rafmacalaba/datause-encoder-data huggingface dataset:
status aects mostly the accuracy of male responses and schooling has no eect on the accuracy of knowledge and adoption responses. Regarding the former, single and formerly married men tend to provide more accurate responses for strip tillage outcomes and inaccurate responses for intercropping outcomes. This appears counter-intuitive to the extent that, autonomous individuals are often the decision-makers in their household and may be more motivated to acquaint themselves with available farming practices and their trade-os. We expected autonomy to be especially relevant for women.

Having ever learned the technique has the strongest association with self-reported accuracy. Having ever learned the technique is signicantly associated with lower levels of error in self-reported measures of knowledge. This is intuitive, as, all else being equal, having received a training on a specic technique should increase the farmer's probability of knowing a technique by name. In contrast, having ever learned a technique is associated with higher adoption misreports on the order of 10 percentage points. The magnitude of the eect is remarkably similar across technique. One interpretation of the opposing eects on knowledge and adoption is that trained respondents are prone to social desirability bias. However, we cannot establish causality nor directly test the mechanisms underlying these combined eects.

Recall decay weakens the precision of self-reported knowledge outcomes only among men, while having known the technique longer leads to higher precision in the case of women. The measurement error seems to increase with the timing of learning for the mulching outcomes of men but not women. Rather, women who learned strip tillage and intercropping techniques over 20 years ago appear to have more precise responses. For women, there is only one instance where recall has a marginally signicant correlation with the adoption measurement error.

As with schooling, there is no consistent impact of wealth on the reporting of knowledge and adoption outcomes. The estimated parameters on the total landholdings' variable are statistically dierent from zero in only three cases. Men on larger farms tend to provide more accurate responses of their intercropping knowledge but less accurate responses for their strip tillage adoption. Women on larger farms tend to provide less accurate responses of their intercropping adoption.

The positive impact of the majority of the signicant parameters on the total number of plots and the number of male adults' variables suggest two possible inuences on false reporting. First, given the opportunity cost of their time, farmers with larger or more productive farms may be less inclined to respond carefully to the survey questions. Such inclinations have been shown in survey 12
# **Annex 1: Data**

### **Table A1: Data Summary**

||O|bs|Mean|Std.Dev|Souce|
|---|---|---|---|---|---|
|CRI|exports|220|206921.6|313076.1|_WITS_|
||output|189|1.08E+08|1.97E+08|_Central Bank_|
||workers|204|13019.16|16937.23|_ILO_|
||wages|141|389.6699|138.9347|_ILO_|
||pollution intensity|220|17075.39|27409.71|_IPPS_|
|HND|exports|198|30170.48|57582.29|_WITS_|
||output|189|8.06E+07|1.51E+08|_Central Bank_|
||workers|84|16390.86|29285.27|_Household Surveys_|
||wages|84|236.7954|202.1501|_Household Surveys_|
||pollution intensity|220|17075.39|27409.71|_IPPS_|
|NIC|exports|198|18320.74|59641.4|_WITS_|
||output|190|1.19E+08|3.20E+08|_Central Bank_|
||workers|111|2408.045|4775.783|_Household Surveys_|
||wages|111|2404.347|1239.8|_Household Surveys_|
||pollution intensity|220|17075.39|27409.71|_IPPS_|
|SVL|exports|220|53119.83|85675.62|_WITS_|
||output|170|2.59E+08|3.34E+08|_Central Bank_|
||workers|146|13620.19|29462.05|_Household Surveys_|
||wages|154|197.7416|38.57555|_Household Surveys_|
||pollution intensity|220|17075.39|27409.71|_IPPS_|

## _Estimating the rate of changes in pollution intensities._

Technical changes, defined here as changes in pollution intensity, are estimated based on the work of Grether, Mathys, and de Melo (2007). This is one of the few articles in the literature that identify technical effects in emissions changes for a large set of countries. The authors focus on changes in sulfur dioxide for the period 1990–2000. Given the lack of more comparable alternatives, we extend their measure to the pollutants studied here. We regress the annual changes in intensity estimated by Grether, Mathys, and de Melo (2007) against a list of variables that could potentially affect technical changes in pollution<sup>12</sup> . Initially we consider a large set of variables (including per capita GDP, trade-to-GDP, FDIto-GDP, Share of manufacturing output, per capita income growth, trade-to-GDP growth, proxies for

> 12 Initially we consider a large set of variables, including per capita GDP, ratio of trade to GDP, ratio of foreign direct investment to GDP, share of manufacturing output, per capita income growth, ratio of trade to GDP growth, proxies for human capital, average year of schooling, secondary education attendance, and tertiary education attendance.

