rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 13: Brazil – trade liberalization<br>Brazil: Weighted tariff rates (%)<br>50<br>45<br>Electrical machinery<br>40<br>35<br>30<br>Non-electrical machinery<br>25 Transport equipment<br>Iron & steel<br>20<br>15 Food Manuf.<br>10<br>Industrial chemicals<br>5<br>Petroleum refineries<br>0<br>Brazil 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Paper & products Printing, publishing and allied industries<br>Industrial chemicals Petroleum refineries<br>Iron & steel Non-electrical machinery<br>Electrical machinery Transport equipment<br>Food Manuf.<br>Source: TRAINS database<br><!-- End of picture text --> Brazils’ catch up has suffered in part because of a lack of sustained effort to build its technological strength which underlies competitiveness. Undoubtedly, macro disturbances in the 1980s and 1990s also conspired against innovation. With one notable exception, the declining RCA in Brazilian industries illustrates the declining competitiveness of the manufacturing sector. A review of Brazil’s innovation policy suggests that post reform, the only industry that evolved into a world class industry is Food products (including ethanol-related industries). The latter benefited disproportionately from S&T-oriented public investments in EMBRAPA which is a government agricultural research organization created in 1974. Similarly, public innovation funds channeled into Petrobras nurtured world-class petroleum-related activities.

While the Brazilian government has been spending more public funds in financing R&D (close to 1 percent of GDP), synergies between public-sector funded research institutes and private firms in the manufacturing sector remain limited. Compared with about 30 percent in the United States, about 55 percent of the total investment in technological innovation in Brazil is publicly funded (World Bank, 2007) but it has been less successful at energizing technological innovation or patents that can be commercialized. Its import substitution policies shielded its private sector, undermined incentives for private R&D and led to underperformance in innovation. Recent initiatives such as the Innovation Law and the Sector Funds that are aimed at encouraging firms to invest in innovation are attempts to correct these distortions (Katz, 2000).<sup>13</sup>

> 13  It has also been noted that overly “theoretical” research in publicly funded universities partially explains why the latter are of little use to entrepreneurial scientists and engineers who do not interact with universities. As an example, Brazil accounts for nearly 2 percent of articles published in internationally recognized research journals, but only 0.18 percent of the patents (OECD S&T Outlook, 2008).  Its average educational attainment is the nextto-lowest gross enrollment rate among the larger Latin American countries. Only 8 percent of the labor force has tertiary-level educational qualifications.

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implies that there was a sizeable compositional shift over time that led to a more skewed distribution of trade in 2005 as compared with 1992.

# 6.2. Variety growth

To utilize the more disaggregated trade data at the 8- and 10-digit levels, we examine the contribution of new varieties to export growth using two complementary methods. The fi rst is the Feenstra index of net export variety growth which provides an indication of the importance of new varieties in trade. The second is a decomposition of export growth into new, disappearing, and existing varieties and o ff ers more information on the magnitude of export creation and destruction. We present the de fi nitions and discuss the strengths and weaknesses of each measure below.

Feenstra’s (1994) seminal work on measuring import prices incorporating new goods leads to a natural index of variety growth that has been widely used in the literature. Denoting I as the set of varieties available in both periods, I ⊆ (It ∩ It − 1), the Feenstra index of net variety growth is de fi ned as the fraction of expenditure in period t-1 on the goods i ⊂ I relative to the entire set i ⊂ It − 1 as a ratio of the fraction of expenditure in period t on the goods i � I relative to the entire set i � It, minus one.<sup>6</sup> Let Vti be the value of trade at time t in product i (Vti = ptiqti ), then The index will be equal to zero if there is no growth in varieties relative to the base period and positive if the number of varieties has grown. This measure has the nice feature that if HS trade classi fi cations are split, and their share of total trade remains unchanged, the index remains unchanged. However, if growth classi fi cations are split (or reclassi fi ed) to a greater extent than shrinking classi fi cations are merged, the index will tend to overstate the extensive margin. A disadvantage of the index for measuring the relative importance of new varieties in export growth is that if there is a lot of churning, with an equal amount of export

> 6 From Feenstra (1994) this is the inverse of the lambda ratio minus one.

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-4-

administered in November, 1988. The overall purpose of the study was to explore differences in the relative effectiveness of various types of school in enhancing student achievement and in reducing performance differences between students from different social class backgrounds.

# Sample

5. For each city, a stratified random sample of schools was identified, with replacements; stratification was based on school type (Federal Technical, SENAI schools,1' teacher training [magisterio] schools, and general secondary schools), time of shift (day or night) and ownership (public or private); it was desigred to be representative of the actual distribution of schools and students in each state. Within each school, all students present on the day of test administration were requested to complete the test and accompanying questionnaire, although participation was voluntary.

