rafmacalaba/datause-encoder-data huggingface dataset:
income distribution in the early 1990’s (Bourguignon and Morrison, 1989, 1992; Bourguignon, de Melo, and Morrison, 1991; Bourguignon, de Melo, and Suwa, 1991).

Of the more recent studies, Löfgren (2000) is representative of the way many applied GE models are currently used to analyze trade and poverty issues.<sup>6</sup> Löfgren investigates how reduced agricultural and industrial protection affect representative Moroccan households in the short run. The general equilibrium model is calibrated to a 1994 Social Accounting Matrix that captures the pronounced rural/urban disparity in economic structure, wages, and education that is characteristic of Morocco. Four household groups are distinguished according to whether they are rural or urban, poor or non-poor. The model relies on standard neoclassical assumptions and is set up in “real” terms, meaning there are no asset markets, money is neutral, and all agents make decisions as a function of relative prices. Löfgren’s simulations assess the impact of removing border protection under different assumptions about labor market rigidity. Domestic trade liberalization in agriculture is found to produce gains for the country as a whole, while the rural poor lose out. Investments in rural education and infrastructure, plus government transfer payments are necessary if rural households are to benefit from liberalization.

On the methodological side, Löfgren finds his results to be strongly influenced by the commodity, factor, foreign exchange, and government budget links between agriculture and the rest of the economy (these correspond to links (a), (b), (c), and (e) listed in section II above). Of the potential linkages, Löfgren excludes only the investment and innovation link (d), and the risk and adjustment cost link (f). Ignoring these two effects likely results in systematic underestimation of the long-run _benefits_ and short-run _costs_ of trade liberalization, respectively. However, determining the ultimate importance of these two linkages requires specification of a stochastic, dynamic model, which is beyond the scope of this study.

6 Some of the other studies in this general category are: Devarajan and van der Mensbrugghe (2000); Sadoulet and de Janvry (1992); Dorosh and Sahn (2000); Coxhead and Warr (1995); Bautista and Thomas (1997); Harrison, Rutherford, and Tarr (2003); Levin (2000); and Cockburn (2001). Summaries are provided in the tables of Reimer (2002). See also Löfgren, Robinson, and El-Said (2003) for a brief introduction to this topic. Cloutier, Cockburn, and Decaluwé (2004) provide an extensive modeling survey of CGE analysis of poverty and trade liberalization.

14
105 whom, whether the firm, donor, or user pays for the services, and the reported costs (usually anticipated expenditures) of **the** program.

The TIPPS project, **USAID's** operational research project (Columbia University) in Nigeria, Mexico's Industrias Unidas project and Enterprise Program's Medical Service Micro-enterprises in Mexico are attempting to measure net costs both ex-ante (all TIPPS projects) **and** ex-post (both Mexico projects). Even where the cost of the project is recorded, in-kind donations are frequently ignored even where the donation consists of **all** contraceptives, vhich constitute t e bulk of program recurrent costs in developing countries. Only one company hus measured the net financial benefits of a company family planning program, the Havaiian Philippine Company.

The company's annual costs for the family planning program were $ (P$35,000.00), which covered salaries of clinic staff and motivator,a **s** vell as contingencies. The clinics were already furnished, the vasectomy operating room had been donated to the company, **and** contraceptives vere donated by the government. The company's costs vere modest. Their savings came from not having to pay the following maternity and birth costs per voman: pre-natal consultations and medications (U.S. $37.00), the full cost of delivery (U.S. $40.50), maternity leave benefits (U.s. $102.15 for 42 days), and post natal care (U.S. $10.10), vhich amounted to $189.85 in current U.S. dollars (P$1,405) per averted birth.18 In addition, the **company** saved on social services for dependents, particularly savings for health care and education. The company estimates that between 1972 and 1976, they saved a net total of $59,190 (PS 438,007). Thus significant savings can accrue from the establishment of family planning. Hovever, the true cost of the service should take into account the 18. Exchange rate for 1975 vas 1$ * 7.4 ***** U.S. S 1.00.

SlEfP:Fawly Planing **Paper** Ubole)
2

# **1 Post-harvest loss in perspective**

With global food supply struggling to keep up with demand since the 2007-2008 world food crisis (World Bank, 2011a), all supply and demand components have come under scrutiny once again. This also holds for food loss and waste occurring along the chain from farm-to-fork (Lipinski et al., 2013). FAO (2011) estimated it at 32 percent of all food produced. In Africa South of the Sahara, where hunger and food insecurity remain highest,<sup>2</sup> it is estimated to be as high as 37 percent.

