rafmacalaba/datause-encoder-data huggingface dataset:
# **IV. Descriptive Analysis**

_Table 1_ presents some descriptive statistics using data on 23 SSA countries (Table A1 presents a list of these countries) that have data on both manufacturing GVC and skilled/unskilled labor contents of exports. First, let’s look at the GVC participation index. The larger the ratio, the greater the intensity of a country’s involvement in GVCs. _Table 1_ shows that on average the intensity of involvement for SSA countries in manufacturing GVCs is approximately 44 percent. This means that less than half of total manufacturing exports in SSA are GVCs related exports. Since GVC participation has only two components reflecting upstream and downstream linkages in the chain, _Table 1_ provides suggestive evidence that the intensity of involvement for SSA countries is greater in backward (24%) than in forward (20%) manufacturing GVCs. This signifies that SSA countries incorporate more foreign inputs in their manufacturing exports (backward linkages) than they supply intermediate manufacturing inputs to the exports of other countries (forward linkages).

**Table 1: Summary Statistics – African Countries**

|**_Variable_**|**_Obs_**|**_Mean_**|**_Std. Dev._**|**_Min_**|**_Max_**|
|---|---|---|---|---|---|
|DVX|508|7,593,820|5,179,819|1,887,196|17,500,000|
|DVA|508|34,800,000|20,500,000|11,700,000|73, 500,000|
|FVA|508|8,972,219|5,984,821|1,936,014|19, 900,000|
|DVX_share|508|0.1995|0.0150|0.1683|0.2203|
|FVA_share|508|0.2400|0.0284|0.1958|0.2914|
|GVC Participation|508|0.4395|0.0348|0.3642|0.4903|
|GDP per capita|508|1865.609|2320.022|214.0448|8313.271|
|LACEX_usk|508|0.0245|0.0410|6.85e-08|0.4224|
|LACEX_sk|508|0.0046|0.0080|2.95e-09|0.0851|
|VA_usk|508|0.1082|0.0889|6.97e-08|0.5867|
|VA_sk|508|0.0200|0.0178|3.00e-09|0.1179|
|HCI|508|0.4129|0.0606|0.3415|0.6258|
|Migrant Stock|508|453441.3|459822.8|15543|2095185|
|Migrants as a share of total<br>population|508|2.6103|2.0745|0.1371|11.0895|
|Remittances-to-GDP ratio|472|0.0074|0.0076|0.0001|0.0306|
|Average Distance from the<br>3 major hubs|508|10050.35|1144.898|8390.698|11801.62|

Note: _LACEX_usk and LACEX_sk stand for labor content of export for unskilled and skilled workers respectively while VA_usk and VA_sk denote the shares of value added by unskilled and skilled workers respectively._ According to _Table 1_ , we observe that the average human capital index for SSA countries is 0.41 which means that the productivity of a newborn as a future worker is on average 59% below what could be achieved with complete education and full health. The global average is 0.66 which means 17
while the payoff for the community is: The problem is to determine _A_ , _R_ and _Z_ ; this problem is solved imposing the first order condition on the equation _Y_ = _Y_ 1 + _Y_ 2 . Very often it is assumed that not all these parameters are on the bargaining table, because sometimes communities want the area _A_ or the rotation _R_ to be fixed. Thus, the problem is to fix _Z_ and the bargaining process gives the solution to the problem. The authors apply their model to a case study from West Bengal (India).

55
48

# TABLE B.1 EXPERIENCE LEVEL OF FIRMS

. ........................

