rafmacalaba/datause-encoder-data huggingface dataset:
# **5 Conclusions**

Accurate projections of electricity demand are essential for planning power systems and appraising investment projects in developing countries. Nonetheless, demand forecasting issues are not rigorously studied and are not always given adequate attention among development practitioners. This study demonstrates that time-series econometric methods yield highly accurate forecast predictions for the majority of developing countries. Econometric forecasts significantly outperform simple heuristical rules used by practitioners, who frequently assume that electricity demand grows at some exogenous rate or is proportional to real GDP growth. These improvements notwithstanding, relying on time-series econometric methods alone may produce inaccurate forecasts in some developing countries. We show that econometric forecasts of electricity demand are challenging for developing countries that are in the process of rapid economic and structural transformation or are prone to conflicts and environmental disasters. These include, among other, the countries in Sub-Saharan Africa region, the low-income countries, and the countries with small electricity generation systems. For those countries, in particular, a more rigorous forecasting approach, using a combination of micro-econometric and computational modeling methods would be preferred.

# **References**

- [1] Abdel-Aal, R. E., and Al-Garni, A. Z. (1997). Forecasting Monthly Electric Energy Consumption in Eastern Saudi Arabia Using Univariate Time-series Analysis. _Energy_ , 22(11), 1059-1069.

- [2] Allcott, H., Collard-Wexler, A., and O’Connell, S. D. (2016). How Do Electricity Shortages Affect Productivity? Evidence from India. _American Economic Review_ , 106(3), 587-624.

- [3] Armstrong, J. S., and Collopy, F. (1992). Error Measures for Generalizing about Forecasting Methods: Empirical Comparisons. _International Journal of Forecasting_ , 8(1), 69-80.

- [4] Baltagi, B. H., Bresson, G., and Pirotte, A. (2002). Comparison of Forecast Performance for Homogeneous, Heterogeneous and Shrinkage Estimators: Some Empirical Evidence from US Electricity and Natural Gas Consumption. _Economics Letters_ , 76(3), 375-382.

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that unilateral reforms offer the possibility of attaining the underlying objective at less cost to society.

# **1.1. Agricultural markets**

For a number of reasons, many agricultural commodities tend to be protected relatively heavily in the industrial countries. These reasons include: (i) that food tends to be a small share of the consumption expenditures of consumers in these countries; (ii) that many agricultural commodities are final goods, for which there is little countervailing pressure from organized using industries (other than processors who can pass on the higher costs); (iii) the number of farmers tends to be relatively small in high-income countries, making it relatively easy for them to coordinate in order to apply political pressure; and (iv) that farmers in these countries are commercially-oriented, selling virtually all of their output, and using substantial amounts of purchased intermediate inputs—creating leverage between their output prices and their net returns.

Historically, agricultural products in developing countries have tended to be taxed, for reasons that are the obverse of those applying in the high income countries. These include: (i) the fact that food expenditures are frequently a large share of the income of most people; (ii) that the number of farmers tends to be large, making it hard for them to organize politically; (iii) that urban consumers are a relatively small group, able to organize on an issue like food prices; (iv) that farmers are mainly subsistence-oriented—selling only part of their output and using relatively few intermediate inputs. In some developing countries, taxation of agricultural exports has historically been an important source of revenues, one that was particularly important before the emergence of the value-added tax.

