rafmacalaba/datause-encoder-data huggingface dataset:
# **References:**

Ahearne, Alan G., John G. Fernald, Prakash Loungani, John W. Schindler 2006. ―Flying Geese or Sitting Ducks: China‘s Impact on the Trading Fortunes of other Asian Economies‖ Board of Governors of the Federal Reserve System, International Finance Discussion Papers, no. 887. Dec 2006.

Arthukorala, Prema-chandra, 2009 ―Outward Foreign Direct Investment from India‖ Departmental Working Papers 2009-14, Australian National University, Department of Economics.

Barro, R. J., & Lee, J. W. 2001. International data on educational attainment: updates and implications. Oxford Economic Papers , 53, 541-563.

Centre for Chinese Studies (CCS), 2011. ―Assessing China‘s FDI in Southern Africa‖.

Cai, Fang, Du Yang, and Wang Meiyan. 2009. ―Employment and Inequality Outcomes in China‖, _Institute of Population and Labour Economics, Chinese Academy of Social Sciences; paper presented to OECD conference._ Davis, Steven J., R. Jason Faberman and John Haltiwanger 2006. ―The Flow Approach to Labor Markets: New Data Sources and Micro-Macro Links. _Journal of Economic Perspectives_ , 20(3): 3-26.

<u>Depew, Chauncey (ed.),</u> _One Hundred Years of American Commerce 1795-1895_ p 111.

Farole, T., 2011. _Special Economic Zones in Africa: Comparing Performance and Learning from Global Experiences_ , Washington D.C., World Bank.

FIAS, 2008. _Special Economic zones: Performance, Lessons Learned, and Implications for zone Development_ , Washington D.C., IFC-World Bank.

Gerschenkron, Alexander, 1962. _Economic Backwardness in Historical Perspective, a book of essays_ . Cambridge, Mass.: Belknap Press of Harvard University Press.

Gelb, Stephen, 2006. ―South-South Investment: The Case of Africa‖ chapter 16, in _Africa in the World Economy - The National, Regional and International Challenges Fondad, The Hague, December 2005, www.fondad.org_ Gill, Indermit, Homi Kharas. 2007. ―An East Asian Renaissance: Ideas for Economic Growth.‖ The World Bank Publications.

Gorg, Holger and Strobl, Eric. 2003. _'Footloose' Multinationals?. The Manchester School, Vol. 71, pp. 1- 19, 2003. Available at SSRN: http://ssrn.com/abstract=381648_ N. Haraguchi and G. Rezonja, 2010, _In Search of General Patterns of Manufacturing Development,_ UNIDO, Working Paper 02/2010.

Harrison, A. and Rodríguez-Clare, A., 2010, ―Trade, Foreign Investment, and Industrial Policy for Developing Countries‖, in D. Rodrik ed. _Handbook of Economic Growth,_ Vol. 5. pp. 4039-4213.

Huang, Yiping and Jiang Tingsong. 2010. ―What does the Lewis Turning Point mean for China: a Computable General Equilibrrium analysis.‖ CCER working paper E2010005, Peking University.

Hughes, H. and A.O. Krueger. 1983. ― Effects of Protection in Developed Countries in Developing Countries‘ Exports of Manufactures.‖ Essay.

Imbs, J., and R. Wacziarg, 2003., ―Stages of Diversification,‖ _American Economic Review_ , 93(1), 63-86.

Ito, Takatoshi. 1992. The Japanese Economy, The MIT Press, Cambridge, MA, London England.

J. Ju, J.Y. Lin, and Y. Wang, ―Endowment Structure, Industrial Dynamics, and Economic Growth,‖ manuscript.

55
**Table 5: Duration of Individual Questionnaire Modules on Assets, by Respondents’ Asset Ownership Status**

|||**Camb**|**odia**|||**Ethio**|**pia**|||**Tanz**|**ania**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||Number of||||Number of||||Number of|
|Module Name|Mean|Median|Std|<br>Obs.|Mean|Median|Std|<br>Obs.|Mean|Median|Std|<br>Obs.|
|**Non-Owners**|||||||||||||
|Financial assets|0.98|0.55|1.23|3459|0.75|0.63|0.38|9306|0.97|0.68|0.89|2346|
|Land|1.11|0.77|1.22|250|1.16|0.75|1.53|4066|1.81|1.27|1.94|275|
|Mobile phones|0.56|0.37|0.52|449|0.22|0.17|0.18|6784|0.57|0.45|0.45|626|
|Livestock|0.73|0.55|0.55|350|||||||||
|**Owners**|||||||||||||
|Financial assets|3.61|2.88|2.74|200|2.97|2.43|2.12|5767|2.59|2.18|1.33|81|
|Land|12.04|9.58|13.26|3298|10.08|5.92|13.43|10852|18.36|13.17|16.73|1977|
|Mobile phones|1.76|1.37|1.37|2967|1.07|0.92|0.63|8056|1.39|1.17|0.80|1792|
|Livestock|2.87|2.35|2.11|1720|||||||||

