rafmacalaba/datause-encoder-data huggingface dataset:
we demonstrate the importance of history and of expectations in determining outcomes. Essentially, a city can be built in a manner which makes it difficult to attract tradable production. This might be a consequence of low (and self-fulfilling) expectations or of history.

While the paper is primarily theoretical, we start by outlining three features of African cities that are illuminated by the model. The first is African cities’ failure to create jobs in internationally tradable goods or service sectors. Gollin et al. (2016) investigate this, principally at the national level, establishing the adverse effect of natural resource sectors on manufacturing employment. Natural resource dependence is only part of the story, and there are also significant regional differences. Focusing on Africa, Jones (2016) compares manufacturing shares of GDP in African and non-African economies at different stages of urbanization. In non-African economies, the manufacturing share of GDP rises from 10% to nearly 20% as the urban population share rises to 60%, above which it falls back. In Africa, the manufacturing share remains flat (or somewhat falling) at around 10% of GDP through a cross-section of urbanization rates ranging from 10% to 70%.<sup>2</sup> A more urban focus can be derived by using spatially disaggregated IPUMS data.<sup>3</sup> These are sample data collected at the individual level, with selfdeclared sector of employment. Table 1 reports the share of employment declaring to be in manufacturing in areas classified as urban. The data are presented by city (selected by size within country) and by country (in India this is urban area by state, with separate city level data unavailable). While manufacturing is not synonymous with tradable goods, the data indicate clearly the extent to which Africa is different from other regions. Simple averages across countries suggest manufacturing shares of urban employment nearly three times greater in Asia than in Africa, and increasing through time, whereas Africa’s shares have declined slightly. Accra appears as the African city with the highest share of employment in manufacturing, at 14.9%, whereas shares in Asian cities rise well in excess of 30%.<sup>4</sup>

> 2 Jones (2016).  Manufacturing shares are predicted values from a quadratic function fitted to the cross section data.

> 3 - Integrated Public Use Microdata Series (iPums) available for download at: https://usa.ipums.org/usa <u>action/variables/group</u>

> 4 Ethiopian data dates from 1994; recent manufacturing investments suggest that it could now exceed this.

3
**Table 3.D. Directional accuracy: Pesaran and Timmerman (2009) test**

|**Commodit**<br>**Horizon**<br>**(months)**|**Consensus**<br>**forecasts**|**Futures**<br>**c**|**Bivariate**<br>**rrelations**|**BVAR**|**OEM**|**Machine**<br>**learnin**|
|---|---|---|---|---|---|---|
|**y**<br><br>Oil<br>3|1.7*|5.2***|5.2***|1.4†|3**|**g**<br>3.6***|
|(U.S. dollars per barrel)<br>6|2.6**|5.2***|4.3***|-0.1|2.3*|3.6***|
|<br>9|2.6**|4.7***|5.2***|-2.2|3**|2.9**|
|12|2.7**|4.7***|4.8***|-1.1|3**|1.7*|
|15<br>18|1.6†<br>3.1**|...<br>...|...<br>...|-1.3<br>0.4|3**<br>2.9**|1.5†<br>1.2|
|21|3.6***|...|...|0.7|2.9**|0.3|
|24|3.2***|...|...|0.7|2.1*|0.9|
|Aluminum<br>3|4***|2.4**|4***|3.1***|4***|5.2***|
|6|4***|3.3***|5.2***|3.9***|4***|5.6***|
|9<br>|3.6***<br>***|3.7***<br>***|4.4***<br>***|3.1***<br>*|4***<br>***|5.6***<br>***|
|12|4|3.3|4.8|2.3|3.5|5.2|
|15|3.6***|...|...|3.1***|3.5***|4.8***|
|18|3.6***|...|...|3.5***|3.5***|4.8***|
|21|4***|...|...|3.9***|3.5***|4.8***|
|24|3.6***|...|...|3.4***|2.3*|3.6***|
|Copper<br>3|0.5|4.1***|5.2***|0|3**|3.6***|
|6|0.5|4.1***|4.8***|1.1|3.5***|3.3***|
|9|0.4|4.1***|4.8***|0.3|3.5***|3**|
|12|0.1|4.1***|4.8***|-1|2.8**|2.5**|
|15|0.4|...|...|0.4|2.8**|-0.1|
|18|0.1|...|...|0.8|2.8**|-0.8|
|21|0.8|...|...|0|2.8**|-0.9|
|24|1.7*|...|...|1.2|2.1*|-0.9|
|Lead<br>3|0.9|3.7***|4.8***|0.4|2.3*|2.5**|
|6|1.3†|3.7***|4***|0|1.3†|2.9**|
|9|0.9|3.7***|3.6***|-0.4|2.3*|2.9**|
|12|0.2|3.3***|4***|0|1.8*|2.9**|
|15|-0.2|...|...|0|1.7*|1.5†|
|18|0.2|...|...|1.2|1.7*|-0.5|
|21|1.7*|...|...|0.4|1.7*|-1|
|24|-0.1|...|...|1.2|0.7|-0.6|
|Nickel<br>3|2.5**|4.8***|5.2***|0|3.5***|4.1***|
|6|2.5**|4.8***|4.8***|0.8|2.9**|4.1***|
|9|2.5**|4.8***|4.8***|1.2|2.9**|2.5**|
|12|0.9|4.8***|4.8***|0.4|2.9**|2.5**|
|15|0.9|...|...|0.4|2.3*|0.9|
|18|0.5|...|...|1.2|2.3*|0.1|
|21|0.9|...|...|0.8|2.3*|0.1|
|24|0.9|...|...|1.9*|3.5***|-0.3|
|Tin<br>3|2.1*|4.2***|5.6***|2*|4***|4.2***|
|6|2.2*|4.5***|5.6***|2*|3.4***|2.8**|
|9<br>12|1.8*<br>2.3*|4.5***<br>4.8***|4.4***<br>4.8***|1.6†<br>0.8|3.4***<br>3.4***|2.6**<br>1.5†|
|15<br>|2.6**<br>|...|...|2*<br>|3.4***<br>|0.3<br>|
|18|2.3*|...|...|2*|2.7**|-2.1|
|21|2.6**|...|...|1.6†|-0.4|-2.9|
|24|1.8*|...|...|2.4**|2.8**|-2.1|
|Zinc<br>3|2.4**|4.1***|5.6***|0.1|3**|3**|
|6|2.1*|4.1***|4.7***|0|3.5***|2.7**|
|9|2.4**|4.1***|4.3***|-0.8|4***|2.7**|
|12|24**|38***|43***|-02|4***|21*|
|15|.<br>2.4**|.<br>...|.<br>...|.<br>0|4***|.<br>2.1*|
|18|2.5**|...|...|0.9|4***|2.7**|
|21|3**|...|...|0.9|4***|2.5**|
|24|1|...|...|1.8*|2*|2.1*|

