rafmacalaba/datause-encoder-data huggingface dataset:
|LAKE VICTORIA ENV. PROJ.(GEF)|Ernst Lutz|
|---|---|
|LAKE VICTORIA ENV. PROJ.(GEF)|Ernst Lutz|
|Lake Victoria Env.(GEF)|Ernst Lutz|
|Lake Victoria Env.(GEF)|Ernst Lutz|
|Lake Victoria Env.(GEF)|Ernst Lutz|
|Lake Victoria Env.(GEF)|Ernst Lutz|
|Lake Victoria Env.(GEF)|Ernst Lutz|
|Lake Victoria Env.(GEF)|Ernst Lutz|
|Lake Victoria Env.(GEF)|Ernst Lutz|
|TZ: LAKE VICTORIA ENV. PROJ.(GEF)|Ernst Lutz|
|TZ: LAKE VICTORIA ENV. PROJ.(GEF)|Ernst Lutz|
|TZ: LAKE VICTORIA ENV. PROJ.(GEF)|Ernst Lutz|
|TZ: LAKE VICTORIA ENV. PROJ.(GEF)|Ernst Lutz|
|TZ: LAKE VICTORIA ENV. PROJ.(GEF)|Ernst Lutz|
|TZ: LAKE VICTORIA ENV. PROJ.(GEF)|Ernst Lutz|
|ForestryDevelopment in Poor Areas Project|Jin Liu|
|ForestryDevelopment in Poor Areas Project|Jin Liu|
|ForestryDevelopment in Poor Areas Project|Jin Liu|
|ForestryDevelopment in Poor Areas Project|Jin Liu|
|ForestryDevelopment in Poor Areas Project|Jin Liu|
|ForestryDevelopment in Poor Areas Project|Jin Liu|
|ForestryDevelopment in Poor Areas Project<br>|Jin Liu<br>|
|ForestryDevelopment in Poor Areas Project|Jin Liu|
|ForestryDevelopment in Poor Areas Project|Jin Liu|
|Judicial Reform Project|David F. Varela|
|Judicial Reform Project|David F. Varela|
|Judicial Reform Project|David F. Varela|
|Judicial Reform Project|David F. Varela|
|Judicial Reform Project|David F. Varela|
|Judicial Reform Project|David F. Varela|
|Judicial Reform Project|David F. Varela|
|Judicial Reform Project|David F. Varela|
|Judicial Reform Project|David F. Varela|
|Judicial Reform Project|David F. Varela|
|Judicial Reform Project|David F. Varela|
|Rehabilitation Project|Eduardo Somensatto|
|Microenterprise Project|Hamid R. Alavi|
|Microenterprise Project|Hamid R. Alavi|
|Microenterprise Project|Hamid R. Alavi|
|Microenterprise Project|Hamid R. Alavi|
|NGO Trust Fund for the West Bank & Gaza Project|Sima W. Kanaan|
|NGO Trust Fund for the West Bank & Gaza Project|Sima W. Kanaan|
|NGO Trust Fund for the West Bank & Gaza Project|Sima W. Kanaan|
|CommunityDevelopment Project|Kanthan Shankar|
|Legal Reform Project|Claus Pram Astrup|
|Legal Reform Project|Claus Pram Astrup|
|Legal Reform Project|Claus Pram Astrup|
6 The remainder of the methodology section outlines the method for constructing three types of indexes: the Anderson-Neary type indexes for individual countries; instrument level indexes for individual countries to gauge the importance of different policy measures in the overall degree of agricultural policy distortions of OECD countries; and commodity market indexes for that group of countries.

# **_Country level trade and welfare reduction indexes_**

To capture distortions imposed by each country’s border and domestic policies on its economic welfare and its trade volume, we adopt the methodology from Lloyd, Croser and Anderson (2010). These authors define a Welfare Reduction Index (WRI) and a Trade Reduction Index (TRI), each of which can be estimated by considering separately the distortions to the producer and consumer sides of the economy (which can differ when there are domestic measures in place in addition to or instead of trade measures). As their names suggest, the two indexes respectively provide a single empirical indicator of the (partial equilibrium) welfare- or trade-reducing effects of distortions to consumer and producer prices of farm products from all agricultural and food policy measures in place.