Notes **:** The tables describe the modules length for asset modules for each individual respondent.

Due to the disaggregated nature of the paradata, we are also able to further estimate how average total time spent in the household varies by the number of adults targeted for individual interview. Figure 1 provides a visual comparison of the average total time spent at household (for the administration of household and individual questionnaires) versus the average total duration of individual interview targets in each household. The estimates are presented in accordance with the number of individual interview targets and the average duration estimates for the land, labor, health and education modules are included as well. Total time spent at household increases with the number of individual interview targets. In Cambodia, interviewers spent on average 103 minutes in total for the administration of household and individual questionnaires when the household only had one individual interview target. Moving from one target to two, the average total time in household increases to 152 minutes. The marginal time for each additional individual interview target, however, does decrease with the exception of Tanzania. In Ethiopia, for example, the difference in time in household with one target versus two is 41 minutes; when shifting from one targeted individual to two, the additional time per individual target is less than 22 minutes. Tanzania’s increase in additional time per interview target is the largest relative to other countries, given the number of modules the survey administered, as high as 191 minutes when moving from three to four targets and as low as 81 minutes when moving from one to two targets.

13
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

### IRR = internal rate of return.

The country’s current irrigated area could be increased substantially with good economic returns. Simulations suggest that with a threshold internal rate of return (IRR) of 6 percent it would already be economically viable to develop a further 25,796 ha of land for irrigation, of which 98 percent would be large-scale projects. If the threshold IRR is raised to 12 percent, the economically viable area for irrigation does not change substantially. The required investment for attaining this expansion is $52 million (table 6). The area with irrigation potential is concentrated around Gaborone and Francistown (figure 10b), where the most fertile soils are located and most of the crop production takes place.

**Table 6. Botswana’s irrigation potential**

|||Large scale|||Small scale|||Total||
|---|---|---|---|---|---|---|---|---|---|
||Investment|IRR|Area<br>increase|Investment|IRR|Area<br>increase|Investment|IRR|Area<br>increase|
|Cutoff<br>(%)|$ million|%|ha|$million|%|ha|$ million|%|ha|
|0|49|20.0|25,243|3|0.0|553|52|19.5|25,796|
|6|49|20.0|25,243|3|0.0|553|52|19.5|25,796|
|12|49|20.0|25,243|3|0.0|553|52|19.5|25,796|
|24|0|0.0|0|2|0.0|342|2|0.0|342|

_Source:_ Derived from You and others (2009).

_Note:_ Water for irrigation can be collected in two ways: through large, dam-based schemes or through small projects based on the collection of runoff from rainfall. The investment costs of large-scale irrigation development reflect only irrigation-specific infrastructure, such as distribution canals and on-farm system development. The potential for small-scale irrigation is assessed not only on the basis of agroecological conditions, but also in terms of market access, since irrigation is typically viable only if the increased yields can be readily marketed. The unit cost for large-scale projects is set at $3,000/ha and for small-scale projects at $2,000/ha.

IRR = internal rate of return; ha = hectare.

# Water supply and sanitation

## **Achievements**

Botswana has one of the highest access rates to improved water sources in Africa. About 95 percent of the total population has access to improved water sources, a level comparable to what is observed in the upper range of MICs in Sub-Saharan Africa (93 percent).

This achievement reflects a conscious policy decision. Botswana has made important progress toward moving its population up the water supply and sanitation ladder. Only 3.3 percent of the population still relies on surface water, and a major effort has been made to upgrade access to utility water from stand posts to direct piped connections (table 7). As of 2006 access to piped water, at 64.9 percent, was above the level of other MICs, at 52.1 percent. Reliance on surface water was only one-quarter of that registered in similar African peers.