Such saving opportunities suggest a need for greater attention to post-harvest loss (PHL) in addressing the world’s food challenge (World Bank, 2011b). It also begs the question why such significant losses have persisted in the face of continuous undernourishment and hunger, and sluggish yield gains, especially in SSA. Why have farmers “tolerated” them and why has the international community not acted more forcefully to reduce them?

Interest in the reduction of post-harvest loss (PHL) of staple crops is indeed not new. As now, it stood high on the international agenda after the mid-1970s food crisis, with considerable development investment going into it (FAO, 1981; World Bank, 2011b). But international attention ebbed away, once real commodity prices resumed their historical downward trend and the economic benefits from preventing PHL declined.

More strikingly, in a thought-provoking article in 1982, Lipton also questioned the widely held premise of high post-harvest loss between farm and fork, quoted in the 30–50 percent range at the time and which was considered especially high in traditional post-harvest technology systems among smallholders. He further doubted that reducing it by modern capital intensive post-harvest technologies could be done cost effectively and, even if it could, that it would make a major dent in the incidence of hunger. Based on a review of the available literature and a series of case studies, he found that on-farm grain losses seldom exceed 10-12 percent of grain output and typically range

> 2 This holds when looking at the share of the population with insufficient caloric intake. According to the Global Hunger Index (which also accounts for the proportion of under-five- year-olds that are underweight and the under-five mortality rate), South Asia was the most hungry continent in 2013, with SSA ranking second (IFPRI, Concern Worldwide, and Welthungerhilfe, 2013).
and electricity use and monthly bills from utilities’ customer billing records with socioeconomic data collected through a household survey.

Using a sample of households with both private electricity and water connections for the exclusive use of household members in Addis Ababa, we find that both the electricity and the water IBT subsidies disproportionately target richer households. When these poor targeting outcomes are considered jointly, the regressive results are striking. Our sample’s 366 households with private water and electricity connections were substantially richer than the general population of Addis Ababa. Yet even within this middle- and upper-income sample, the cumulative electricity and water subsidies were disproportionately received by the richer households. Households in the wealthiest quintile received an average combined subsidy that was more than twice as large as households in the lowest wealth quintile ($38 vs. $16 per month).

The water IBT’s targeting of subsidies was somewhat worse than that of the electricity IBT. One reason could be that the water consumption of most households was within the first (lifeline) block of the IBT; in contrast, the electricity consumption of households was more evenly spread throughout the seven blocks of that utility’s IBT. This difference might reflect the greater ease with which people can visualize the extent of their water consumption (which is measured in cubic meters) than that of their electricity consumption (measured in kilowatt-hours).

However, we find no difference between water use and our wealth indicator (+0.48, p value=0.00) and between electricity use and our wealth indicator (+0.48, p value=0.000).<sup>22</sup> Figure 7 and Figure 8 illustrate the distribution, by wealth quintile, of (respectively) electricity and water use among our sample of households with private connections. These figures confirm the relatively low correlation between resource use and wealth.

> 22 When the correlations are measured not with respect to our wealth quintiles but instead to self-reported income, we obtain values of 0.36 ( _p_ = 0.000) for water consumption and 0.24 ( _p_ = 0.0000) for electricity.

26
Sandmo, Agnar, 1975. “Optimal Taxation in the Presence of Externalities.” _The Swedish Journal of Economics,_ 77(1):86-98.

- Shah, Anwar and Bjorn Larsen, 1992. “Carbon Taxes, the Greenhouse Effect, and Developing Countries.”   World Bank Policy Research Working Paper Series No. 957, The World Bank, Washington, D.C.

Summers, Lawrence H., 1991. “The Case for Corrrective Taxation,” _National Tax Journal,_ 44(3): 289-92.

- _________ (2007). “Pratical Steps to Climate Control,” _The Financial Times,_ May 28, 2007.

- Stern, Nicolas, 2007. _The Economics of Climate Change – The Stern Review._ Cambridge: The Cambridge Universoty Press.

- Timilsina, Govinda R. and Ram M. Shrestha, 2002. “General Equilibrium Analysis of Economic and Environmental Effects of Carbon Tax in a Developing Country: Case of Thailand.” _Environmental Economics and Policy Studies._ 5: 179-211.

van Der Merwe, C., 2002. _SAM Presentation_ . National Treasury. 12 December 2002.

Van Heerden, Jan, Reyer Gerlagh, James Blignaut, Mark Horridge, Sebastiaan Hess, Ramos Mabugu, and Margaret Mabugu, 2006. “Searching for Triple Dividends in South Africa: Fighting CO2 Pollution and Poverty While Promoting Growth,” _The Energy Journal,_ Vol. 27, No.2: 113-41.