24
# **3.2 Educational Status**

**_Literacy rates._** Paraíba has made great strides in literacy during the last three decades, reducing illiteracy from 55 to 28 percent (Table 3.2). However, it is among the five states with the highest illiteracy rate in Brazil. Rather than being a corollary of economic growth, which was high only in the 1970s and weak during the 1980s and 1990s, gains arose from changes in policy.  Illiteracy rates in Paraiba, and Brazil as a whole, are still high compared to other contries in the region, for  example in Chile only 4.1 percent are illiterate and 8.3 percent in Mexico.

|**Table 3.2: Literacy**<br>**1970 and 2000**|**in Bra**<br>**(perce**|**zil**<br>**nt)**|
|---|---|---|
||**1970**|**2000**|
|**North**|63.0|83.7|
|**Northeast**|46.1|73.8|
|**Ceará**|44.6|73.5|
|**Rio Grande do Norte**|45.6|74.6|
|**Paraíba**|45.0|72.4|
|**Pernambuco**|50.3|75.5|
|**Bahia**|49.4|76.9|
|**Southeast**|77.1|91.9|
|**South**|76.5|92.3|
|**Center West**|67.5|89.2|
|**Brazil**|67.0|86.4|
|_Source:_Census 1970 and 2000.|||

Efforts to lower illiteracy further are hampered by the fact that many of the illiterates are adult—the result of years of educational neglect. Efforts to improve adult literacy have been undertaken, but with poor results, because it is more difficult to teach basic skills to adults than to children.

In 2000, about 15 percent of those aged 10-14 were illiterate, and 18 percent of those aged 20-24 were illiterate. The majority of illiteracy is still in the 50 and older age group, 42 percent can not read and write. Moreover, there is a gender difference in the illiteracy rates—24 percent of females are illiterate compared to 32 percent of males (Figure 3.1). Illiteracy is also worse in rural than in urban areas—48 percent compared to 25 percent for males, and 34 percent compared to 20 percent for females.

17
-4-

critical level than the rate is devalued. The current and capital accounts are fully endogenous in our model, and we will be able to derive cettain analytic conclusions concerning whether or not a particular program af public expenditure cuts lead& :o unsustainable losses in foreign reserves. The model simultaneously incorporates both public infrastructure as well as public crowding out of the private sector. Government debt financing affects the domestic interest rate, and since private investment is debt-financed and interest sensitive, it can be crowded out by increased public spending.

The next section will give a brief review of background literature as well as provide an intuitive explanation of our model. Section III will formally derive the analytics of the model, while section IV will sketch a proof of the existence of an intertemporal equilibrium. Section V will give some policy simulations using Mexican data, while Section VI will be a conclusion.

