rafmacalaba/datause-encoder-data huggingface dataset:
**_Energy demand models for policy_** **_<u>formulation</u>_**

Koopmans, C. C. and D.W. te Velde, 2001, Bridging the energy efficiency gap: using bottom-up information in a top-down energy model, Energy Economics, 23, pp. 5775.

Kulshreshtha, M. and J. K. Parikh, 2000, Modeling demand for coal in India: Vector Autoregressive models with cointegrated variables, Energy, 25, pp. 149-168.

- Kulshreshtha, M., B. Nag and M. Kulshrestha, 2001, A multivariate cointegrating vector auto regressive model of freight transport demand: evidence from Indian railways, Transport Research, part A, pp. 29-45.

- Labys, W. C. and Asano, H., 1990, Process Models, Special issue of Energy, Energy, 15, 3&4, pp. 237-48.

- Laitner, J.A., S. J. DeCanio, J. G. Coomey and A. H. Sanstand, 2003, Room for improvement: increasing the value of energy modeling for policy analysis, Utilities Policy, 11, pp. 87-94.

- Lapillonne, B. and B. Chateau, 1981, The MEDEE models for long term energy demand forecasting, Socio-Economic Planning Sciences, 15(2), pp. 53-58.

Lapillonne, B., 1978, MEDEE-2: A model for long-term energy demand evaluation, RR-

- 78-17, International Institute for Applied Systems Analysis (IIASA), Austria (see http://www.iiasa.ac.at/Publications/Documents/RR-78-017.pdf).

- LEPII-EPE and Enerdata, 2006, Energy scenarios, technology development and climate policy analysis with the POLES modelling system, University of Grenoble, Grenoble, France (see http://web.upmf-grenoble.fr/iepe/textes/poles-0506.pdf).

LEPII-EPE, (2006), POLES model - State of the Art, University of Grenoble, France (see http://web.upmf-grenoble.fr/iepe/Recherche/Rech5.html).

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Policy Research Working Paper 7151

# **Abstract**

This paper analyzes Zambian export patterns using a new transaction-level trade data set for the period 1999–2011. The data show that, in international comparison, Zambian exports are exceptionally concentrated (on mining products). This reliance has been increasing in recent years. Zambia’s exports are also characterized by a high level of churning of firms and products. Multivariate models of survival probabilities suggest that exchange rate volatility and difficult access to imported inputs significantly inhibit diversified and stable exports. The econometric analysis is complemented with a qualitative study of the Zambian export sector. The analysis concludes that one of the main policy levers for unleashing Zambia’s full potential as an exporter is by facilitating access to imported inputs. Additional measures that ease foreign exchange transactions, simplify export and certification requirements, and increase the predictability of Zambia’s trade regime could be effective to promote Zambia’s nontraditional exports.

This paper is a product of the Trade and Competitiveness Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at Marius.Brulhart@unil.ch, ndihel@worldbank.org, and mkukenov@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 3. The average impact of an increase of one percentage point in the average gross wage on inflation, semi-structural model

|**Impact on core inflation**|**Impact on CPI inflation**|
|---|---|
|0.05-0.12|0.03-0.07|

Source: authors, based on NBR estimations Note: The impact on CPI inflation was determined by multiplying the impact on core inflation with the core index weight in the CPI basket.

The results may be counter-intuitive at first sight, as a large increase in minimum wage, over a relatively short time period, has a very small impact on inflation. There are a number of possible explanations for this small impact. First and probably foremost, the whitening of the labor market (the “Efeito Farol”) which means that the same amount of money was exchanged between employers and employees before and after the increase of the minimum wage, but in the latter case more is shown and subject to taxation. This explanation is supported by our finding regarding the impact on the shadow economy. Second, the very low starting point means that people earning the minimum wage probably used some of the higher income to repay their debt or to cover for essential needs for which they should have paid anyway, therefore not creating inflation. Nevertheless, we do not have sufficient data to validate this hypothesis, but it cannot be ruled out. Third, the minimum wage earners may have increased the demand for low-cost, locallyproduced goods and services, some of which are being produced by the companies they work for. Therefore, the relationship between domestic demand and domestic supply is reinforced.