Vehmas, Jarno,2005. Ënergy-Related Taxation as an Environmental Policy Tool – The Finnish Experience 1990-2003.” _Energy Policy_ 33 No.17 (November): 2175-82.

Winkler, Harald, 2007. “Long-Term Mitigation Scenarios,” Project Report, Energy Research Center, University of Cape Town.

- Winkler, Harald and Andrew Marquard, 2009. “Analysis of the Economic Implications of “a Carbon Tax,” Energy Research Center, University of Cape Town (February).

World Bank, 2008. Ethiopia: A Country Study on the Economic Impacts of Climate Change. Report No. 46946-ET.

World Bank, 2010 World Development Report (WDR): Development in Changing Climate. Forthcoming.

Xie, Jian and Sidney Saltzman, 2000. “Environmental Policy Analysis: An Environmental Computable General-Equilibrium Approach for Developing Countries.” _Journal of Policy Modeling_ 22(4):453-89.

31
# **I. Introduction**

Maritime transport costs have a profound influence on international trade. In some cases, their trade-inhibiting effect dwarfs that of customs duties.' More generally, economic research has highlighted the role of transport costs in determining geographical patterns of trade, production, industrial structure, and income.<sup>2</sup> Interesting new work even suggests that transport costs (as an element of trade costs) help explain a variety of puzzles in the field of international macroeconomics, such as the well-known home biases in consumption and investment, and the excessive volatility of exchange rates.<sup>3</sup> These observations are interesting from a policy point of view, however, only if something can be done about these costs. Are transport costs exogenously determined by technological developments or can they be influenced by policy?

It has been argued that maritime transport costs are kept high by restrictive trade policies, notably the cargo reservation schemes and monopoly rights granted to providers of port and auxiliary services.4 It has also been argued that private anti-competitive practices, primarily but not exclusively of the maritime conferences, are responsible for keeping costs high.<sup>5</sup> However, most observers also argue that both public and private trade-restrictive policies are becoming less and less important.<sup>6</sup> Yet the available evidence suggests that transport costs, especially for liner trade, are not falling-despite dramatic improvements in technology, especially in the form of containerization (Hummel, 1999). Could it be that the disappearance of restrictions, like the demise of Mark Twain, has been prematurely announced?

This paper seeks to assess the relative importance of public and private trade-restrictive actions in explaining the price of maritime transport services. To measure these prices, we used newly published data on US waterborne transport from the United States Department of Transportation. A major advantage with this data was that it is broken down by type of service-liner, bulk and

> ' This has been demonstrated in several studies over the years. See Waters (1970), Finger and Yeats (1976), Sampson and Yeats (1977), Conlon (1982), and Amjadi and Yeats (1995).

> **2** See, for instance, Venables and Limao (1999).

3 See Obstfeld and Rogoff (2000).

4 Bennathan (1989), Amjadi and Yeats (1995), Francois et al. (1996), and Hummel (1999).

5 Francois and Wooton (1999), Hummel (1999).

**6** White (1988), Franck and Bunel (1992), WTO (1998).

4
# **A EBRD transition indicators**

Full details of the EBRD transition indicator score methodology are available at www.ebrd.com. The transition indicators cover up to 29 Eastern European and former Soviet Union economies from 1989 to 2007: Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, FYR Macedonia, Georgia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Moldova, Mongolia, Montenegro, Poland, Romania, Russian Federation, Serbia, Slovak Republic, Slovenia, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan.

The transition indicator scores are based on a judgement of an economy’s progress relative to defined classifications. Scores range from 1 (little change from centrally planned economy) through to 4+ (defined as 4.33) which represents norms of advanced industrial economies. Trade measure also includes restrictions to current account convertibility. In addition to the banking sector measure indicated in the figures below there is also an indicator for non-bank institutions and securities markets.

