rafmacalaba/datause-encoder-data huggingface dataset:
equal to the corresponding sub-national AgGDP values. The last equation (15) is a natural constraint for the percentage of AgGDP, which is also the probability in the cross-entropy model. The modeling framework is flexible in that more constraints can be added if more data are available and/or more reasonable assumptions on how AgGDP should be spatially disaggregated are discovered.<sup>10</sup> Last but not least, we multiply the total regional agricultural GDP by the probability in the cross-entropy model to derive the final pixel level agricultural GDP:

# **2.4 Natural hazards**

We use measures of two natural hazards to gain insight into the spatial distribution of agricultural activity with regards to drought and water scarcity. We calculate the Standardized Precipitation-Evapotranspiration Index (SPEI) (Sergio M Vicente-Serrano, Beguerıa, and L ́opez-Moreno, 2010), which measures the difference between observed precipitation and estimated potential evapotranspiration with a 3 month interval using the base climatology of 1980 to 2019, which is implemented in R (Beguer ́ıa and Sergio M. Vicente-Serrano, 2017) using climate data from Harris et al. (2020). Extra dry years are defined as the number of years that are less than or equal to -2.0 during the period from 2000 to 2009. Figure 5 shows the results of the SPEI.

The Water Crowding Index (WCI) is a measure of water scarcity considering the local population as the annual water availability per capita (Falkenmark, 1986; Falkenmark, 2013). Veldkamp et al. (2015) model global water crowding index with return periods. We take the mean of any pixels of the ensemble WCI with a 10 year return period within an agricultural GDP pixel. Following the literature (e.g. Alcamo, Fl ̈orke, and M ̈arker, 2007; Arnell, 2003; Kummu, Ward, et al., 2010; Veldkamp et al., 2015), we categorize the WCI into four categories: Absolute is less than 500 m<sup>3</sup> /capita per year; severe is less than or equal to 1000 m<sup>3</sup> /capita per year; moderate is less than or equal to 1,700 m<sup>3</sup> /capita per year; and low is the remainder (Figure 6). Then, we evaluate water shortage events using a threshold of 1,700 m<sup>3</sup> /capita per year with a return period of 10 years.

> 10For instance, market access may play a role in determining the spatial distribution or spatial structure of AgGDP and can be included as a constraint in the model. However, we provide a parsimonious model without market access.

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|Higher Education FinancingProject|Andreas Blom|
|---|---|
|Higher Education FinancingProject|Andreas Blom|
|Higher Education FinancingProject|Andreas Blom|
|Higher Education FinancingProject|Andreas Blom|
|Higher Education FinancingProject|Andreas Blom|
|Higher Education FinancingProject|Andreas Blom|
|Higher Education FinancingProject|Andreas Blom|
|Higher Education FinancingProject|Andreas Blom|
|Rural and Small Towns Water Supplyand Sanitation Project(PRAGU|Franz R. Drees-Gross|
|Rural and Small Towns Water Supplyand Sanitation Project(PRAGU|Franz R. Drees-Gross|
|Rural and Small Towns Water Supplyand Sanitation Project(PRAGU|Franz R. Drees-Gross|
|Rural and Small Towns Water Supplyand Sanitation Project(PRAGU|Franz R. Drees-Gross|
|Rural and Small Towns Water Supplyand Sanitation Project(PRAGU|Franz R. Drees-Gross|
|Rural and Small Towns Water Supplyand Sanitation Project(PRAGU|Franz R. Drees-Gross|
|Rural and Small Towns Water Supplyand Sanitation Project(PRAGU|Franz R. Drees-Gross|
|Anhui Provincial HighwayProject|Aurelio Menendez|
|Anhui Provincial HighwayProject|Aurelio Menendez|
|Anhui Provincial HighwayProject|Aurelio Menendez|
|Anhui Provincial HighwayProject|Aurelio Menendez|
|Anhui Provincial HighwayProject|Aurelio Menendez|
|Anhui Provincial HighwayProject|Aurelio Menendez|
|Anhui Provincial HighwayProject|Aurelio Menendez|
|Anhui Provincial HighwayProject|Aurelio Menendez|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Education for All Project|Nathalie Lahire|
|Health Financing& PrimaryHealth Care Development Project|Toomas Palu|
12 Needless to say, there are numerous challenges in applying the above methodology, especially in less developed economies with poor-quality data. Ways to deal with the standard challenges are detailed in Anderson et al. and the country-specific challenges are discussed in the analytical narratives in the regional and global volumes listed in footnote 3 above.