The Lloyd, Croser and Anderson (2010) methodology requires data on the production and consumption sides of the economy separately. Since PSE and CSE information is available from the OECD on an annual basis, this methodology is well suited to focusing on the trade and welfare effects of agricultural and trade policy in OECD member countries. Indeed it provides something closer than the PSE or CSE to what a sector or economy-wide computable general equilibrium model can provide in the way of estimates of the trade and welfare (and other) effects of price distortions, while having the advantage of providing an annual time series.

The derivation of the measures in Lloyd, Croser and Anderson (2010) for _n_ import-competing sectors leads to the expressions in Box 1 for the TRI and WRI for the import-competing sector of a country. The import-competing TRI and WRI are constructed from appropriately weighted averages of the level of distortions of consumer and producer prices. The same weights are used to construct both indexes, but the TRI is a mean of order one measure, while the WRI is a mean of order two. Because the WRI is a mean of order two, it better reflects the welfare cost of diverse agricultural price-distorting policies than the PSE or CSE since it captures the disproportionately higher welfare costs of peak levels of assistance or taxation. The
- Holden, S. T. (2019). Economics of Farm Input Subsidies in Africa. _Annual Reviews of Resource Econimics_ , _11_ , 501–522. https://doi.org/doi.org/10.1146/annurev-resource-100518- 094002

- Holden, S. T., & Lunduka, R. W. (2012). Who Benefit from Malawi’s Targeted Farm Input Subsidy Program? _Forum for Development Studies_ , (March 2017). https://doi.org/10.1080/08039410.2012.688858

- Ickowitz, A., Powell, B., Rowland, D., Jones, A., & Sunderland, T. (2019). Agricultural intensification, dietary diversity, and markets in the global food security narrative. _Global Food Security_ , _20_ (November 2018), 9–16. https://doi.org/10.1016/j.gfs.2018.11.002

- IPC. (2022). _Malawi- IPC Acute Food Insecurity Analysis July 2021 - March 2022_ . Rome, Italy. Retrieved from

- https://www.humanitarianresponse.info/sites/www.humanitarianresponse.info/files/docume nts/files/iipc-malawi-acute-food-insecurity-2021july2022mar-report.pdf

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- Jayne, Thomas S, Mason, N. M., Burke, W. J., & Ariga, J. (2018). Review: Taking stock of Africa’s second-generation agricultural input subsidy programs. _Food Policy_ , _75_ (October 2017), 1–14. https://doi.org/10.1016/j.foodpol.2018.01.003

- Jones, A. D., Shrinivas, A., & Bezner-Kerr, R. (2014). Farm production diversity is associated with greater household dietary diversity in Malawi: Findings from nationally representative data. _Food Policy_ , _46_ , 1–12. https://doi.org/10.1016/j.foodpol.2014.02.001

- Kankwamba, H., Kadzamira, M., & Pauw, K. (2018). How diversified is cropping in Malawi? Patterns, determinants and policy implications. _Food Security_ , _10_ (2), 323–338. https://doi.org/10.1007/s12571-018-0771-x

- Karamba, R. W. (2013). _Input Subsidies and their effect on cropland allocation, agricultural productivity, and child nutrition: evidence from Malawi._ American University.

- Logistics Unit. (2017). _Final Report on Implementation of Farm Input Subsidy Programme 2016-17_ . Lilongwe, Malawi.

Mason, N. M., & Ricker-Gilbert, J. (2013). Disrupting Demand for Commercial Seed: Input 26
# Selected Bibliography

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- Catholic University of Peru, 1988, Discussions with members of the Department of Economics, July.

- Chow, G.C., 1960, Tests of Equality between Sets of Coefficients in Two Linear Regressions, Econometrica 28, 3, 591-605.

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- Jacoby, H., 1988, The Returns to Education in the Agriculture of the Peruvan Sierra. Mimeo (Population and Human Resources Department, The World Bank, Washington, D.C.).

- Kafka, F., 1984, El Sector Informal Urbano en la Economia Peruana (Centro de Investigacion, Universidad del Pacifico, Lima).

