rafmacalaba/datause-encoder-data huggingface dataset:
PRE Working PAper Sorigs

|J3iba|Author<br>'|Contact<br>J2al<br>P=2|
|---|---|---|
|WPS675 Are Buybacks Back? Menu-Driven<br>Debt-Reduction<br>Schemes<br>with<br>Heterogenous<br>Creditors|lshac Diwan<br>Mark M. Spiegel|May 1991<br>S. King-Watson<br>33730|
|WPS676 Higher Education<br>in the Republic<br>of Yemen: The University<br>of Sana'a|Viswanathan<br>Selvaratnam<br>Omporn L. Regei|May 1991<br>C. Cristobal<br>33640|
|WPS677 On Economic<br>Transformation<br>in East-<br>Central Europe: A Historical<br>and<br>Intemational<br>Perspective|Andres Solimano|May 1991<br>E. Khine<br>39361|
|WPS678 Economic'Growth:<br>A Review of the<br>Theoretical<br>and Empirical<br>Literature|David Reneit|May 1991<br>R. Luz<br>34303|
|WPS679 Poverty Alleviation in Mexico|Santiago Levy|May 1991<br>M. Stroudo<br>38831|
|WPS680 On Hunger and Public Action|Martin Ravallion|May 1991<br>C. Spooner<br>30464|
|WPS681 Political-Economy<br>Arguments<br>for<br>UniformTariff|Arvind Panagariya<br>Dani Rodrk|May 1991<br>K. Cabana<br>37947|
|WPS682 Interte'nporal<br>Substitution,<br>Risk<br>Aversion, and Private Savings in<br>Mexico|Patricio Arrau<br>Sweder van Wijnbergen|May 1991<br>'*4(ing-Watson<br>'ffO47|
|WPS683 Vocational Schooling, Occupationa<br>Matching, and Labor Market Eaming<br>in Israel'|l Shoshana<br>Neuman<br>s Adrian Ziderman|May 1991<br>C. Cristobal<br>33640|
|WPS684 The Value of Intra-household<br>Survey<br>Data for Age-based<br>Nutritional<br>Targeting|Lawrence<br>Haddad<br>Ravi Kanbur|May 1991'<br>J. Sweeney<br>31021|
|WPS685 Children and lrgra-household<br>Inequality: A Theoretical<br>Analysis|Ravi Kanbur|May 1991<br>J. Sweeney<br>31021|
|WPS686 Lending for Learning: Twenty Years<br>of World Bank Support for Basic<br>Education|Adriaan<sup>Verspoor</sup>|May 1991<br>C.Cristobal<br>33640|
|WPS687 Brazilian Frozen Concentrated,<br>Orange Juice: The Folly of Unfair<br>Trade Cases|CarlosAlberto Primo Brag<br>Simao Davi Silber|a May'1991<br>N. Artis<br>37947|
|WPS688 Macroeconomics<br>of Public Sector<br>Deficits:The Case of Zimbabwe|Felipe Morande<br>Klaus Schmidt-Hebbel|May 1991<br>S. Jonnakuty<br>39074|
there is so much opposition to these reforms. The average decline in Japan is 16 % under the Doha scenario and 28 % under the Full Liberalization scenario and 6% and 13% respectively in the EU. On-farm income losses in the US are much smaller – indeed they are negligible under the Doha scenario, while Canadian and Australia/New Zealand producers see gains in real on-farm income.

However, as noted above, farm households in many of these countries are quite diversified in their earnings. If we factor in the change in real, off-farm income, which tends to rise (albeit modestly, since there are no reforms outside of agriculture), the total impact on real farm household income is considerably moderated. Indeed, in Japan, the losses drop by a full order of magnitude – from -16 % to just -1.4 % under the Doha scenario. In the US, the losses become negligible, even under full liberalization. The dampening factor is less prevalent in Europe, where the role of off-farm income is much more modest than in Japan and US.

