PPP**<br>**but no**<br>**autonomous**<br>**regulatory agency**|**Countries with an**<br>**autonomous**<br>**regulatory agency**<br>**but no PPP**|**Countries with no**<br>**autonomous**<br>**regulatory agency**<br>**and no PPP**|
||||**PPP**<br>**Full Sample (177**|**countries)**|||
|**Number**|80|157|69|89|11|8|
|**Share (in %)**|45.2|88.7|39.0|50.3|6.2|4.5|
|||**Deve**|**loping country sam**|**ple (123 countries)**|||
|**Number**|55|108|48|60|7|8|
|**Share (in %)**|44.7|87.8|39.0|48.8|5.7|6.5|
|||**Dev**|**eloped country sam**|**ple (54 countries)**|||
|**Number**|25|49|21|29|4|0|
|**Share (in %)**|46.3|90.7|38.9|53.7|7.4|0.0|

**Source: Bertomeu and Estache (2017)**

What we do know, from solid theoretical and empirical research, is that the creation of a separate regulatory agency does not guarantee accountability since the water sector continues to be seen as rent extracting, as documented by Transparency International for instance, despite the institutional changes adopted in many countries.<sup>44</sup> We also know that accountability can be influenced by the degree of commitment, the skills of regulatory staff or civil servants, the protection from political interference and from capture by operators (e.g. Laffont (2005)). The relevance of legal context (Guerreiro (2011), culture (Athias and Wicht (2014)) or the regulatory board composition (World Bank (2017))) has also been demonstrated. And anthropologists and sociologists have added that the relevance of local norms of acceptance or resistance are key drivers influencing accountability, particularly in the context of changes in power structure that can be associated with imported governance structures (e.g. Ostrom (1990) or more recently Pal (2009)). All of these dimensions interact to define the level and nature of accountability observed for any given WSS sector. Not all of these accountability drivers can be acted upon by regulators, but some can and this is where there is

> 44 <u>http://www.transparency.org/topic/detail/water</u>

40
**Table 11.E. Model comparison: Diebold and Mariano (1995) test for nickel prices**

|||||**Bivariate**|||**Machine**|
|---|---|---|---|---|---|---|---|
|**Approach**|**Horizon**|**Consensus**|**Futures**|**correlations**|**BVAR**|**OEM**|**learning**|
|Consensus|3|...|||**||**|
|Futures|3|*|...||***||***|
|Bivariate correlations|3|*||...|***||***|
|BVAR|3||||...|||
|OEM|3|*||†|*|...|**|
|Machine learning|3||||||...|
|Consensus|6|...|||**||*|
|Futures|6|**|...||**||***|
|Bivariate correlations|6|||...||||
|BVAR|6||||...|||
|OEM|6|*|||*|...|**|
|Machine learning|6||||||...|
|Consensus|9|...|||**||*|
|Futures<br>|9<br>|**|...|†|**<br>||***|
|Bivariate correlations|9|||...|†|||
|BVAR|9||||...|||
|OEM|9|**||†|*|...|**|
|Machine learning|9||||||...|
|Consensus|12|...|||**||**|
|Futures|12|**|...|†|**|*|***|
|Bivariate correlations|12|||...||||
|BVAR|12||||...|||
|OEM|12|***||†|***|...|**|
|Machine learning|12||||||...|
|Consensus|15|...|...|...|**||**|
|Futures|15|...|...|...|...|...|...|
|Bivariate correlations|15||...|...||||
|BVAR|15||...|...|...|||
|OEM|15|**|...|...|**|...|***|
|Machine learning|15||...|...|†||...|
|Consensus|18|...|...|...|***||**|
|Futures|18|...|...|...|...|...|...|
|Bivariate correlations|18||...|...||||
|BVAR|18||...|...|...|||
|OEM|18|**|...|...|**|...||
|Machine learning|18||...|...|†||...|
|Consensus|21|...|...|...|***||*|
|Futures|21|...|...|...|...|...|...|
|Bivariate correlations|21||...|...||||
|BVAR|21||...|...|...|||
|OEM|21|**|...|...|***|...||
|Machine learning|21||...|...|||...|
|Consensus|24|...|...|...|***||*|
|Futures|24|...|...|...|...|...|...|
|Bivariate correlations|24||...|...||||
|BVAR|24||...|...|...|||
|OEM|24||...|...||...||
|Machine learning|24||...|...|**||...|

_Source_ : World Bank.