In the case of sanitation, Botswana has managed to improve service options by moving people from traditional to improved latrines and by increasing access to flush toilets, therefore reducing the practice of 19
differed from other countries, we first investigate determinants of urbanization across countries.

**Figure 1. Africa's urbanization in comparative perspective**

<!-- Start of picture text --> Urbanization and income by region<br>(own regional average calculations, annually 1960-1995)<br>T 090 -_._.<br>° 0.80<br>0.70<br>i 0.60<br>e 0.50<br>0.40<br>S 0.30<br>fj0. 0<br>~0.00<br>4 5 6 7 8 9 10 Is<br>GDP Per capita (logs, 1995 US $)<br>| * Easia * South Asia Africa LAC * MENA OECD|<br><!-- End of picture text -->

# **_A_** _caveat: nowhere does urbanization stop during economic downturns_

Urbanization without growth is not a uniquely African phenomenon. The quasi totality of countries that experienced negative growth continued to urbanize. Out of 187 observations in our database that show average annual negative growth over a period of five years, 183 experienced positive urbanization. This is true across regions (Figure 2.)

**Figure 2. Urbanization continues even in periods of negative growth, 1960-95**

<!-- Start of picture text --> Urbanization continues even during periods of negative growth<br>0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.05<br>0.00<br>East Asia South Asia Europa Central Middle East Latin America Sub Saharan<br>Asia North Atrica Atrica<br>0 Percent of observations with both negative growth and positive<br>urbanization<br>o Percent of observations with negative growth<br><!-- End of picture text --> 4
Equivalently, one could think of **R**<sup>**A**</sup> and **R**<sup>**G**</sup> as measures of compensations needed to make the household as well off as they were prior to the price change; in other words, household utility or well-being is kept constant or at base period levels. In the case of the arithmetic or Laspeyres index, this is consistent with the items and quantities of consumption being kept constant, with underlying preferences that can be described by a Leontief utility function (i.e., no substitution). In the case of the geometric index of relative price change, the estimate reflects Cobb-Douglas preferences, with expenditure shares constant across time and allowing for substitution away from relatively more expensive goods while keeping household utility constant.<sup>23</sup> Appendix Table 3. Two-Good Example

|||||Base Period||
|---|---|---|---|---|---|
||||Base Period|Expenditure||
||||Expenditure|Shares||
|Goods|Q<sup>t</sup>|P<sup>t</sup>|P<sup>t</sup>Q<sup>t</sup>|(a), (1-a)|P<sup>t+1</sup>|
|Good X|1.00|1.00|1.00|0.50|1.50|
|Good Y|1.00|1.00|1.00|0.50|1.00|

For example, suppose a household consumes only two goods (X and Y). Appendix Table 3 reports the base period prices and consumption as well as the expenditure.<sup>24</sup> Assume that the base period prices are both 1.00 and total expenditure is 2.00. Suppose the price of good X rises in the next period to 1.50.

Using the arithmetic index (equation 3), the relative price change is equal to 1.25 = [(0.5*1.5)+(0.5*1.00)] representing a 25 percent increase in prices or a 25 percent fall in real household income. Using the geometric index, the price change would only be 22.5 percent, or 22.5 = [(1.5)<sup>0.5</sup> *(1.0)<sup>0.5</sup> ], as it allows changes in spending patterns in response to changes in relative prices. To compensate for the fall in real income, the arithmetic index implies a compensation of 0.50 = 0.25*(2.00). The household can then spend 2.50 on the base period quantities and keep Leontief utility constant, U = min{X,Y}.

The geometric index, however, implies only a compensation of 0.45 = 0.225*(2.00). In this second case, the household can spend 2.45 on goods X and Y while keeping expenditure shares constant. In particular, the household can buy 0.82 unit of

> 23 The expression for RG is equal to the ratio of the Cobb-Douglas expenditure function for the reference period to the expenditure function of the base period (see also Pollak 1989)

> 24 The example is drawn from BLS (1997).