# 1. Number of Firms by Experience Level (X)

||77-82(a)|1983|198U|1985|1986|1987|1988|1989(c)|
|---|---|---|---|---|---|---|---|---|
||------------------------------|----------------------|------||||||
|i)Less than 3 year|s<br>16|<br>26|<br>30|<br>NA|29|<br>Z6|<br>26|<br>16|
|<br>ii) 3 -4 years|17|<br>16|<br>17|<br>NA|19|<br>20|<br>20|<br>22|
|iii) 5--9 years|28|<br>18|<br>19|<br>NA|21|<br>22|<br>22|<br>24|
|iv) 10 or more year|s<br>39|<br>40|<br>34|<br>NA|31|<br>32|<br>32|<br>38|
|Total|100|<br>100|<br>100||100|<br>100|<br>100|<br>100|

# 2. Capital Resources by Experience Level (X)

||77-82(b)|1983|1984|1985|1986|1987|1988|1989(c)|
|---|---|---|---|---|---|---|---|---|
|i)Less than 3 year|s<br>)|6|9|NA|9|8.5|<br>7|<br>7|
|ii) 3-- 4 years|)23|-|13|<br>NA|11|<br>9.5|<br>10|<br>17|
|iii)<br>5--9years|11|-|15|NA|19|21|25|<br>18|
|iv) 10 or more year|s<br>66|<br>69|<br>63|<br>NA|61|<br>61|<br>58|<br>58|
||.**......**................|..|...............................|.........|||||
|Total|100|<br>100|<br>100||100|<br>100|<br>100|<br>100|

Notes: (a) >10 yrs for category (iv); 6-9 yrs for (iii); and 4-5 yrs for (ii).

(b) 7-9 yrs for (iii).

(c) data corresponds to first half of 1989.

Source: Venture Capital Journal, various issues.
1997‐2007. Its flow measure was much higher than Mexico because it built up its stock more slowly. And India is not alone as other economies like the U.S., EU, Turkey and Argentina each had flow measures that (by count) averaged more than 0.40% of imported products per year during 1997‐2007.

Combined, these results suggest that a number of major economies in the WTO system may create substantial trade policy uncertainty for foreign exporters through the way they use TTBs. While applied tariffs are quite low in this period (especially in historical terms), many exporters experienced the possibility of a trade policy adjustment by being subject to a TTB investigation which had a reasonable chance of resulting in imposition of a new trade barrier.

Finally consider the table 2 data which examines flow information on potential new TTBs during the recent global economic crisis. In particular, columns (3) and (4) present information from 2009 on the investigations initiated in response to domestic industry petitions for new import protection. In 2009, 0.63% (0.50%) of U.S. imports by count (value) was subject to a new TTB investigation. This data is somewhat surprising given the historical context; at the time, there was a substantial fear that new import protection would result from the deep recession, as injured industries and high levels of unemployment would result in industry and labor unions placing demands for new barriers. However, the 2009 U.S. figures are well below the average annual share of imported products subject to new U.S. TTB investigations during 1997‐2007, which is 1.01% (0.92%) by count (value). This pattern is similar for the EU, South Korea, Japan and South Africa – economies which all registered smaller flow measures (new investigations) in 2009 than their 1997‐2007 annual (pre‐crisis) averages. On the other hand, India (1.74% of imports, by count), Argentina (1.32%) and Turkey (0.80%) are countries for which the opposite is true. Each of these economies had substantially higher 2009 flows than their 1997‐2007 averages. I further describe these and other notable 2008‐2009 crisis trends in the data in more detail in section 3 below.

# **2.4 Policy coverage and focus on antidumping**

Before turning to a more detailed discussion of the 2008‐2009 crisis, I point to one last feature of table 2. Column (2) presents information on the extent to which each of the G20 economies was particularly reliant on antidumping, relative to its total cumulation of products that were affected by at least one TTB during the 1990‐2009 period.

19
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lower: 12 percent of male-headed households had received extension visits, whereas 2 percent of female-headed households had received extension visits in one region, and zero percent had received extension visits in two other regions. Indeed, even in male-headed households, only 2 percent of spouses received an extension visit. This finding is particularly striking in light of the fact that of the three countries, Ghana had the highest share of female extension officers. In Karnataka, India, 29 percent of land-holding maleheaded households received an extension visit, whereas 18 percent of female-headed households did. For livestock extension, 79 percent of female-headed households had contact with an extension agent, compared with 72 percent for male-headed households (World Bank and IFPRI 2010, pp xxviii-xxix).