TabL_I1l: Mcxico: Maero-Variables, 1973-85

||1973|1974|1975|1976|1977|1978|1979|1980|1981|1982|1983|1984|1985|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Real**GDPc/|8.4|<br>6.1|<br>5.5|<br>4.3|3.4|<br>8.2|9.2|<br>8.3|<br>7.9|-0.5|-5.3|3.7|<br>2.7|
|Publie sector<br>revenues k/|20.0|21.0|23.0|23.6|24.2|25.5|25.2|28.5|27.5|30.3|34.4|34.2|32.2|
|Public<br>expendituro c/|25.6|26.7|31.8|32.0|29.5|31.0|32.2|35.5|41.1|46.5|42.7|41.5|40.5|
|Budget**balance**|-5.6|-S.7|-8.8|-8.4|-5.3|-5.5|-6.0|-7.0|-13.6|-16.2|-8.3|-7.3|-8.3|
|Trade balance<br>d/|-1.2|-3.4|-5.0|-3.0|**0.1**|**-0.5**|-1.2|-0.9|-1.7|6.2|10.0|**7.5**|4.9|
|Inflation<br>o/|16.0|22.4|10.4|22.4|41.2|15.8|18.3|24.5|**25.5**|57.5|99.3|63.6|**55.2**|
|**Capital**flight<br>_I/_|668|**755**|784<br>2|944|**686**|-66|231<br>-|678<br>|9733<br>|8225<br>|2415<br>2|332<br>|1917|
|&/<br>Percentage<br>c|hange.|**Source**|**:**<br>Ind|icadore|s<br>Eco|nomics.||||||||

k/ Total revenues of **the** public **sector as a percent** of GDP. Source: ibid. _j/_ Total expnditures of **the** public **sector as** a **percent** of GDP. **Z..urc*:** ibid. ;/ As percent of GDP. **Source:** ibid. &/ **Percentage** change in **the** annual **average** of **the wholesale** price index. Source: ibid. **_V/_** In **millions** of US dollars. **source:** Zedillo **(1986).**
WPS3688 Preference utilization and tariff reduction in European Union imports from Africa, Caribbean, and Pacific countries Miriam Manchin<sup>1</sup> Tinbergen Institute, Rotterdam Contact address: <u>manchin@few.eur.nl</u>

# **Abstract**

Despite the long relationship between the European Union and the African, Caribbean and Pacific (ACP) countries aimed at encouraging their exports while stimulating growth and investment, the ACP states still face difficulties in integrating into the world economy. This paper examines the non-least developed ACP countries’ preferential trade with the EU. The objective is to explain the determinants of preferential exports of ACP countries towards the EU and to assess the impact of preferences on trade volumes. We also investigate the existence of a threshold in the offered duty reduction under which traders have no incentives to ask for preferences.

**Key words** : Preferential trade agreements; preference utilization; African, Caribbean and Pacific (ACP) countries; European Union

**JEL:** F13, F14, O19

# World Bank Policy Research Working Paper 3688, August 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1 I am thankful for helpful comments from Paul Brenton, Joe Francois and Eric Strobl. I am also thankful to Micheal Henry for providing me with programs for the threshold estimations.
sponsored children were in attendance on the day of enrollment. Schools and parents varied in their compliance. If a child intended for treatment was not present, then a replacement was selected from a list. If that replacement was not present, another was chosen. Because of that, some children who won the lottery for child sponsorship were ultimately assigned to the comparison group and vice-versa. ICS registered 809 children into the program. As a result, we use treatment assignment as an instrumental variable to estimate a local average treatment effect (LATE). The LATE parameter measures the average effect of treatment on compliers, i.e., those students who received a uniform in 2002 because they were present on the day of the lottery and were randomized into the program.

Table 1 presents the first-stage results: what is the probability that a child, having been randomized into the project, was actually registered or went on to receive a uniform? Table 1 indicates that 77 percent of children who won the lottery were actually registered as part of the child sponsorship program. Seventy-four percent of children who won the lottery went on to receive uniforms in June of that year. Being randomized into the program thus acts as an appropriate instrumental variable for actual enrollment and later uniform receipt.

# **3. Data and Empirical strategy**

The final data set we use comprises six smaller survey and administrative data sets with information about (1) students who won the sponsorship lottery in 2002, (2) students who were present in school on the date of enrollment in 2002, (3) a pupil questionnaire administered in 2002, (4) students who received uniforms in 2002, (5) pupil attendance from 2002 to 2006, and (6) a tracking questionnaire administered in 2010. Figure 1 demonstrates the relationship between the Pupil Attendance data set and the other data sets. We have attendance information for each of the 12 schools from 2002 through the end of 2005. Attendance was gathered as field officers made unannounced visits to each school multiple 9
Policy Research Working Paper 5671

