rafmacalaba/datause-encoder-data huggingface dataset:
# **4.4. Key Measurement Domains and Methods**

The measurement methods in each of the key measurement domains of interest, namely maize production, plot area, and soil fertility, are summarized below.

# **4.4.1. Plot Area Measurement**

After walking the perimeter of a given plot with the plot manager to identify the boundaries, the enumerators re-paced the perimeter and measured the area with a Garmin eTrex 30 handheld GPS device. The area was recorded on the questionnaire in square meters, and the raw GPS track outline was stored for the remote sensing work program; linking relevant geospatial variables to the plot location; and calculating plot shape metrics.<sup>12</sup> As noted above, the competing yield measures in our study are all anchored in GPS-based plot area measurement.

# **4.4.2. Maize Production Measurement**

# **4.4.2.1. Crop Cutting**

Crop cutting has been recognized as the gold standard for yield measurement since the 1950s by the Food and Agriculture Organization of the United Nations (FAO). Besides the cost- and supervision-intensive nature of the exercise, several concerns have been raised regarding the accuracy of the method. Even if one places only one random crop cutting sub-plot within the sampled plot, the resulting yield estimate may carry a sampling error if the yields exhibit withinplot heterogeneity. (1) More thorough-harvesting of crop cut sub-plots vis-à-vis the typical farmer harvesting practices, (2) possible rounding of crop cut production estimates obtained through scales; (3) using faulty or inappropriate scales; (4) omitting to net out the weight of the measurement container from the measured production; (5) including plants that fall outside of the sub-plot; and (6) non-random placement of crop cut sub-plots have also been suggested as possible sources of error (Fermont and Benson, 2011).

By implementing a well-supervised crop cutting operation that relied on (a) random sub-plot placement and high-precision digital weighing scales in both rounds, (b) full plot harvests in a subsample of MAPS II plots, and (c) field staff and local crop cut monitors that were intensively trained to circumvent the abovementioned criticisms, our crop cutting-based maize yield estimates, in comparison to those that rely on farmer-reported production, are assumed, and later shown, to be more accurate approximations of the true levels. Further, given our interest in the IR, the main

> 12 Each plot manager was also asked to estimate the full area in acres, consistent with the practice of the UNPS. Up to two decimal points were recorded. However, we use the GPS-based plot areas for all analyses, given the systematic errors in farmer-reported plot areas that have been documented, as reviewed above, in Uganda and elsewhere in SubSaharan Africa.

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|ln_EDU_|0.036|(0.080)|0.092|(0.073)||
|---|---|---|---|---|---|
|_CONC_|0.190|(0.136)|0.284|(0.131)|**|
|_TOIL_|0.120|(0.112)|-0.011|(0.114)||
|_MOTO_|0.215|(0.389)|0.374|(0.388)||
|_BIKE_|0.193|(0.252)|0.269|(0.236)||
|_CHIPATA_|-0.028|(0.213)|0.453|(0.273)|*|
|Constant|2.107|(1.784)|1.676|(1.680)||
|Obs|753||697|||
|R-squared|-1.995||-2.012|||
|Wald chi2|373.99||388.95|||
|Exogeneity test:||||||
|C statistic (chi2)|19.888|***|15.052|***||
|Overidentifying restriction:||||||
|Hansen's J statistic (chi2)|0.000||0.016|||
|Marginaleffect|-0.419|(0.302)|0.273|(0.145)|*|

The dependent variable is the log of total crop sales, _SALE_ . Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.

# **VI. CONCLUSION**

Rural access is among the most important infrastructure elements to stimulate economic growth in rural and remote areas. Many rural farmers and local businesses are still likely faced with a crucial constraint to have access to input and output markets. The literature is generally supportive of the importance of feeder road connectivity in rural areas. Without the feeder road network maintained in good condition, it is difficult to improve agricultural productivity and encourage farmers to particulate in market transactions.

One of the most important challenges lies in maintenance of feeder roads. It is often neglected under budget pressure given limited available resources. Particularly, unpaved feeder roads require more frequent maintenance. Output- and performance-based road contracting (OPRC) is considered as an important policy instrument to ensure sustainability of road maintenance after the road rehabilitation. It normally requires contractors to not only improve roads but also maintain them through routine and emergency works.