Given the very modest aggregate farm household losses in the US, the question arises: Why is the opposition to reform so strong? This becomes quite clear when we delve more deeply into the US impacts, using the farm household survey data and associated methodology outlined above. Table 9 reports the welfare impacts on representative households in each of the 11 wealth classes across the five US producer groups. It is clear that under the Rich-Agr Full Liberalization scenario, the losses to the richest, and likely most influential, producer groups are very large– nearly 20% of income in the case of the wealthiest rice producers. The wealthiest sugar producers are also hard-hit, as are cotton producers across the board. The surprising thing about the results in Table 9 is the impact on rice producers under the Doha scenario. Here, they 29
longer significant in all three sets of regressions. Household characteristics, especially consumption expenditures, account for observed within-ward differences. The situation is quite different for distance to towns: results here are stronger than before the introduction of household characteristics. As before, crcp purchases decrease with distance from towns and, as a result, self-sufficiency increases. The effect remains strong and significant. The consumption of farm products depends on distance to towns, but in ways that become clearer after income effects are controlled for. This is shcwn in Figure 16. In particular, rice consumption is much higher near towns, while the consumption of other crop and livestock products increases with distance to towns. This is in line WithL studies that have brought to light the role of rice as an urban food, possibly because of ease of preparation (e.g. Savadogo and Brandt 1988, Nagy, Sanders and Ohm 1988).

# 6. Conclusion

Combining household level data with information on distance to markets and towns, we have examined the spatial division of labor in Nepal. A semi-parametric model was used to avoid restricting the shape of the relationship between spatial specialization and distance to markets and towns. Distance is measured in hours of travel time. To control for possible endogeneity, city population and travel time are instrumented using physical characteristics of surveyed wards and districts. Coefficient estimates with and without instrumentation are very similar, thereby suggesting that endogeneity of city size and road construction is not a serious concern.

Result indicate a strong spatial division of labor. Non-farm employment - our indicator of non-farm production - is heavily concentrated in markets and in and around towns. The effect is strong for towns of all sizes. Agricultural wage employment, in contrast, is concentrated in rural areas sufficiently close to cities that they can specialize in commercial crops but neither so 32
# **References**

- [1] Afsah, S., B. Laplante, and D. Wheeler, Regulation in the information age: Indonesian public information program for environmental management, Research Paper, World Bank, Washington, DC. 1997.

- [2] Arora, S. and T. Cason, Do community characteristics influence environmental outcomes? evidence from the toxics release inventory, _J. Appl. Econ._ 1:413-453 (1998).

- [3] Blackman, A. and G.J. Banniseter, Community pressure and clean technology in the informal sector: an econometric analysis of the adoption of propane by traditional Mexican brick makers, _J.  Environ. Econ. Manage._ 35:1-21 (1998).

- [4] Boyle, M.A. and K. A. Kiel, A survey of house price hedonic studies of the impact of environmental externalities, _J. Real Estate Lit._ 9:117-144 (2001).

- [5] Bui, L.T. and C.J. Mayer, Regulation and capitalization of environmental amenities from the toxic release inventory in Massachusetts, _Rev. Econ. Stat._ 85:693-708 (2003).

- [6] Dasgupta, S., H. Wang, and D. Wheeler, Disclosure strategies for pollution control, In The International Yearbook of Environmental and Resource Economics 2006/2007: A Survey of Current Issues. Tietenberg, T. and Folmer, H. (Ed.), Northampton, MA: Edward Elgar Publishers, pp. 93-119 (2006).

- [7] Dasgupta, S., B. Laplante, and N. Mamingi, Pollution and capital markets in developing countries, _J. Environ. Econ. Manage._ 42:310-335 (2001).

- [8] Foulon, J., P. Lanoie, and B. Laplante, Incentives for pollution control: regulation or information? _J. Environ. Econ. Manage._ 44:169-187 (2002).
# **References:**

Abdulai, A. and Delgado, C. (1999) Determinants of nonfarm earnings of farm based husbands and wives in northern Ghana. _American Journal of Agricultural Economics_ , 81(1) pp. 117-130 Bardhan, P. (1979). Labor supply functions in a poor agrarian economy. _American Economic Review_ , 69(1) pp. 73-83

- Belasen, A. and Polachek, S (2009) How disasters affect local labor markets: the effects of hurricanes in Florida. _Journal of Human Resources_ , 44(1), pp. 251-276

Bell, C. (1988) Credit markets and interlinked transactions. _Handbook of Development Economics_ , vol. 1, pp. 763-830 Hornbeck, R and Naidu, S (2012) When the Levee Breaks: labor mobility and economic development in the American South. _NBER Working Paper 18296_ Jayachandran, S. (2006) Selling labor low: wage responses to productivity shocks in developing countries. _Journal of Political Economy_ , 14 (3), pp. 538–575.

Kochar, A. (1999) Smoothing consumption by smoothing income: hours-of-work responses to idiosyncratic agricultural shocks in rural India. _Review of Economics and Statistics_ , 81(1), pp. 50-61 Rose, E. (2001) Ex ante and ex post labor supply response to risk in a low-income area. _Journal of Development Economics_ , 64(2), pp. 371–388.

