rafmacalaba/datause-encoder-data huggingface dataset:
Table 3. Sub-Saharan Export Products Facing MFN or Preferential Duties in the European Union (No. of tariff line tems)

|Exporting Country|Tariff Lines<br>Facing a Zero<br>MFN Duty|Zero Preference<br>Rate Lines|Total Lines with<br>Zero Duties*|Non-Zero<br>Preference Rate<br>Lines|Zero Duty or<br>Preference Rate<br>Lines*|Non-Zero MFN<br>Duty Lines|Total Tariff<br>Lines Exported|
|---|---|---|---|---|---|---|---|
|Angola|25|106|131 (98)|1|132 (98)|2|134|
|Botswana|33|94|127 (99)|0|127 (99)|1|128|
|Cameroon|95|411|506 (98)|4|510 (99)|4|514|
|Central African Rep.|41|83|124 (98)|0|124 (98)|3|127|
|Chad|21|_51_|72 (97)|1|73 (99)|1|74|
|Congo|57|173|<br>230 (100)|0|<br>230 (100)|0|230|
|Cote d'lvoire|121|_595_|716 (97)|6|722 (98)|14|736|
|Ethiopia|67|111|178 (98)|1|179 (99)|2|181|
|<br>Gabon|55|261|<br>316 (100)|0|<br>316 (99)|1|317|
|Ghana|63|250|313 (99)|2|315 (99)|2|317|
|Guinea|50|109|159 (100)|0|159 (100)|0|159|
|Kenya|108|438|546 (98)|4|550 (98)|10|560|
|Liberia|57|84|141 (96)|1|142 (97)|_5_|147|
|Madagascar|63|278|341 (97)|2|343 (98)|8|351|
|Malawi|28|140|168 (98)|2|170 (99)|1|171|
|Mali|34|146|180 (99)|1|181 (99)|1|182|
|Mauritania|18|125|143 (99)|0|143 (99)|4|144|
|Mauritius|46|588|634 (99)|2|636 (99)|6|642|
|Niger|29|151|180 (99)|1|181 (99)|1|182|
|<br>Nigeria|127|392|519 (99)|3|521 (99)|4|525|
|Senegal|90|375|465 (98)|4|469 (99)|6|475|
|Sierra Leone|30|78|108 (98)|0|108 (98)|2|110|
|Sudan|55|84|139 (99)|1|140 (99)|1|141|
|Swaziland|20|118|138 (98)|5|143 (96)|6|149|
|Togo|48|170|218 (96)|2|220 (97)|6|226|
|Uganda|25|60|85 (97)|0|85 (97)|3|88|
|<br>United Rep. Tanzania|79|219|298 (99)|1|299 (99)|2|301|
|<br>Zaire|97|217|<br>314 (98)|0|<br>314 (98)|7|321|
|Zambia|48|125|173 (93)|8|181 (97)|5|186|
|Zimbabwe|91|451|<br>542 (97)|8|<br>550 (99)|6|556|
|MEMO ITEM||||||||
|Taiwan, China|192|0|192 (4)|0|192 (4)|4,080|4,272|
|Republic of Korea|153|0|153 (4)|0|153 (4)|3,350|3,503|

Source: World Bank-UNCTAD SMART Database. These statistics relate to 1988 trade patterns.

* Figures in parentheses show the share of the country's tariff line exports that receive the indicated tariff treatment.
# **1 Introduction**

As countries grow and develop, their economies restructure away from agriculture into manufacturing and services.<sup>2</sup> Accompanying this process is an occupational shift towards more remunerative non-farm activities, though usually only with a lag, instigating inequality (World Bank, 2008). Historically, great diversity has been observed in these processes and much has been written about how the nature and speed of countries’ sectoral and occupational diversification, i.e. their structural transformation, affects economic growth and the speed of poverty reduction (Timmer, 2009; Szirmai, 2012).

Along with countries’ structural transformation usually also comes urbanization, i.e. a spatial transformation, with people relocating from rural to urban areas. Great diversity also exists in how these structural and spatial transformation processes interact. In some countries, the structural transformation goes along with rapid agglomeration in mega cities (as for example in South Korea and the Philippines), while in others, people diversify out of agriculture into the rural nonfarm economy and secondary towns (e.g. Taiwan, China, and Thailand) (Otsuka, 2007; Christiaensen, 2007). And just like different processes of economic growth and structural transformation may yield quite different distributional and poverty outcomes<sup>3</sup> , so, different patterns of rural-urban transformation may be associated with different rates of economic growth, and especially poverty reduction.