Using the instrumental variable technique, the paper examined the impact of the recent OPRC experience in Chipata and Katete Districts, Zambia. By comparing the two areas: one
Policy Research Working Paper 6302

# **Abstract**

This note examines in detail Brazil’s export performance over the past 15 years, focusing not only on growth and composition, but also on different performance dimensions, including diversification, sophistication, and firm dynamics. The analysis uses international comparisons to better situate the Brazilian performance, and explores different databases, including firm-level data recently published by the World Bank. The note uses a recent diagnostic toolkit developed by the World Bank in order to suggest some hypotheses about the factors that have been inhibiting exports and industrial production expansion. Among the latter, it is noted how service sectors, as the largest beneficiaries from favorable terms of trade, accommodated larger wage increases and “exported” cost pressures to other sectors of the economy. Furthermore, although a stronger currency can be appointed as one of the elements behind the lower competitiveness in Brazilian exports, sluggish productivity performance and a real wage uptrend explain a significant part of the overall loss of competitiveness. This diagnostic reinforces the importance of resuming the agenda of microeconomic reforms, increasing the investment-to-gross domestic product ratio, and advancing toward better-skilled human capital.

This paper is a product of the Poverty Reduction and Economic Management Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ocanuto@worldbank.org, mcavallari@worldbank.org or jreis@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
47 -

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GRAPH lb: ACTUAL COMMODITY PRICE CHANGES ALICP33 FORECASTS AND ERROR BARS <!-- Start of picture text --> ALICP33 FORECAST-<br>.50_<br>.399<br>.4.,~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~1<br>1979 19080 1981 1982 1983 1984 1985 198C LOS! 1988 1989<br><!-- End of picture text --> Forecasted Sample Period: 1979-1988
Policy Research Working Paper 0

# **Abstract**

This study documents the long-term welfare effects of household non-traditional agricultural export (NTX) adoption. The analysis uses a unique panel dataset, which spans the period 1985–2005, and employs differencein-differences estimation to investigate the long-term impact of non-traditional agricultural export adoption on changes in household consumption status and asset position in the Central Highlands of Guatemala. Given the heterogeneity in adoption patterns, the analysis differentiates the impact estimates based on a classification of households that takes into account the timing and duration of non-traditional agricultural export adoption. The results show that while, on average, welfare levels have improved for all households irrespective of adoption status and duration, the extent of improvement has varied across groups. Long-term adopters exhibit the smallest increase in the lapse of two decades, in spite of some early gains. Conversely, early adopters who withdrew from non-traditional agricultural export production after reaping the benefits of the boom period of the 1980s are found to have fared better and shown greater improvements in durable asset position and housing conditions than any other category.

This paper—a product of the Poverty and Inequality Team, Development Research Group—is part of a larger effort in the department to document the long-term welfare effects of household non-traditional agricultural export adoption. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at gcarletto@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
uncertainty as to what cost reductions actually will be achieved, since these are conditional on significant technological breakthroughs.

**Table 14: Total investment required for an EHF process**

|Cost category|Installation costs (M$)|Share (%)|
|---|---|---|
|Pre-treatment|31.52|8.95%|
|Heat exchangers|10.87|3.09%|
|Stills|4.27|1.21%|
|Fermentation section|12.86|3.65%|
|Compressors|0.31|0.09%|
|Steam turbine|44.5|12.64%|
|Water Waste Treatment|10.4|2.95%|
|Onsite installation costs|114.74|32.59%|
|Other costs and investments<sup>a</sup>|237.28|67.41%|
|**Total project investment**|**352.02**|**100.00%**|

Source: Piccollo and Bezzo (2009). The plant is assumed to process 700 thousand dry tones of biomass per year.<sup>a</sup> Piccollo and Bezzo (2009) did not include this category (only onsite installation costs and total investments were reported) .

A breakdown of the production costs for lignocellulosic ethanol from four studies is presented in Table 15. Given the variability in feedstock costs reviewed in the previews section, it is to be expected that production costs of second-generation biofuels would be wide-ranging. A recent literature review reported current production costs of secondgeneration ethanol in the $0.60-1.30/liter range (IEA 2008). Technological advances are expected to drive production costs down to as low as $0.30-0.40 per liter by 2020 (IEA 2005; Perlack et al. 2005). More ambitious targets for production cost reductions (achieving costs of $0.28 per liter by 2012) were included in the U.S. Biofuels Initiative (US DOE 2008).