- King, E., 1988, Does Education Pay in the Labor Market? Women's Labor Force Participation, Occupation and Earnings in Peru, Mimeo (Population and Human Resources Department, The World Bank, Washington, D.C.).

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- Mincer, J., 1974, Schooling, Experience, and Earnings (National Bureau of Economic Research, New York).

- Moock, P. and R. Bellew, 1988, Vocational and Technical Education in Peru, Policy, Planning, and Research Working Paper No. 87 (The World Bank, Washington, D.C.).

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- Jepsen, C., K. Troske, and P. Coomes. 2014. “The Labor-Market Returns to Community College Degrees, Diplomas, and Certificates.” _Journal of Labor Economics_ 32(1):95–121.

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- Minaya, V., and Scott-Clayton, J. (2022). Labor market trajectories for community college graduates: How returns to certificates and associate's degrees evolve over time. _Education Finance and Policy_ , _17_ (1), 53-80.

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36
relative poverty rate for a specific country, we take the median consumption level for that country, estimate the country-specific SPL for survey and reference years, and pass the value of the SPL back to PovcalNet to extract the estimated poverty rate.<sup>31</sup> Because the SPL results in a relative poverty line for each country that is directly linked to the overall wellbeing in that country, we suggest that this can be interpreted as defining poverty at a level that has social relevance to the country. (This can also be inferred from the fact that the parameterization of a dollar plus 50% of median consumption or income was selected because it fits national poverty lines well.) Another way of assessing social relevance is to pose the question of whether the SPL results in an estimated poverty rate that corresponds with national poverty headcount rates. The top two graphs in Figure 5 plot the officially reported poverty rates at national poverty lines and the estimated societal poverty rate for each country based on our preferred SPL and the SPL with the $1.90 lower bound. In both figures, the societal poverty rates closely correspond to national poverty rates, as reflected by the relatively tight scatter of poverty rates around the 45-degree line (indicating perfect correspondence between national poverty and relative poverty as measured by the various SPLs). While we note above some differences in the RMSE between the SPL and the lower-bound variant of the SPL, from visual inspection of Figure 5, these differences appear relatively inconsequential for the resulting poverty measures. Indeed, Table 3 provides the mean deviation and mean absolute deviation

> 31 Estimates are produced from the version of PovcalNet updated October 1, 2016, available at <u>http://iresearch.worldbank.org/PovcalNet/home.aspx (accessed April, 2017). China, India and Indonesia</u> have separate rural and urban distributions in PovcalNet and no national median is therefore readily available. For these countries, the national median is derived by combining the rural and urban population weighted distributions available in PovcalNet and estimating the median of the joint national distribution. The resulting national median is used in defining the SPL used for these countries. For high-income countries, we replicate the alignment of the closest surveys to the reference years using National Accounts data, as done for countries included in PovcalNet’s reference-year aggregation.

25
Table 1: GATT/WTO - 60 Years of Tari¤ Reductions

|Implementation<br>Round<br>Weighted|tari¤|Weights based on|
|---|---|---|
|period<br>covered<br>reducti|on|MFN imports|
|||(year)|
|1948<br>Geneva (1947)<br>-26||1939|
|1950<br>Annecy (1949)<br>-3||1947|
|1952<br>Torquay (1950-51)<br>-4||1949|
|1956-58<br>Geneva (1955-56)<br>-3||1954|
|1962-64<br>Dillon Round (1961-62)<br>-4||1960|
|1968-72<br>Kennedy Round (1964-67)<br>-38||1964|
|1980-87<br>Tokyo Round (1973-79)<br>-33||1977 (or 1976)|
|1995-99<br>Uruguay Round (1986-94)<br>-38||1988 (or 1989)|
|e: WTO (2007), Table 5. Notes: MFN tari¤ reduction of industria<br>ing petroleum. Tari¤reductions for the ...rst ...ve rounds refer to the<br>rage rates of reductions are weighted by MFN import values.<br>able 2: Applied tari¤ rates of selected GATT/WTO <br>Economy|l countri<br>United S<br>Membe<br>1952|es for industrial pro<br>tates only. The calcu<br>rs, 1952 and 20<br>2005|
|Austria|17|4.2|
|Benelux|9|4.2|
|Denmark|5|4.2|
|France|19|4.2|
|Germany|16|4.2|
|Italy|24|4.2|
|Sweden|6|4.2|
|United Kingdom|17|4.2|
|European Union (EU-25)|-|4.2|
|Canada|11|3.8|
|United States|16|3.7|