18
# **3 Evidence**

We use two sources of data, firm-level data and customs records. The firm-level data come from the Encuesta Nacional Industrial Anual (ENIA), an annual industrial census run by Chile’s Instituto Nacional de Estad ́ıstica that interviews all manufacturing plants with 10 workers or more. It is a panel. The customs data provide administrative records on firms exports by destination. We manually matched both databases for the period 2001-2005. As a result, we built a 5-year panel database of Chilean manufacturing firms.

The data have several modules. The main module contains information on industry affiliation, ownership type, sales, exports, input use, imports of materials, workers and wages. Industry affiliation is defined at the 4-digit ISIC Revision 3 level, which totals 113 industries.

We are mostly interested in the employment information. The data on workers are presented at detailed categories, which allows us to explore the demand for different tasks. From the detailed employment records, we define the following tasks: management (directors), administrative services (accountants, lawyers), engineers (specialized skilled production workers), blue-collar activities (non-specialized unskilled production workers), and general maintenance services (unskilled non-production workers).

Table 1 presents summary statistics for the key variables in our model. We present the unconditional averages as well as averages for exporting firms and non-exporting firms. In terms of employment, exporters are larger than non-exporters, as expected. On average, 39 percent of workers in Chilean firms are skilled workers and 61 percent are unskilled workers. Exporters utilize a higher share of skilled workers (41 percent) than non-exporters (39 percent). Within the skilled and unskilled categories, we are especially interested in engineering and blue-collar employment. The share of engineering employment is roughly 18.1 percent at both at exporters and non-exporters, while the share of blue-collar employment is 52.0 and 54.6 at exporters and non-exporters, respectively. The ratio of engineers to blue-collar workers is 4.7 at exporters and 2.1 at non-exporters. Finally, the average exporter ships around 32 percent of its sales abroad. Among all firms, exports account for only 5 percent of total firm sales.

To formally study the relationship between exporting and the composition of employment, we work with the following regression model

> (11) _yijt_ = _γEijt_ + **x**<sup>_′_</sup> _ijt_<sup>_β_+</sup><sup>_φi_+</sup><sup>_φjt_+</sup><sup>_εijt,_</sup>

8
# **Table 9: Labor Indicators Panel Regression with Household and Temporal Fixed Effects**

|||**xt.Con**<br>|**sider Migr**<br>|**ate= 1**<br>|||**xt.Cu**<br>|**rrent Migr**<br>|**ant= 1**<br>||
|---|---|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|**(10)**|
|Share Adults UZB Working<br>|-0.033**<br>(0.016)|||||-0.048**<br>(0.024)|||||
|Share UZB+Mig Working||-0.104***|||||0.384***||||
|||(0.019)|||||(0.031)||||
|Members Looking for Job|||0.020***|||||-0.036***|||
||||(0.005)|||||(0.006)|||
|Ln(Household Income)||||-0.009***<br>(0.003)|||||0.037***<br>(0.005)||
|"Labor Market is Bad"|||||0.030***|||||-0.001|
||||||(0.011)|||||(0.011)|
|Constant|0.031***|0.051***|0.018***|0.142***|0.020***|0.161***|0.046***|0.158***|-0.371***|0.149***|
||(0.005)|(0.006)|(0.004)|(0.046)|(0.004)<br>|(0.009)|(0.010)|(0.005)|(0.067)|(0.005)|
|Number of observations|12,137|12,137|12,137|10,368|12,137|12,137|12,137|12,137|10,368|12,137|
|Calendar Month FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Household FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Adjusted R2|0.006|0.010|0.009|0.005|0.007|0.008|0.059|0.016|0.026|0.008|
|r2_w|0.007|0.011|0.010|0.006|0.008|0.009|0.059|0.017|0.027|0.008|
|r2_o|0.006|0.007|0.016|0.005|0.014|0.013|0.029|0.001|0.023|0.003|
|r2_b|0.001|0.002|0.019|0.000|0.021|0.033|0.015|0.000|0.034|0.001|
|sigma_u|0.119|0.119|0.118|0.122|0.118|0.293|0.294|0.296|0.302|0.295|
|sigma_e|0.177|0.176|0.176|0.174|0.177|0.199|0.194|0.198|0.186|0.199|
|rho|0.311|0.314|0.309|0.331|0.308|0.684|0.696|0.690|0.726|0.687|

note: .01 - ***; .05 - **; .1 - *; 23
Luxembourg, the United States (U.S.), and the United Kingdom (U.K.). Most bond funds are domiciled in Denmark, Germany, Ireland, Israel, Italy, Luxembourg, the U.S., and the U.K.