A recent comprehensive study of agricultural distortions led by Kym Anderson (2009) shows that agricultural distortions in the industrial countries generally remain large, although there are signs that they may have begun to decline from their high levels in the mid-1980s. This is particularly the case when we consider the protection that is not decoupled from output decisions. In developing countries, the average rate of taxation of agriculture has declined sharply, as shown in Figure 1, and switched to modestly positive assistance. The changes in these rates of assistance reflect a dramatic sea-change in the pattern of agricultural distortions in developing countries, perhaps related to the high rates of economic growth in developing 9
22

**Table 1: Party age and Political Budget Cycles (extended sample):**

|Dependent Variable:<br>Total central government<br>expenditure/GDP|(1)<br>Pooled<br>OLS|(2)<br>Fixed<br>Effects OLS|(3)<br>Fixed<br>Effects OLS|(4)<br>Extended<br>Sys-GMM|(5)<br>Extended<br>Diff-GMM|
|---|---|---|---|---|---|
|Avg. party age|-0.002|0.011|0.012*|-0.002|0.011|
||(0.002)|(0.008)|(0.006)|(0.005)|(0.008)|
|Election|0.517**|0.482**|0.508**|0.674***|0.614***|
||(0.258)|(0.237)|(0.236)|(0.246)|(0.208)|
|**Avg. party age X**|**-0.006***|**-0.005***|**-0.005***|**-0.009****|**-0.008*****|
|**Election**|**(0.004)**|**(0.003)**|**(0.003)**|**(0.004)**|**(0.003)**|
|Lagged Dependent|0.975***|0.820***|0.839***|0.929***|0.594***|
|Variable|(0.008)|(0.023)|(0.025)|(0.023)|(0.091)|
|Log GDP per capita|0.126**|-0.849|0.940|0.578***|1.523|
||(0.058)|(0.522)|(0.953)|(0.195)|(0.091)|
|Growth|-0.123***|-0.163***|-0.145***|-0.120***|-0.119***|
||(0.031)|(0.039)|(0.037)|(0.031)|(0.025)|
|Ruling coalition size|-0.379|-0.619|-1.028|0.118|-0.388|
||(0.453)|(0.717)|(0.638)|(0.859)|(0.835)|
|Unified government|-0.010|-0.211|-0.179|1.024***|0.847**|
||(0.142)|(0.234)|(0.221)|(0.318)|(0.432)|
|Constant|1.086***|6.858***|1.622|-0.821|6.434|
||(0.381)|(1.349)|(2.570)|(0.885)|(9.226)|
|Time dummies|No|No|Yes|Yes|Yes|
|AR (1)||||-4.28***|-3.96***|
|AR (2)||||0.38|0.34|
|Number of observations|1,128|1,128|1,128|1,128|1,128|
|Number of countries|65|65|65|65|65|

Standard errors in parentheses (robust standard errors for OLS). *** p<0.01, ** p<0.05, * p<0.1. Instruments are restricted to 3 lags to minimize the instrument count. Endogenous variables are the lagged dependent variable and growth. First order serial correlation in first-differenced residuals (AR 1 significant) with no second order serial correlation (AR 2 insignificant) supports the claim that instruments for both GMM analyses are valid. Lagged dependent variable coefficient for Difference-GMM below fixed effects (FE) coefficient suggests a weak model fit; LDV coefficient of System-GMM between OLS and FE coefficients suggest a good model fit.

Further supporting the use of the System-GMM estimation is that the first and second order serial correlation is consistent with the validity of the GMM instruments. First order serial correlation in the first-differenced residuals (AR 1) is significant and negative. There is no second order serial correlation, since AR(2) is insignificant. We therefore rely on System-GMM for most of the remaining specifications.
<!-- Start of picture text --> Figure 3 : Public Sector Wage Bill Growth<br>2000 2005 2011 2014<br>year<br>Wage Bill in TND '000<br>Wage Bill/Total Expenditure<br>Source : INSAF 2014.<br>Figure 4 : Unsustainability of Wage Bill Growth<br>2000 2005 2011 2014<br>year<br>Government Revenue/GDP<br>Government Expenditure/GDP<br>Wage Bill/GDP<br>Source : INSAF 2014, Government Finance Statistics 2014.<br>10000 .38<br>8000 .36<br>6000 .34<br>Wage Bill in TND '000 .32<br>4000<br>Wage Bill/Total Expenditure<br>2000 .3<br>.3 .13<br>.125<br>.28<br>.12<br>.26<br>.115<br>Wage Bill/GDP<br>.24 .11<br>Revenue or Expenditure/GDP<br>.22 .105<br><!-- End of picture text -->