The NRAs and CTEs are similar to the PSEs and CSEs computed by OECD (2008), except that each of the OECD’s measures is expressed as a percentage of the distorted rather than the undistorted price. Thus, it is lower than the comparable NRA or CTE, and has a maximum value of 100 percent. The OECD does not attempt to estimate rates of distortion to prices of non-covered farm products, thereby implicitly assuming they are the same as the average for the roughly 70 percent of farm production that is covered by direct price comparisons. Nor does the OECD take into account distortions to non-farm sectors or to the market for foreign exchange, and it does not estimate indicators such as the WRI and TRI.

The estimates by Krueger, Schiff and Valdés (1988; 1991) distinguish for each country a ‘direct’ and ‘indirect’ rate of ‘protection’ as measures of distortion to agricultural incentives. Their ‘direct’ rate is not identical to the agricultural NRA described above, but is the closest for comparative purposes.<sup>14</sup> Their ‘indirect’ rate is the number of percentage points by which the ‘direct’ rate for each product, or the production-weighted average for a country’s covered farm products, should be reduced because of the adverse macroeconomic influence on farmer incentives of that country’s non-farm policies (most notably protection to the manufacturing sector and overvaluation of the country’s currency). Their ‘total protection’ rate, therefore, is not identical to the above RRA, but again it is the closest for comparative purposes. It is not identical to the RRA partly because the K/S/V measure is an attempt to estimate econometrically the indirect effect on farm distortions of those non-farm policies, whereas the RRA explicitly uses an estimate of the NRA for non-farm tradable sectors alongside the estimated NRA for the tradable farm sector and both of those NRAs explicitly incorporate an estimate of the trade-taxing effect of multiple exchange rates. Since there are now plenty of sectoral and economy wide models of national and global markets available, the study summarized in Anderson leaves it to modelers to determine how much the estimated domestic price distortions influence a country’s real exchange rate and the international relative price of farm products (see footnote 8 above). The other important

> 14 Formally, K/S/V’s ‘direct protection’ measure is the ratio of (a) the difference between the relative producer price and the relative border price and (b) the relative adjusted border price measured at the equilibrium exchange rate and in the absense of all trade policies, where the ‘relative price’ refers to the price of the farm product relative to the price of all non-farm products.
FEMA has a multipronged approach to encouraging compliance with land use regulations: deterrence of deforestation on nonlicensed properties; encouragement of landowners to license their properties; and enforcement of regulations on licensed properties. In the first prong, satellite and field inspections of high deforestation areas identifies landowners who are out of compliance with regulations. Many of these, especially in the early phases of implementation, will have unlicensed properties. These violators are subject to severe civil and criminal penalties. However, FEMA’s preferred approach is offer a reduction of up to 90% in fines if they repair the damage done to RL or APP areas. This is done through the licensing process. Landowners may also voluntarily opt to license their properties, motivated in some cases by lenders’ requirements to show compliance with regulations.

In either case, recruitment of a property into licensed status facilitates enforcement of regulations. A first step in the licensing process involves detailed mapping of the property, and determination of compliance with RL and APP requirements. Landowners who are out of compliance are given an opportunity to redeem themselves by reestablishing native vegetation, by purchasing a compensating area of RL from another private property, or by purchasing for the state a private property within an already-gazetted conservation unit. Landowners who are already in compliance may apply for an authorization for legally compliant deforestation. Because the maps of licensed properties are stored in a GIS, subsequent violation of license terms can in principle be easily and unambiguously detected. Moreover, the owners of licensed properties are legally bound to observe the licensing terms, so that violation of these terms exposes them in principle to swift and severe penalties.

Initial studies of the environmental control program suggested that it was contributing to an observed reduction in aggregate deforestation. FEMA credited the control system with the decrease in deforestation rates (per total area) from 2% in the biennium 1998-99<sup>4</sup> to 1.35% in the biennium 2000-01 (FEMA 2002, 19). Fearnside’s municipal-level study of FEMA’s data for

> 4 That is, the two year period ending in April 1999.

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capital accumulation decisions. The model combines the labor supply model with worker mobility costs of Artuc, Chaudhuri and McLaren (2010) with the labor demand model with capital adjustment costs of Cooper and Haltiwanger (2006) and Bloom (2009). The labor supply side is characterized by a rational expectations optimization problem of workers facing mobility costs and a time-varying idiosyncratic shock. The labor demand side is characterized by the rational expectations intertemporal profit maximization problem of firms facing costs for adjusting their capital stock.

Bet, Brambilla and Porto (2011) fit the model to plant-level panel data and household survey data from Argentina. They estimate large costs of adjusting both capital and labor. For capital, the fixed cost of adjustment is around 14.5 percent of the firm’s capital stock, and the quadratic cost parameter is 0.11. The partial irreversibility cost parameter is 0.91, suggesting that the selling price of installed capital is 9 percent lower than the buying price of capital. As discussed in section 5.1, the labor mobility costs are equivalent to around 9 times the annual value of wages.