The TNAs of mutual funds increased significantly over time, reaching large values at the end of the sample (Figure 1). In 2011, the equity (bond) funds in our sample had 1.2 trillion (303 billion) U.S. dollars in TNAs. Moreover, funds in our combined dataset capture an important part of the assets held by the industry of international funds. For example, our sample of U.S.-domiciled equity funds had 442 billion dollars in TNAs, while the Investment Company Institute (ICI) reports that, during the same period, U.S. (non-domestic) international funds held 1.4 trillion dollars including the numerous country funds that we exclude due to our interest on country weights. Similar estimates for Europe from the European Fund Asset Management Association (EFAMA) show that our sample accounts for approximately 53 percent of the international funds in this region. Explicit indexing funds (mostly ETFs) represent a fast growing but still relatively small share of the industry. By also including closet indexing funds, both the level and growth rate of the funds that closely track benchmark indexes increases significantly.<sup>18</sup>

# **3. Benchmark construction and behavior**

Benchmark weights are assembled with the portfolio weights of individual securities included in a benchmark index, aggregated at the country level according to the market where the security was issued. That is, international benchmark indexes are typically constructed using a bottom-up approach and consist of composite stock (or bond) market indexes that include securities from many countries as constituents. The following example from MSCI, the provider of the most prevalent equity indexes, illustrates more details on how benchmarks are assembled (other companies use a similar approach).<sup>19</sup> MSCI first defines the main scope of an index (such as, geography, industry, and type of firms) and in which category each country is classified at each point in time

> 18 The trends exhibited by the share of total assets of ETFs in our sample also appear in (unreported) data on U.S. mutual funds from the Investment Company Institute (ICI), which does not identify closet indexing funds.

> 19 While benchmark indexes have become very popular over the past years and there exist a high diversity of indexes, a large percentage of mutual funds declare a concentrated subset of these indexes. For instance, the MSCI Emerging Markets Index, the MSCI World Index, or the MSCI Europe Index are followed by 52 percent of our equity mutual fund sample. J.P. Morgan constructs many of the indexes bond funds follow. Appendix 2 provides more information on the benchmark index industry.

9
_page 7_ the terriination of capital inflows, and debt-overhang/uncertainty effects. Using quarterly investment data for 68 private-sector industries between 1981 and 1985, he found that the main mirroeconomic mechanism driving the investment decline was the rise in the relative price of imported investment goods, and further that the terms of trade decline (driven by falling world oil prices) explains much of the increase in this relative price.

Morley (1992) examined stabilization programs in a broad sample of LDC's. Using panel data, he found that the terms of trade had a significant positive impact on investnent and output. The same was true of real appreciations, which as we have argued are a likely consequence of terms-of-trade shocks.

Etherington & Yainshet (1988), using time-series methodology, found that the price of coffee, the key Ethiopian export, had a significant positive impact on Ethiopian capital good imports and domestic capital formation.

De Gregorio (1992) analyzed growth determinants in twelve Latin American countries over the period 1950-1985. Controlling for investment and macroeconomic stability, he did not find a significant effect of the terms of trade on growth. However, he did not consider that investment itself might be affected by the terms of trade.