The clustering of a country’s urban population in few localities, known as urban concentration<sup>4</sup> , could for example generate more economic growth and jobs given economies of scale and agglomeration (World Bank, 2009). On the other hand, off-farm jobs generated in

> 2 The process mirrors people’s income inelastic demand for agricultural products (Engel’s Law). Nonetheless, it is important to note that when the process is successful, the absolute size of agriculture continues to grow, even though its share in the economy declines,

> 3 In this, agricultural growth is for example often considered key as driver of growth and poverty reduction early on in the development process. However, as countries take off and transition through their structural transformation, non-agriculture usually takes over as engine of growth, while agriculture maintains its superior poverty reducing powers, at least for the poorer of the poor (Montalvo and Ravallion, 2010; Loayza and Raddatz, 2010; Christiaensen, Demery, and Kuhl, 2011).

> 4 Urban concentration needs to be distinguished from urbanization. While the latter concerns the share of the population residing in urban areas, the former refers to distribution of the urban population across the system of cities, with urban primacy, the share of the urban population living in the largest cities, one common measure of urban concentration.

2
Policy Research Working Paper 10494

# **Abstract**

Internet surveys may never replace in-person surveys as a gold standard, but they remain important tools for rapid, remote, and low-cost data collection. The West Bank and Gaza Poverty and Equity team had a unique opportunity to compare a Facebook survey with an in-person survey covering conflict exposure and potentially associated socioeconomic and mental health outcomes over a similar time period. It is reasonable to expect that the estimates from internet surveys and in-person surveys would differ. In this case, the Facebook survey estimates more severe outcomes (e.g., higher exposure to conflict and worse mental health) than its in-person counterpart for most topics and populations. Multiple mechanisms may have contributed to this difference in estimates, including overrepresentation in the Facebook sample of respondents who were interested in the survey topics, reduced sensitivity bias in the context of a self-administered online questionnaire, and reporting more severe outcomes than personally experienced to encourage resource flows to perceived needs. Estimated outcomes tend to be more similar for people in Gaza, possibly because of greater homogeneity in socioeconomic experiences and exposure to violent conflict and broader interest in a survey on the effects of the May 2021 violence. The main results are robust to different ways of controlling for observable characteristics; neither alternative weights nor sample restrictions erase the systematic differences between the surveys.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at afinn1@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
point estimate, we cannot completely rule out the possibility that the high initial pro...t women receiving the cash treatment are also investing it in the business and bene...t through higher pro...ts.

# 6.2 How do the low and high initial pro...t women di¤er?

We have seen that the impact of the grants di¤ers greatly between women with low initial pro...ts and women with high initial pro...ts. It is therefore worth examining in more detail the composition of these two subsamples. The ...rst point to note is that these groups don’t di¤er greatly in the industry or type of business, just in the scale. The low initial pro...t group is made up of 31 percent food sales, 18 percent beauty and hair, 9 percent sewing, and 42 percent trade, compared to 37 percent food sales, 9 percent beauty and hair, 6 percent sewing and 47 percent trade for the high initial pro...t group. Even when we look more ...nely within these broad sectors, we see a similar broad range of types of ...rms in both subgroups: kenkey and banku (both traditional prepared foods) sellers, dressmakers, beauty salons, used clothes sellers, and retail trade.

In contrast, the scale of the ...rms di¤ers substantially. Table 6 compares the pre-treatment characteristics of these two subgroups of female ...rms to each other and to the male-owned ...rms. The ...nal column also o¤ers a comparison to the sample of female microenterprises from Sri Lanka used in de Mel et al. (2009a). We see that mean and median monthly pro...ts for the low initial pro...ts female subsample is 37-38 cedis, approximately US$1 per day, while mean and median pro...ts are 4 to 6 times this level in the high pro...t group. Similarly, mean and median sales di¤ers by a factor of 5 to 6 between the low and high initial pro...t groups. Mean capital stock for the low initial pro...ts group is 251, versus 456 for the high pro...ts group. Comparing to the other two groups, we see that the high initial pro...t females have larger pro...ts than the average male-owned ...rms in the sample, while the low initial pro...ts group are similar in size to the female-owned ...rms in the Sri Lankan study.

