rafmacalaba/datause-encoder-data huggingface dataset:
allocated to one of the four survey assignments. The total sample is 1,344 households, with 336 households assigned to each of the four survey assignments. Although the sample of 1,344 is not designed to be nationally representative of Tanzania, the districts were selected to capture variations between urban and rural areas and along other socio‐economic dimensions.

The basic characteristics of the sampled households generally match the nationally representative data from the Household Budget Survey (2006/07) (results not presented here). Household interviews were conducted over a 12‐month period, but because of small samples, we do not explore the survey assignment effects across seasons (such as harvest time with peak labor demand and dry seasons with low demand). The random assignment of households is validated when examining different household characteristics, as reported in Table 2 Panel A.

Individuals are classified on the basis of the survey assignment they actually received, which is the result of the initial assignment of their household (to one of the four survey assignments), whether the individual is selected to be a proxy respondent or a self‐report, and whether the proxy/self‐report assignment is realized. In the case of self‐report modules, up to two persons over age 10 are randomly selected to self‐report. If persons randomly selected to self‐report are unavailable, an alternative person is selected at random. In the case of proxy assignment, one person in the household over the age of 15 is selected to self‐report and to proxy report on up to two random household members. Thus, in the proxy assignment, one household member actually self‐reports in addition to reporting on other household members. Therefore, the number of self‐reports should be about half the number of proxy reports for households in the proxy assignment. In total, by design, there are more self‐reports than proxy reports. Because the survey experiment highly emphasized the importance of avoiding proxies when not assigned to this treatment, the project was successful at completing self‐reports when assigned. In about five percent of cases, the team was unable to interview a person selected for self‐report. While there were small deviations from the original design during the implementation, the overall realised design remained very close to the planned design, as shown in Table A.2. in annex. The results presented in this paper are unchanged if we exclude the observations where we had to deviate slightly from the planned design, or when we restrict ourselves to spouse instead of random proxies to reflect the more common approach in household surveys. Panel B of Table 2 reports balance tests at the individual level, and shows that allocation across treatments is generally well balanced. There appear to be some imbalances across individuals related to age, 15
Policy Research Working Paper 4982

# **Abstract**

This paper examines the energy/non-energy commodity price link, based on a reduced form econometric model and using annual data from 1960 to 2008. The transmission elasticity from energy to the non-energy index is estimated at 0.28. At a more disaggregated level, the fertilizer index exhibited the largest elasticity (0.55), followed by precious metals (0.46), food (0.27), metals and minerals (0.25), and raw materials (0.11). By contrast, only a few price indices responded strongly to inflation, although the trend parameter estimate (often viewed as a proxy for technological progress) is negative for agriculture and positive for metals. A key implication of the pass-through results is that for as long as energy prices remain elevated, most non-energy commodity prices are expected to be high.

This paper—a product of the Global Trends Team, Development Prospects Group—is part of a larger effort in the department to analyze and understand the key determinants of commodity price movements. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at jbaffes@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
29 run to shed further light on the specific characteristics associated with PD programs that improve student learning. The ITTSI data were already updated once in this way in 2018, increasing the number of evaluated programs in our sample from 26 to 33.

Third, there are challenges in comparing evaluated PD programs with at-scale PD programs. As the data demonstrate, at-scale PD programs tend to be larger programs designed by governments, often at the national level, and aimed at providing broad trainings to teachers. In light of these differences, we highlight the fact that top-performing programs—regardless of their core objectives—share certain common sets of characteristics that most at-scale programs do not share. These characteristics may be useful in the conceptualization and implementation of future teacher PD programs in low and middle-income countries, including large-scale programs funded by governments.

Improving in-service teacher professional development may be a clear win for governments. They are already spending resources on these programs, and there is broad support for these programs among teachers and teachers’ unions. Interventions such as the above provide learning opportunities for country governments and stakeholders seeking to design effective teacher PD programs. While no single characteristic of top-performing PD programs may transform an ineffective PD program into an effective one, this paper highlights trends in topperforming programs, such as including incentives, a specific subject focus, and lesson enactment. These are characteristics that, if included and implemented successfully, have the potential to improve the quality of teacher PD programs, and ultimately, the quality of instruction and student learning.
# **5. Conclusion**

Until the end of 2015, currency controls in Argentina led to a widening spread between the official exchange rate and parallel market rates. On December 16, 2015, the government announced its decision to remove the currency and capital controls and currency restriction and let the peso to float within certain limits. As a result, the currency immediately depreciated by about 30 percent and the gap with parallel rates virtually closed.