6. The voluntary nature of participation may have affected the overall estimates of achievement for individuals, schools and cities. Private school and day shift students were slightly oversampled in Fortaleza but undersampled in Sao Paulo. In Salvador, public schools were oversampled. Strong collaboration from the State Secretary of Education in Parana resulted in the most representative sample for this city.

I/ SENAI secondary schools are financed by the federal government but administered by the National Confederation of Industry (a private association of industrial employers). The 17 SENAI secondary schools in Brazil offer a four-year program that combines a full secondary education with technical specialization.
of roughly 0.25 standard deviations in general reasoning (Raven’s Matrices). Considering the impact of early childhood deworming spillovers on the first principal component and reasonsing measures, I take the ratio of coefficients in Table 2 to those in Appendix Tables A4 and A5. Thus, the effects of early deworming spillovers that I document are comparable to between 0.5 and 0.8 additional grades in school. That Raven’s Matrices are so responsive to the intervention suggests that even mild disease burdens early in childhood can alter cognitive development. One of the key issues in the child development literature is the decreasing plasticity of physiological and neural development with age. The early age at which spillover effects of community deworming can impact child cognition has not been documented before, and may shed light on child development more generally.

While these cognitive effects are robust to a number of specifications, the effect of community deworming spillovers on height, height-for-age, and stunting all appear statistically indistinguishable from zero. These estimates may be thought of as lower bounds, because even respondents in the excluded (comparison) group lived in communities that received treatment starting when they were aged two and older, and thus still may have experienced some beneficial effects.<sup>17</sup> The absence of effects on stature is less surprising than it might appear at first glance, for at least two reasons. First, a well-known effect of reducing worm infections is the concomitant reduction of anemia. This, for example, was demonstrated in Miguel and Kremer (2004), Table V. However, while hemoglobin levels may affect cognition, they do not directly affect stature. Second, only 22 percent of the population in this study experiences stunting; other populations are not so lucky. For example, among children over 30 months old in a deworming study in Zanzibar, 41 percent were stunted - a rate almost twice as high as in the present study (Stoltzfus, et al., 2004).<sup>18</sup> Extreme malnutrition thus does 17Extreme stunting, defined as height-for-age Z score below -3, occurs with a frequency of roughly 4 percent in the sample. As with other measures of stature, there is also no discernible effect on extreme stunting.

> 18At first glance, the Demographic and Health Surveys from Kenya appear to disagree with the stunting rate I measure: Table 11.1 of the 2008-2009 DHS report shows that 35.3 percent of Kenyan children are stunted; 34.2 percent in Western Province are stunted (Kenya National Bureau of Statistics 2010). However, the underlying data reveal that the rate of stunting in the two DHS clusters nearest to the site of the present study is actually 21.2 percent (author’s calculations): almost exactly the same level I measure in this original data collection. Though the precision of averages based on only a few DHS clusters is low, one can marginally reject (at the 10 percent level) that the rate of stunting in these two clusters is equal to the national or provincial means of 34 or 35 percent; intra-cluster correlation in stunting rates is significantly different from

14
**<u>Table A10: Linear trend in share of fertilizer-using households that could not access desired amount</u>**

||(1)<br>Burkina Faso|(2)<br>Ethiopia|(3)<br>Malawi|(4)<br>Uganda|
|---|---|---|---|---|
|Survey round|-0.0411|-0.132***|0.000192|-0.238***|
||(0.0521)|(0.0417)|(0.0328)|(0.0712)|
|Constant|0.498***|0.630***|0.688***|0.619***|
||(0.0836)|(0.0696)|(0.0543)|(0.134)|
|Observations|789|945|1,474|295|

Note: Results from OLS regression of the outcome on a variable indicating the n'th data point over time for this variable. The variable is 1 for the first round in which data on the outcome was collected, 2 for the second instance, and so on. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

31
paved roads and railways to estimate an aggregate production function. This dataset includes time series of usable length for key infrastructures (excluding water) for all countries included in our exercise. Second, it is in theory possible to build time series of infrastructure stocks based on investment data together with the perpetual inventory method – just as time series of _K_ are normally constructed. Unfortunately, in practice financial data on infrastructure (in monetary terms or as percentage of GDP) are scarce for the sample countries. Also, some authors (see Pritchett, 1996) have warned against the poor quality of financial indicators of public investment. For these reasons, we concentrate on physical indicators of infrastructure.

With respect to explanatory variables, we have used data from the Canning database in five countries: Indonesia, Philippines, Thailand, South Korea and Singapore. The following series of physical infrastructure indicators from the Canning (1998) database, as extended with the World Bank’s infrastructure database, have been used:

- Number of telephones and telephone main lines;

- Electricity generating capacity;

- Total roads (railways and paved roads).