Table 6 also shows that women in the high initial pro...ts group are more educated, have richer households (which may be a consequence of the higher pro...ts rather than a cause), are more likely to keep accounts and to have had a formal loan, and have been in business slightly longer than the low initial pro...ts ...rms. When it comes to the reasons for choosing a particular sector, women in the high pro...ts group are more likely to say they chose their sector for earnings potential and less likely to say they chose it because it had a low capital requirement.

30
Mexico), and both were aggregated to 2- and 3-digit levels for consistency with the R&D data (16 industries).

# _5._ Estimation Results

# 5.1 Panel Unit Root Test

Before turning to the econometric analysis, we need to consider the issue that two or more variables may be trended and contain unit roots, making the regression results spurious (unless the variables are co-integrated). Levin and Lin(1992, 1993) developed a specific procedure to test for panel data unit roots. This paper adapts the method in Levin and Lin (1993) to conduct unit root tests for each of the variables of interest: total factor productivity _(TFP),_ trade-related foreign R&D from the US and Canada _(NRDN),_ and trade-related foreign R&D from the rest of the OECD _(NRDOT)._ The model under Pi consideration is Ay,, _= aO, + a,1 1_<sup>_t + 'yY,,._</sup> _1_<sup>_+_</sup> _9iLAYi,-L +_ e, _0_ where L=1,..., **Pi;** t=1,.... _L=I_ T;i=1,..., N; and Pi is the number of lags included in each panel. The null hypothesis (variables contain unit roots) is that 6, = 0 for all i and the alternative hypothesis is that 3,S < 0.

As shown in Table 1, whether P = 1 or P = 2, we reject the hypothesis that there is a panel unit root for all three variables: _TFP, NRDN_ and _NRDOT._ Therefore, any panel regression results in the paper are unlikely to be spurious.

9
Often we need to answer questions such as: Do capital controls also serve as a conduit for favors to firms outside crises periods? What are the institutional characteristics that create this type of connection between firms and governments? Are there other policies that are used by other countries as substitute for capital controls to funnel resources to connected firms? Several approaches have been tried in the literature to address such questions. The simplest is to posit a relation between the variable of interest and a set of plausible determinants. One then uses multivariate techniques to describe the association between the variables of interest and the postulated instruments. Evidence of association is taken to support the existence of the postulated relation. This approach is problematic in that many of the plausible determinants may themselves not be truly exogenous, leading to well-known biases.

A second, widely adopted, approach follows Rajan and Zingales‟ (1998) study of the effect of financial development on the growth rate of industries. They posit that industries that depend on external financing grow faster in countries with well-developed financial systems. The identification problem here, though, is that in any country, the extent to which a firm depends on external financing is itself endogenous and influenced by the development of the financial system. The solution that Rajan and Zingales (1998) adopt is to use the extent industries in the U.S. depend on external finance to classify industries into financially and financially independent industries. This classification does not depend on conditions in developing countries or growth rates of industries in developing countries.

Once industries are classified using U.S. data, Rajan and Zingales use a difference-indifferences approach where they use variation across industries in their dependence on external finance and variation across countries in their level of financial development to assess the impact of finance on industry growth. The specific equation they estimate is as follows: Industry Growthi,k =  _i +_  _k +_  (External Dependencek x Financial Developmenti) _+_  Industry Sharei,k +  (Industryk x Countryi) +  _i,k_ (1) where Industry Growth is growth of value added in industry _k_ in country _i_ ;  and  are vectors of industry and country dummies; External Dependence is industry k‟s dependence on external finance; Financial Development is a measure of financial development in country _i_ ; Industry Share is the initial share of industry k‟s value added in total manufacturing value added of 16
# **Table 2.  Empowerment Indicators, Nepal Rural Water and Sanitation Project**<sup>32</sup>