Rosenzweig, M. (1980) Neoclassical theory and the optimizing peasant: an econometric analysis of market family labor supply in a developing country. _Quarterly Journal of Economics_ , 94(1), pp. 31-55

> xii In previous studies, labor supply elasticities in developing countries are shown to be small (Bardhan (1979) for rural households in West Bengal; Abdulai and Delgado (1999) in Ghana). Rosenzweig (1980) estimates that the long-run labor supply curve for men is even backward-bending.

xiii For example, fishermen might not be able to exit from fishing if they owed a debt obligation of replaying debt to the middlemen (called _Toke Bangku_ in Aceh). This type of interlinkage between borrower and lender through trade-credit arrangement is widespread in many agricultural economies (Bell, 1988). When borrowers default on their contractual obligation, lenders have various options (such as bonding labor and extracting resources of borrowers) until borrowers satisfy the debt obligation.

xiv Hornbeck and Naidu (2012) shows a large compositional change in the black-white ratios after the 1980s Dust Bowl in the U.S. south, and claims that the compositional shift actually promoted the mechanization of American agriculture.

41
# **Tables:**

|Table 1: Causes of Corruption: Full Specification.............................................19|
|---|
|Table 2: Corruption in Developing Countries...................................................22|
|Table 3: Decentralization and Corruption: Unitary vs. Federal States.......................23|

# **Appendices:**

|Appendix A: Sources for the 1998 Corruption Perceptions Index...........................29|
|---|
|Appendix B: Sample Space........................................................................30|
|Appendix C: Data Sources and Descriptions....................................................31|
|Appendix D: Composite Measures...............................................................34|
5 negative – just as they could be for producing non-GM crops, which also are not captured in our model. On the one hand, many GM crop varieties have some attributes that are more environmentally friendly than their conventional non-GM counterparts. They also are less dangerous to farmers and the soil where they require reduced applications of pesticides. On the other hand, there is concern that some long-term and possibly irreversible negative environmental effects might occur in the future, although we are not aware of significant scientific evidence of such adverse effects.<sup>3</sup>

# **4. Model simulations and results of GM cotton adoption**

To simulate the economic effect of adoption of GM cotton, we assume total factor productivity (TFP) in cotton production would rise by 5 percent in most adopting countries, net of any higher cost of GM seed.<sup>4</sup> This output-augmenting, Hicks-neutral TFP shock is a conservative estimate of the gain to farmers, according to experience to date (FAO 2004, Table 7; Marra, Pardey and Alston 2002; Qaim and Zilberman 2003; Huang et al. 2004) and bearing in mind that typically, in a small number of years after GM cotton adoption is allowed, more than four-fifths of production moves to GM varieties. In India and Sub-Saharan Africa other than South Africa, however, we assume a TFP shock of 15 percent. Even that higher value is conservative for those countries, according to Qaim and Zilberman (2003), because those countries’ yields per hectare with conventional varieties are less than half the yields in the rest of the world (see last

> 3 Federoff and Brown (2004) give reasons why that null finding is not surprising from the viewpoint of a molecular biologist.

> 4 In the GTAP database, cotton is part of a sector called ‘plant-based fibers’ but it represents well over 90 percent of the value of that sector. The only country for which this is likely to be of any significance is Bangladesh, which is still a large flax producer.
**<mark>Figure 5a: Bangladesh’s exports and imports up sharply starting in 2005</mark>**

_(Bangladesh’s exports and imports as a share of GDP, 1970–2016, %)_ <!-- Start of picture text --> 30<br> 25<br> 20<br> 15<br> 10<br> 5<br> -<br>Exports Imports<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text --> _Source:_ World Development Indicators database at the World Bank.

A rise in exports could be the result of increasing supply (due to changes in Bangladesh) or a rising demand (due to changes in the global market). One way to differentiate between a change in supply and a change in demand is to follow the change in prices over time. Figure 5b shows the U.S. unit value (in dollars per square-meter-equivalent, or SME) from OTEXA for U.S. imports from Bangladesh both alone and divided by the unit value of SME imports from the rest of the world. Figure 5b clearly shows a sharp increase in the U.S. price for Bangladesh imports that begins when the MFA ends at the end of 2004.