What seems to be the reason for this differential access? One factor is the bias of service delivery toward men, which stems from the belief that men are the decision makers and women are marginal farmers, if they farm at all (see, for example, World Bank and IFPRI, 2010). Related to this belief is the perception that when men are educated, knowledge will be shared with other members of the household (Meinzen-Dick et al., 2010). This assumption may be unrealistic, particularly when men and women perform different tasks or even grow different crops.

Of course, this bias may be due to the discriminatory norms of the institution. Systematic evidence of this discrimination does not exist, but individual cases indicate that this may be an issue. For example, in 2011, the U.S. government settled a class action lawsuit brought by women farmers alleging systematic discrimination by the U.S. Department of Agriculture. A second factor, which echoes the theme of interlinked 27
**Figure 15: Falling rural – urban ratios in health outcomes and rising urban – rural ratio in income per capita, 1991‐2009**

<!-- Start of picture text --> 3.5<br>3<br>IMR rural/urban ratio<br>2.5<br>U5MR rural/urban<br>ratio<br>MMR rural/urban rate<br>2<br>urban/rural income<br>ratio<br>1.5<br>1<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> Source: China Health Statistical Digest 2010 27
23

# Regression 9

|Dependent<br>variable:<br>Number<br>of<br>observations:|Standa|rd<br>Deviation<br>114|of<br>Terms<br>of<br>Trade<br>Shocks<br>1960-95|
|---|---|---|---|
|Mean of dep.|var.|<br>.038019|LM het. test=1.87192 [.171]|
|Std. dev. of dep.|var.|<br>.021479|Durbin-Watson<br>=2.07124|
|[<.8691||||
|Sum of squared resid|uals|=.026595|Jarque-Bera test=2.17388 (.337]|
|Variance of resid|uals|=.250897E-03|Ramsey's RESET2 = .137189 (.712]|
|Std. error of regres|sion|.015840|F (zero slopes)=14.5415 [.000]|
|1R-sq|uared|=.489871|Schwarz B.I.C. = -8.03086|
|Adjusted R-squ|ared|=.456183|Log likelihood= 314.945|
|Estimated||Standard||
|Variable<br>Coefficient||Error|t-statistic<br>P-value|
|SSA<br>.04:2971||.419929E-02|10.2330<br>[.000]|
|ASIA<br>.022997||.323085E-02|7.11797<br>[.000]|
|ECA<br>.016725||.219593E-02|7.61616<br>(.000]|
|MENA<br>.039738||.690534E-02|5.75471<br>[.000)|
|LAC<br>.037033||.317373E-02|11.6685<br>[.000]|
|OIL<br>.02:3363||.503847E-02|4.63685<br>[.000]|
|COMMOD<br>.740058E|-02|.452971E-02|1.63379<br>[.105]|
|MICROSTATE<br>.01:3328||.473321E-02|2.81585<br>[.006]|
|Standard Errors are|heter|oskedastic-cons|istent<br>(HCTYPE=2).|
In the second case, road level of service (technical standard, width...) should be reduced<sup>24</sup> aiming at ensuring passability of intermediate means of transport, which would mean the importance of working exclusively on critical points/obstacles (small bridges for instance).

Like pointed out in Raballand et al. (2010) using Cameroon, Burkina Faso and Uganda examples, there is a continuum of integration to markets for most households in Africa. In some cases, a road may be non-passable for cars, a motorcycle driver may, for instance, dismount the motorcycle and walk it around the trouble spot in the road and then continues his trip. Therefore, from an economic perspective, most rural populations are _somehow_ connected to markets whereas most data analysis or policy prescriptions are based off of binary classifications of connectivity as either 0 or 1. Hence, from a public policy perspective, investments in roads might have a lower impact on economic development than expected due to the fact that transport connectivity is only one component of rural development, and sometimes not the most important.<sup>25</sup> It may also explain why despite major investments in rural roads in some countries, poverty reduction has not reduced significantly.

In any case, economic and social data (such as traffic data, vehicle operating costs for minibuses/IMTs, purpose of minibus usage) need to be collected for policy-makers to decide on which investment choice is required to enable which type of service provision, based primarily on demand assessment. Surveying transport demand in the Western part of the country, Zambia JCTR (2008) found that 92% of the local farmers surveyed do not produce more than 50 bags of 50 kilos each of agricultural products in a year, which means that without consolidation, the current demand does not justify transport services to develop.