_Note_ : Diebold Mariano statistic tests whether the forecasts in the model indicated in the row is more accurate than the forecast of the model indicated in the column. *** indicates statistically significantly more accurate model in the row at the 0.1 percent significance level, ** at the 1 percent level, * at the 5 percent level, and † at the 10 percent level.

56
welfare. In the first measure, the scale measures agreement with the statement “All things considered, I am satisfied with my life now” in five steps (strongly disagree, disagree, neither agree nor disagree, agree, and strongly agree). In the second measure, the scale is numeric, ranging from 1 to 10, where 1 is labeled “very dissatisfied”,10 is labeled “very satisfied”, and the intermediate values are unlabeled.

The two measures are strongly correlated – at country-level one of the scales accounts for 73 percent of the variation in the alternative scale.<sup>19</sup> However, country rankings are not robust to the choice of the life satisfaction measure. In figure 4 below, countries are sorted in ascending order by the share of the country’s population who either agrees or strongly agrees with the question “All things considered, I am satisfied with my life now”. While life satisfaction in Sweden is highest by either of the two measures, Italy ranks ten positions higher – and Kyrgyzstan 14 positions lower – if ranked by the 1-10 life satisfaction measure rather than the 5-step qualitative scale.

**Figure 4:** Ranking SWL across countries with alternative scales

<!-- Start of picture text --> 100% 9.0<br>90% 8.0<br>80% 7.0<br>70%<br>6.0<br>60%<br>5.0<br>50%<br>4.0<br>40%<br>3.0<br>30%<br>20% 2.0<br>10% 1.0<br>0% 0.0<br>Share of population satisfied with life, left axis<br>Average satisfaction score (1-10), right axis<br>Romania HungaryArmenia GeorgiaSerbia Ukraine Moldova BiH FYROM BulgariaLithuania Latvia Albania AzerbaijanMontenegroRussia Croatia KyrgyzstanItalyTurkeyBelarus Slovakia Kazakhstan Kosovo Overall Estonia Czech MongoliaPoland Slovenia Uzbekistan France UK TajikistanGermanySweden<br><!-- End of picture text --> Verbal responses to Likert scale questions (in the LiTS case “strongly disagree”, “disagree” etc.) may be preferred because these categories may be more understandable to the respondents than the 0-10 scale. On the other hand, such verbal categories may not carry the same meaning to everyone. There is some evidence that the numeric scale is less ambiguous, and potentially less problematic for interpersonal comparability (van Praag 2004).

We investigate the consistency between the two life satisfaction measures. Table 3 presents, for each of the countries in the LiTS II sample, the median life satisfaction score on the 1-10 scale for each of the categories of the life satisfaction measures based on the strongly disagree – strongly agree scale.

> 19 The implied correlation coefficient between the two variables is 0.85.

13
20

# C. Program Implementation

The data set on conditionality and implementation, with a much expanded sample of loans approved in FY79-88, showed most of the same patterns found in the fir-st Report, and revrealed some new ones (Webb and Shariff, 1990). Countries begin implementing their structural adjustment program before the adjustment loans become effective and frequently continue implementation after disbursement ends. Progress in implementation is measured by the share of conditions in the loan agreements that have been implemented by the time of final tranche release. Of all conditions in the loan agreements in the sample, 84 percent had been implemented at least substantially -- better than found in the first Report -- and 66 percent had beeen implemented fully or more than fully by the time of final tranche release.

Implementation rates increased during the 1980s, both for countries that had received adjustment loans since the early 1980s and for countries that started more recently. For the loans in the sample that had final tranche release in FY89, i.e., since the first Report, 99 percent of the conditions were implemented at least substantially, and 80 percent of the conditions as originally written were implemented fully or more. In the rare cases when a condition as originally written does not seem necessary, the Bank waives the condition, with approval from the Board. Not counting the one loan in the sample for which this occurred in FY89, this would raise the proportion of fully implemented conditions from 80 to 88 percent. Thus, the final tranches were released only when all conditions in the loans were at least substantially fulfilled. Implementation rates are lower for countries with higher rate of inflation. This finding illustrates the importance of macroeconomic stability once again.
-20-

# C. Overtime Returns to Education

Among the eighteen Latin America countries, eight country data sets allow us to examine the change in returns to education over the past decade and of comparing the results shown in Table 6 to those in Table 1. There exists a slight declining trend in the returns to education, while the ten year difference in the returns to education in Argentina and Panama is only marginal. (See also Psacharopoulos (1987), Psacharopoulos and Velez (1988) and Riveros (1990), on the experiences of other countries).