_Source_ : World Bank.

_Note_ : Test statistics indicate statistically significantly accurate forecast directions for the model indicated in columns. *** indicates statistically significantly more accurate model in the row at the 0.1 percent significance level, ** at the 1 percent level, * at the 5 percent level, and † at the 10 percent level.

37
_Uganda’s roads are on average in better condition compared to several East African countries_ (figure 9) _._ Around 87 percent of Ugandan roads are in good or fair condition compared to only 72 percent of the roads in its low-income peers (see table 4 and figure 9). And when compared to its East African peers, Uganda boasts some of the best quality roads—although Uganda’s share of roads in good condition is relatively low, its share of roads in fair condition is exceptionally high.

**Figure 9. Main road network conditions in East Sub-Saharan Africa, 2005**

<!-- Start of picture text --> Uganda<br>Tanzania<br>Ethiopia<br>Kenya<br>Sudan<br>Eritrea<br>0% 20% 40% 60% 80% 100%<br>Percentage Main Road Network Length<br>Good Fair Poor<br><!-- End of picture text --> _Source:_ AICD Road Sector Database on Southern Sub-Saharan African countries.

_A number of recent efforts have aimed at strengthening road sector institutions_ . The Ministry of Works and Transport, which is responsible for policy formulation, leads and periodically formulates and updates the government’s National Transport Policy and Strategy (NTPS), which includes a Road Sector Development Program (RSDP). Following a long-term transitional arrangement that created the Road Agency in Formation Unit (RAFU), which is mostly externally funded, the government established the Uganda National Roads Authority (UNRA), which become operational in 2008. The UNRA is responsible for executing the road development and maintenance plans of national roads as well as projects financed by donors and development agencies. The Uganda Road Fund (URF) was legally established in 2008 as a Second Generation Road Fund and become operational only in 2010. The URF has private participation in its board and has been given the mandate to provide funding for maintenance on all roads. The legislation makes provision for road user charges including fuel levy, license fees, and others. However, the only source of funds applied to date have been appropriations by parliament from Ministry of Finance consolidated funds. This contravenes the principle of the URF and does not satisfy the intention of the Act. The finances applied to maintenance of the roads are not clearly related to road usage but are decided arbitrarily within the normal budget process.