**Wage bill growth has become increasingly difficult to control since the Revolution** (Figure 4). While public revenues fell from 24.9 percent of GDP in 2010 to 23.6 percent in 2013, with an even stronger fall in absolute terms, public expenditures increased from 25.2 percent of GDP in 2010 to 29.7 percent in 2013. Since the wage bill represents approximately 37 percent of total expenditures, as discussed above, its growth is an important determinant of the increase in public expenditures relative to GDP.

**Tunisia’s public sector currently includes 583,000 employees in the central and regional administration and 33,000 in local authorities, as well as 180,000 employees in public companies** (CGFP 2014). Central administration employees are distributed across more than 25 ministries, many of which also have regional offices and administrative agencies. The five

6
Canada also drops since Canada’s option value diminishes significantly.

While issues related to international transit migration receive relatively little attention in economics, they have a more prominent place in other fields, such as demography and sociology, with varying definitions and approaches. More specifically, the phrase ‘transit migration’ is used to describe the migration patterns emerging in Europe following restrictions on legal migration and border controls that were introduced in the 1990s. As a result, many migrants from Eastern Mediterranean and African countries moved to countries at the periphery of the European Union, such as those in North Africa and Eastern Europe, while waiting for a chance to move to the European Union countries (Collyer and de Haas, 2012; Collyer et al, 2012). The paths of most recent refugees from Syria, Iraq and other source countries follow similar transit routes, such as through Turkey, as they try to reach Western Europe. The phrase is also used extensively to refer to low-skilled and mostly undocumented migrants from Central America who travel through and temporarily live in Mexico before reaching the United States. In other words, transit migration has a strong affiliation with irregular and undocumented migrants and refugees as well as a strong sense of temporariness (see Duvell, 2012). Even among the few papers in the economics literature (such as Djajic, 2013), this is the general perception.

We use the expression ‘transit migration’ to describe any migrant who lives in a foreign country before moving to a second foreign country. Our data from the American Community Survey indicate that transit migrants, defined as those living in a country other than their birth country ‘one year’ prior to their move to the United States, are somewhat different than those the demography and sociology literatures focus on. First, transit migrants to the United States are more likely to have tertiary education as opposed to primary or secondary education. Second, most transit migrants are born in high-income European (and some African and East Asian) countries as opposed to most Latin American migrants who come directly from their countries of birth.

In terms of international migration, our paper is related to the large literature on the deter5
Interesting to discuss are also the signs of the estimates for the control variables in table 3. First, the findings suggest a strong complementarity between the investment in job training of the workforce and the stock of human capital of the workforce, measured by the share of skilled workers in the firm. This finding is supportive of the idea that, the more educated is the workforce in the firm, the higher is the return to this investment, regardless of firm size. This is fully aligned with the empirical evidence exploring household level surveys where more educated workers have higher returns and are more likely to receive job training than less educated workers (see e.g., Tan and LopezAcevedo, 2003, Johansen and Van Adams, 2004). Second, the findings also suggest a strong complementarity between the investment in job training and the degree of firm openness, on the one hand and between the investment in job training and technological adoption on the other hand (see e.g. Bee and Batra, 1998).<sup>17</sup> Third, we do not find strong support of the view that all else constant older and publically owned firms are more likely to invest in job training.

Table 4 tests whether the positive correlation across firm size and the investment in job training is driven by other factors related with the firm geographical location and the sector of activity. First, we test whether within each country, the positive correlation across firm size and the investment in job training could be driven by the fact that larger firms disproportionally locate in the country capital or in other very large cities.<sup>18</sup> And, we conjecture that firms located in the capital or in large cities could have better institutions, a better access to information on the quality of trainings or possibly face lower training costs, due to the proximity to training centers. Therefore, larger firms could be, all else constant, more likely to invest in on the job training simply because of their geographical location. Column (1) of table 4 reports the estimates for the baseline specification when restricting the sample only to firms located outside the capital city.