# **Abstract**

Despite the importance of agriculture to economic development, and a vast accompanying literature on the subject, little research has been done on the quality of the underlying data. Due to survey logistics, agricultural data are usually collected by asking respondents to recall the details of events occurring during past agricultural seasons that took place a number of months prior to the interview. This gap can lead to recall bias in reported data on agricultural activities. The problem is further complicated when interviews are conducted over the course of several months, thus leading to recall of variable length. To test for such recall bias, the length of time between harvest and interview is examined for three African countries with respect to several common agricultural input and harvest measures. The analysis shows little evidence of recall bias impacting data quality. There is some indication that more salient events are less subject to recall decay. Overall, the results allay some concerns about the quality of some types of agricultural data collected through recall over lengthy periods.

This paper is a product of the Poverty and Inequality Team, Development Research Group. 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 author may be contacted at kbeegle@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
nationality and corporate policy of the firm, the type of industrial production, labor market conditions and the country's institutions and regulations.

**Figure 7. Employment in Export Processing Zones**

<!-- Start of picture text --> 18 8<br>16 -<br>_ ~~14-<br>12 -<br>at0 10<br>8-<br>E<br>6-<br>E 4<br>N ~~2<br>W9 0 9 09<br>00~~~~~1<br><!-- End of picture text --> _Source:_ Constructed using data gathered by Madani (1999).

Most of the jobs in export-processing zones are held by women. In the Caribbean zones approximately 80 percent of the workforce is female, and the percentage is almost as high in the Philippines. This female bias is especially strong in garment production. Several reasons have been advanced to explain why women, many young and single, are sought after as prospective employees. Women are said to be more diligent and have more dexterity than men. Also, the fact that most of them marry and leave after a few years implies that they tend not to get involved with trade unions. Last but not least, women tend to be paid less than men. Madani concludes that, despite their lower pay, women might be the unintended beneficiaries from the formation of export-processing zones. Many would have remained fully or partially employed in the informal sector, or stayed at home, were it not for them.

The pattern is similar in countries that have not relied massively on export-processing zones, but where exporting firms have tapped local labor markets, attracting workers from the surrounding villages. Industries such as textiles and electronics have massively hired young, literate, largely single women, who frequently ended up earning more than in traditional sectors, such as agriculture or cottage industries. This female bias has been observed even out of the wage sector. Evidence from Ghana and Uganda reveals that women had substantial economic mobility in response to economic reforms. In these two countries, rural women became increasingly engaged in non-farmn employment activities, moving into the non-farm sector at faster rates than men (World Bank, 2001).

15
- Basu, Kaushik, 2013, “World Bank Goals on Eliminating Extreme Poverty and Boosting Shared Prosperity:  Implications for Data and Policy,”  Presentation at the Human Development and Capability Association annual conference in Managua, Nicaragua, September 10, 2013.  Mimeo:  The World Bank.

- Basu, Kaushik, 2000, “On the Goals of Development,” in Meier, G.M. and J. E. Stiglitz, eds., _Frontiers of Development Economics_ , World Bank: Washington D.C.

- Deininger, Klaus and Lyn Squire, 1998, “New Ways of Looking at Old Issues: Inequality and Growth,” Journal of Development Economics, 57(2), pp. 259-87.

- Dollar, David, T. Kleineberg and A. Kraay, 2013, “Growth Still Is Good for the Poor,” World Bank Policy Research Working Paper 6568.  World Bank:  Washington D.C.

- Dollar, David and Aart Kraay, 2002, “Growth is Good for the Poor,” _Journal of Economic Growth_ , 7, 195-225.

- Foster, James E., and Amartya Sen, 1997, “ _On Economic Inequality_ after a Quarter Century,” in Amartya Sen, ed., _On Economic Inequality_ , 2<sup>nd</sup> Edition, Oxford: Clarendon Paperbacks.

- Milanovic, Branko, 2012, “Global Inequality by the Numbers:  In History and Now,” World Bank Policy Research Working Paper 6259.  November 2012.

- Perotti, Roberto, 1996, “Growth, Income Distribution, and Democracy: What the Data Say,” _Journal of Economic Growth_ , 1(2), pp. 149-87.