Despite small differences in the relative weight of some cost categories, estimates are largely consistent with each other for the first three studies, yielding an estimated cost of $0.6 per liter of lignocellulosic ethanol. Capital costs account for roughly 40% of the overall costs in the studies. It should be noticed, that these sources used very similar assumptions in terms of feedstock costs, implying comparable costs for all the other 29
We set the government share in the oil sector = 0.7, which is the historical average of oil revenues (as share of oil GDP) in Angola over 2001-2013 (IMF-RR ττ WEC, most recent data available). Private investment is set to 20 percent of GDP to match the average observed in 2000-2017 (IMF-FAD). Due to exceptionally large investments in energy infrastructure, public investment has been historically very high in Angola (above 10 percent of GDP), but is expected to fall significantly over time, especially due to the likely decline in oil revenues. Accordingly, we assume that public investment falls from 6 percent of GDI in 2023 to 2 percent by 2050. The marginal propensity to spend under the BBR is set to B = 0.2, as public investment averaged 20 percent of total government expenditure from 2000 RR θθ to 2017 (IMF-FAD) (recall D = 0.2 and = 1). Finally, in the baseline we set the actual and structural price of oil to 50 dollars RR HHRR θθ θθ per barrel, which is the estimated unconditional mean estimated with data since 1960 (see Appendix Figure 5 for details). Also, we assume discoveries of 400 million barrels of oil per year in the baseline, equal to the 25<sup>th</sup> percentile over 19902017 (BP-Energy data, see Figure 6 panel A).

**Calibration to Angola (External Balance submodel).** For the External Balance submodel, we set the private savings rate and FDI to 19 and 1 percent of GDP, respectively, similar to their averages over the past 20 years. The public investment share in expenditure is set = 0.2 until 2050 (also the historical average). Finally, we assume that = 0, which means a balanced budget for the tt ηη BBR and a structural balance for the SSR. We assume the default private tt bb<sup>�</sup> investment crowd-in parameter (Chinn and Ito 2011). For more details of the calibration of the External Balance submodel refer to Appendix Table 4. λλ= 0.15

# _A. Baseline Growth in Angola: LTGM-NR versus Standard LTGM_

In this subsection, we (i) present the baseline GDP per capita growth simulated by the LTGM-NR-default until 2050, (ii) discuss how this baseline is explained by each of the variables of the model, and (iii) illustrate the importance of using a model with a resource sector (by comparing it with a naïve calibration using the standard LTGM).

27
_55_ _E&wrV Policy and Prices_ The externally supported adjustment program set in motion in 1983 laid down long-tum development plans for the energy sector. A goal was to reduce dependence on imported crude oil, t largemt sige import item equivalen **t** o as much as 40 percent of Ghana's total export earins during lh 1980s. Improvement in energy use via pricing reflecfing opporunity costs was identified as e most ipoat insument to achieve tis objective. Domestic petroleum product pnices pnor to dh incept ofthe adjusme program in 1983 were highly subsidized, and the first step toward achieving Ike goal **set** forward inthe long-term energy sector development plans was an upward adjustm in the admmisted petrolewn product prices. Figure 4.1 shows the changes in domestic energy prices during 1983-87.

As figure 4.1 shows, Ghana experienced sizable domestic petroleum price changes from 1983 through 1987. By 1987, kerosene and diesel prices had risen, in real terms by 161 and 214 perent, respectively, and gasoline prices by 156 percent _Rgwe 4.1: Ghn= Domestic EnergyPrce Changes tercentgep.a in real terms)_ <!-- Start of picture text --> 100.00%<br>50.00%<br>0.00%<br>-50.00%<br>1981 1982 1983 1984 1985 1198 1987 1988 1989<br>Diesel Gasoline M Kerosene<br><!-- End of picture text -->