The removal of the controls boosted the debate on exchange rate equilibrium levels for Argentina. This study aims at contributing to this debate by: (a) identifying which are the macroeconomic fundamentals affecting the exchange rate equilibrium level in Argentina; and (b) estimating an equilibrium exchange rate, based on these fundamentals.

With this objective, the study adopts the behavioral equilibrium exchange rate (BEER) approach using monthly data for the period 1980-2015 and takes into account important features specific to the Argentine economy during the period considered, such as the relevance of agriculture exports and the potential underestimation and overestimation of inflation and economic activity figures, respectively.

According to the analysis, the set of fundamental factors driving Argentina’s real exchange rate are identified as: the terms of trade, productivity differentials with trade partners, US real exchange rate, trade openness, and reserves. Based on the data available for these fundamentals, the peso was overvalued by 39%, as of October 2015. To ensure robustness of results, the analysis is repeated using different specifications, frequencies, and estimation approaches.

Moreover, estimation results shed light on the long-run impact of economic fundamentals on the equilibrium exchange rate in the context of Argentina. The analysis suggests that, for every 1% increase in real commodity (soybean) prices, the real exchange rate appreciates by approximately 0.6% in the long run. A 1% increase in the ratio of CPI-to-PPI relative to trade partners results in nearly a 1.5% appreciation. A 1% increase in the level of real reserves implies a 0.3% appreciation. Finally, a 1% increase in trade openness, measured as real total trade, depresses the real exchange rate by 0.6% in the long-run. The speed of adjustment coefficients (error correction) yield half-lives between 2 and 5 years, in line with the empirical literature.

28
In a more recent study, Assaad et al. (2012) examines the patterns of inequality of opportunity in child health outcomes in Egypt and a number of Arab countries and Turkey using several DHS data. The outcome variables used in this study as indicators for stunting or wasting are standardized height and weight of children. They find that for Egypt, total inequality is increasing over time and geography is the most prevalent circumstance affecting height and weight of children, followed by demographic variables and parents' education. Our study differs from Assaad et al. (2012) and the other existing studies in that we consider several other health outcome variables in addition to stunting and wasting, including maternal health outcome variables, such as antenatal care, the presence of skilled birth attendants in delivery, as well as postnatal care and immunization.

Inequality of opportunit in education has also received considerable attention in the literature on early childhood development. A number of cross-country studies that included Egypt, such as Filmer and Pritchett (1999), Filmer (2005), Smits (2007), and Huisman and Smits (2009), find socioeconomic status to be an important variable affecting access to and achievements in education. Filmer (2005) also noted that the prevalence of gender gaps in educational enrollment varies by regions across the world. Al-Qudsi (2003) finds household wealth to be an important factor affecting school enrollment. Zhao and Glewwe (2010) in China and Tansel (2002) in Turkey reach similar conclusions on the importance of household wealth/income and parents' education status in influencing a child's enrollment in school. Salehi-Isfahani et al. (2012) has looked at inequality of opportunity in education in Egypt, alongside several other Middle East and North Africa countries, using the Trends in Mathematics and Science Study data.

# **3. Data and Methdology**

## **Data**

The paper uses data from two main sources: (a) the Egypt DHS for 2000 and 2008; and (b) the Egypt HIECS for the corresponding years. The DHS is a nationally representative population and health 6
share of biofuels in the total liquid fuel consumption in the road transportation is presented in Table 3.

The second scenario is the announced biofuel targets ( _AT_ ) scenario. This scenario considers the implementation of biofuel use targets consistent with what countries already have announced. Table 3 shows shares of biofuels under the AT scenario in 2020. There are notable increases in the biofuel penetration as compared to that in the BAU case for India, Thailand, France, Germany, Italy, Spain, United Kingdom, and in the rest of European Union. On the other hand, biofuel penetrations for Brazil, United States and Malaysia are not much different from that in the _BaU_ scenario as existing policies and incentives, if continued will be sufficient to meet the AT scenario.