There is a recurrent lack of data collection on the demand side and many investments are carried out on the multiple assumptions that roads improvement translates into reduced vehicle

> 24 Where repressed demand is not high.

> 25 Ruijs et al. (2004) find out that the direct effect of transport costs reductions on food prices, such as cereals, requires some nuance and tempered expectations in the case of Burkina Faso, notably due to the organization of markets.

17
|_E_(_Y |T_ = 1_, X_)_−E_(_Y |T_ = 0_, X_)<br>No Regressors<br>Basic Regressors<br>Same Name<br>0.040<br>0.012<br>0.013<br>(0.044)<br>(0.038)<br>(0.038)<br>0.013<br>0.025<br>0.033<br>(0.181)<br>(0.161)<br>(0.165)<br>0.066<br>0.051<br>0.035<br>(0.077)<br>(0.071)<br>(0.068)<br>0.042<br>0.025<br>0.021<br>(0.031)<br>(0.027)<br>(0.027)<br>-0.004<br>-0.001<br>-0.004<br>(0.038)<br>(0.034)<br>(0.034)<br>0.029<br>0.027<br>0.018<br>(0.025)<br>(0.026)<br>(0.023)<br>andard deviations in brackets. Columns 3-5 report the<br>) for continuous outcomes and probit regression (13) for<br>e specifcation in column 4 includes controls for: Tigre<br>ntrols additionally include a dummy =1 if village name<br>vations are clustered at village level and robust standard|
|---|
|Control<br>0.282<br>(0.450)<br>0.618<br>(1.978)<br>0.39<br>(0.898)<br>0.125<br>(0.399)<br>0.219<br>(0.483)<br>0.046<br>(0.372)<br>ups, with st<br>egression (12<br>controls. Th<br>column 5, co<br>sions, obser|
|Variables<br>Treatment<br>Over the 6 months before the survey:<br>1. Respondent participated in LHM<br>0.322<br>(0.468)<br>In the last month before the survey:<br>2. Days spent by household in LHM<br>0.632<br>(2.774)<br>3. Household members who participated in LHM<br>0.456<br>(1.007)<br>4. Male household members_>_15 years old<br>0.167<br>who participated in LHM<br>(0.462)<br>5. Female household members_>_15 years old<br>0.215<br>who participated in LHM<br>(0.47)<br>6. Household members_<_15 years old<br>0.075<br>who participated in LHM<br>(0.467)<br>Note: Columns 1 and 2 report means for treatment and control gro<br>difference between treatment and control groups, estimated using LS r<br>binary outcomes. The specifcation in column 3 does not include any<br>tribe, Muslim religion and sub-zone dummies. In the specifcation in<br>was not changed from List 1 to List 4, and =0 otherwise. In all regres<br>errors are reported in parentheses. *** p_<_0.01, ** p_<_0.05, * p_<_0.1.|

19
Policy Research Working Paper 7911

# **Abstract**

This paper illustrates how the capabilities of GIS and satellite imagery can be harnessed to explore and better understand the urban form of several large African cities (Addis Ababa, Nairobi, Kigali, Dar es Salaam, and Dakar). To allow for comparability across very diverse cities, this work looks at the above mentioned cities through the lens of several spatial indicators and relies heavily on data derived from satellite imagery. First, it focuses on understanding the distribution of population across the city, and more specifically how the variations in population density could be linked to transportation. Second, it takes a closer look at the land cover in each city using a semi-automated texture based land cover classification that identifies neighborhoods that appear more regular or irregularly planned. Lastly, for the higher resolution images, this work studies the changes in the land cover classes as one moves from the city core to the periphery. This work also explored the classification of slightly coarser resolution imagery which allowed analysis of a broader number of cities, sixteen, provided the lower cost.