# **Challenges**

_Over half the district (that is, rural or feeder) roads are in poor condition._ District roads connect rural areas to markets, health centers, educational <!-- Start of picture text --> 0.40<br>0.30<br>0.20<br>0.10<br>19<br>0.00<br>Uganda Rwanda Kenya Tanzania Ethiopia<br>Figure 10. Uganda significantly underspends on road maintenance, 2005<br>Source: AICD calculations. Implicit Levy Optimal Levy<br>US dollars per litre<br><!-- End of picture text -->
# **1. Introduction**

Sovereignty implies policy autonomy. Demands for policy autonomy are frequent and can produce conflict with the central government. Unmet demand for autonomy is behind more than a third of all civil wars since 1945. These wars, and many more lower-level conflicts, both violent and nonviolent, are fought over a wide spectrum of policy autonomy demands, ranging from demands for fiscal policy autonomy to movements for national independence. The presence of sovereignty demand of any kind reveals conflict between the state and a geographically concentrated minority group (usually an ethnic minority). This paper asks if economic factors matter for conflicts over sovereignty in economically or politically decentralized states.

We argue that economic factors matter because of a basic tradeoff between income and sovereignty. Sovereignty, or autonomy over policies that can affect the production of groupspecific public goods, is itself a public good that is costly to produce. A group that is sovereign in a given geographical region can provide citizenship, education, security, and other goods to its members. The costs associated with producing those goods should moderate the scope and intensity of demands for sovereignty. But the economic tradeoff is not the only determinant of sovereignty demand. We try to understand the role of economic factors while taking into account demographic and other determinants of sovereignty demand.

The effect of income on sovereignty demand is still an open question in the literature. Some political scientists (most prominently, Horowitz 1985) advance a social-psychological perspective, arguing that poorer or more ―backward‖ groups are more likely to want to secede in an effort to increase their group’s social status. By contrast, economists and rational choice sociologists (Spolaore 2008; Hechter 2001) concentrating on the economic costs of sovereignty posit the opposite relationship, since groups or regions with higher income should be in a better position to provide public goods. Our approach is consistent with this economic perspective and considers demands for sovereignty as bids for a measure of decentralization. Thus we draw on the intuition of earlier economic studies of separatism in which the ability to ―exit‖ is partly a 2
other as possible. In view of this, we conduct our analysis using a range of treatment and comparison groups. The results (discussed in section 5) appear to be fairly robust to the alternative definitions of the groups used. However, the main focus is on the model where the control group is chosen to be children aged 14 years. This is so because in terms of both supply and demand, labor of 13 and 14 year olds can probably be considered close substitutes.<sup>11</sup> The models given in equation (5) and (6) are estimated using different sub-samples from the data set, according to the control and treatment groups used.<sup>12</sup> In addition to the above specification issues, concern is that since the data used is from household survey, any introduction of a minimum age law might prompt households to systematically misreport the age of working children to avoid children from being out of work.<sup>13</sup> We checked the data set for any systematic decrease in children of age 13 and an increase in children age 14 after the introduction of the law. As can be seen in figure 3, the proportion of children for age 10-14 does not show any systematic shift over the months, in particular over the pre and post intervention periods.

# **4 - Results**

# **_4.1 - Comparing children’s and adult employment over January-April and May-December_**

# **_1991_**

> 11 Our analysis uses other control groups as robustness checks to identify if there has been a similar systematic decrease in other age groups.

> 12 All of the regression results are clustered by province age and time (months or years).

> 13 The age comes from actual reported age in the survey and not from the year of birth.

17
# **8. References**

Aterido, Reyes, Mary Hallward‐Driemeier, Carmen Pages (2009), “Big Constraints to Small Firms’ Growth? Business Environment and Employment Growth across Firms,” World Bank Policy Research Working Paper No: 5032.

Beck, Thorsten, Asli Demirgüç‐Kunt and Vojislav Maksimovic. 2005. “Financial and Legal Constraints to Growth: Does Firm Size Matter?” Journal of Finance 60(1): 131‐177 Cooley, Thomas F. and Vincenzo Quadrini (2001), "Financial Markets and Firm Dynamics," American Economic Review, 91(5):1287‐1310.

Dunne, Timothy, Mark J. Roberts, Larry Samuelson (1989),”The Growth and Failure of U.S. Manufacturing Plants,” The Quarterly Journal of Economics, Vol. 104, No.4, pp.671‐698.

Evans, David S. (1987a), “The Relationship between Firm Growth, Size, and Age: Estimates for 100 Manufacturing Industries,” Journal of Industrial Economics, Vol. 35, pp.567‐82.

