rafmacalaba/datause-encoder-data huggingface dataset:
4

## A. Growth experience of developing countries

The issue of which policies affect long-run growth has become especially critical as many developing countries seek to reestablish (or in some cases, establish for the first time) conditions for growth in the 1990's, after the poor record of the 1980's.

## 1. Experience over 1965-88

Table 2 presents growth rates for 1965-89 and 1980-89 for different country classifications. Three important facts stand out: (1) growth rates over the last 25 years vary widely across countries and regions; (2) developing countries have not in general grown more rapidly than developed countries over the last 25 years; (3) the 1980’s was a disastrous decade for many developing countries. Sub-Saharan Africa stands out with a poor growth record on all counts, East Asia with an outstanding growth record. However, wide divergences exist among countries in the same region. This record suggests that large variation in growth rates across countries needs to be explained; it is critical to understand the extent to which national policies can do so.

Table 2 also presents some ranges of growth rates for growth in socialist countries, illustrating the uncertainty surrounding socialist economic performance. The high values of estimates imply respectable performance, which has been interpreted by some to support the conclusion that high distortions (such as those induced by planning) do not have growth effects.” The low estimates imply a strong effect of distortions, since investment rates for most of these countries were very high.

Preliminary analysis of developing country data indicates some suggestive regularities. We find that growth over time is not very stable--the correlation for growth in countries across successive 5-year periods is low.’ Moreover, poorer countries have more unstable growth rates, which may be related to greater policy instability in the poorest countries. The nigh variance of growth rates is at least suggestive that relatively frequent changes in policy may help explain changes in growth rates over successive periods (and possibly that uncertainty itself depresses growth). We will discuss the analysis of the lack of persistence in growth rates below.

# 2. Long-run experience

While this project will focus mainly on the recent period for which most of the daia is available, a longer-run perspective is also helpful. Figure 1 shows some estimates of long-run growth (since 1870) in developing and developed countries for which long time-series are available. The siriking fact that emerges from this graph is that growth is much more unstable for

> 2Lucas (1988). However, opinion seems to be swinging towards lower estimates. IECSE is conducting research on the estimation of growth in socialist countries in transition.

> 5The cross-period correlations are generally below .4 and in many cases below .2 (these results are available in Levine and Renelt (19906).
41. Rodrik, Dani, Arvind Subramanian and Francesco Trebbi (2004) “Institutions Rule: The Primacy of Institutions over Geography and Integration in Economic Development,” Journal of Economic Growth, 9, 131-165.

42. Soloaga, Isidro and L. Alan Winters (2001) “Regionalism in the nineties: What e¤ect on trade?” North American Journal of Economics and Finance, 12, 1–29.

43. Svaleryd, Helena, and Jonas Vlachos (2002) “Markets for risk and openness to trade: how are they related?” Journal of International Economics, 57, 369–395 44. Svaleryd, Helena, and Jonas Vlachos (2005) “Financial markets, the pattern of industrial specialization and comparative advantage: Evidence from OECD countries,”European Economic Review, 49, 113-144.

23
- Singh, I, L Squire and J Strauss, eds (1986), _Agricultural Household Models_ , A World Bank Publication, Johns Hopinks University Press.

- Statistics South Africa (2001), “A Survey of Time Use: How South African Women and Men Spend Their Time”, Mimeo, Statistics South Africa, Pretoria, South Africa.

- Strauss, J and K Beegle (1996), _Intrahousehold Allocations: A Review of Theories, Empirical Evidence and Policy_ , Department of Agricultural Economics and Department of Economics, Michigan State University, Michigan.

- Strauss, J and D Thomas (1995), “Human Resources: Empirical Modeling of Household and Family Decisions”, in J Behrman and TN Srinivasan, eds., _Handbook of Development Economics_ , Vol III, Ch 24, 1881-2023.

- Udry, C (1996), “Gender, Agricultural Production, and the Theory of the Household”, _Journal of Political Economy_ , 104, 1010-1046.

- World Bank (2001), _Engendering Development_ , A World Bank Policy Research Report, World Bank and Oxford University Press.