This paper is a product of the Social, Urban, Rural and Resilience 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 nlozano@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
**7 Tables**

**Table 1:** Impact of RCT treatments

||Knowledge of<br>pollution<br>|Actions to avoid<br>pollution|Health<br>symptoms|
|---|---|---|---|
||Leafet Treatment|||
|Treatment efect<br>N|-0.3493<br>(0.3254)<br>458|-0.0262<br>(0.1350)<br>458|-0.0571<br>(0.1518)<br>375|
||Outdoor Treatment|||
|Treatment efect|0.4912<br>(0.3258)|0.3107<sup>_∗∗_</sup><br>(0.1427)|0.2468<br>(0.1575)|
|N|457|457|368|
|Ou|tdoor and Indoor Treatm|ent||
|Treatment efect|0.7597<sup>_∗∗_</sup>|0.3079<sup>_∗_</sup>|-0.3692<sup>_∗∗_</sup>|
||(0.3057)|(0.1566)|(0.1752)|
|N|375|375|309|
|**Pre-period outcome means:**||||
|Leafet treatment:|10.90|1.35|1.49|
|Outdoor treatment:|10.75|1.23|1.30|
|Outdoor & Indoor treatment:|11.23|1.49|1.77|

Notes: The outcome in all columns is the difference in the count of self-reported knowledge, adaptive behaviors, or health symptoms of pollution exposure from each household in the survey. The omitted group is the control group. The survey asked about adaptive actions and health symptoms during the 30 days prior to each survey round. The actions we ask about are: avoiding busy traffic areas, avoiding high levels of outdoor activity on high pollution days, using a ventilation fan while cooking, opening windows to vent the house. The symptoms we ask about are: eye irritation, sore throat, headache, cough, wheezing, sinus pain, or shortness of breath. Heteroskedasticity robust standard errors clustered by census block - the sampling unit- are in parentheses (* p _<_ .10 ** p _<_ .05 *** p _<_ .01). We report results controlling for household wealth and housing age in Table A2.

20
# **1.  Introduction**

When is foreign aid effective in achieving its desired objectives? A vast empirical literature has sought to answer this question. Most of this literature has focused on the aggregate country-level impact of aid (typically on GDP growth), and has necessarily also focused on country-level factors that determine the growth effects of development assistance.<sup>1</sup> An important limitation of the aggregate country-level approach is that it has little to say about the very large variation in the success or failure of individual aid-financed development projects. In this paper, we contribute to the much smaller literature that seeks to provide empirical evidence on the factors contributing to the outcomes of individual projects, using data on over 6,000 World Bank-financed projects undertaken in 130 developing countries since the 1970s.

Our dependent variable consists of a subjective rating of the extent to which individual World Bank projects were able to attain their intended development objectives. These ratings are generated through internal World Bank project evaluation procedures, which we describe in more detail below. While we acknowledge upfront that these ratings are only imperfect indicators of actual project success or failure, we will for terminological convenience refer to these ratings as "project outcomes". We begin by documenting a set of very robust partial correlations between project outcomes and basic measures of country-level policy and institutional quality observed over the life of the project. This echoes other findings in the literature on macro-level determinants of aid effectiveness, which emphasize the role of country-level proxies for macroeconomic stability and the quality of policies and institutions in driving project outcomes. Most notably, we find a very strong partial correlation between the World Bank’s Country Policy and Institutional Assessment (CPIA) ratings and project performance. While this result holds for all World Bank projects, it is particularly noteworthy in the context of projects financed by the International Development Association (IDA), which is the World Bank’s fund for the poorest countries. IDA resources are allocated across countries using a Performance Based Allocation (PBA) process which directs more resources to countries that (a) are poorer, and (b) perform better on the CPIA ratings. It is therefore encouraging to see that this _ex ante_ emphasis on country-level policy performance is reflected

> 1 This line of research has produced a wide variety of conflicting results, to the point where Temple (2010) suggests that it “must be regarded as a work in progress”.  Recent assessments over the past decade range from cautiously optimistic Burnside and Dollar (2001), Clements, Radelet, Bhavnani  (2004), Hansen and Tarp (2000),  Minoiou and Reddy (2009) to ambivalent Roodman  (2007) to skeptical and pessimistic Easterly and Levine ( 2004), Doucouliagos and Paldam (2008), and Rajan and Subramanian, (2008).

2