____ (1987b), “Tests of Alternative Theories of Firm Growth,” Journal of Political Economy, Vol. 95, pp.657‐74 Gauthier, Bernard and Mark Gersovitz (1997), “Revenue Erosion through Exemption and Evasion in Cameroon 1993,” Journal of Public Economics, Vol.64, pp.407‐24.

Jovanovic, Boyan (1982), "Selection and the Evolution of Industry," Econometrica, 50(7): 649‐67 Klepper, Steven and Peter Thompson (2007), "Submarkets and the Evolution of Market Structure," Rand Journal of Economics, 34(4):862‐888.

Klette, Tor J, and Samuel S. Kortum (2004), "Innovating Firms and Aggregate Innovation," Journal of Political Economy, 112(5):986‐1018.

Sleuwaegen, Leo and Micheline Goedhuys (2002), ”Growth of Firms in Developing Countries, Evidence from Cote d’Ivoire,” Journal of Development Economics, Vol. 68, pp.117‐35.

Van Biesebroeck, Johannes (2005), “Firm size matters: growth and productivity growth in African manufacturing,” Economic Development and Cultural Change, Vol. 53(3), pp.545– 83.

World Bank (2010), _Investment Climate Assessment of Turkey._ Washington DC.

11
This paper introduces a new cross-country data set collected through a survey of regulators at the country level, and expands on the existing literature to analyze access to finance by SMEs. The paper draws on the report and database of _Financial Access 2010_ (CGAP and WBG, 2010), the second in a series of annual reports by CGAP and the World Bank Group (WBG) on the financial inclusion agenda based on survey data collected from financial regulators around the world. More specifically, this new data set collects information on the level of lending to SMEs, the definition of SMEs, and country-level initiatives through which lending to SMEs is monitored. It is also the first attempt at collecting global comparable SME statistics.

This new database collects data from financial regulators, mostly central banks and bank superintendents, around the world. Although regulators collect a wide array of portfolio data from regulated financial institutions, SME lending data are not necessarily among the regularly collected statistics, and some parts of SME lending are done through unregulated institutions. Nevertheless, according to _Financial Access 2010_ , the experience of some countries shows that collection of data on lending to SMEs is possible on a monthly basis with a clear SME definition and sound reporting requirements.

The analysis of the new data indicates two main findings. First, many regulators collect data on SME financing; however, there is no unique definition of an SME. At the same time, the results indicate that differences in definitions across countries are not statistically significant in explaining the differences in SME lending volumes. Second, given this, the paper goes on to estimate the global SME lending volume at $10 trillion, roughly two-thirds of the current size of the US economy. The bulk of this volume, 70 percent, is in high-income OECD countries. On average, SME loans constitute 13 percent of GDP in developed countries and 3 percent in developing countries.

The rest of the paper is organized as follows. Section 2 introduces the dataset. Econometric analyses are reported in section 3, and predictions of global SME lending volume are discussed in section 4. Section 5 concludes.

5
**_Famiy_** _Planning Success in 21o Cities in Zaire_

_26_

## B. P!unIly Planning in Kanariga, 1987-90

# _1._ **_JImionW_** _of Ex,stlng Service Delivery_

Despite the high infant mortality, high fertility, and high fertility ideals described above, these were not the major program barriers in Kananga. Family planning had never taken hold because of the ineffectiveness and inertia of existing family planning programs.

Prior to the operations research project (referred to herein as the "Kananga Project") which began in 1987, there were a few health centers in the area offering limited family planning services. The national PSND project-which had identified Kananga as one of its target citieshad trained a few nurses from these centers and supplied them with some contraceptives, but couid claim credit for no new family planning activities in Kananga. AZBEF, the IPPF affiliate, had established a regional committee, but the group had not met for several years.

One of the main service delivery problems in Kananga was obtaining contraceptives. The AZBEF office in Kinshasa had been the major contraceptive supplier in Zaire for 15 years. They usually had contraceptives but did not deliver them; someone had to travel to Kinshasa (800 kilometers away), go to the warehouse, purchase and carry away the supplies, and arrange air transport to Kananga.

In the mid-1980s, PSND became the main supplier of contraceptives in Zaire; the project had not only contraceptives but also the means to ship them to the interior. PSND administrative procedures, however, were cumbersome and tended to discourage the Kananga health centers from obtaining their supplies through this channel. Each individual health center had to sign an agreement of cooperation to obtain an initial supply of contraceptives; they then had to submit quarterly service statistics to the Kinshasa office (even though there was no reliable mail or courier service). The national office then began a long bureaucratic procedure to "replace" the contraceptives as stocLc were drawn down. At best, these supplies arrived in Kananga 6-12 months after being requested.

