rafmacalaba/datause-encoder-data huggingface dataset:
down substantially, except in exceptional cases such as „remote villages and hamlets‟. India now relies on a mix of mechanisms including various tax and generation-based incentives, renewable purchase obligations, capital subsidies and accelerated depreciation. Yet, the accumulation of incentive programs and the failure to coordinate them is thought to hinder the development of renewable energy resources in India as it results in unnecessary delays and conflicts (ESMAP, 2011a).

In the Philippines, the portfolio of policy instruments includes duty-free importation of equipment, tax credits on domestic capital equipment and services, special realty tax rates, income tax holidays, net operating loss carry-over, accelerated depreciation and exemption from the universal charge and wheeling charges (WWF, 2008).

# **6.2. Implementation challenges**

Sensitivity to policy costs is more significant in developing country markets such as India, China, Brazil, Philippines and Bangladesh than in more developed economies. Thus, a common approach toward renewable energy technologies, seen in developing countries, is to “rationalize development and deployment strategy” (MNRE 2006) of renewable energy technologies. For instance, India planned in its eleventh Five-Year plan (2007-2012) to install 15,000 MW of grid-connected renewable energy and it was widely believed that this market expansion would be driven by wind, micro-hydro and biomass, as the plan recognized that solar PV would be an option only if the prices come down to levels comparable to micro-hydro.

More recently, the National Solar Mission promoting solar power in India has been launched. The first phase (2009-2013) targets increases in the utility grid power from solar sources, including CSP, by over a 1 GW (ESMAP, 2011a). By 2022, 20 GW of solar capacity is to be added in India. The approach to the renewable energy mix in China, Philippines and Bangladesh represents similar priorities of rationalizing the policy costs. In Brazil, as in other developing countries, the minimal policy cost is ensured via technology-specific and reserve energy auctions (ESMAP, 2011b) as the cheapest renewable energy projects are implemented first.

Solar PV is recognized as serving a niche market that is very important in developing countries – electrification of rural and peri-urban areas that do not yet have access to the electric 35
**<u>Table 6.2.  Estimated Annual Cost of Crop Losses Due to  Land Degradation</u>**

||Total<br>Low|Loss(billion Rs<br>Mean|)<br>High|% of GDP<br>2010|% GDP of<br>thepoor|
|---|---|---|---|---|---|
|Salinitylosses|63|110|148|0.2%|1.1%|
|Waterlogged land losses|103|133|163|0.2%|1.2%|
|Erosion losses|320|460|600|0.7%|4.1%|
|Total CropLand Degradation Losses|480|703|910|1.1%|6.4%|
|<br>_Source:_Staff estimates.||||||

# **_6.3 Rangeland Degradation_**

Land use reported in India suggests that the main causes of rangeland degradation in India are irrational land use management practices leading to denudation of vegetation from rangelands which, exacerbated by intermittent droughts, has resulted in many pockets of desertification. According to land use data from Indiastat.com about 10 million hectares are classified as permanent pastures. At the same time, about 1.5 times more land, including that under miscellaneous tree crops and groves and cultivable waste land, is also used as pastures. There is a substantial share of degraded lands within all these land categories. Forest lands that are used as pastures are estimated in the next section to avoid double-counting. An estimated 60 percent of livestock grazes in the forest area (Kapur et al., 2010).

The loss in yield is valued in two ways. In the first method the reduction in fodder production is valued at the price of fodder. In the second method the loss of fodder is converted into a loss of livestock based on livestock feed requirements and a value is attached to the loss of livestock. In both cases the hypothetical value against which losses are calculated is one in which original productivity prevails.

The estimated annual cost of rangeland degradation for the two methods is summarized in Table 6.3. The mean of two estimates is 405 billion Rs at 0.6% of GDP in 2010 or 3.6% GDP of the poor.

**Table** **<u>6.3. Annual Cost of Rangelands Degradation in India</u>**

||Billion RS.|% of GDP|% of GDP<br>of thepoor|
|---|---|---|---|
|Market value of fodder losses|400-800|0.6-1.2%|3.6-7.2%|
|Foregone livestock income from fodder losses|170-256|0.3-0.4%|1.5-0.2.3%|
|Mean cost|405|0.6%|3.6%|

_Source:_ Staff estimates.