Source: WTO (2007), Table 5. Notes: MFN tari¤ reduction of industrial countries for industrial products, excluding petroleum. Tari¤reductions for the ...rst ...ve rounds refer to the United States only. The calculation of average rates of reductions are weighted by MFN import values.

Table 2: Applied tari¤ rates of selected GATT/WTO Members, 1952 and 2005 Source: WTO (2007), Appendix Table 7. Notes: computed as simple average of 52 products in 1952 and of all tari¤ lines in 2005.

121
**<u>Table 6. Simulated and actual business cycles in the OECS countries: Volatilities</u>**

||% St<br>|andard devia<br>|tion<br>|<br>|% Standard<br>% Standard d<br>|deviatio<br>eviation<br>|n of x<br>of GDP<br>|
|---|---|---|---|---|---|---|---|
||GDP|TB/GDP|Int rate|Inv|Gov exp|Cons|Employment|
|OECS data|3.72|5.21|2.69|4.57|2.55|2.66|n/a|
|**Independent fiscal policy**||||||||
|**1. Independent country risk**||||||||
|(a) all shocks|3.72|4.79|2.69|4.57|2.55|1.40|0.89|
|(b) no z shocks|1.81|4.46|2.69|7.09|5.25|2.03|1.44|
|(c) no G shocks|3.72|3.89|2.69|4.57|0.00|1.39|0.89|
|(d) no R*, D shocks|3.20|3.17|0.00|3.20|2.96|1.11|0.62|
|**2. Induced country risk**|3.72|6.55|2.69|7.74|2.55|1.41|0.94|
|(d) no R*, D shocks|1.91|2.89|0.00|3.16|4.97|1.11|0.62|
|**Pro-cyclical fiscal policy**||||||||
|**3. Independent country risk**||||||||
|(a) all shocks|3.72|4.68|2.69|4.57|2.55|1.31|0.89|
|(b) no z shocks|1.81|4.58|2.69|7.09|5.61|2.03|1.44|
|(c) no G shocks|3.72|3.89|2.69|4.57|1.02|1.31|0.89|
|(d) no R*, D shocks|3.20|2.99|0.00|3.20|2.96|0.97|0.62|
|**4. Induced country risk**|3.72|6.49|2.69|7.74|2.65|1.35|0.94|
|(d) no R*, D shocks|1.91|2.89|0.00|3.16|5.15|0.95|0.62|

_Source_ : Authors’ calculations.

We begin by examining Table 6, which presents the results for volatilities. The benchmark model (panel 1.a) replicates the volatilities of the macro variables in the OECS data quite closely. It matches the volatilities of GDP, interest rate, investment, and government spending spot on because these moments were targeted in the calibration. But we did not target the volatility of consumption and trade balance: while the model comes very close to replicating the volatility of TB to GDP ratio, it underpredicts the volatility of consumption in the OECS countries. At the same time, it is important to note that the model is successful in generating consumption that is more volatile than GDP, in line with the data facts for the OECS countries.

Under a pro-cyclical fiscal policy, the model with all shocks (panel 3.a) predicts lower volatility of trade balance and consumption (the rest of the volatilities, again, were targeted in the calibration). The reason for this lower volatility is the counteracting effect that government expenditures have to productivity changes under the pro-cyclical fiscal policy (see impulse responses in Figure 2a).