22
TABLE D.2: Comparison of rent variables, ACCRA

||Rent value||Self estima|ted rent|Combined s|elf estimated rent|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Directly affected by food|-0.024||-0.033||-0.198*||
||(0.100)||(0.048)||(0.112)||
|Flood risk perception||-0.150||-0.052||-0.253**|
|||(0.139)||(0.060)||(0.106)|
|Job density|-0.264*|-0.257*|-0.285**|-0.278**|-0.814***|-0.789***|
||(0.151)|(0.153)|(0.123)|(0.123)|(0.274)|(0.276)|
|Number of rooms|0.641***|0.600***|0.485***|0.480***|0.391***|0.369***|
||(0.123)|(0.110)|(0.070)|(0.073)|(0.114)|(0.111)|
|Number of rooms squared|-0.108***|-0.099***|-0.047***|-0.047***|-0.048***|-0.045***|
||(0.017)|(0.015)|(0.008)|(0.008)|(0.013)|(0.012)|
|Unimproved house|-0.461**|-0.458**|-0.313***|-0.312***|-0.310*|-0.302*|
||(0.182)|(0.177)|(0.070)|(0.071)|(0.157)|(0.153)|
|Unimproved roof material|0.208***|0.206***|0.142***|0.141***|0.487***|0.481***|
||(0.077)|(0.075)|(0.045)|(0.045)|(0.096)|(0.097)|
|Unimproved wall material|-0.200**|-0.203**|-0.259***|-0.258***|-0.337***|-0.337***|
||(0.092)|(0.097)|(0.043)|(0.045)|(0.118)|(0.118)|
|Unimproved foor material|-0.037|-0.018|-0.102|-0.113|-0.044|-0.107|
||(0.145)|(0.165)|(0.105)|(0.108)|(0.234)|(0.253)|
|Unimproved drinking water source|-0.051|-0.036|-0.071|-0.071|-0.076|-0.081|
||(0.070)|(0.079)|(0.046)|(0.046)|(0.097)|(0.093)|
|Unimproved toilet facility|0.175|0.184|-0.248***|-0.248***|-0.078|-0.088|
||(0.219)|(0.210)|(0.047)|(0.046)|(0.111)|(0.100)|
|Not collected solid waste|0.034|0.035|0.045|0.041|0.097|0.076|
||(0.107)|(0.103)|(0.044)|(0.045)|(0.110)|(0.108)|

_Continued on next page_
The most prominent contributions of the first stream are Falvey (1981), Falvey and Kierzkowski (1987) as well as Flam and Helpman (1987).<sup>4</sup> Falvey (1981) constructs a model in which countries differ in their initial labor and capital endowments. Since the production of higher quality products requires the use of relatively more capital compared to labor, trade leads to a specialization in production. The country that has a relatively higher capital stock exports capital-intensive, higher quality goods and imports labor-intensive, lower quality products. In this Heckscher-Ohlin (HO) type set-up the factor endowment differences result in intra-industry trade of different vertical varieties. Falvey and Kierzkowski (1987) present a similar model of trade based on dissimilar factor endowments. However, they add a product which becomes traded according to technology differences along Ricardian lines. Flam and Helpman (1987) use also a Ricardian type approach to model quality trade. They assume that one country has a comparative advantage in high quality products and that under free trade this country exports high quality products and imports low quality goods. A recent paper by Feenstra and Romalis (2006) uses the HO framework with a continuum of industries. In their model, the industries in each country choose a price as well as quality level, depending on factor prices and trade costs.

The second strand of literature focuses on difference on the demand side between countries. In Stockey (1991) consumers in the rich country consume more of the same good than consumers in the poor country. Murphy and Shleifer (1997) construct a Ricardian model in which tastes differ between countries. High quality producing countries have a consumption preference for high quality goods and low quality producing countries for low quality goods. As a consequence of these consumption differences, countries might not even find it beneficial to start trade.

A growing number of empirical studies test these or similar models and show that quality aspects have indeed a major role to play in explaining international trade pattern. Schott (2004) studies very detailed US trade data and finds that countries tend to specialize within products and not as assumed so far across products. His study further indicates that the unit value of trade within one product is higher for high-wage countries. Schott (2004) concludes that the higher unit-value must come from additional features or quality that high-wage countries are able to add to their products. In their empirical investigation Hummels and Klenow (2005) reveal that richer countries export higher quality goods. Using import data from 76 countries at the six-digit level of the Harmonized System the authors also report that the quality margin

> 4 Sutton (2007) develops a insightful model in which countries differ with respect to quality as well as productivity. He founds several interesting outcomes for different market equilibria.