**Table 3: Share of biofuels in total liquid fuel demand for transportation** **<u>(biofuel penetration)</u>**

||||Biofuelpen<br>|etration<br>|
|---|---|---|---|---|
||BaU||AT|ET|
|Countries/regions|2009|2020|2020|2020|
|China|2.0|2.4|3.7|7.3|
|Japan|0.4|1.0|1.0|1.2|
|Indonesia|1.8|3.3|5.0|10.0|
|Malaysia|1.8|5.1|5.1|5.1|
|Thailand|1.3|2.8|5.2|10.4|
|India|3.1|4.9|16.7|16.7|
|Canada|1.0|2.5|4.1|8.2|
|United States|3.4|8.3|8.3|8.3|
|Argentina|1.4|3.3|5.0|10.0|
|Brazil|9.5|18.8|18.8|19.0|
|France|1.5|4.5|10.0|20.0|
|Germany|2.2|5.9|10.0|20.0|
|Italy|0.9|2.5|10.0|20.0|
|Spain|0.8|2.3|10.0|20.0|
|United Kingdom|0.3|1.0|10.0|20.0|
|Russia|2.7|5.3|5.3|5.3|
|South Africa|3.1|5.3|5.3|5.3|
|Rest European Union and EFTA|0.5|1.5|10.0|20.0|
|Rest of Latin America and Caribbean|0.8|2.1|2.1|3.0|
|Australia and New Zealand|0.4|1.0|1.2|2.5|
|Rest of East Asia and Pacific|0.4|1.0|1.5|2.5|
|Rest of South Asia|0.5|0.9|0.9|0.9|
|Rest of Europe and Central Asia|0.8|1.9|1.8|1.9|
|Middle East and North Africa|0.1|0.2|0.1|0.1|
|Rest Sub-Saharan Africa|1.3|3.4|3.4|3.4|

_Mandates for cellulosic ethanol are not included._ 15
Table 9: The impact of PAL on nutrition

|_Outcome =_|(1)<br>Energy|(2)<br> (kcal)|(3)<br>Pro|(4)<br>teins|(5)<br>Vitam|(6)<br>in C|(7)<br>Ir|(8)<br>on|
|---|---|---|---|---|---|---|---|---|
|In-kind|48.128|38.504|1.760|1.374|25.221***|23.887***|1.058***|1.130***|
||(41.529)|(46.073)|(1.760)|(1.931)|(5.209)|(5.617)|(0.385)|(0.421)|
|Cash|0.996|-5.474|1.933|1.726|25.257***|23.958***|0.547|0.538|
||(48.650)|(52.738)|(1.990)|(2.237)|(7.627)|(8.681)|(0.421)|(0.453)|
|State FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Pre-program controls|YES|YES|YES|YES|YES|YES|YES|YES|
|Restrc. to those with ENLACE|NO|YES|NO|YES|NO|YES|NO|YES|
|Observations|2,381|1,856|2,419|1,880|2,419|1,880|2,419|1,880|
|Outcome mean in control group|967.54|980.10|32.48|33.48|31.89|32.37|6.81|6.79|
|**_Effect size:_ In-kind - Cash**|47.13|43.98|-0.17|-0.35|-0.04|-0.07|0.51|0.59|
|**_H 0 :_ In-kind = Cash (p-value)**|0.21|0.26|0.92|0.84|1.00|0.99|0.20|0.14|
|**_H 0 :_ In-kind = Cash**|||||||||
|**(Randomization Inference p-**|0.22|0.28|0.92|0.85|1.00|0.99|0.21|0.17|
|**value)**|||||||||
|_Outcome =_|Zi|nc|Cal|cium|Ret|inol|Principal compo<br>nut|nent macro/micro<br>rients|
|In-kind|1.155***|1.098***|76.264***|80.725***|114.716**|107.959**|0.252***|0.247***|
||(0.265)|(0.291)|(25.014)|(28.578)|(48.289)|(50.538)|(0.070)|(0.078)|
|Cash|0.518*|0.444|19.666|16.556|27.197|16.344|0.132*|0.119|
||(0.270)|(0.296)|(30.679)|(34.199)|(54.694)|(56.472)|(0.076)|(0.084)|
|State FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Pre-program controls|YES|YES|YES|YES|YES|YES|YES|YES|
|Restrc. to those with ENLACE|NO|YES|NO|YES|NO|YES|NO|YES|
|Observations|2,419|1,880|2,419|1,880|2,419|1,880|2419|1,880|
|Outcome mean in control group|4.28|4.36|467.54|468.09|360.17|342.23|-0.32|-0.31|
|**_Effect size:_ In-kind - Cash**|0.64|0.65|56.60|64.17|87.52|91.61|0.12|0.13|
|**_H 0 :_ In-kind = Cash (p-value)**|0.02|0.02|0.05|0.04|0.05|0.04|0.10|0.09|
|**_H 0 :_ In-kind = Cash**|||||||||
|**(Randomization Inference p-**|0.03|0.04|0.05|0.04|0.05|0.05|0.12|0.12|
|**value)**|||||||||

Notes: *** p<0.01, ** p<0.05, * p<0.1 (1) Standard errors are clustered at the village level.