**Figure 5b: Unit Values of U.S. Imports from Bangladesh**

<!-- Start of picture text --> 1990 2000 2010 2020<br>Year<br>US Bangladesh/World Price P Bangladesh<br>1 3<br>.9<br>.8<br>2.5<br>.7<br>P Bangladesh<br>US Bangladesh/World Price .6<br>2<br>.5<br><!-- End of picture text --> Source: U.S. Department of Commerce, Office of Textiles and Apparel (OTEXA). Unit Values are calculated as the total import value divided by the total quantity of apparel measured in square-meterequivalent. World Price is measured as the total U.S. value of imported apparel (OTEXA Category 1 (Apparel)) divided by the total U.S. SME import quantity.

12
calculate the effects of tariff changes on domestic activity levels, tariff revenue, and welfare. This model is designed to be sufficiently simple that it can be calculated on an EXCEL spreadsheet, and to be flexible enough to allow application under differing degrees of availability of domestic data. The spreadsheet model yields an index of the effect of the preferential tariff reductions which is dependent on the assumptions made regarding the domestic production structure.

The structure of this model reflects the desire to combine information on tariffs and trade volumes, which is available at a very fine levels of product classification, with data on domestic input usage and consumption, which is available only at much more highly aggregated levels. By assuming separability between domestically produced goods (traded and non-traded) and imported goods in the production functions and utility function, we are able to combine data from these two different sources and obtain simple measures of the welfare effects of preferential tariff cuts. Chile presents an interesting example application of the model for several reasons. First, the initial tariff structure in Chile is essentially a uniform external tariff. The literature on tariff reform (Hatta (1977), Corden (1984)) has suggested that under certain conditions, welfare improvements can be obtained by either (i) a proportional reduction in all tariffs or (ii) tariff changes that move in the direction of a uniform external tariff (e.g. a reduction in the highest tariff rate and an increase in the lower tariff rate). This raises questions about the benefit of entry into a preferential trading arrangement for Chile, since it results in a movement away from a uniform external tariff. Second, the availability of two alternative preferential trading arrangements has the potential to provide insights about which types of preferential trading arrangements would be most desirable.

# I. Aggregation of Trade Data

In this section, we present a procedure for aggregating tariff line level data using nested CES functions that is compatible with domestic data on consumption and input use. The trade data consists of import values and base tariff rates for N tariff lines from H different source countries. We will 2
45 Table 14 (contimued) Euatps **Niddie Ernt, ad** Nos d **Africm (EM) 9egihn Caw U.S.S.R.)** Europe and U.S.S.R. Albania Country Department I Country Departmnnt III (cant.) Austria Pakistan Oatar Belgium Turkey Saudi Arabia Bulgaria Syria Chamnel Islands Country Department 11 United Arab Emirates Czechoslovakia Algeria Yemen Arab Rep. 13 Denmrk Libya Yemen, PeopLes DOm. Rep. of finland Nalta France Morocco Country Department IV Germsn Dem. Rep. Tunisia Cyprus Germnry Federal Rep. of Hungry Greece Country Department III Poland Iceland Afghanistan Portugal Ireland BShrain Romania Italy Egypt, Arab Rep. of Yugoslavia Luxembourg Iran, slsmic Rep. of Netherlands Iraq Nonborrowers Norway Jordan Spain Kuaiat North Africa Sweden Lebanon Other North Africa Switzerland Omen United Kingdom Other Europe U.S.S.R.

Latin wci awd_ the Caewihbn C(AC) Region (C _O_ karthe _mn_ ric)_ Country Department IV Argentina Country Department I Country Department III Chile Brazil Antigua and Barbuda Ecuador Bah Paraguay Country Department 11 Barbados i Peru Costa Rics Belize Uruguay El Salvador Bolivia Guatem6la Colarbia Nonborrowers HNndiras Doomnica Mexico Dominican Rep. Latin America and the Caribbean Nicaragua ren da Cuba Panama Guyana Guadeloupe Hafti Nartinique Jamaica Netherlands Antilles Montserrat Virgin Islands (U.S.) St. Kitts and NeMis Other Latin America St. Lucia St. Vincent and the Mrenadines Northern America Suriname Canada Trinidad and Tobago Puerto Rico Venezuela United States of America Other Northern America
# **Appendix A. Mortgage Lending Policy: Regulatory DTI Ratio**

The regulatory debt-to-income (DTI) ratio is the total annual amount of redemption in principal and interest of the loan against the borrower’s annual income (Box A.1).

Box A.1. Calculation of Debt-to-Income (DTI) Ratio The redemption amount of principal per year for the relevant mortgage loan <u>+ the redemption amount of interest per year for other loans</u> X 100 Annual income _Source_ : FSS.