# **VII    Forest Degradation**

The cost of deforestation and degradation of forests is the aggregate social loss associated with degraded or deforested lands. These losses include, in theory, a wide range of local, regional, national, and even global costs. Examples include direct losses of timber, fuel wood and nontimber products, recreation and tourism losses and indirect use losses (such as those associated 25
> _N_ % ˆ δ δ _jPMG_ = <u>∑</u> _<u>i</u>_ = <u>1</u> _ij_ , _j_ = 1, K , _q_ − 1, , and θ<sup>ˆ</sup> _PMG_ = θ<sup>%</sup> . This highlights both the pooling implied _N_

by the homogeneity restrictions on the long-run coefficients and the averaging across groups used to obtain means of the estimated error-correction coefficients and other short-run parameters.

We briefly discuss one important modeling issue. The PMGE is legitimate only where long run parameters are homogeneous across groups. Tests of homogeneity of error variances and/or short- or long-run slope coefficients can be easily carried out using LogLikelihood Ratio tests, since the PMG and dynamic fixed effects (DFE) estimators are restricted versions of (possibly heterogeneous) individual group equations. However, we note that the finite sample performance of such tests is generally unknown and thus unreliable. An alternative would be to use Hausman (1978) type tests. The mean group (MG) estimator<sup>3</sup> provides consistent estimates of the mean of the long-run coefficients, though these will be inefficient if slope homogeneity holds. For example, under long-run slope homogeneity the PMG estimators are consistent and efficient. Therefore, the effect of both long-run and short-run heterogeneity on the means of the coefficients can be determined by the Hausman test (hereafter _h_ test) applied to the difference between MG and PMG or DFE estimators. It is this approach that is adopted in the present study.

As long as sector-homogeneity is assured, the PMG estimator offers efficiency gains over the MG estimator, while granting the possibility of dynamic heterogeneity across sectors unlike the DFE estimator. In the presence of long run homogeneity, therefore, our preference is for the use of the PMG estimator.

Finally, it is worth pointing out that a crucial advantage of the estimation approach of the present paper is that dynamics are explicitly modeled.

# **_The data_**

The summary statistics for the fully balanced (1,472 observations for each variable), 52country panel data for the period 1980-2002 from the World Bank World Development Indicators database are provided in Table 1. The list of countries is provided in Appendix A.

The dependent variables are (i) electricity production per capita (in kwh) (elepropc), and (ii) total telephone lines (fixed lines and cellular phone subscribers) per 1,000 population (telpc). The independent variables are: GDP per capita in PPP terms (constant US$, 2000) (gdppc), shares of agriculture (yag), manufacturing (yman) and services (yserv) in real GDP, and a dummy variable for a structural break arising from the emergence of the cellular lines (in 1996).

Demand for electricity production and telephone lines were estimated separately for each infrastructure service. Table 1 shows means, standard deviations, and other summary

> 3 See Pesaran and Smith (1995).

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10

# **3. Background**

The total global building volume equals 1,593 billion m<sup>3</sup> ( _∼_ 1.52 million Empire State Buildings), resulting from a total building area of _∼_ 300,000 km<sup>2</sup> and a mean height of _∼_ 5 m. We obtain 1,558 billion m<sup>3</sup> in WSF3Dv1, implying that it missed 2% of global volumes. WSF3Dv2 mostly improves upon WSF3Dv1 for cities, where WSF3Dv1 missed 5% of global volumes (we get 9% for cities above 5 million today). Indeed, WSF3Dv1 disproportionately missed high-rise buildings and skyscrapers typically found in cities. This holds as well in the developing world where the same missing shares are 2%, 4%, and 8%, respectively. Focusing on cities, the volume discrepancy between WSF3Dv2 and WSF3Dv1 is then larger for the developing economies of Latin America & the Caribbean (LAC) (7%) than in developing Asia (3%) or Africa (1%).

Regional statistics from WSF3Dv2 are provided in Table 1. Developing economies account for two-thirds of global volumes. Upper-middle, lower-middle, and low income economies account for 70%, 25%, and 5% of total volumes in the developing world, respectively. Africa accounts for 9% of global volumes despite comprising almost 18% of the world’s population.