The authors also study transitional dynamics following a tariff cut in the Food and Beverages sector. Figure 4 reports the evolution of employment in the manufacturing sectors (top left panel), employment in the non-manufacturing sector (top right panel), real wages (bottom left) and workers’ present values (bottom right). Overall, Bet, Brambilla and Porto (2011) observe a sizeable effect of trade liberalization on labor markets and a sluggish adjustment of employment. For instance, labor reallocation towards the new steady state is complete after more than 15 years. In particular, labor allocation in the food and beverages sector decreases from a steady-state value of approximately 10 percent to a new steady-state of around 6 percent. Labor reallocates to all other sectors, with the largest increases in Nonmanufactures and Other Manufactures.

In the bottom panels, as expected, real wages in the food and beverages sector decrease as a result of trade liberalization. Real wages in the shocked sector overshoot its long-run value with a sharp initial drop and a lower new steady-state level. In particular, after the sudden shock of the drop in food and beverages prices, workers begin to move out of the sector bringing wages up. The other sectors exhibit gains in real wage levels because of the drop in consumer prices. Additionally, some of these sectors also exhibit overshooting (but in the opposite direction and because of similar reasons). In spite of the fact that real wages fall in the food and beverages sector, the present discounted value increases in this sector, exactly as in Artuc, Chaudhuri, and McLaren (2010).

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Second, the impact of these trade agreements on production sectors are somewhat more significant, and generally similar across agreements. Under either agreement, there is some flow of labor out of import-competing (primarily textile and machinery) and non-traded goods (services and transportation) and into exportable (mining and food products) sectors. The major difference between the two agreements seems to be that NAFTA has a more favorable impact on the agricultural sector, but a more unfavorable effect on the other manufacturing sector (primarily chemicals). This reflects the differences in comparative advantage of the NAFTA and MERCOSUR countries.

Third, the sectoral effects of either of these agreements is significantly smaller than the one that would result form a complete elimination of trade barriers. The negative impact on the textile and machinery sectors of free trade would be several times larger than that of either preferential agreement (even under high assumptions regarding the degree of trade diversion), probably because of the presence of important suppliers in Europe and Asia that would not be part of either preferential arrangement.

It should be emphasized that the results reported above do not include the gains to Chile generated by improvements in the terms of trade resulting from tariff cuts in partner countries. For the case of NAFTA, suppose we view the US domestic price as being the world price of exports. A reduction in the US tariff will allow Chile to sell its exports at the US domestic price, which result in a terms of trade improvement for Chile. Incorporation of this effect is likely to increase the attractiveness of NAFTA relative to MERCOSUR, given the greater size of the US market. Effects of this type could be incorporated by utilizing data on the tariffs faced by Chile in the partner market. In the NAFTA case, it would seem reasonable to model the terms of trade improvement for Chile as the amount of the reduction in the US tariff. For Mercosur, where Chile is likely to have a significant impact on prices in partner countries, it would be preferable to endogenize the prices in the partner countries. For example, Harrison, Rutherford, and Tarr (1996) find that the effects of 23
# **1. Mining Sector Background**

9. Ghana is endowed with sizable deposits of manganese, diamonds, and bauxite, but gold remains the key resource for the country’s economy (see Table 1), accounting for more than 95 percent of mineral revenues. Although the output of other minerals has decreased, the production of gold grew from 63 tons in 2004 to approximately 80.5 tons in 2008 (Ghana Chamber of Mines 2000–08; Mineral Commission 1996–2006). The country is the secondlargest gold producer in the continent after South Africa. The largest gold deposits are found in the Ashanti, Western, Central, and Brong-Ahafo Regions. The mining sector currently contributes approximately 41 percent of total exports earnings, 14 percent of total tax revenues, and 5.5 percent of Ghana’s gross domestic product (GDP) (for official statistics, see appendix A). Despite the fact that mineral exports increased 50 percent and tax revenues derived from mining almost tripled between 2004 and 2008, as a share of total tax revenues they have decreased in the same period (see ISSER 2009).

**Table 1: Production of Mineral Commodities (thousand metric tons unless otherwise**

|**specified)**<br>|||||||
|---|---|---|---|---|---|---|
|**Commodity**|**Unit**|**2004**|**2005**|**2006**|**2007**|**2008**|
|Bauxite, gross weight||498|727|886|748|738|
|Diamond|thousand carats|920|1,013|973|895|643|
|Gold|kilograms|63,139|66,852|69,817|83,558|80,503|
|Manganese:|||||||
|Ore, processed||1,597|1,715|1,659|1,173|1,090|
|Mn content||559|600|580|410|380|
|Salt||265|250|123|124|150|
|Silver|kilograms|3,329|3,571|3,142|3,300|3,200|
|Steel||25|25|25|--|--|

_Source_ : USGS Mineral Commodity Review, Ghana 2009.