(2) Pre-program controls include gender, age, and indicators for whether the household head is married, whether the house has running water, and whether the closest school offers morning shift.

(3) Sample only includes individuals aged 6 or less in 2003.

(4) Principal component includes proteins, iron, zinc, calcium and retinol.

37
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20
|family labor)|||||||
|---|---|---|---|---|---|---|
|Working capital|3,239.3|8,004.7|1,285.0|3,031.2|1,756.9|3,557.5|
|Value of the enterprise|73,327.8|86,196.9|67,478.4|120,897.9|107,755.1|107,303.9|

Note: Figures are CPI-adjusted (2000=100). Operating cost includes all business expenses including cost of raw material, rent, fuel, hired labor and other cost incurred by the enterprise. Since family labor cost is not actually incurred by the enterprise it was imputed (calculated) by multiplying man-days spent by family members according to the prevailing wage rate at the village level.

Source: HIES 2000, 2005 and 2010

**Table 10: Microenterprise productivity measures by activity: 2000-2010**

|**Productivity measure**<br>|**Manufacturing**<br>**and processing**<br>**200**<br>|**Transport**<br>**0 (N=1,427)**<br>|**Trading**|**Service**<br>|**Other**<br>**misc.**<br>**activities**|**All**<br>**activities**|
|---|---|---|---|---|---|---|
|Profit margin|35.5|61.6|44.3|26.6|41.3|32.7|
|Rate of return|0.665|0.758|0.599|0.613|0.662|0.638|
||**200**|**5 (N=1,425)**|||||
|Profit margin|39.0|61.6|20.0|20.9|33.1|25.7|
|Rate of return|0.641|0.757|0.542|0.625|0.540|0.628|
||**201**|**0 (N=1,909)**|||||
|Profit margin|31.8|62.3|37.9|31.5|46.6|36.7|
|Rate of return|0.477|0.683|0.526|0.500|0.466|0.519|

Note: Profit accounts for family labor, and this definition of profit has also been used in subsequent tables. The profit margin is defined by the profit as a percentage of the revenue. Rate of return is calculated by dividing profit by the sum of working capital and the value of enterprise assets.

Source: HIES 2000, 2005 and 2010

**Table 11:  FE estimates of microenterprise rate of return (log of profit)**

|**Explanatory variables**|**Model 1**|**Model2**|**Mean of**|
|---|---|---|---|
||||**explanatory**<br>**variables**|
|Duration of activity|0.028|0.027|9.5|
||(3.08)|(3.02)||

42
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31 investment encourages savings to stay in the developing economy and not to go into capital flight.

Investment is more efficient with relatively nondistortionary policies* and therefore policy changes that reduce the distortions in resource allocation not only raise the baseline level of efficiency but also tend to raise growth in the long term. The size of the initial distortions and the magnitude of the reduction affect how much growth will respond. Growthenhancing policies include lowering tariffs, relaxing import quotas, raising or decontrolling domestic interest rates, reducing reserve requirements or mandatory government bondholdings, reducing government subsidies for consumption or production of particular goods, and reforming taxes to reduce or eliminate differential treatment of sectors or inputs.

Reforms to improve incentives can proceed in several steps. For example, tariffs can initially be substituted for quotas, both to increase the transparency of incentives and to raise public revenue. Later, tariffs can be reduced as other revenue sources are expanded. Institutional development may be necessary to strengthen the private responses to such changes in incentives. In low-income countries, the lack of well-developed public and private institutions may be an important hindrance to growth even if trade and financial incentives are not distorted. A stable system of civil liberties, well-defined property and contractual rights, and predictable and equitable regulation are widely believed to be particularly important in harnessing the energies of entrepreneurs in Africa.<sup>20</sup> A comparison of the long-run growth experience among many developing countries concluded that govenments' administrative competence was the single most important factor explaining the 20 World Bank (1989b).