Table 1: Selected summary statistics for several groupings of countries, 2012/2019

|Economies|Volume km<sup>3</sup>|Per Cap. m<sup>3</sup>|% Outward|% Upward|% Low-Rise|% High-Rise|
|---|---|---|---|---|---|---|
|World|1593|207|52|48|46|2|
|Developed|577|467|43|57|53|4|
|Developing|1016|157|54|46|45|1|
|High-Income|577|467|43|57|53|4|
|Upper-Middle-Inc.|705|246|46|54|52|2|
|Lower-Middle-Inc.|254|87|59|41|40|1|
|Low-Income|55|83|68|32|32|0|
|Africa|148|113|65|35|35|0|
|Asia|749|164|50|50|48|2|
|Europe|372|498|43|57|55|2|
|Latin America & Carib.|115|177|54|46|43|3|
|North America|193|526|47|53|51|2|
|Pacifc|16|375|54|47|45|2|

_Notes:_ This table shows summary statistics for four groupings of countries. Developed Status is based on the World Bank (WB)’s classification of countries in 2019 (= high-income economies). Outward volume = built area x 2.5m. Upward volume = volume - outward volume. High-rise volume = from high-rises ( _≥_ 50 m). Low-rise volume = from low-rises (between 2.5m and 50m).

In general, the mean volume per capita in the world is 207m<sup>3</sup> . Assuming housing accounts for half the building stock and a ceiling height of 2.5m, the average resident occupies 40m<sup>2</sup> . But volume per capita in low- (LIC), lower-middle (LMIC), upper-middle (UMIC), and high-(HIC) income countries is 83, 87, 246, and 467m<sup>3</sup> , respectively. Thus, the average resident in LICs, LMICs, UMICs, and HICs possibly occupies 17, 17, 49, and 94m<sup>2</sup> of living space.
timing by borrowing. The cost of delaying is higher future inflation once the speculative attack takes place. This raises the issue of what monetary policy the government should pursue. Section 5 shows that in our simple model, the optimal policy is to abandon fixed exchange rates as soon as information about higher prospective deficits arrives. Since this result reflects, in part, the absence of nominal rigidities in our model, it serves primarily to highlight the costs of delaying a speculative attack. In reality, these costs must be counterbalanced against the benefits (if any) of delaying.

Section 6 offers empirical evidence in support of the two key assumptions in our model: (i) large losses in the banking sector are associated with increases in governments' prospective deficits; and (ii) the public knows that banks are in trouble before the exchange rate crisis occurs. To address (i) we use information on pre and post currency crisis loan default rates to generate rough estimates of governments' total implicit liabilities to the financial sector. To address (ii) we construct and analyze stock market based measures of the value of financial and nonfinancial sectors in the crisis countries. We find strong evidence that in Korea, Thailand, and to a lesser extent Malaysia and the Philippines, the value of the financial sector had been declining, in both absolute and relative terms, well before their currency crises. Coupled with the institutional information summarized by Corsetti, Pesenti and Roubini (1998a), IMF (1998) and Lane et al (1999), this constitutes strong evidence in favor of assumption (ii). Finally, Section 7 presents concluding remarks and discusses some shortcomings of our analysis.

# **2.** A Benchmark Model

In this Section we describe a continuous time, perfect foresight endowment economy populated by an infinitely lived representative agent and a government. All agents, including the government, have access to international capital markets.

5
levied on imports originating in Mercosur countries (Brazil, Paraguay, and Uruguay). Our source for this exercise is the Commercial Liberalization Program included in the Asunción Pact (1991). This program established a progressive, automatic and linear reduction in the tariffs assigned to imports from the Mercosur countries.

First, we computed the share of the imports from the Mercosur in the year 1990, prior to the signing of the agreement. Next, we adjusted the original import tariffs considering the estimated ratio and the scales of tariff reduction established by the Asunción pact: Import Tariffit = Intrazone Tariffit ∗ Ratio + Original Import Tariffit ∗ (1—Ratio), where i is a 3-digit ISIC code and t is a year.