Finally, the TFP growth rates calculated by the Asian Productivity Organization (APO, 2004) for the five countries under analysis have been used as the dependent variable, as in (7). The APO has calculated (not estimated) TFP growth rates following the standard methodology that is, following equation (3) and, in addition, taking into account changes in labor quality.

# **2.3. Results**

The main results from individual growth accounting regressions are reported in Table 4. First, in South Korea and Singapore, which are the two most developed economies in our sample (Figure 1), we cannot reject the hypothesis that the coefficients on the three infrastructure variables are zero. Again, recall that the interpretation for this result is not that infrastructure is not productive but rather that there is no evidence from this exercise that it is more productive than other types of capital.

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differencing to the area of common support. We weighted the differences for each treated case (i.e. VRF borrower) by 1, and each comparison case by _p/(1-p)_ – where _p_ is the propensity score – as recommended by Imbens (2004; also Ravallion 2006). The results, shown in the bottom panel of Table 12, were similar to those of the unweighted, unconstrained estimates: there is a hint of an impact on expenditure per capita, and on non-farm income per capita, but none of the effects are statistically significant at the conventional levels.

It is also possible to confine the double differencing to those who did not borrow in 2004 (and look at the effects of borrowing in 2002); or to those who did borrow in 2002 (and look at the effects of continuing to borrow in 2004).<sup>i</sup> None of the results in these cases (not shown here) were statistically significant.

# Panel Instrumental Variables

As a final exercise we undertook an instrumental variables analysis using the (rural) panel data and incorporating household fixed effects. The linear first-stage equation uses, as instruments, the presence of a VRF in the village, this measure multiplied by the educational level of the household head, and the size of the village multiplied by the educational level of the head. The full equations, for the case where the outcome is the log of expenditure per capita and the comparison is between those who borrowed from the VRF in 2004 and those who borrowed neither in 2002 nor in 2004, are shown in Appendix Table A2.

The key results are summarized in table 13. Households that borrowed from the VRF in 2004 had 15% more income and 18% more expenditure than those who borrowed in neither year, holding other influences constant; these increases are statistically significant, but also rather large. If, instead, the comparison is between those who borrowed both in 2002 and 2004 and those who borrowed only in 2004, the impact of the second year of borrowing was to raise income by 8% and spending by 10%. These statistically significant rises are within the bounds of plausibility.

# **8. Conclusions**

This study of the impact of the Thailand Village Fund is based entirely on data from the socio-economic surveys of 2002 and 2004, undertaken just one and three years after the VRF was launched. In the absence of random assignment, we were obliged to use quasi-experimental methods to quantify the effect of the VRF on outcome variables. The propensity score matching approach generates reasonable results: the Village Revolving Fund does appear to have had an impact, raising expenditures by 3.3% and income by 1.9% in 2004. These results are tolerably robust to most specifications of matching, and we may interpret these numbers as reflecting the average impact of the VRF program.

_Village Fund, draft of June 29, 2009_ _Page 19 of 34_
# **Introduction**

Crude oil prices have been sufficiently low for most of the past two decades, or at least low enough not to upset policy makers and consumers in oil importing countries. Between 1983 and 2002 they averaged US$20 per barrel in nominal terms or US$22 per barrel in real (1990) terms (see Figure 1). That changed in 2004. Between January 2004 and August 2005 crude oil prices doubled (from $31 to $62 per barrel). The _Economist_ concluded that “... the basic fact is that the equilibrium price of oil has risen ...” (August 27, 2005, p. 56). Given strong demand, especially by large emerging economies such as China and India, and capacity constraints on the supply side, analysts agree that oil prices in the range of $55 to $65 per barrel will be the norm for the next five to ten years.<sup>**1**</sup> The World Bank (2007) forecasts oil prices to fluctuate from $50 to $60 per barrel between 2007 and 2010. The IMF’s (2007) range is even higher: $60 to $65 per barrel.

Oil price changes affect, to various degrees, most sectors of most economies. Moreover, the channels through which such effects take place are numerous.<sup>**2**</sup> This paper focuses on one such channel: the pass-through of crude oil price changes to the prices of 35 internationally traded primary commodities.

Crude oil prices affect the prices of other commodities in a number of ways. On the supply side, crude oil enters the aggregate production function of most primary commodities through the use of various energy-intensive inputs (e.g., fertilizer and fuel for agricultural commodities) and, often, transportation over long distances, an equally energy demanding process. Some commodities have to go through an energy-intensive primary processing stage (e.g., some metals such as aluminum). Other commodities can be used to produce substitutes for crude oil (e.g., maize and sugar for ethanol production or rapeseed and other oils for biodiesel production). In other cases, the main input may be a close substitute for crude oil, such as nitrogen fertilizer, which is made directly from natural gas. On the demand side, some commodities are competing with

**_— 1 —_**
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# Water Pumping Model Assumptions

# Village Water Supply

79. Each system is sized to provide 20 liters of water/capita/day. This represents a minimal health standard daily water requirement.