The behavior of rates of return to schooling by gender over time demonstrates an interesting pattern (Table 7). In the earlier period females did not have such a clear advantage over men in the returns they realized on their schooling investment; while in the early eighties, only three out of the eight countries show that the average rate of return to males is greater than their female counterparts.
# **5 Concluding remarks**

This paper aims to further characterize the business cycles of emerging market economies —and, especially, Latin American countries— relative to industrial economies. More specifically, we apply the BBQ algorithm developed by Harding and Pagan (2002) on the quarterly series of real GDP over the period 1980.Q1‐2006.Q2 for a sample of **35** countries, of which **23** are emerging market economies and **12** are industrial economies.

One of the main contributions of this paper is computing business cycle features for a wide array of emerging market economies using quarterly data.<sup>16</sup> We confirm the evidence that output fluctuations in emerging markets are more volatile than those of industrial countries. More specifically, we find that: (i) peak‐to‐trough phases ( _i.e._ contractions) are deeper and more frequent in emerging markets, and (ii) trough‐to‐peak phases (i.e. expansions) are more sizable but more volatile among emerging markets than among industrial economies.

Using the information on duration and amplitude, we also computed the cumulative variation in both the peak‐to‐trough and trough‐to‐peak phases of the cycle (say, output losses and output gains, respectively). We find that the cost of recessions (i.e. cumulative variation from peak‐to‐trough) is typically higher in emerging markets —which approximately triples the amount of output foregone from peak‐to‐trough in OECD economies. On the other hand, the cumulative output variation during expansionary phases of the cycle is larger among emerging markets —especially, among East Asian economies— than among industrial ones.

We then conducted an exploratory analysis on the determinants of the cost of recessions, as measured by the average output loss per year during the peak‐to‐trough phase of the cycle. We identify 126 recession episodes and, due to data availability for the

> 16 Typically, cross‐country studies for emerging markets and developing economies use annual data (e.g. Hausmann, Rodriguez, and Wagner, 2006)

33
surveys<sup>2</sup> , allowing us to utilize country fixed effects. Table 1 shows the countries included in our analysis along with the year in which firm-level surveys were conducted. This table also shows the year in which credit bureaus and/or credit registries were introduced.

In Figure 1 (Figure 2), we compute the annual mean for each measure of firm financing separately for the CB (CR) reform sample and control sample, and plot the time trend of these averages<sup>3</sup> . Concentrating on the first graph of the dichotomous variable for whether the firm has access to a loan, line of credit or overdraft, we notice that firms in both the treatment and control countries on average have increased access over the sample period. A reflexive comparison of the impact of CB reform on access to finance for only the countries that introduced a CB would have falsely attributed the secular upward trend to the impact of CB introduction. Our use of a counterfactual allows us to assess the causal impact of CB/CR reform in relation to a control group that did not introduce such reform. At the beginning of the sample period, the two groups have similar mean levels of access, but the increase in the treatment group as countries steadily introduce CB reforms is at a greater pace than the control sample. While in our regression analysis we will introduce various firm and country level controls, as well as well as fixed effects, Figure 1 provides preliminary suggestive evidence that the increase in access to finance is greater among the reform group than the control group. Our estimation strategy, after controlling for all relevant factors, will be able to identify the impact of credit information reform on access to finance as the differential increase of the reform sample over the control sample. The rest of the graphs in Figure 1 and Figure 2 show a qualitative similar summary.

> 2 Each round of survey consists of a repeated cross-section with a new nationally representative sample. A small sub-sample of firms in certain countries are re-interviewed to form a firm-level panel.

> 3 We regress country mean access to finance variables on year dummies, and plot the resulting predicted values to get the time trend.

9
alternative scenarios are compared. These across scenarios comparisons are the real value added of the modeling exercise rather than the levels the variables are estimated to be in a particular future year.

The BaU’s GDP and labor markets macro trends are linked to developments at the sectoral level (shown in Table 4). Output growth rates are slightly lower for the agricultural sectors than for the non-agricultural ones. Agriculture exports, due to falling primary commodity international prices, grow at a slightly lower pace than nonagriculture exports. Additionally, in agricultural sectors, employment of unskilled workers is stalled or reduced, whereas demand for skilled workers, whose wages are increasing at a contained pace, is increasing. Productivity gains dictate that less workers are needed to achieve the same output, and rising wages, in particular for unskilled workers, induce producers to substitute (although with a low level of substitution) skilled workers for unskilled ones. The rightmost panel of the table shows the relative sizes of sectors in terms of employment and the skill intensities of each sector. Services are the largest employers of both skilled and unskilled workers but, on average, they use skilled labor more intensively. Agriculture employs almost a third of unskilled workers and uses this factor quite intensively, whereas manufacturing labor intensities are in-between agriculture and services.