|DOMAIN|SUB-DOMAIN|ASSET AND OPPORTUNITY STRUCTURE<br>|DEGR|EES OF EMPOWERMENT INDI|CATORS|
|---|---|---|---|---|---|
|||INDICATORS<sup>33</sup>|Fed.|Intermediary|Local|
|STATE|Justice|•<br>Literacy levels<br>•<br>Monthly income levels<br>•<br>Size of land household owns<br>•<br>Perceived levels of wealth<br>•<br>Participation in training events on the rights of low<br>castes/indigenous peoples<br>•<br>Membership in (1) externally organized groups,<br>such as credit/saving group, users’ group,<br>women’s group, (2) traditional indigenous<br>organizations||Extent to which respondents<br>find it easy to approach (1) the<br>police, (2) a court<br>Extent to which respondents<br>feel that they are treated fairly<br>(1) by the police, (2) by the<br>court|Extent to which respondents<br>find it easy to approach (1)<br>the police, (2) a court<br>Extent to which respondents<br>feel that they are treated<br>fairly (1) by the police, (2)<br>by the court|
|||•<br>Perceived levels of respect with which members of<br>one caste/ethnicity are treated by members of other<br>castes/ethnicities<br>•<br>Perceived degree of security relative to other<br>castes/ethnicities<br>•<br>Perceived levels of opportunity for improvement<br>relative to other castes/ethnicities<br>•<br>Respondent is restricted from entering certain<br>public areas, such as village district office<br>•<br>Perceived levels of improvements in the status of<br>indigenous people’s rights since 1990||||

> 32 Asset indicators are listed in the shaded boxes, opportunity structure indicators in the non-shaded ones.

48
# **References:**

Aitchison, J. and Brown, J. A. C., 1957, _The Lognormal Distribution_ . Cambridge, UK: Cambridge University Press.

Burton, I., 1997, Vulnerability and Adaptive Response in the Context of Climate and Climate Change. _Climate Change_ , 36, 185-196.

Dixit, A. K. and Pindyck, R. S., 1994, _Investment Under Uncertainty_ . Princeton, N.J.: Princeton University Press.

Dynkin, E.B. (1965), _Markov Processes_ , Vol I. Berlin: Springer Verlag.

Fankhauser, S., Smith, J. B. and Tol, R. S. J., 1999, Weathering Climate Change: Some Simple Rules to Guide Adaptation Decisions. _Ecological Economics_ , 30, 67-78.

Fischer, A. C., 2000, Investment Under Uncertainty and Option Value in Environmental Economics. _Resource and Energy Economics_ , 22, 197-204.

Harrison, J. M., 1985, _Brownian Motion and Stochastic Flow Systems_ . New York: Wiley.

Heal, G. and Kriström, B., 2002, Uncertainty and Climate Change. _Environmental and Resource Economics, 22, 3-39._ IPCC, 2007, Inter-Relationships Between Adaptation and Mitigation. Chapter 18 in WGII Fourth Assessment Report, Inter-Governmental Panel on Climate Change.

Karatzas, I. and Shreve, S. E., 1988, _Brownian Motion and Stochastic Calculus_ . Berlin: Springer Verlag.

Øksendal, B., 2007, _Stochastic Differential Equations_ . 4<sup>th</sup> Edition. Berlin: Springer Verlag.

Pindyck, R. S., 2000, Irreversibilities and the Timing of Environmental Policy. _Resource and Energy Economics_ , 22, 223-259.

Pindyck, R. S., 2002, Optimal Timing Problems in Environmental Economics. _Journal of Economic Dynamics and Control_ , 26, 1677-1697.

Tol, R. S. J., 1998, Economic Aspects of Global Environmental Models. In J. C. van den Berg and M. W. Hofkes (eds.): _Theory and Implementation of Economic Models for Sustainable Development_ ; pp 277-286. Doordrecht: Kluwer.

Tol, R. S. J., 2005, Adaptation and Mitigation: Trade-Offs in Substance and Method. _Environmental Science and Policy_ , 8, 572-578.

Tol, R. S. J., Fankhauser, S., and Smith, J. B., 1998, The Scope for Adaptation to Climate Change: What can we Learn from the Impact Literature? _Global Environmental Change_ , 8, 109-123.