World Bank (1983), _World Development Report_ , OUP, New York.

64
14

# Tables and Figures

Table 1 - Small States

|||Population|Average Per Capita|
|---|---|---|---|
|||(Thousands)<br>GDP,|1985 PPP-Adjusted Dollars|
|ATG|Antigua and Barbuda|63|5329|
|BHR|Bahrain|419|10342|
|BHS|Bahamas, The|237|11136|
|BLZ|Belize|178|3548|
|BMU|Bermuda|58|15356|
|BRB|Barbados|247|5341|
|BWA|Botswana|880|1516|
|COM|Comoros|340|632|
|CPV|Cape Verde|295|746|
|CYP|Cyprus|638|5084|
|DJI|Djibouti|344|1479|
|FJI|Fiji|602|3149|
|GAB|<br>Gabon|777|3853|
|GMB|Gambia, The|628|803|
|GNB|Guinea-Bissau|739|644|
|GRD|Grenada|92|2632|
|GUY|Guyana|719|1630|
|ISL|Iceland|223|9689|
|KNA|St. Kitts and Nevis|42|4399|
|LCA|St. Lucia|148|3264|
|LUX|Luxembourg|358|11934|
|MDV|Maldives|201|1908|
|MLT|Malta|341|4049|
|MUS|Maurtius|916|4092|
|QAT|Qatar|384|18278|
|REU|Reunion|496|2253|
|SLB|Solomon Islands|299|1845|
|SUR|Suriname|378|2877|
|SWZ|Swaziland|556|2358|
|SYC|Seychelles|59|2214|
|VCT|<br>St. Vincent and the Grenad|107|3312|
|VUT|Vanuatu|145|1633|
|WSM|Samoa|160|1844|
and a large effort was required to convert these into continuous lines and junctions so that a usable road network was available for calculating travel distance and time.

One way of reducing the effort required to obtain a usable roads network dataset is to digitize only the main roads and then assume a network of feeder roads, which will automatically have nicely aligned junctions. This approach is used by Staal et al. (2000, 2002) who study market access and its effect on market participation and technology adoption for smallholder Kenyan dairy farms. To build a road network linking their sample of farms to Nairobi and other urban areas they use topographic maps to digitize three classes of roads: 1) all weather, bound surface, 2) all weather, loose surface, and 3) dry weather only roads. Since this left many of the surveyed farms still off the actual road network they add a 4-kilometer grid of assumed feeder roads to fill in the areas between existing roads. It is not clear how much error is introduced by using this combination of actual and assumed roads.

# **Confidentiality Issues**

The accuracy of GPS in measuring either household or community locations also poses a challenge for maintaining the privacy of survey respondents. VanWey et al. (2005) discuss how the ethical need to ensure confidentiality of information collected about human research subjects may conflict with a desire to link the characteristics and actions of individuals or households to a particular geographic location. Uncertainty about how best to proceed in these circumstances may mean either that spatially explicit data are under-utilized, undermining the role of data sharing and data preservation in advancing science, or that researchers inadvertently disclose information that can identify survey respondents. These conflicts affect not only the original producers of data but also any data archivists charged with maintaining the database and providing it to other researchers while continuing to honor the commitments to confidentiality made when the data were first collected.

Moreover, this conflict between the confidentiality and usability of GPS data is not limited just to the sharing of data. It also affects the reporting and display of results based on geo-referenced data. For example, to show the confidentiality challenges posed by mapping point data, Curtis, Mills and Leitner (2006) re-engineer (i.e., reverse address match back to an individual residence) a newspaper map showing the locations in New Orleans where deaths occurred during Hurricane Katrina. The location marks in the newspaper each covered

- 22 -
29

**(a) Sugar (b) Milk (c) Rice (d) Beef (e) Cotton** (51 countries, (46 countries, (36 countries, (47 countries, (19 countries, global TRI = 54.8) global TRI = 44.5) global TRI = 42.9) global TRI = 32.0) global TRI = -4.1)

