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

Sustained economic growth is always accompanied by urbanization. But in Africa, urbanization occurred without growth. Is the implication that Africa's urbanization process was distorted? Or rather that urbanization is not always accompanied by sustained growth. And that in economic downturns, the poor and the migrants don't necessarily flock back to rural areas.

The initial motivation for this paper was to understand why once again, Africa appeared to be "different." To do so, we investigate the determinants of urbanization across countries, over the last 40 years. Most analysis of this kind focus on individual's decision to migrate. Here, we rely on macro data and cross-country comparisons instead. This paper then is about the determinants of urbanization everywhere. It is not limited to Africa, although it does focus on trying to answer the puzzle raised by the phenomenon of urbanization without growth in Africa.

Why this interest in urbanization at a macro level? A central hypothesis of the paper is that individuals move (with varying degrees of ease) in response to economic incentives and follow economic opportunities. If location incentives are distorted, so presumably is the growth process. Distorted location incentives may also result in higher than necessary social and environmental costs, that are not offset by the increase in productivity and wealth that usually accompanies urbanization.

An additional objective of the paper is to show that urbanization is part and parcel of the structural changes that accompany economic development. And that there is very little point in trying to stem it. Where governments have tried, they have generally failed (China, Soviet Union, Vietnam), creating substantial pent-up demand that led to brutal adjustments (colonial Africa, Trujillo's Dominican Republic) and generally harmed the poorest (Nyerere's Tanzania.) Understanding the dynamics of urbanization can help policy makers mitigate its costs rather than worsen them. In many cases, slums are created and remain locus of environmental and human disasters for decades because of the myth that slums dwellers are there temporarily and will return to the country. Fortunately, in most cases, the income of the slum dwellers eventually increase with growth, and the slums are upgraded and turned into middle class neighborhood.

But the growth and upgrading process is slow. And it can fail to happen. The average African country's urban population grew by 5.2% per annum over the 1970-95 period, while its GDP per capita was falling at an annual rate of 0.66%. This implies that urbanization occurred without generating the resources (public or private) and employment opportunities to accommodate this surge in urban population. Today, up to two thirds of African urban dwellers live in informal settlements with inadequate transport, water, sanitation, electricity, and health services. Housing finance systems are non-existent or limited to upper income sectors. Crime is a major problem in many African cities, and worsening with increased poverty and deteriorating living conditions.

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Note that, for developing countries, the best indicator of ICB governance seems to be on ICB practice (usually measured by turnover of ICB governors) rather than on the quality of the legal framework - which is the best indicator for OECD countries. (See Section 2.5 for further discussion.)

- **_2) Recent developments in the literature on telecommunications reform and regulation.  This is increasingly showing that independent regulatory agencies are associated in developing countries with higher investment (eg mainline penetration rates) and faster productivity growth either directly and/or by augmenting the effects of liberalization/competition and privatization._**

In this context, Gutierrez (2003) is important in demonstrating the contribution to effective estimation of the effects of regulation that can be made by using an index of regulatory indicators. (See Section 2.4 for further discussion.)

- **_3) The results from studies of electricity and Latin American concession contracts provide some preliminary support for the effectiveness or regulatory agencies in promoting investment and/or preventing major concession contract problems._**

The position is similar to that of investigating the effects of telecom regulation 3-4 years ago when developing country telecom regulators were, on average, still very young and when there was little purpose-designed data with which to test the impact of regulation on industry outcomes. For electricity, the lack of available and comparable data on investment in transmission and distribution is a significant problem.

Of course, having an effective regulator may be necessary but is by no means sufficient to generate sustained growth and this may help explain why the results have, so far, been a lot less than conclusive. Besides an effective regulatory agency, the following issues arise

- A well-designed industry structure is crucial – good regulation cannot overcome bad design of industry and market structures;

- The industry and the regulatory arrangements must be based within an effective governance framework – effective regulation cannot take root in corrupt systems or ones where the law courts are unreliable; and

- Commercialization has to be accepted as the basis of utility service provision, including pricing which avoids large scale implicit subsidy of prices to final users – the political economy of pricing reform has destroyed many utility reforms and new regulatory systems and threatens many more.  This has been and remains a serious problem in electricity but seems to be worse in water and perhaps (passenger) railways.