These organizational difficulties remained the same throughout the period 1987-90, while the Kananga Project was active. The PSND continued to train a few Kananga nurses each year in a month-long course held several hundred kilometers away. It sent contraceptives to replace the ones used by some of the Kananga providers, but PSND did not simplify its contraceptive supply procedures. The PSND did appoint a Regional Coordinator (nurse-supervisor) to cover the two central regions in 1989, but this man had no car or motorcycle and rarely visited Kananga.

# _2. Projed_ **_Activilies_**

The Kananga Project began in 1987. It was carried out by the Institut M6dical Chretien du Kasai (IMCK, Christian Medical Institute of the Kasai) with support from USAID through a grant to the Population Council. The IMCK was a 35-year-old Zairian training institution, a pioneer in preventive health programs; IMCK's hospital and outpatient polyclinic offered primary, secondary and tertiary health care.
TABLE A9: Impacts of text messages on socio-emotional outcomes for children aged 1-3 years

||(1)<br>Behavioral index|(2)<br>Control|(3)<br>Affect|(4)<br>Ability|
|---|---|---|---|---|
|Panel A: Equation 1|||||
|ITT|0.02|-0.14|0.04|0.09|
||(0.08)|(0.09)|(0.09)|(0.09)|
|Observations|717|717|717|717|
|Panel B: Equation 2|||||
|Nutrition & Stimulation|- 0.01|-0.16|0.01|0.10|
||(0.11)|(0.11)|(0.11)|(0.11)|
|Nutrition|0.07|-0.27<sup>_∗∗_</sup>|0.11|0.22<sup>_∗∗_</sup>|
||(0.11)|(0.11)|(0.11)|(0.11)|
|Stimulation|0.02|0.05|-0.00|0.01|
||(0.12)|(0.11)|(0.11)|(0.11)|
|Observations|717|717|717|717|
|Panel C: Equation 3|||||
|ITT Leader|0.02|-0.01|0.04|0.00|
||(0.10)|(0.09)|(0.09)|(0.11)|
|Observations|577|577|577|577|

_Note:_ The dependent variable in the first column is the first principal component of 3 sub-scales of the early childhood behavior questionnaire: effortful control (column 2), negative affectivity (column 3) and socialibility (or extraversion, column 4). Controls in panel A and B as in Table 3, controls in panel C as in Table 4. Standard errors (in parentheses).<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 57
# **I. Introduction**

Egypt achieved rapid growth during 2005-2008. In this period reforms and economic shocks produced gains for some groups, and losses for others. The main objective of this paper is to analyze to what extent the rapid growth experienced in Egypt between 2005 and 2008 has been pro-poor. Pro-poor growth is about changing the distribution of relative incomes through the growth process to favour the poor. This paper will refer to the “relative” definition of pro-poor growth when discussing to what extent growth benefitted the poor in Egypt between 2005 and 2008. There are two definitions for measuring pro-poor growth used in recent literature and policy-oriented discussions. The first and relative definition compares changes in the incomes of the _poor_ with changes in the incomes of the _non-poor_ . Using this definition, growth is pro-poor when the distributional shifts accompanying growth favour the poor. This is the definition that will be used throughout this paper. The second and absolute definition considers growth to be pro-poor if and only if poor people benefit in absolute terms, as reflected in some agreed measure of poverty (Ravallion and Chen, 2003; Kraay, 2003). In this sense, growth in Egypt was indeed benefitting the poor between 2005 and 2008, as the country achieved a substantial reduction in the poverty headcount (from 23.4 to 18.9)<sup>2</sup> .

The paper presents a detailed picture of how different groups above and below the poverty line were affected by this period of positive growth. This analysis is based on the Household Income, Expenditure and Consumption Panel Survey (HIECPS) conducted by CAPMAS (Egypt’s national statistical agency) to trace household consumption and living standards over 2005-2008. The survey is the first large scale data collection in Egypt to monitor the situation of same households over extended period of time.

The study compares Growth Incidence Curves (GIC) based on a cross section of data with GICs based on the panel data. Panel data follows the same households over time (unlike the cross section which has an anonymous approach) and allows the analysis of factors that affects household’s welfare over time. It captures the dynamic aspects of poverty and growth and it is therefore an essential tool in assessing social mobility. This study finds indeed opposite results in terms of pro-poor growth between cross-section and longitudinal data. It therefore attempts to identify the main factors behind these apparently contradictory results.

This paper is divided in seven sections. The next section provides a brief review of the literature, and the subsequent (third) section contains description of data and methodologies used. Main facts about changes

> 2 These data refer to the panel component of the Household Income, Expenditure and Consumption Panel Survey (HIECPS), i.e. they are comparing data from February 2005 with February 2008.

2