In order to provide some external consistency check on our tariff series, we compared our data with some of the indicators provided by Berlinski (1994) and Berlinski (2003). These are some of the most comprehensive accounts of the history of trade policies in Argentina. Overall, the numbers on tariff protection reported by Berlinski are quite similar to our numbers. For example, he reports that the average tariff before the liberalization of the 1970s was 99 percent (our estimate is around 100 percent). For the 1990s, Berlinski’s estimates of the average tariff fluctuate around 16-19 percent, and our averages are around 18 percent. During the 1980s, he reports estimates for 1988, before the reform of the late 1980s and early 1990s, of 48 percent. We instead find that the average tariff is close to 30 percent. Even though there are differences in methodologies, mostly in aggregating the average tariffs, it is clear that our estimates are pretty consistent with those in Berlinski. It is important to remark that while Berlinski built tariff measures for selected years, in this paper we need a full time series of historical trade data. The series produced here are, we believe, an important contribution of our work.

# The Permanent Household Surveys

Data on wages and worker characteristics come from the Encuesta Permanente de Hogares (EPH, Permanent Household Survey). These surveys collect information on household 20
# **Figure 7:  Labor force participation, with interaction with age at arrival in the US. Males.**

<!-- Start of picture text --> 0.02<br>0<br>-0.02<br>-0.04<br>-0.06<br>-0.08<br>-0.1<br>-0.12<br>Whites out <10Whites out 1020Whites out> 20Blacks inBlacks out < 10Blacks out 1020Blacks out > 20Asians inAsians out < 10Asians out 1020Asians out > 20Hispanics inHispanics out < 10Hispanics out 1020Hispanics out > 20<br><!-- End of picture text -->

**Figure 8: Labor force participation, with interaction with age at arrival in the US. Females.**

<!-- Start of picture text --> 0.06<br>0.04<br>0.02<br>0<br>-0.02<br>-0.04<br>-0.06<br>-0.08<br>-0.1<br>-0.12<br>-0.14<br>Whites out <10Whites out 1020Whites out> 20Blacks inBlacks out < 10Blacks out 1020Blacks out > 20Asians inAsians out < 10Asians out 1020Asians out > 20Hispanics inHispanics out < 10Hispanics out 1020Hispanics out > 20<br><!-- End of picture text --> Data from the 2000 Census IPUMS data (5% sample), population aged 25-65. The figures display the marginal coefficients and their 95% confidence intervals on interaction terms for racial groups, foreign born status (“in” for natives, “out” for foreign born; “<10” for arrived younger than 10, “1020” for arrived between 10 and 20, “>20” for arrived older than 20) and age at arrival in the US in probit regressions with labor force participation as the dependent variable. “White natives” is the omitted dummy. The regression results are further detailed in table A3.

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**Figure 26  Growth of Different Segments of the Third Pillar**

<!-- Start of picture text --> 240<br>220<br>200<br>180<br>160<br>140<br>120<br>100<br>1996 1997 1998 1999 2000 2001 2002 2003<br>Pillar 3a (CP) Pillar 3b (CP) Pillar 3a (A) Pillar 3b (A)<br><!-- End of picture text --> Source: Bundesamt für Privatversicherungen (2003) Seventy-five percent of the accumulated pillar 3a capital in insurance companies is invested in one of the top five players, i.e., _Basler Life, Generali Person, Rentenanstalt (Swiss Life), Winterthur Life_ and _Zürich Life_ . Within this group, _Rentenanstalt (Swiss Life)_ is the leader, with a share of almost 22 percent of the total capital. The remaining 25 percent is distributed among many different insurance companies. Note that even some foreign companies, such as _Império_ , are active in this part of the Swiss pension system. The situation with respect to pillar 3b is similar. The same few companies cover the majority of the invested capital. Nevertheless, with _Patria_ and _Providentia_ there are two additional players in that market that have no weight in the market of pillar 3a.

# **6.4 Volume of the Third Pillar (Banks)**

Table 34 presents the conditional precautionary capital (i.e., pillar 3a with capital payments<sup>72</sup> ) managed by banks. In 2004, the total amount reached almost 24 billion CHF. This corresponds to an increase of 46 percent, compared to the 16.4 billion CHF in 2000. Prior to this date the investment remained almost constant around 17 billion CHF (c.f. also Figure 27). In 1997, the money allocated to banks represented 29 percent of the amount at life insurance companies (IC). The different growth patterns of banks and insurance companies can be seen in Figure 27.