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# Figure 2.6. Sovereign Spreads: The Role of Global Factors a. Contribution of First Principal Component to Overall Variation of Spreads

(Percent) <!-- Start of picture text --> 100%<br>90%-<br>80% -<br>70% -<br>60% -<br>10 in (0 (0 t~- t~- co co 0) a) 0) 0 - C- N<br>0 , . . . . . . . 9 9 . 9 9 9<br><!-- End of picture text --> b. Contribution of First Principal Component to Variation in Individual Country Spreads (Percent) <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>] ~~~~ARGENTINA<br>20% -,i&-*BRAZIL<br>-'&MEXICO<br>0%<br>10 10 a0 (0 ° C,D 0, CD - C0 $ 0)P 0 9 9 0o o o<br>1= > n : o > cmf c c f i<br>(U 0 S u aQ~ .<br>7~ 7~ Z U) IL .. 7 a 0 i < 7 Z U )O<br><!-- End of picture text --> Source: Fiess (2002) 14
Note: 1) according to WTO rules, three methodologies can be used in order to estimate normal value: domestic price, third country price and constructed value; 2) DDA/DM measures the ratio between the antidumping duty applied and the dumping margin; and 3) na = not available.

Table 8 shows the reasons why a negative determination was made in some antidumping investigations. Likely enough, in view of the experience of other countries – the United States, for example (Finger & Murray, 1993, and Niels, 2000) – approximately 34.9% of the cases were dismissed because no injury resulting from dumping was found, whereas dumping could not be evidenced in 24.4% of all actions. Two cases were turned down as a result of direct government intervention, in order to avoid an impact on domestic prices. In the other eight cases, other measures were adopted to protect the affected industries.

Table 8 Rejected Cases, Classified by Motives

|Description|No of cases|(%)|
|---|---|---|
|No injury|30|34.9|
|No dumping|21|24.4|
|No causation|9|10.5|
|Government interest|2|2.3|
|Requested by petitioner|7|8.1|
|Other support measures|8|9.3|
|Other|9|10.5|
|Total|86|100.0|

Source: Customs, Compendium of Laws on Foreign Trade. Author's calculations.

To find out whether the antidumping measures adopted had been used to support the monopoly power of domestic enterprises, the cases were distributed into groups according to the number of manufacturing companies: one single firm (monopoly), two to five (oligopoly) and more than five (competition). Given the highly concentrated industrial market structure, the majority of actions involved the first two categories of firms. Despite of that, the proportion of accepted cases (82.4%) is almost equivalent to that of the rejected ones (88.4%).

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||(1.303)|(1.879)|(1.952)|(3.038)|(1.298)|(1.875)|
|---|---|---|---|---|---|---|
|Landlocked-ness|-0.347*|-0.274|0.0898|0.296|-0.361*|-0.294|
||(0.193)|(0.240)|(0.272)|(0.396)|(0.192)|(0.240)|
|Log land area (Ha.)|-0.0234|-0.0460|0.0708|0.130|-0.0243|-0.0475|
||(0.0542)|(0.0790)|(0.0818)|(0.129)|(0.0540)|(0.0789)|
|Terrain ruggedness index|4.97e-05<br>(0.00046)|1.17e-05<br>(0.00048)|0.000448<br>(0.000663)|0.000719<br>(0.00068)|1.30e-05<br>(0.000457)|-2.91e-05<br>(0.000477)|
|Log GDP per capita at<br>|-0.0873|-0.0573|-0.116|0.0577|-0.0888|-0.0637|
|appraisal|(0.0800)|(0.0868)|(0.124)|(0.135)|(0.0795)|(0.0863)|
|Theory of change|0.163***|0.161**|0.172*|0.158|0.159***|0.157**|
||(0.0608)|(0.0674)|(0.0946)|(0.104)|(0.0605)|(0.0671)|
|Private sector consultation|0.203*|0.320**|0.315*|0.412*|0.185|0.303**|
||(0.123)|(0.143)|(0.185)|(0.214)|(0.122)|(0.142)|
|Degree of private sector<br>|-0.435***|-0.383***|-0.692***|-0.616***|-0.431***|-0.380***|
|involvement|(0.128)|(0.128)|(0.196)|(0.196)|(0.127)|(0.128)|
|Degree of connectivity<br>i|-0.0571|-0.112|-0.0665|-0.191*|-0.0583|-0.112|
|ncrease|(0.0687)|(0.0753)|(0.103)|(0.115)|(0.0684)|(0.0750)|
|Investment at border|-0.0501|-0.0141|-0.494|-0.496|-0.0378|0.00369|
||(0.248)|(0.270)|(0.397)|(0.426)|(0.247)|(0.269)|
|Geographic scope|-0.126|-0.0964|-0.117|-0.149|-0.126|-0.0945|
||(0.0878)|(0.0932)|(0.135)|(0.140)|(0.0875)|(0.0928)|
|Openness at approval|0.0482|0.0288|1.111*|1.060|0.0489|0.0415|
||(0.386)|(0.453)|(0.603)|(0.788)|(0.384)|(0.451)|
|Any DPO in past 5 years||0.0676||0.921***||0.0541|
|||(0.253)||(0.325)||(0.252)|
|DPO: Industry-trade-<br>||-0.0955||-0.460||-0.0636|
|services||(0.335)||(0.521)||(0.333)|
|DPO: Public<br>iii||-0.213||-0.469||-0.234|
|admnstraton||(0.482)||(0.703)||(0.481)|
|DPO: Transport||-0.173||-0.132||-0.168|
|||(0.171)||(0.269)||(0.170)|
|Observations|16,248|16,248|7,165|7,165|16,166|16,166|
|R-squared|0.326|0.342|0.353|0.375|0.328|0.343|
||||Exclude<br>20% at the|Exclude<br>20% at the|Exclude if<br>Human<br>Settlement|Exclude if<br>Human<br>Settlement|
|Sample|All|All|nodes|nodes|Index > 2.5|Index > 2.5|
|Specification|No DPOs|With DPOs|No DPOs|With DPOs|No DPOs|With DPOs|