**<u>Table 7. Simulated and actual business cycles in the OECS countries: Co-movement with GDP</u>**

Correlation of GDP with 22
**Table 2. Share of Villages with a Regularly Scheduled Election**

||**All**|**1995**|**1996**|**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|**2003**|
|---|---|---|---|---|---|---|---|---|---|---|
|All|0.154|0.145|0.145|0.182|0.182|0.182|0.091|0.182|0.182|0.091|
|Anhui|0.123|0.111|0.111|0.222|0.056|0.278|0.056|0.167|0.111|0|
|Henan|0.139|0.125|0.125|0.125|0.125|0.25|0.063|0.125|0.25|0.063|
|Jiangsu|0.222|0.273|0.182|0.091|0.455|0.091|0.182|0.364|0.182|0.182|
|Shanxi|0.156|0.1|0.2|0.3|0.2|0|0.1|0.1|0.2|0.2|
|Number of villages|55|55|55|55|55|55|55|55|55|55|

Source: 2004 Village Governance Survey and Land Use Survey, RCRE, Ministry of Agriculture.

_Land Tenure Insecurity and Migration, Page 36_
conceptual framework developed by **Gardner** (1988) and Lins (1988) They examine **the** gains and **losses** associated with the wheat and corn programs by means of a quantitative graphical analysis of the vaLious policy instruments th;:ough which wheat and feed grain conunodity policies are administered. The crop supply models are estimated using annual data while the crop demand models and meat supply and demand models are estimated using quarterly data. **The** Crop SupplV **Structure** The basic form of the acreage equations is as follows. First, acreage in a market free of government programs is assumed to follow (1) Af - Af(X,t,X,aAf,.l)

# where

- Af - free market acreage of the crop in question

- -n ' antic,pated short-run profit per acre from production of the crop in question with free market price

- anticipated short-run profit per acre from production of competing crop(s)

- Af -1 - lagged free market acreage (to represent production fixities, etc.)

Profit per acre is defined by price times yield less per acre production cost, e.g., (2) **_itn_** m Y- C where Pm - market price

- Ya - expected yield

- C - short-run cost per acre.

2
**Figure 8. Direct and indirect value added exports as shares of GDP, select services, 1995-2009**

<!-- Start of picture text --> Financial Services Business Services<br>BGR BEL<br>BEL<br>MLT<br>DEU LUX<br>NLD<br>AUT<br>CHN<br>NLD<br>KOR FRA<br>SVNHUNPRT SWE<br>IND LVAEST<br>DNK<br>IRL<br>CZEFRAAUSDEUITA SWE GBR SVN ESTHUNAUTGBR<br>JPN CAN CZE FIN MLT<br>BRALTUTURMEXFINROMPOLCYPIDNESP USA KORITALVAPOLSVKDNK<br>SVK AUSCAN<br>USAPRT<br>RUS GRC MEXRUSJPNLTUCYPROMESP<br>BGRCHN<br>BRA<br>TUR<br>GRC IND<br>IDN<br>0 .05 .1 .15 0 .2 .4 .6<br>Direct Value Added Exports/GDP Direct Value Added Exports/GDP<br>.5<br>.15<br>.4<br>.1<br>.3<br>.2<br>.05<br>Indirect Value Added Exports/GDP Indirect Value Added Exports/GDP<br>.1<br>0 0<br><!-- End of picture text --> Source: Author’s calculations based on OECD Trade in Value Added (TiVA) data. Note: the panel for financial services does not include Ireland and Luxembourg.

For financial services, figure 8 shows:

- In the bottom left quadrant are Greece, the Russian Federation, and Turkey. The low competitiveness of financial services in these countries is reflected in the low share of direct exports and the low level of embodied exports—which could reflect the low financial services intensity of goods production, the reliance primarily on imported financial services, or both.

- In the top left quadrant are China, India, and the Republic of Korea, which are not yet sufficiently competitive to export financial services directly, but which do export a significant share indirectly. That goods sectors in these countries rely significantly on domestically produced financial services could be because financial services in these countries have reached an intermediate level of development at which they can compete in the domestic market but not yet internationally. It could also be that restrictions on cross-border imports in these countries oblige goods producers to use domestically produced services.

- In the top right quadrant are Austria, Belgium, and the Netherlands, whose more developed financial services sector exports both directly and indirectly.

- In the bottom right quadrant are such “mature deindustrializers” such as Britain and the United States, where the domestic tangible industries have shrunk in importance and financial services are mostly exported directly.

19