# **Effets of Eer Price Chnage**

_Detel Price ncreases 1983-88: T7e Industral Sector_ Given6the importmwe ofpetroleum products in tie industrl sector, it is reasonable to qwt dhat cag m domestic energy prices would have had significant cosequences for the industi sector, **especily** adjustnts for dieseL Wha was the unpact of the diesel pnce increase for this seor? Table 4.2 gives the cost shares and the developments in the index of industri production for a slected group **of industreL**
In 2004, a further review of asset allocation strategy resulted in an increased allocation to alternative asset classes, including real estate, private equity and commodities, of up to 18 percent of total assets. The objective was to increase the fund's potential long-term return without substantially changing its risk profile. The Commission also decided to increase its small cap allocation from 2 to 4 percent and to allocate 2 percent to emerging market equities.

After reviewing the assumptions underpinning the strategic asset allocation, the Commission introduced in 2006 slight adjustments to the targets adopted for 2009 (Table 4.2). The main adjustments included a reduction in large cap equities and an increased allocation to emerging markets. The latter underscores the opportunities for higher returns offered by these markets but also recognizes the progress made by many developing countries in promoting financial stability and strengthening the regulation and supervision of their financial markets.

Unlike public pension funds in Canada, the Commission has not advocated engaging in principal investing or short-term trading. Much of the portfolio in bonds and large caps continues to be passively managed, while investments in alternative classes are made through investment funds.

Table 4.2: <u>Strategic Asset Allocation: 2009 Targets</u>

|percent|2004 Review|2006 Review|
|---|---|---|
|Large Cap Equity|63|56|
|Small Cap Equity|4|5|
|Emerging Markets Equity|2|5|
|**Total Quoted Equity**|69|66|
|Private Equity|8|10|
|Property|8|8|
|Commodities|2|2|
|**Total Alternative Assets**|18|20|
|Bonds|13|13|
|Currency Funds||1|
|**Total Monetary Assets**|13|14|

Source: NPRF

# **4.5 Implementation of Investment Strategy**

The appointment of NTMA as the operational manager of the fund is a natural choice for the NPRF. The NTMA is responsible for managing the public debt of Ireland with the objective to minimize the cost of public debt subject to a prudent level of risk. This is very similar to asset management objective to maximize the investment return subject to a prudent level of risk. The NPRF is not allowed to invest in Irish government securities and thus there is no direct conflict of interest between the two main functions of the NTMA. When investments in infrastructure projects are considered, proper Chinese walls are put in place between the managers of the NPRF and the managers of infrastructure projects.

50
**Table 4. Mean Weekly Hours-Worked and Secondary Occupation Shares**

|**Type of Occupation**|**Hours-**<br>**Ma**<br>**Occup**|**worked**<br>**in**<br>**ation **|**Adjusted**<sup>**1**</sup><br>**worked**<br>**Occup**|<sup>**/**</sup>**Hours-**<br>**Main**<br>**ation **|**Hours-**<br>**Secon**<br>**Occup**|**worked**<br>**dary**<br>**ation **|**Hours-**<br>**Total**|**worked**<br>**Hours**|**S**<br>**Occu**|**econdary**<br>**pation S**|<br>**hare**|
|---|---|---|---|---|---|---|---|---|---|---|---|
||**Urban**|**Rural**|**Urban**|**Rural**|**Urban**|**Rural**|**Urban**|**Rural**|**Nal.**|**Urban **|**Rural**|
|Primary Teacher Pub. Sch|29.4|29.1|23.2|23.0|2.3|3.4|31.8|32.6|13.4|<br>12.3|21.2|
|Primary Teacher Priv. Sch|33.4|n.d.|26.3|n.d|0.4|n.d|33.8|n.d|3.0|<br>3.0|n.d.|
|L-Secon. Teacher Pub. Sch.|34.0|33.0|26.8|26.0|2.1|2.3|36.2|35.3|16.4|<br>14.6|25.6|
|L-Secon. Teacher Priv. Sch|30.3|n.d|23.9|n.d|0.4|n.d|30.7|n.d|2.5|<br>2.5|n.d.|
|U-Secon. Teacher Pub. Sch|29.4|21.0|23.2|16.5|3.7|0.0|33.0|21.0|22.0|<br>22.2|0.0|
|U-Secon. Teacher Priv. Sch|21.6|n.d|17.0|n.d|7.4|n.d|29.0|n.d|30.5|<br>30.5|n.d.|
|**The mixed-skilled group**|**46.0**|**43.9**|**43.4**|**41.3**|**1.2**|**5.6**|**47.2**|**49.5**|**8.7**|<br>**6.1**|**24.4**|
|The agricultural group|38.2|36.7|36.0|34.6|5.2|6.8|43.4|43.5|25.6|<br>18.5|27.6|
|The low-skilledgroup|42.2|41.4|39.8|39.0|1.2|3.4|43.5|44.8|7.7|<br>6.5|15.4|