# Irrigation

80. Systems are sized to pump peak daily water demand Qmax = 1.76 x Q and Q 0 34 m3 daily average per hectare. This peak ratio is derived from Kenyan irrigation practices as described in Small-Scale Solar-Powered Pumping Systems: The Technology. Its Economics and Advancement. UNDP/World Bank, June, 1983. Irrigation water costs include only the costs of the pumpset and well.

# AR Systems

81. In water lifting applications, the daily hydraulic demand is measured by the volumehead product: m<sup>4</sup> = daily water demand(m<sup>3</sup> ) x lift(m). Since the size and cost of a pumping system is directly related to the hydraulic demand it is designed to serve, water costs are commonly compared over a range of hydraulic demands to determine where economic tradeoffs occur. The computed water costs include annualized power source capital cost, fuel, O&M, and the costs of storage and wells.

82. Pumping systems are sized for a solar resource of SkWh/m<sup>2</sup> /day and winds of 4m/sec during the critical demand month. These assumptions represent a normal solar regime for equatorial countries and a good wind resource.

|Discount rate|10%|
|---|---|
|Period of analysis|20 years|
|Euel inflation|0%|
|Useful life||
|mechanical equipment<br>non-mechanical equipment|10 years<br>20 years|
|Maintenance costs||
|mechanical equipment<br>non-mechanical equipment|10% of capital costs<br>1% of capital costs|
|Diesel efficiency|7.5% fuel to hydraulic energy|
_page 20_ (18) Value added in Manufacturing (MANUFACT), constant prices, LCU.

(19) Value added in Construction (CONSTRUC), constant prices, LCU.

(20) Value added in Public Utilities (UTILITY), constant prices, LCU.

(21) Value added in Services (SERVICES), constant prices, LCU.

(22) Value added in Transportation and Communications (TRANSPORT), constant prices, LCU.

(23) Value added in Wholesale and Retail Trade (TRADE), constant prices, LCU.

(24) Value added in Other Services (OTHRSERV), constant prices, LCU.

(25) Total Labor force (LABOR).

Variables 1, 4.. 14 and 25 came from the World Ban's DAD database (except for Mexico's terms of trade, obtained from Mexico's central bank), and variables 2 and 3 are as described above, They are available for all countries for the whole period.

Variables 17, 18 and 21 were from the World Bank's STARS database, and all other variables came from World Bank Country Economic Memoranda. They were not available for Iraq, nor before 1969.

In all cases, the ultimate sources are national central banks, national statistical services, and estimates by World Bank missions.
what the facilitator should concentrate on in making these notes. One important function of the notes should be to capture the main areas and points of consensus and discrepancy between the participants, as well as to record of particularly interesting or novel ideas put forward by the participants.

## **Dealing with expectations**

In any type of participatory assessment it is essential, at the outset, to articulate clearly to the participants what the expected outcomes and follow-up of the assessment will be. This is in order to avoid raising people's expectations (for instance with regard to possible follow up support) as well as to give people an idea of why they should spend their valuable time on the discussions. In this type of consultation on Government policies, it is especially important to explain the purpose of the study to local leaders and officials. Even so, in some locations, it was found that difficulties arose because local staff and participants began discussing project related activities and inputs. In this type of situation it is important for the facilitators to re-emphasize that the consultations are not intended to be directly related to project / program activities, but are a way of informing Government policies that will in turn influence the way in which projects and programs are carried out in the future. This may seem an obvious point to make, but in practice, it is essential to have clarity on this with the participants so the discussions are focused on the task in hand.

# **3.5 Aggregating and analyzing the data and results**

The local consultations yielded large data sets of numerous viewpoints and ideas, priorities and recommendations put forward by the local participants. The main steps involved in processing this information included: (i) compiling and aggregating the data from the focus group meetings and participant groups in each location; (ii) comparing the results within and between these participant groups in order to identify the most important policy messages and recommendations; and (iii) preparation of draft summary reports from the individual study locations. This section describes how this was done using specific examples to highlight various aspects in the interpretation of the results.

## **Compilation and aggregation of the data**

The basis of the analysis was to aggregate the frequency and/or ranking given to particular responses or recommendations as made by the participant groups in each locality. The degree to which this could be done systematically depended on the way in which the information was recorded in the field. An example of the type of output emerging from the community level groups is given in Table 3 from Lao Cai Province. This is from one Womens' group discussion on the topic of improving education.

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