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# Measuring Legislative Predictability:

The Case of the Kingdom of Jordan and Implications for the MENA Region Mihály Fazekas<sup>1,2</sup> , Dominik Brenner<sup>1</sup> , and Peter Ladegaard<sup>3</sup> 1 Department of Public Policy, Central European University 2 Government Transparency Institute 3 World Bank JEL Classification: G28 Key Words: Regulatory Impact Assessment, Regulatory Governance and Transparency, Jordan, Regulatory Management, Legislative Predictability
For the “early” 19<sup>th</sup> century I use the “time window” of 1750-1880. The time window is wide. This is due to the fact that, in order to have a sufficiently comprehensive coverage of the world, I need to include both India and China. Now, the social table for the Moghul India is available for the year 1750, while the first social table for China is for the year 1880. The dates of these two social tables therefore frame our time window. In-between, from Europe, I have social tables from Old Castille 1752, France 1788, England and Wales 1801, the Netherlands 1808, and Kingdom of Naples 1811; from Latin America, I have Nueva Espaňa (Mexico) 1790, Chile 1861, Brazil 1872 and Peru 1876. Finally, there is an additional social table from Asia (Java 1880), and one from Africa (Maghreb 1880).

In reality, as this list makes clear, the future advanced (developed) countries have their social tables up to the early 19<sup>th</sup> century only. That was intentionally done in MLW paper, which dealt with pre-industrial inequality, so as to limit the investigation to not yet industrialized countries. As is conventionally believed, after the end of the Napoleonic wars, Great Britain, France, Belgium and the Netherlands were already engaged in industrialization. But for other countries, MLW collected social tables up to a much later date (since these countries were still preindustrial). In the context of this paper, it means that one can argue that treating the whole sample as giving a snapshot of pre-industrial global inequality, around early to mid 19<sup>th</sup> century, does not involve a significant bias. This is because GDP per capita of countries that enter our sample at later dates (the three Latin American countries, Maghreb, Java and China) registered no appreciable economic progress between the early 19<sup>th</sup> century and 1860-1880 when they enter the sample. According to Maddison (2004), China’s GDP per capita decreased from $PPP 600 in 1820 to $PPP 530 in 1870 (no datum for 1880 is given). For Indonesia, the change was from $PPP 612 in 1820 to $PPP 654 in 1880. For Brazil, it was from $PPP 647 in 1820 to $PPP 718 in 1870. Since most of global inequality after 1820 was driven by fast growth of the industrializing nations while economies and income distributions of other nations were basically stagnant between the early 19<sup>th</sup> century and 1870-1880 when they enter 5
The main clients for engineering consulting services are the public sector (water, highways, buildings, schools, hospitals), private investors (buildings, environmental studies, mega-projects in gas, coal, dams), large multinational or domestic firms, and donors.

# **3.4 IT Services**

The definition of this type of services can be somewhat difficult given the possible overlap between (i) the provision of post-sale and maintenance IT services for firms that sell hardware and/or software and (ii) the provision of customized IT solutions and systems for user firms. It is possible that given the embryonic stage of development of the IT sector in Mozambique, disentangling those two types of services is not relevant and moreover, the provision of services of type (ii) may still be rare.

Based on Infodev (2007) there are about 73 providers of IT services in Mozambique: 30 firms whose primary activity is the sale of hardware, 24 firms whose primary activity is the sale of software, and 18 firms whose primary activity is networking.<sup>13</sup> Based on the enterprise census data shown in Table 5, as of 2002 there were 50 firms providing ITrelated services in Mozambique, up from 50 firms in 2002. In this census, IT services cover programming and consulting, maintenance, equipment consulting, and other. The majority of IT services firms are Mozambican-owned.

There is no information on the market structure in IT services in revenue terms, but we can provide a sense of the distribution of firm sizes in the various segments in terms of their number of employees. Of the 30 firms whose primary activity is the sale of hardware, 6 are large (more than 30 employees), 10 are medium (6-29 employees), and 14 are small (1-5 employees). Of the 24 firms whose primary activity is the sale of software, only 1 is large, 7 are medium, and 16 are small. Of the 18 firms whose primary activity is networking, only 1 is large, 7 are medium, and 16 are small.

# **3.5 Medical Services**

Most of the medical services in Mozambique are provided by the public sector. Ferrinho and Omar (2006) provide a detailed description of both public and private providers of health services in Mozambique. Here we simply note that the distribution of private providers across types in 2006 is as shown in Table 7. Most of the public providers are domestic-owned with the exception of 3 foreign-owned clinics.

The demand for private medical services comes from the very small share of highincome individuals and households in Mozambique, including many expatriate professionals.

> 13 Note that we exclude from our discussion ISP providers and website providers also identified in Infodev (2007) as IT sector firms.

22