**Figure 1: Bauxite and Gold Production (19802009)**

<!-- Start of picture text --> 900 90<br>800 80<br>700 70<br>600 60<br>500 50<br>400 40<br>300 30<br>200 20<br>100 10<br>0 0<br>Bauxite Gold<br>Bauxite Thousand Metric Tons Gold Thousand Toz<br>1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006<br><!-- End of picture text --> _Sources:_ USGS 2009.

**Figure 2: Contributions of the Mining Sector (2004-2008)**

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>2004 2005 2006 2007 2008<br>Mineral Exports as Percent of Total Exports<br>Mneral Revenue as Percentage of Total Tax Revenue<br>Percentage<br><!-- End of picture text --> _Source:_ Revenue Agencies Governing Board (RAGB).

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# **III. The role of initial conditions, growth, policy, and other factors**

**_Initial conditions count in MDG performance, but subsequent growth and policy also matter greatly--or more._** In most cases, countries that are doing better (those on or close to the target) exhibited favorable starting conditions around 1990 (the reference year). A higher per capita GDP in 1990 is generally associated with better MDG performance (figure 3).

**Figure 3. MDG performance is stronger in countries with good initial conditions**

<!-- Start of picture text --> MDG performance and Initial income conditions MDG performance and Initial institutional<br>7000 conditions<br>6000<br>3.60<br>5000 3.40<br>4000 3.20<br>3.00<br>3000 2.80<br>2000 2.60<br>10000 on targetclose to target 2.402.202.00 on target<br>far from target close to target<br>far from target<br>Source: Authors’ calculations based on data from the World Development Indicators database.<br>Note: A country is ―close to the target‖ if its distance to getting on target (that is, its gap of trajectory) is smaller than the average<br>gap of all lagging countries. Otherwise, it is ―far from the target‖ (that is, its distance is greater than the average gap).<br>GDP per capita, 1990, CPIA index, 1996<br>PPP (constant 2005 international $)<br><!-- End of picture text --> Although there is no perfect indicator of the overall quality of policy and institutions in developing countries, the World Bank’s annual Country Policy and Institutional Assessment (CPIA) provides a broadly consistent framework for assessing country performance on 16 items grouped in four clusters: economic management, structural policies, policies for social inclusion and equity, and public sector management and institutions. The score is from 1 (low) to 6 (high) for each policy that covers a wide range of issues.<sup>11</sup> The index focuses on policies and institutional arrangements--the key elements that are within the country’s control--rather than on actual outcomes (for example, growth rates) that are influenced by elements outside the country’s control. Over time, good policies and institutions are expected to lead to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations caused by external factors.<sup>12</sup> Using the 1996 CPIA, the earliest index with comparable scale and criteria available,<sup>13</sup> suggests that countries starting with good policy and institutions tend to do better in the MDGs.

> 11 Issues include macroeconomic and fiscal policy, debt policy, trade, human development policy in education and health, gender equality, social protection, budgetary and financial management, and corruption in the public sector. 12 See World Bank (2009).

> 13 An earlier version of the CPIA goes back to 1970s but uses a different scale and criteria. For example, the assessment of governance issues was not included in the earlier CPIA.

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Table 20: CHOICE OF EMPLOYMENT STATUS - PROBIT RESULTS

|Intercept|-2.67|(8.2)|
|---|---|---|
|Years of Primary Education|.028|(1.2)|
|Years of Secondary Education|-.048|(1.2)|
|Years of Higher Education|-.049|(0.5)|
|Years of Secondary VTE|.091|(1.7)|
|Years of Other VTE|.473|(2 0)|
|Years of Apprenticeship|-.010|(0.5)|
|CEPE|.669|(4.1)|
|BEPC|1.15|(3.5)|
|Secondary VTE Diploma|1.27|(4.5)|
|Other VTE Diploma|2.29|(3.6)|
|Other Diploma|1.53|(2.9)|
|Age<br>|.133|(7.0)|
|(Age)<sup>2</sup>|-.0018|(6.8)|
|Female Without Child 5 years|-.947|(5.5)|
|Female With Child 5 years|-.970|(9.5)|
|Non-Ivorian|.052|(0.5)|
|Years of Residence Since Migration<br>|.017|(1.4)|
|(Years of Residence Since Migration)<br>2|-.0006|(1.7)|
|Household Enterprises|-.363|(7.4)|
|Abidjan|.762|(7.3)|
|Rural Areas|-1.37|(13.8)|
|Log-likelihood|-636.34||
|Chi-squared|1893.6||
|Significance<br>level|0.1E-6||
|Number of cases|4597||
|% correct predictions|95||

Note: Coefficients pertain to the likelihood to become a wage-earner. Absolute asymptotic t-values are in parentheses.
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