_<u>Table 4: BaU’s output and trade sectoral growth rates, and employment intensities</u>_

||<br>|<br>Annual av|<br>erage gro|<br>Labor D<br>wth rates|<br>emand|<br>by sec<br>Emp|<br>tor<br>loyment|<br>by s<br>percentag|<br>kill<br>es|
|---|---|---|---|---|---|---|---|---|---|
||Output|Imports|Exports|Skilled|Unsk.|Skilled|Unsk.|Skilled|Unsk.|
|CerealGrains|3.2|2.5|2.3|0.3|0.1|0|5|2|98|
|OilSeeds|3.1|2.2|2.4|0.1|-0.1|0|1|6|94|
|RawSugar|3.2|||0.2|0.1|0|1|4|96|
|OtherCrops|2.9|1.3|2.5|0.0|-0.1|1|12|3|97|
|Livestock|3.2|1.5||0.3|0.1|2|4|10|90|
|RawAnimalProducts|3.3|2.5|1.6|0.4|0.3|0|3|1|99|
|OilMinerals|3.3|3.0|2.9|1.5|1.7|0|0|15|85|
|LightManufacturing|3.3|0.8|3.7|1.0|1.2|1|2|16|84|
|AgriIndustriesExp|3.2|0.5|3.4|1.0|1.2|2|3|16|84|
|WoodProductsPaper|3.3|0.9|3.5|1.0|1.2|2|2|15|85|
|ChemicalsOilPr|3.3|1.8|2.9|1.1|1.3|2|1|30|70|
|MetalMineralProducts|3.5|1.8|3.3|1.2|1.4|2|2|17|83|
|MachineryEquipment|3.6|1.9|3.5|1.4|1.6|3|2|28|72|
|OtherServices|3.0|2.6|1.7|2.1|2.3|58|30|33|67|
|Construction|3.2|||2.3|2.5|2|8|6|94|
|TradeCommunication|3.1|2.4|1.8|2.2|2.4|15|18|17|83|
|PublicServices|3.1|2.7|1.7|2.2|2.4|9|4|41|59|
|Agri|3.0|1.9|2.4||0.0|4|27|6|96|
|Non-Agri|3.2|2.0|3.1||2.2|96|73|29|76|
|Economywide|3.2|2.0|3.1|2.0||100|100|28|76|

Source: Authors’ calculations. Note: the mapping of this table sectors and GTAP sectors is shown in appendix 7.5, see Table 24 and Table 25.

29
# 1. **Introduction**

In the mid-1980s, Mexico began a process of structural reform that fundamentally changed the economic environment facing productive enterprises. Liberalization began in 1984 and accelerated when Mexico joined the General Agreement on Tariffs and Trade in 1986. In 1989, the government began radical policy reforms to reduce government regulation and liberalize trade. The adoption of the North American Free Trade Agreement with the U.S. and Canada in 1994 intensified liberalization. These reforms replaced quotas with smaller tariffs, eliminated price ceilings and floors, simplified trade regulation, eased foreign direct investment restriction, and privatized state-owned enterprises.

Because of this international openness, technology now lies at the heart of Mexican economic activity. Globalization-induced competition has made firms increase the speed and efficiency of new technology adoption (TA). It has also inspired firms to increase research and development (R&D) budgets (OECD 2000).

However, liberalization has worsened Mexican wage equality (World Bank 2000). The Gini coefficient, which measures income inequality and is especially sensitive to changes around the population median, rose from 0.47 in 1984 to 0.53 in 1998 (Table 1). While the poorest quintile lost 1.3 percent of its income during this period, the richest decile increased its wealth by 7.7 percent. In relative terms, all strata except the richest lost income during this period (Figure 1).

**Table 1. Inequality in Mexico, Measured by Gini Coefficient**

|**Year**|**National**|**Urban**|**Rural**|
|---|---|---|---|
|1984|0.47|0.44|0.45|
|1989|0.52|0.50|0.44|
|1992|0.53|0.50|0.43|
|1994|0.53|0.51|0.42|
|1996|0.52|0.49|0.45|
|1998|0.53|0.495|0.48|

_SouIrce:_ Author's calculations based on ENIGH.

3