- PovcalNet Database, 2013, The on-line tool for poverty measurement developed by the Development Research Group of the World Bank, http://iresearch.worldbank.org/PovcalNet/index.htm

- Virmani, Arvind, 2008, “Growth and Poverty: Policy Implications for Lagging States,” Economic and Political Weekly, Vol.XLIII, No. 2, January 12-18.

- --------------, 2007, “The Sudoku of Growth, Poverty and Malnutrition:  Policy Imiplications for Lagging States,”  India Planning Commission, Working Paper No. 2/2007-PC.

- World Bank, 2013a, “A Common Vision for the World Bank Group,” Development Committee Paper.  Spring Meetings 2013.  World Bank: Washington D.C.

World Bank, 2013b, _World Development Indicators_ .

15
sources can be found in Appendix IV. We add each variable individually, as adding them all to the model simultaneously reduces the sample size to only 36 countries, resulting in extremely few degrees of freedom and a sample largely composed of developed countries.

# _Education_

Higher levels of education lead to a more productive and entrepreneurial workforce, which may increase discovery activity (as well as imitation) over and above the effects of education on GDP. We use measures of average rates of enrollment in tertiary education as well as average educational attainment to evaluate this relationship.

# _Innovative and Absorptive Capacity_

It is believed that national learning and absorptive capacity are functions of spending on R&D (Baumol, Nelson and Wolf 1994). Absorptive capacity makes countries more knowledgeable of what foreign goods they could potentially produce and more able to adapt production to the local context. However, it may also make them more adept at imitating the successes of their fellow nationals. We use a measure of the quantity of scientific and technical articles published in major journals by researchers residing in each country to capture basic scientific and research capacity, as well as the number of patents granted by the U.S. and E.U. agencies, weighted by the amount of commerce directed to these two markets (see Appendix IV for details).

31
measured by national R&D expenditures as a percent of GDP, _M_ n is manufacturing sector as a share of all production, _BC_ n is the share of employment in blue collar jobs, and _E_ n is average years of education of the labor force.

This parametric methodology is not feasible for several reasons. First, the studies in the sample do not use a consistent definition of skills. Some studies ask which skill is most important while others ask which skill is most lacking and yet others ask about the most important skill in the future. This leads to error in the measure of the dependent variable. Second, many of the country-level sample frames are not nationally representative, which limits the predictive power of the national-level independent variables. Third, some studies do not present “national” averages, instead disaggregating the data along certain categories. For example, the Indonesia survey results are presented as occupation-industry disaggregations.

Instead, we perform a pseudo meta-analysis by reviewing results by each dimension that we would have included in our regression. To capture heterogeneity by level of economic development ( _GDP per capita_ ), we summarize regional results. We explore differences by size of the economy ( _GDP_ ) – where smaller, less diversified economies may have distinct skills demands than more diversified large economies – by comparing results from three island states to results from some of the largest economies in the world. To capture cross-industry skills differences ( _M_ ), we compared skills demands in the manufacturing and the service industry within country, thus holding constant other factors that may affect skills demand in that country. We also aggregate results by industry across countries. Three countries allow us to explore skills demand differences within country by degree of export-orientation ( _X_ ) of the firms interviewed. Two studies allow us to compare skills demand by employers in innovative firms as compared to more traditional firms. Similar to the analysis carried out by industry, we carry out an analysis for occupation ( _BC_ ), comparing managers and workers both within country and by occupation across countries. Finally, we explore different expectations of employers for their workers of different skill levels, defined by education level of the worker ( _E_ ).

We consider three sets of skills. Cognitive skills are knowledge and thinking skills. We differentiate between basic (reading, math) and higher-order (logic, abstract thinking) cognitive skills, but still recognizing that the former are the foundation for building the latter. Technical skills are jobspecific skills. Many surveys do no go into detail on technical skills, so “job relevant skills”, computer skills, and “work experience” are included in this set. Socio-emotional skills capture behaviors (including social skills) and personality traits. Since the individual studies do not necessarily classify their skills 11