The trade and foreign exchange (FX) indicator ranges from a value of 1 through to 4+ (graded by the EBRD as 4.33) with “1: Widespread import and/or export controls or very limited legitimate access to foreign exchange; 2: Some liberalization of import and/or export controls; almost full current account convertibility in principle, but with a foreign exchange regime that is not fully transparent (possibly with multiple exchange rates); 3: Removal of almost all quantitative and administrative import and export restrictions; almost full current account convertibility; 4: Removal of all quantitative and administrative import and export restrictions (apart from agriculture) and all significant export tariffs; insignificant direct involvement in exports and imports by ministries and state-owned trading companies; no major non-uniformity of customs duties for nonagricultural goods and services; full and current account convertibility; 4+: Standards and performance norms of advanced industrial economies: removal of most tariff barriers; membership in WTO.” The corresponding definitions for the banking reform and interest rate liberalization transition indicator are “1: Little progress beyond establishment of a two-tier system; 2: Significant liberalization of interest rates and credit allocation; limited use of directed credit or interest rate ceilings; 3: Substantial progress in establishment of bank solvency and of a framework for prudential supervision and regulation; full interest rate liberalization with little preferential access to cheap refinancing; significant lending to private enterprizes and significant presence of private banks; 4: Significant movement of banking laws and regulations towards BIS standards; well-functioning banking competition and effective prudential supervision; significant term lending to private enterprizes; substantial financial deepening; 4+: Standards and performance norms of advanced industrial economies: full convergence of banking laws and regulations with BIS standards; provision of full set of competitive banking services.” 30
The high business service export growth rates for developing countries have resulted in their share of global trade rising to 22 percent. Most of this increase reflects expanding exports of Asian countries, which doubled their global market share to reach 14.8 percent in 2005. However, trade in business services continues to be dominated by the high-income countries, whose global market share in 2005 was 78 percent, down from 86 percent in 1995. The country with the highest annual rate of business services export growth during 1995-2005 was Ireland, at 31.6 percent.

As mentioned, the characteristics of services make FDI a major channel for foreign providers to contest services markets. It may well be that increased use of services outsourcing will result in cross-border trade in services coming to dominate the value of sales of services by affiliates of foreign-owned firms at some point in the future, but this is not the case today.<sup>12</sup> Moreover, as noted by Bhagwati, Panagariya and Srinivasan (2004) among others, the share of high-income countries in global stocks of FDI and flows of services is very high, implying that rich countries remain the dominant net exporters of services. For the United States, sales of services by foreign affiliates of US firms (i.e., through outward FDI) has grown more rapidly than cross border trade in services since the mid 1990s. The global stock of FDI stood at some $10 trillion in 2004, of which about 60 percent was in service industries, up from only 25% in 1970.<sup>13</sup> This rapid increase is in part the result of changes in policy towards FDI and large-scale privatization of service sector firms in many countries during the late 1980s and the 1990s.

These developments have also led to a marked shift in the composition of global services FDI flows. UNCTAD (2005) reports that in 1970 finance and trade (distribution) accounted for 65% of the total stock; this dropped to 45% in 2003. Conversely, the share of telecoms, energy, and business services has risen from 17% to

> 12 See, e.g., Jensen  and Kletzer (2005). In the case of the US, which collects and publishes detailed data on both cross-border trade in services as reported in the BOP and sales by affiliates of US services firms in host countries, the overall value of FATS (some $500 billion in 2004) dominates cross-border services trade (close to $350 billion in 2004), illustrating that although services have become more tradable in recent years, geographic proximity remains crucial for the provision of many services. As the stock of outward FDI by the US in 2003 was some $1.8 trillion, of which $1.3 trillion was in services (UNCTAD 2005), given FATS of $477 billion in 2003, this gives a sales/stock ratio of 0.35. Assuming some $5 trillion global FDI in services, this generates a global FATS guesstimate of $1.6 trillion.

> 13 See http://www.unctad.org/Templates/Page.asp?intItemID=1923&lang=1.

11
1992; Card and Lemieux, 2001), and distinguish between workers with college education (or high ~~-s~~ killed) and the less educated (or low ~~-s~~ killed), denoted by S = (H, L). Finally, in terms of age groups, we split the labor force between young (25 ~~-~~ 44 years of age) and old (45 ~~-~~ 64 years of age) workers, denoted by A = (Y,O). We describe below the production technology that determines the labor demand in the economy, the preferences that govern the labor supply decisions of workers, and the competitive labor market equilibrium of our benchmark model. Variants of the benchmark model are described at the end of the section.

# 2.1 Aggregate production function

In each country, output (y) is produced using a CRS (constant returns to scale), Cobb ~~-~~ Douglas function of physical capital (k) and composite labor (q): where A is a scale parameter capturing the total factor productivity (TFP), and a is the share of capital in total income.

Assuming capital is internationally mobile and each country is too small to affect the world capital markets, the price of capital () is equalized across countries and is taken to be fixed.? Setting the marginal productivity of capital equal to its price determines the ratio of capital per worker in the economy: Substituting Equation (2) into Equation (1) allows us to write the aggregate output as a linear function of the aggregate labor composite q: where A = A ~~ra (3)~~ <u>TMe</u> is the modified TFP term. A fraction a of total output serves to remunerate physical capital, while labor compensation accounts for the remaining *In the extensions section, we add a simulation where the capital stock is fixed and we discuss the changes in the results.

6