> 72Some banks also offer annuities, but on behalf of life insurance companies. Thus, these annuities appear in the balances of the insurance company.
parameters. Our method is computationally efficient and can utilize standard statistical software, widely used to estimate gravity equations in the context of migration and trade flows<sup>4</sup> . Similar to the other non-iterative solution methods, the state space has to be small compared to the backwards solution methods.

In the next section, we present a representative discrete choice model that can be estimated with our method. In the following sections, we summarize our estimation strategy, and provide an example application, and present simulation results.

# **1 Model**

Consider an economy with infinitely-lived _L_ agents and _N_ sectors, where each agent is in a discrete state _s ∈ S_ . Sectors can be industries, occupations, cities, countries, or any combination of such choices, while the state could be the type of agent such as education level, gender, age or other individual characteristics. It is also possible to consider economic policies as a part of state space, such as trade policy, migration policy, or education policy. We can also incorporate unobserved types, which is omitted from this section for the sake of clarity.

A type _s_ agent chooses a sector _i ∈{_ 1 _,_ 2 _,_ 3 _, .., N }_ in the end of period _t −_ 1, and receives instantaneous utility _u_<sup>_i,s_</sup> _t_ at time _t_ defined as where _wt_<sup>_i,s_</sup> is the observed sector specific random payoff common to all type _s_ agents working in sector _i_ with finite moments, and _η_<sup>_i,s_</sup> is the unobserved sector specific iid utility shock also common to all type _s_ agents. Hence, the state of each agent can be summarized with the pair ( _i, s_ ) where _s_ is the type and _i_ is the current sector.

> 4PPML estimation is based on an orthogonality condition, which has an analytical derivative. This computational convenience makes the estimation process much faster than alternatives. For example, it converges within minutes even with hundreds of choices and many structural parameters.

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We aim to answer three main questions. First, is there a gap between students’ aspirations and expectations, which would indicate that they feel constrained when imagining their future education? If yes, how large is it and how does it differ by gender? Second, how do aspirations, expectations, and having a gap between the two correlate with schooling outcomes such as grades and school attendance, and are these relationships different for girls and boys? Such correlations can suggest the extent to which aspirations and expectations relate to students’ current effort in school. Third, what factors do students perceive as their main barriers to fulfilling their aspirations, and do these factors vary by gender?

Our study has at least three main contributions. First, we compare the relationship between aspirations and outcomes with the relationship between expectations and outcomes from one of the few surveys of students measuring both concepts. Second, this is one of the first studies to discuss students’ constraints related to their aspirations. Third, our analysis systematically assesses gender differences, a relatively understudied topic, and our results are based on a large-scale survey from a middle-income country.

We find that most Indonesian students aspire to high levels of education but only half of the students expect to complete their aspired level. Three-quarters of the students aspire to postsecondary education, dominantly university education. However, on average, students expect educational levels that represent three years less of education than their aspired level. A non-negligible 15 percent of students have a 9-year aspirations-expectations gap, which represents aspiring for an 8-year university degree and expecting to complete the equivalent of high school. While boys tend to aspire to lower levels of education, girls aspire higher but expect much less than they aspire to: while 86 percent of girls aspire to a postsecondary degree (compared to 67 percent of boys), only 55 percent expect it (compared to 42 percent of boys).

Higher aspirations correlate with better grades and, up to a point, lower absenteeism. Aspirations are positively correlated with grades across the distribution, although with decreasing magnitude. Each additional year of aspired education is correlated with fewer missed days of school, up to 19.7 years of aspired education (more than a master’s degree) when this trend reverses. The magnitude of correlation between aspirations and schooling outcomes is notable: aspiring to achieve a bachelor’s degree rather than a high school degree is associated with grades that are 0.14 standard deviation higher and with missing 0.32 fewer days of school per month. Whereas the relationship between aspirations and grades is similar for boys and girls, the relationship between aspirations and absenteeism is stronger for boys, suggesting that girls may have less control over the reasons for which they miss school.

3