# **III.** The Data

We consider the period 1965-1989. Our sample consists of the following 18 oil-exporting countries:

|Algeria|Gabon|Mexico|Trinidad and Tobago|
|---|---|---|---|
|Bahrain|Indonesia|Nigeria|United Arab Emirates|
|Congo|Iran|Oman|Venezuela|
|Ecuador|Iraq|Saudi Arabia||
|Egypt|Kuwait|Syria||

For each country, on the expenditure side we examined data on consumption and investment (by the private sector, by the government, and in the aggregate), savings, the trade balance, and GDP. At a sectoral level, we analyzed value added in the oil sector and the non-oil sector. The non-oil sector was further broken down into the following categories: agriculture, manufacturing, construction, public utilities, and
7 and North 1986 make the general<sup>point that services enhance productivity in the</sup> goods producing sectors--whether<sup>they are orovided inside or outside the firm.6</sup> Romer refines this argument<sup>by pointing out that the development of services</sup> facilitates specialization and<sup>hence productivity growth in firms producing</sup> goods.<sup>7</sup> Giarini, among others, argues<sup>that financial and informational services</sup> reduce risk and uncertainty, hence<sup>reducing overall costs. Although plausible,</sup> such ideas are not yet backed<sup>up with empirical work.</sup> A further point in considering<sup>services as a source of growth is that slow</sup> downs in productivity growth in<sup>services experienced by the developed countries</sup> will not necessarily apply to<sup>the developing countries, which can benefit from</sup> technological advances achieved<sup>elsewhere.</sup> Thus, there is strong reason<sup>to</sup> believe that former socialist<sup>countries, where services were repressed, will</sup> benefit from a wide range of service-related<sup>productivity enhancements.</sup>

# B. International and Soviet Data<sup>on Services</sup>

The service sector is defined<sup>here to cover all economic activities other</sup> than agriculture, mining, manufacturing,<sup>construction, and utilities.8The many</sup> remaining activities included in<sup>the service sector differ according to function,</sup> factor intensity, technological<sup>sophistication, and size.</sup> It is therefore important to sub-divide services into various sub-sectors, to arrive at more homogeneous activity groups. There is little standardization of sub-sector groupings (not only between socialist and non-socialist countries,<sup>but also among</sup>

# 6 See also Gershuny 1987 and Ott 1987.

# 7 See also Grossman 1989 and Grossman<sup>and Helpman 1989.</sup>

x There are diverse views about the<sup>definition and measurement of services.</sup> Some definitions, for example, also<sup>include construction and utilities.</sup>
# **I. Introduction**

Poverty has steadily decreased in India over the past decade. Since India makes up a quarter of the world’s poor (i.e., those living under $1.25 a day), which is roughly half again its share of the world’s population (17 percent), reducing poverty in this country would not only impact its welfare alone but would also register a significant impact on global poverty estimates.<sup>1</sup> What is particularly striking is the acceleration of poverty reduction that appears to be taking place. Between 2004/5 and 2009/10 poverty declined from 37.7 percent to 29.9 percent. Over the subsequent two years, poverty declined by a further 10 percentage points, to 20.0 percent. These achievements in poverty reduction have been widely remarked on and celebrated.<sup>2</sup> The aim of this paper is to consider the recent experience of poverty decline from two perspectives that have not historically received a great deal of attention in the Indian context – due most likely to the scarcity of nationally representative panel survey data.<sup>3</sup> First, we ask whether there is any suggestion that those who are currently non-poor remain at a heightened risk of falling into poverty. Second, we investigate to what extent one can discern a core subset of the population

> 1 We use the poverty rates and population data respectively from the World Bank’s PovCalNet database (http://iresearch.worldbank.org/PovcalNet/index.htm) and Development Indicators database. All figures are estimated averages for the two years 2011 and 2012.

> 2 There have been nevertheless some concerns raised around the credibility of the most recent episode of poverty decline, we come back to more discussion in the next section. Unless otherwise noted, all the poverty rates are based on the national poverty lines.

> 3 Smaller panel surveys have been fielded for India, but none of these provide nationally representative data; see Dercon and Shapiro (2007) for a recent review. For recent studies that use these panel surveys, see, e.g., Munshi and Rosenzweig (2009), Krishna and Shariff (2011), and Dercon, Krishnan, and Krutikova (2013) respectively for analysis of the REDS panel between 1982 and 1999, the NCAER panel between 1993-94 and 2004-05, and the ICRISAT panel between 1975 and 2006. While panel surveys allow more in-depth analysis of mobility, Rosenzweig (2003) discusses potential issues that can bias these surveys (that are not nationally representative) such as split-offs or attrition. A new nationally representative panel survey (IHDS) fielded by the University of Maryland and NCAER promises much improvement over the previous panels (http://ihds.umd.edu/index.html). But note that compared to the NSSs, the IHDS has less than half the sample size and collects a much reduced version of household consumption data (i.e., 47 consumption items in the latter vs. more than 400 items in the former).