> 17 It is also interesting to note that, in column (3) of table 3, the positive correlation between the investment in job training on the one hand and the degree of firm openness on the other hand is not solely explained by differences across firms in their technological innovation. Notice that these point estimates are still large and statistically significant in columns (3) and (4) after controlling for differences across firms in the degree of technological adoption.

> 18 In particular, in our sample 40% of the micro and 45% of the small firms are located in the capital or in other very large city, while 50% for the large firms are located there.

17
- Weinthal, Erika. 2002. _State Making and Environmental Cooperation: Linking Domestic and International Politics in Central Asia._ Cambridge, MA: The MIT Press.

- Wolf, Aaron, Kerstin Stahl, and Marcia F. Macomber. 2003. ―Conflict and Cooperation within International River Basins: The Importance of Institutional Capacity,‖ _Water Resources Update_ 125: 31-40.

- World Meteorological Organization. 2008. Manual on Low-flow Estimation and Prediction. Operational Hydrology Report No. 50. WMO No. 1029.

- World‘s Water Report. 2010. Table 12: Number of Dams, by Continent and Country 1998-1999. Retrieved from http://www.worldwater.org/data.html, accessed 2010.

- Yoffe, Shira, Aaron Wolf, and Mark Giordano. 2003. ―Conflict and Cooperation Over International Freshwater Resources: Indicators of Basins at Risk,‖ _Journal of the American Water Resources Association_ , 39(5): 1109-1126.

31
# **Appendix C: Estimates of the Ad Valorem Equivalents of Poor Trade Facilitation**

Our estimates of the ad valorem equivalents (AVEs) of poor trade facilitation are based on the path-breaking work of David Hummels and his co-authors (Hummels, 2007; Hummels and Schaur, 2013; Hummels _et al_ ., 2007). Using the estimates of Hummels and his co-authors, Peter Minor (2013) provided estimates for the regions and products in the GTAP database. We use estimates from Peter Minor, which we aggregate to the sectors and regions of our model.<sup>33</sup> Documentation of the steps we have taken and a brief explanation of the methodology are explained below. (See Minor (2013) for a fuller explanation of the methodology.) Although a central finding of the above studies is that the AVE of time in trade varies across products, most computable general equilibrium modeling of trade facilitation issues have used a single AVE across all products. By basing our estimates on the work of Hummels and Minor, we improve on the sector accuracy of the benefits of trade facilitation, and show that the results are dependent on these sector estimates. We summarize the steps in the estimation of Minor and our aggregation below.

# **1. Estimation of the value of one day saved in transit for over 600 HS4** **_products_** (“the perday value of time savings” by product)

The crucial first step is the estimation of the value of one day saved in transit for each _product_ (“the per-day value of time savings” by product). The key to the estimation is the premium in shipping costs that firms are willing to pay for air shipping to avoid an additional day of ocean shipping. The premium that firms are willing to pay for air shipping varies considerably across products. At one extreme, we have products like crude oil, coal and fertilizers with an AVE of zero for one day saved in transit. Evidently no significant amounts of these products are shipped by air, which reflects no willingness to pay to save time. On the other hand, a significant share of fruits and vegetables are shipped by air, reflecting a willingness to pay to save time in shipping. Hummels _et al._ , (2007, p. 8) estimate that for an aggregate of all fruits and vegetables the AVE of one day saved in 0.9 percent; that is, one additional day in

> 33 We thank Peter Minor for his cooperation with us in this process.