Source: Own calculations based on ENIGH96 survey 1/ Following OECD, it is assumed that teachers worked on average 41 weeks per year and non-teachers worked on average 49 weeks per year. .

# _Income, Labor Earnings and Salaries_

Teachers salaries have been highlighted as a very important issue in school improvement discussions. The level and structure of teacher remuneration are said to affect teacher morale and ability to focus on and devote adequate time to teaching well. Teacher remuneration could also determine the capacity of the education system to attract and retain good teachers. Table 5 presents several definitions of salaries and personal income sources, since much of the argument over teacher compensation refers to what is meant by the term “underpaid.” An issue that may hamper the comparison of net earnings across occupations and locations is non-regular and additional benefits, and the way certain allowances are made available to teachers. The labor earnings and salary figures tell us something about fairness of compensation, while total income refers to the teachers’ standard of living. It is clear from the table below that labor earnings is the largest share of all the teachers' total income. This suggests that the teachers' standard of living basically depends on what they obtain as labor income. On the other hand, total incomes for _the mixed-skilled_ group _, the agricultural_ group _,_ and _the low-skilled_ group are more uniformly distributed among other income sources.

12
It is also likely that the margin enjoyed by traders-particularly in the urban areas-had fallen with the stabilization of the economy.

The stabilization measures saw a remarkable turnaround in the economy. Overall GDP growth rate increased from minus 1 percent to plus 5 per cent in the first five years of the ERP. Total merchandise export grew at 22 per cent per annum, with the traditional exports, logs and cocoa growing the fastest. Real wages recovered to some extent in the formal sector, as the rate of inflation fell. In the public sector some retrenchment allowed a greater part of the wage bill to be used to support recovery of real earnings.

The measures of structural adjustment of the economy seem to have been less successful. The public sector still dominates the formal labor market. Liberalization on the trade front has not led to the expected increase in manufacturing output. Public enterprises continue to be important and generally a drag on the efficient growth of the non-agricultural sector.

# **2.3 Recent Shocks**

An adverse development has been the decline in Ghana's terms of trade since 1987. In 1994 it stood at a level more than 25 per cent below that of 1987. It has been pointed out that the net aid flow has virtually been wiped out by the terms of trade loss (World Bank 1994, figure 4.1,p.155). Some authors have argued that the foreign aid flow has prevented depreciation of the Ghanaian currency in the foreign exchange market to reflect the terms of trade deterioration (Younger, 1994). But it should be remembered that the net foreign aid was less than 5 per cent of Ghana's GDP, well below the average of 8 per cent for sub-Saharan Africa (ibid., p.155).

More recently the fiscal situation has deteriorated. The fiscal shock of 1992 was triggered by a large across-the-board increase in wages for civil servants and was accentuated by a fall in tax revenue and a shortfall in foreign aid. The narrow fiscal account went from a surplus of 1.5 percent of GDP to a deficit of 4.8 percent in 1992 and deficits of 5.6 in 1993 and 4.3 in 1995-- reversing the improving trend of the last seven years. The attendant expansion of money supply triggered a reversal of inflation trend as well. After falling to 10 per cent in 1992, the rate of inflation shot up to 25 per cent in 1993-1994, and more than 50 percent in 1995- 1996. The indirect impact on savings and investment seems to have been severe. The upward trend in gross private investment was interrupted in 1991, has continued to decline since then, and could be directly related to the macro-economic instability. While public investment tried to make up for the shortfall, it could do so only at the cost of public dis-saving, and at the same time private savings fell (World Bank 1995, pp. 9).

7