<!-- Start of picture text --> Mexico<br>Japan<br>France<br>Canada<br>Italy<br>India Germany<br>Indonesia Brazil<br>US Korea<br>Colombia Japan Slovenia<br>Germany US Ukraine US<br>Japan India Czech Rep China<br>France Germany Spain Pakistan<br>China Switzerland India Turkey Brazil<br>Lithuania France Japan Russia India<br>Philippines Colombia Ukraine Cote ...<br>Taiwan<br>Russia Canada US Zambia<br>Pakistan UK Vietnam Colombia Zimbabwe<br>Rep South ... Mexico Korea Argentina Tanzania<br>UK Italy Suda Egypt<br>China<br>Italy Netherlan... Nicaragua Nigeria<br>Turkey Ukraine US Poland Turkey<br>0 5 10 ‐20 0 20 0 20 40 ‐50 0 50 100 ‐1000 0 1000<br><!-- End of picture text -->

**Figure 2. Country Share of the Global Commodity-Specific TRI for Rice, Sugar, Beef, Cotton and Milk, 2000–04**

Source: Authors’ calculations based on NRA and CTE estimates in Anderson and Valenzuela (2008).

Notes: The decomposition over the 5-year period can be greater than or less than 100, even though the decomposition sums to 100 in any one year. We have scaled the 5-year averages, so that the decompositions sum to 100. Focus countries have been omitted from the above charts if their decomposition share has an absolute value of less than 2 percent.
Argentina, South Africa, Brazil, and India. These developing economies are many of the same countries whose textile and apparel industries are already struggling to compete in global markets with China’s textile and apparel exports. Using trade remedies to raise the cost of an integral input is likely to have unintended and yet important downstream competitiveness consequences, including increasing the probability that these developing economies’ textile and apparel producers may themselves be the next in line demanding their own protectionism through trade remedies.

# **3.2 Antidumping, multinational firms, and potential concerns of anti-competitive behavior**

There are a number of additional worries when it comes to the use of policies such as antidumping. Firms may abuse the policy by convincing government policymakers to impose trade barriers that, while in the firms’ interest, are not in the overall interest of the country. There are a number of mechanisms through which firms may manipulate such policies – including by using it as a tool to get government policymakers to assist (perhaps unwittingly) firms to segment markets (raising anticompetitiveness concerns), and as a tool to raise domestic rivals costs. The potential for abuse is increasingly heightened when the key industrial users of the policies are multinational firms with headquarters and subsidiaries situated globally that have the ability to tap into (and to be affected by) trade remedies in many different political jurisdictions.<sup>17</sup> First, the desire to segment markets identified in the U.S.-EC “Sodium metal” cases described in section 3.1 is not limited to rival firms, but it may also take place between subsidiaries of the same firm. A careful examination of the repetitiveness of firm names in trade remedy initiations (compiled into the publicly available _Global Antidumping Database_ ) allows us to identify two potential examples worthy of further investigation.

One example is an instance in November 2008 in which the U.S. imposed definitive AD duties on “Polyethylene Terephthalate (PET) film, sheet, and strip” from China, including 3.49 per cent on DuPont Teijin Films China Ltd. According to its website, DuPont Teijin Films is a 50-50 global joint venture between the American firm DuPont and the Japanese firm Teijin.<sup>18</sup> According to the public records in the case, the American subsidiary DuPont Teijin Films is one of the domestic petitioners behind the U.S. effort to target the Chinese subsidiary of the same firm with new import restrictions that could have the effect of segmenting markets.

A second example took place when India imposed a definitive AD measure on “Compact fluorescent lamps” from China in February 2009. The measure was an agreement that Chinese firms,

> 17 Important contributions to the economic research literature on the ways in which access to antidumping can inadvertently increase the concern for anti-competitive behavior include Prusa (1992), Messerlin (1992), and Veugelers and Vandenbussche (1999). 18 See <u>http://www.dupontteijinfilms.com</u> .

18
# **Example 2: Complex goods**

In a provocative application of network analysis, Hausmann and Hidalgo (2011) have generated an _Atlas of Complexity_ that generates a map of the product space by a factor extracted that is common to goods produced by on the one hand, few countries and on the other, very diversified countries. They term this factor “complexity” and it is argued that more complex goods require more types of skills which poor countries do not have. The possible inter industry externality however, arises from the idea that producing a complex good generates more of these skills- as Hidalgo notes “countries become what they make” – which can, in turn, be used to produce even more complex goods which are presumed to be better.