94
WPS 3224

# **Assessing the Potential Benefit of Trade Facilitation: A Global Perspective**

by John S. Wilson<sup>*</sup> , Catherine L. Mann<sup>+</sup> and Tsunehiro Otsuki<sup>**</sup> World Bank Policy Research Working Paper 3224, February 2004 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> * Transport Unit and Urban Development Department (TUDTR), World Bank, 1818 H Street, NW, Washington, DC.

> + Institute for International Economics, 1750 Massachusetts Ave NW, Washington DC

> ** Development Research Group (DECRG), World Bank.

> Corresponding email: <u>totsuki@worldbank.org.  The views expressed here are those of the authors and</u> should not be attributed to the World Bank or the Institute for International Economics.
# ABBREVIATIONS AND ACRONYMS

|AFIPN<br>AIDS|Africa Technical Department, Population, Health and Nutrition Division<br>Acquired Immuno-deficiency Syndrome|
|---|---|
|ARIS|Annual Review of Implementation and Supervision|
|ASR|Annual Sector Review|
|ECA|Europe an Central Asia Region|
|EDI|Economic Development Institute|
|FY|Fiscal Year (July1- June 30)|
|IBRD|International Bank for Reconstruction and Development|
|IDA|International Development Association|
|IEC|Information, Education and Communication|
|IES|Income and Expenditure Survey|
|KNH|Kenyatta National Hospital|
|LAC|Latin America and the Caribbean Region|
|LATHR|Latin America and the Caribbean Technical Department, Human Resources<br>Division|
|LSMS|Living Standards Measurement Survey|
|MCH|Maternal and Child Health|
|MENA|Middle East and North Africa Region|
|MIS|Management Information System|
|MOHFW|Ministry of Health and Family Welfare|
|MOPH|Ministry of Public Health|
|MPND|Ministry of Planning and National Development|
|NGO|Non-govemmental Organization|
|OED|Operations Evaluation Department|
|PCR|Project Completion Report|
|PHN|Population, Health and Nutrition|
|PHRHN|Population, Health and Nutrition Division of the Population and Human<br>|
||Resources Department (As of January 1, 1993, this division has become the PHN|
||Department)|
|PMP|Private Medical Practitioners|
|PVO|Private Voluntary Organization|
|SAR|Staff Appraisal Report|
|SIMAP|Social Impact Amelioration Program and Agency|
|STD|Sexually-Transmitted Disease|
|UNDP|United Nations Development Programme|
|WDR|World Development Report|
|WHO|World Health Organization|
|WID|Women in Development|
|WPS|Wki P Si|
||orng aper eres|
the latter sector in Jhapa may be related to the substantial tea production, which is the largest in the district (as shown below) and one of the largest in the country.

**Figure 2: Production structure in Indian districts (share of district’s GDP)**

<!-- Start of picture text --> (a) Uttar Dinajpur (b) Darjeeling<br>50% 30%<br>45%<br>25%<br>40%<br>35%<br>20%<br>30%<br>25% 15%<br>20%<br>10%<br>15%<br>10%<br>5%<br>5%<br>0% 0 %<br>2003/04 2006/07 2003/04 2006/07<br>Source: Indian Planning Commission (http://planningcommission.nic.in/plans/stateplan/index.php?state=ssphdbody.htm)<br><!-- End of picture text -->

**Figure 2: Production structure in Nepalese districts (share of district’s GDP), 2011**

<!-- Start of picture text --> 70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Ilam Jhapa<br><!-- End of picture text --> _Source: National Planning Commission estimates for 2014 Nepal Human Development Report (forthcoming)_ While we do not have comprehensive production data for Panchagarh, the employment data from the most recent household survey is also consistent with a production structure dominated by agriculture. In the next section we will try to use production (and consumption) data to assess the extent to which gains from trade could potentially materialize.

# <u>District Transport Connectivity</u>

The three countries are connected by a trade route and border infrastructure that intersect with the so-called Siliguri Corridor, a major trade route linking the mainland and NE States of India. Siliguri is 10