2
# Table 6 Savings Functions for the Panel Data of European Countries, 1960-90

|||Depe<br>(t-|ndent Variab<br>Statistics in|le: Savings ra<br>parenthesis)|te||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|_(5)_|(6)|(7)|(8)|
|GDPpercapitagrowth|4.813<br>(5.30)|_5.135_<br>(6.18)|5.194<br>(8.46)|5.085<br>(7.98)|5.217<br>(4.94)|5.616<br>(5.67)|3.794<br>(4.14)|4.435<br>(4.26)|
|Lagged GDP growth per capita||0.711<br>(0.83)||0.428<br>(0.53)||1.265<br>(1.39)||0.890<br>(0.97)|
|Dependency ratio|0.225<br>(2.09)|0.132<br>_(1.55)_|0.214<br>(3.18)|0.072<br>(1.15)|0.406<br>(3.68)|0.338<br>(2.62)|0.267<br>(2.52)|0.249<br>(2.16)|
|Lagged Savings Rate||0.304<br>(2.08)||0.275<br>(1.90)||0.317<br>(1.97)||0.321<br>(1.99)|
|Overall Education Stock|-0.932<br>(-1.55)|-0.392<br>(-0.70)|||||||
|Lagged Overall Education Stock|1.044<br>(2.23)|0.704<br>(1.52)|||||||
|Prinary Education Stock|||-0.697<br>(-1.02)|-0.521<br>(-0.91)|||||
|Lagged Primary Education Stock|||0.926<br>(1.79)|0.809<br>(1.68)|||||
|Secondary Education Stock|||||0.130<br>(0.64)|0.073<br>(0.41)|||
|Lagged Secondary Education Stock|||||-0.038<br>(-0.21)|0.847<br>(0.54)|||
|Tertiary Education Stock|||||||-0.277<br>(-1.84)|-0.279<br>(-1.90)|
|Lagged Tertiary Education Stock|||||||0.286<br>(1.87)|0.337<br>(2.32)|
|AdjR<sup>2</sup><br>DW<br>Observations|0.332<br>1.84<br>95|0.409<br>2.03<br>95|0.349<br>1.83<br>95|0.410<br>2.03<br>95|0.249<br>1.81<br>95|0.348<br>2.02<br>95|0.263<br>1.83<br>95|0.348<br>2.05<br>95|

All variables are expressed **in** log (with the exception of the GDP growth rate) and in 5 year average. The TSLS have been used in all regression and fixedeffects have been introduced to account for countries' characteristics.
World Bank income groups, we find moderate dominance for Decreasing GGUs in high-income economies and heavy dominance for Increasing GGUs in the other three groups.

In the final section of the paper, we summarize our results and provide an accompanying Excel file with monitoring reports for 775 GGUs, separated into five deciles by estimated EDGAR emissions. For each GGU, we report separate results for the two models, the composite result in the four previously-defined categories, and the dominant source sector.

We also illustrate our results with global maps, with each GGU represented by color-coded icons that display the composite results and dominant source sectors. These maps provide graphic illustrations of the challenges that confront the Global Methane pledge. Although some regions have many Decreasing results (e.g., Western Europe, North Africa, the Gulf Region), most are dominated by Increasing results across several sectors.

We close with two observations. First, the current state of the art enables us to estimate the significance and direction of trends for GGUs, but not the magnitudes of emissions reductions. In consequence, even in areas where Decreasing results dominate, the _levels_ of emissions may remain high for extended periods. Second, our results provide a cautionary reminder of the challenges ahead. We will know that the Global Methane Pledge is beginning to succeed when future versions of Figures 13 and 14 are dominated by Decreasing (Blue) icons. At present, that is far from the case.

33