In addition to the applicable LTV ratio, the DTI ratio for some applicants has been limited since 2005. In October 2005, the DTI ratio was limited to 40 percent for married borrowers whose spouse already had mortgage credit and for unmarried mortgage applicants 30 years or younger seeking housing units in speculative areas (Box A.2).

In March 2006, the application of the DTI ratio was extended to include mortgage applicants purchasing a housing unit in a speculative area whose collateral value exceeded KRW600 million, in addition to the LTV requirement already in effect.

In November 2006, the application of the DTI ratio was expanded to include some additional speculative areas outside Seoul.

## Box A.2. Regulation on the DTI Ratio<sup>a</sup>

The 40 percent DTI regulation was set for a very limited range of cases:

1. For loans secured by apartments in areas of high speculation as designed by the government or certain metropolitan areas designated as areas of excessive investment by the government, with appraisal values of more than KRW600 million where the loan application date is within 3 months of the ownership transfer; or

2. For the following borrowers who: i) have a spouse who already has a loan secured by housing, or ii) are single and under 30 years old, applying for loans secured by apartment(s) located in areas of high speculation as designated by the government.

_Source_ : FSS a. Added to the regulation in July 2007.

In February 2007, the FSS announced proposals for systemic improvement of mortgage loan reviews with the Korea Federation of Banks and the banking industry, to shift loan review practices from being collateral-focused to being based on assessment of borrowers’ ability to repay debt. The proposal encouraged lenders to improve internal loan review processes through their own plan based on best practices.

In March 2007, banks set forth detailed criteria to apply different lending limits to each borrower within a 40 percent to 60 percent DTI ratio, varying according to the size of the housing unit, lending amount, borrower's credit rating, repayment method, interest rate, and evidence of income.

21
<!-- Start of picture text --> Figure 1: Foreign Direct Investnent in Korea<br>(billions of U.S. dollars)<br>16<br>14 -<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1994 1995 1996 1997 1998 1999<br><!-- End of picture text --> The changes in the legal limits to foreign<sup>ownership, combined with the large supply of company</sup> shares following the financial crisis,<sup>makes the Korean sample an excellent choice to study the</sup> investment patterns of foreigners. Within<sup>a very short period of time a number of companies</sup> changed hands. This natural experiment<sup>allows us to test several hypotheses that we derive in the</sup> next section.

# **3. Theoretical Framework**

Existing theories of foreign direct investment<sup>highlight the importance of intangible firm-specific</sup> assets and location in making the decision<sup>to choose FDI over other means of servicing a foreign</sup> market.<sup>5</sup> In general, two conditions should<sup>be satisfied if a firm is better off investing in the</sup> country than exporting. (i) There<sup>is a locational advantage to operating abroad, perhaps because</sup> of transport costs or tariffs. (ii)<sup>The investing firm has an advantage over host country firms,</sup> for example, in technology, managerial<sup>skills, or brand name and marketing. FDI theory has</sup> traditionally focused on greenfield<sup>investment, where the intangible asset is assumed to be the</sup> 5Markusen (1995) surveys the literature.

6
The purpose of this paper is to analyze China’s income-consumption-production dynamics for food using an entirely different approach to those used in the studies cited above— econometric techniques based on data for 154 countries during the period 1980-2009. The paper aggregates both the demand for and the supply of food into resource based cereal equivalents (Yotopoulos, 1985; Rask and Rask, 2004, 2011). This approach takes into account one of the central features of food demand behavior—the shift from reliance on direct consumption of grains and other sources of basic carbohydrates into a more diversified diet including edible oils and protein-rich animal products as incomes grow. On the supply side, agricultural output is specified as a function of income and land endowment, with agricultural output growing in response to the productivity growth that is associated with national output growth per person. The econometric approach used relies on the experiences of a wide range of countries and is intended to complement, rather than replace, more detailed country-specific structural approaches.

Section 2 analyzes the changes that have occurred in dietary patterns in China. Section 3 presents the methodology used for the analysis and implements regression analyses. We first discuss the construction of the cereal equivalent measure of food output and demand. Next, we replicate and extend the income-consumption analysis of Rask and Rask (2004, 2011) to the period 1980-2009. Then, we modify Rask and Rask’s approach on the production side, specifying a regression model relating both income and land endowment to production. Finally, we suggest the implications of these trends for China’s likely net import demands in the future. Section 4 presents conclusions.

4