Though the logic of accumulating skills/ learning by doing is very compelling, how the actual empirics relate to this and whether we have documented market failures pertaining to particular goods in the Atlas is unclear. To begin, we cannot be sure that the relationships they document are emerging from the supply side- they could as well be reflecting that consumers in rich countries both value diversity and consume boutique higher end goods and this is reflected in the country’s production structure (See Imbs and Wacziarg 2005). Second, even if the Atlas were reflecting production considerations, we do not know if the common underlying factor is, in fact, related to complexity somehow, for instance, as the number of steps in production, as do Levchenko (2007), and Krishna and Levchenko (2013). Third, even if there were, there is no guarantee that the absence of a greater alphabet of capabilities is the binding constraint on producing more complex goods. Blanchard and Kremer (1997) and Levchenko (2007) for instance posit weak contract enforcement as the barrier to producing goods with more stages, and there may be no feedback from producing complex goods to improving the institutional environment and hence no externality. Fourth, since we do not know what the “complexity” measure is aside from being highly correlated with income per capita, its robustness in growth regressions is of unclear import. Fifth, the analysis uses final goods export data which do not capture which tasks a country is really undertaking - again, China is not producing sophisticated manufactures when it exports the iPod, it exports assembly tasks.

In sum, we cannot be sure whether there is a market failure to redress pertaining to particular goods and, if so, whether the _Atlas of Complexity_ is helping us locate it. The approach shares the intuition of 9
14 estimates of child gender effect. The examination of correlation coefficient between the two variables in the sample employed above is informative in this regard; this turns out to be close to zero and insignificant (Pearson C.C.=-0.0107/ sig. prob=0.3426)<sup>13</sup> . This seems to suggest that the values reported for the proportion of girls are exclusively due child gender effect on adult consumption, and provide sensible measure of bias against girls.

I end this section by a brief examination of the correspondence between the above results and welfare outcomes for Ethiopian male and female children. This obviously is critical, as the absence of such correspondence would generate doubts about the findings of our study, whilst its presence would make the econometric findings of no evidence of gender bias in other studies equally doubtful. The two most commonly employed welfare indicators are the child population sex ratio, and differences in school attendance by gender. The sex ratio is supposed to be balanced, except to a limited extent among low age child groups. The unbalanced ratio may be partly due to relative under-recording of births, but pronounced unbalanced sex ratio is an indication of neglect and poorer survival of girls compared to boys, see Kynch and Sen (1983). Schooling is also a major determinant of lifetime chances of children. Table 6 provides the ratio of girls to boys aged 0-14 and the ratio of number of girls’ “ever-attended” formal school to that for boys; both obtained from HICES 2005-6 survey- top row for the entire survey, the bottom for the sample employed in table 5. The child (0-14) ratio of number of girls to boys is 0.893 for the entire survey sample, indicating a large gender gap that is hard to explain solely by under-recording. It is, for example, worse than 0.90 reported in Ahmand and Morduch (1993) for the same age group for Bangladesh, a country with some of the worst recorded outcomes for women in the world. School attendance evidence also suggests bias against girls in education. The same applies to the ratios obtained from the sample, bottom row, employed in table 5. There is thus correspondence between our adult goods results on child gender bias, and the welfare outcomes for Ethiopian children.

> <sup>13</sup> Similar lack of correlations were found for all the various samples examined later in this study.
Policy Research Working Paper 7844

# **Abstract**

This paper presents a new demographic profile of extreme and moderate poverty, defined as those living on less than $1.90 and between $1.90 and $3.10 per day in 2013, based on household survey data from 89 developing countries. The face of poverty is primarily rural and young; 80 percent of the extreme poor and 75 percent of the moderate poor live in rural areas. Over 45 percent of the extreme poor are children younger than 15 years old, and nearly 60 percent of the extreme poor live in households with three or more children. Gender differences in poverty rates are muted, and there is scant evidence of gender inequality in poor children’s educational attainment. A sizable share of the extreme and moderate poor, 40 and 50 percent, respectively, have completed primary school. Compared with the extreme poor, the moderate poor are significantly more likely to have completed primary school and are less likely to work in agriculture. After conditioning on other individual and household characteristics, having fewer than three children, having greater educational attainment, and living in an urban area are strongly and positively associated with economic well-being. The results reinforce the central importance of households in rural areas and those containing large numbers of children in efforts to reduce extreme poverty, and are consistent with increased educational attainment and urbanization hastening poverty reduction.