2
In the aftermath of the storm, DSWD waived the program conditionalitites in the affected areas but did not alter the benefits. The agency did provide some in-kind assistance in the affected areas, but this was not linked to being a 4Ps beneficiary. However, the World Food Program delivered two payments of ~~P~~ 1,300 to affected households between December 2013 and January 2014 using Listahanan and the 4P delivery system. This approach was later replicated by UNICEF for households living in the most affected region, Eastern Samar (Bowen 2015, Bowen 2016). For the purpose of this study, only the expansion in 2014 could potentially interfere with the estimation. The data used were collected in the second halves of 2012 and 2015. When Yolanda made landfall in November 2013, the expansion in that year had largely been implemented. Since the 2014 expansion was solely based on the number of pre-existing household members aged 15-18, it can be expected to be orthogonal to the impact of the typhoon. Nonetheless, at the end of this paper I will present a direct test showing that Yolanda had no direct effect on beneficiary status.

# **4 Data and empirical strategy**

The paucity of research on the question of the performance of social protection systems in response to large aggregate shocks most likely stems from the high demands on the data necessary to produce a state-of-the-art study. Ideally, one would want to have longitudinal household and individual level 11
observables (Butler et al., 1987). The direction of this second bias depends on the sign of the correlation between the error in _SAH_ (as an indicator of true health status) and _Y_ . We would conjecture that poorer people (in terms of _Y_ ) tend to over-state their health, as a form of psychological adaptation to their relative ill-health. If that is the case then the two biases work in the same direction, leading to an underestimation of the true gradient in health status.

There are two standard approaches to reducing attenuation bias. The first is to find a better measure (with lower error variance) for the regressor and the second is to find an instrumental variable (IV) that is correlated with the miss-measured regressor but uncorrelated with the error term in the main regression. (The IV can be measured with errors, but these must be uncorrelated with the errors in the regressor.) We use both approaches. Our alternative indicator of _W_ draws on respondents’ perceptions of their own economic welfare, denoted by _SAW_ (for “self-assessed welfare”). This appears likely to better reflect the personal, healthrelevant, aspects of economic welfare that are missing from current incomes.

Some bias will undoubtedly remain. Latent individual personality traits may jointly influencing _SAW_ and _SAH_ — personality traits that are of little or no relevance to the true economic gradient of health. For example, a happy disposition may lead simultaneously to higher perceived economic wellbeing and a more positive health assessment at any given level of health. To address these concerns, we shall also try using objective welfare-covariates of _SAW_ as instrumental variables, under the assumption that the covariates are uncorrelated with the errors implied by using _SAW_ as a measure of economic welfare. (In a linear model, identifying the gradient conditional on control variables will also call for exclusion restrictions.) What about the second bias, arising from a correlation between the measurement errors in _SAH_ (as an indicator of _H_ ) and economic welfare? The IV estimation method will also help 7
reduction of tariffs and the increasing limitations on product and country eligibility imposed by the "graduation" features of various programs. This is because when GSP is taken into account, average applied tariff rates would decline further in major markets as the frequency of items on which duties are set at zero increases substantially - doubling, for example, in the US. Notwithstanding such benefits, the GSP as well as other unilateral preferential schemes may create the wrong types of "dependency" incentives in developing countries and retard rather than promote the fuller integration of developing countries into the international trading system (Finger and Winters, 1998; Srinivasan,1998).

# 3. Peaks

Despite the overall declines in the average applied MFN tariff levels following the URA, there is a number of sectors and product groups in various developed countries where tariffs are at substantially higher levels which would limit market access. The URA tended to increase the tariff dispersion in some developed country markets and decrease it in others (OECD p.<sup>1</sup> 8, 1997; Daly and Kuwahara p.223, 1998). But in all countries there are many products and product groups in which the average MFN applied tariff level following the URA agreements exceeds 12 per cent, or roughly three times the overall average MFN applied tariff level of developed countries. These products and groups can be defined as having tariff "peaks". They exist in both agriculture and manufactures in different developed country markets. But the very high rates typically have been the consequence of tariffication in agriculture. The main products in which such tariff peaks can be observed in various markets are the following:

-- Major agricultural staple food products, such as meat, sugar, milk and dairy products, cereals, where the tariffication of quantitative restrictions has resulted in tariff rates frequently exceeding 100 per cent and ranging, for example up to 550 per cent for rice in Japan.20

> 20These are estimates reported in UNCTAD/WTO, 1997. It is difficul **t** o estimate accurately the ad-valorem tariff equivalents for many agricultural commodities where tariffication involves the adoption of specific duties often combined with quotas.

47