This paper—prepared as a background paper to the World Bank’s _Poverty and Shared Prosperity Report 2016: Taking on Inequality_ —is a product of the Poverty and Equity Global Practice 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 authors may be contacted at dnewhouse@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
# **Choosing Parameters for Clustering Crash Reports into Unique Crashes**

Multiple people often tweet about the same crash. In order to cluster crash reports to unique crashes, we cluster by the kilometer and time distance between reports. To determine optimal kilometer and time parameters, a team manually determined which crash reports refer to the same crash. The dataset was double coded by different team members, resulting in two “truth” datasets. To judge whether crash reports refer to the same crash, the team used the location of the crash, the time of the tweet and looked for details about the crash in the tweet itself (e.g., extent of injuries, types and numbers of vehicles, etc.).

The below table shows summary statistics of the maximum distance and time between any two crash reports in the same clustered or individual crash. Before calculating the statistics, outliers were removed (we define an outlier as a crash cluster where reported crashes occurred over 24 hours or over 5 km from each other). Across both truth datasets, around 52% of tweets were clustered with another tweet, meaning that 48% of tweets are the only tweet reporting one crash.

Table S6: Clustered Tweets Truth Data Summary Statistics

|Variable|Min|Quartile 1|Median|Mean|Quartile 3|Max|
|---|---|---|---|---|---|---|
|||Tru<br>|th Dataset <br>|1<br>|||
|Hours Dif|0|0.133|0.55|1.68|1.693|23.776|
|KMs Dif|0|0|0.013|0.213|0.138|3.328|
|N Tweets|2|2|2|3.324|3|44|
|||Tru<br>|th Dataset <br>|2<br>|||
|Hours Dif|0|0.161|0.597|1.715|2.144|23.417|
|KMs Dif|0|0|0.024|0.276|0.199|3.13|
|N Tweets|2|2|2|3.275|3|19|

We examine two common metrics for evaluating clustering performance: the adjusted Rand index and the Jaccard coefficient[9]. When using our algorithm to cluster crash reports, we test all combinations of 0.1, 0.5, 1, 2 and 3 kilometers and 1, 2, 4, 12 and 24 hours. For truth dataset 1, both the Rand index and Jaccard coefficient show that 12 hours and 500 m leads to best results, while truth dataset 2 shows 2 hours and 500 m (see figure ). The difference in results in the truth datasets likely results from the exercise being partially subjective, particularly when limited or no S21
# **Achieving Accelerated and Shared Growth in Ghana: A MAMS-Based Analysis of Costs and Opportunities**<sup>♦</sup>

Željko Bogetić, Maurizio Bussolo, and Denis Medvedev<sup>♣</sup> .

_Keywords:_ Millennium Development Goals (MDGs), Ghana, Simulation Model _JEL codes_ : D58, E61, O15, O55

> ♦ The views expressed in this document are those of the authors and may not be indicative of the official position of the World Bank, its Executive Directors, or the countries they represent.

> ♣ Eastern Europe and Central Asia Economic Management, Development Prospects Group, and Development Prospects Group, World Bank. The authors may be contacted at zbogetic@worldbank.org, mbussolo@worldbank.org and dmedvedev@worldbank.org. We are thankful to Hans Lofgren, Hans Timmer, and participants of the Ghana Country Economic Memorandum workshops for helpful comments and suggestions. We are indebted to our counterparts at the Ministry of Finance and Economic Planning (MOFEP) and Bank of Ghana in Accra, Ghana for fruitful discussions and sharing of key data. The remaining errors are ours.