_Note:_ Robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

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existence of an “Africa premium”—equivalent to roughly 2 rating notches or 200 basis points— even for relatively better-performing countries with above-median growth and low aiddependence (Gelb, Ramachandran and Turner 2006).

Figure 5. Launch spreads decline with an increase in sovereign rating* <!-- Start of picture text --> 700 Spread (basis points) 2003<br>600<br>500<br>Investment grade premium 2004<br>(basis points)<br>400<br> 2003 191<br> 2004 122 2005<br>300<br> 2005 92<br>2007<br> 2006 64<br>200<br> 2007 67 2006<br>100<br>0<br>Sovereign rating<br>AA AA- A+ A A- BBB+ BBB BBB- BB+ BB BB- B+ B B- CCC+<br><!-- End of picture text -->

* Assuming $100 million sovereign bond issue with a 7 years tenor. Borrowing costs have fallen steadily since 2003 with a slight reversal more recently reflecting changes in the global liquidity situation. The investment grade premium indicates the rise in spreads when rating falls below BBB-. The relationship between sovereign ratings and spreads is based on the following regression: log(launch spread) = 2.58 - 1.2 investment grade dummy + 0.15 sovereign rating + 0.23 log(issue size) + 0.03 maturity - 0.44 year 2004 dummy - 0.73 year 2005 dummy - 1.10 year 2006 dummy - 1.05 year 2007 dummy; N = 200; Adjusted R<sup>2</sup> = 0.70. All the coefficients were significant at 5 percent. A lower numeric value of the sovereign rating indicates a better rating.

_Source:_ Ratha, De and Mohapatra (2007) based on Bondware and Standard and Poor’s.

At the sub-sovereign level, few firms in Sub-Saharan Africa outside of South Africa are rated by the three international rating agencies.<sup>36</sup> Several firms are highly creditworthy in local currency terms but they are constrained by either an absent or low foreign currency sovereign rating.

Model-based estimates indicate that several unrated Sub-Saharan African countries would be rated higher than currently believed. Drawing on the well-established literature on the empirical determinants of sovereign ratings, Ratha, De, and Mohapatra (2007) find that the

> 36 Only 5 banks in all of Sub-Saharan Africa excluding South Africa (4 in Nigeria and 1 in Mauritius) were in Standard and Poor’s global debt issuers list. In contrast, South Africa had nearly 30 firms and banks in the list.

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to become what LSMS has become for household surveys (that is, an excellent source of data useful for answering a range of questions across countries), it may be worthwhile to take into account the observations made in this paper about data and measurement issues.

Many questions about collecting firm data remain unresolved: How do we check for data quality? How do we know whether the numbers come from a false accounting book? What are the best ways to ask questions? How important is survivor bias and recollection errors? Should we pay interviewees based on the percentage of missing information (Philipson, 1997)?

Our analysis however offers a few important lessons:

1. Data issues and hypothesis testing:

- If possible, use panel data.

- Have enough information on productivity to estimate a production funct'ion.

- 3 Avoid the paradigm of "list the severity of the obstacle/problem on a scale of 1 to 5." Instead, ask for data on specific dimensions of the problem that will shed light on alternative hypotheses and policy recommendations.

- Pick particular disaggregated industries, and sample those industries in each survey.

- Identify the most important policy interventions of interest, and consider how you will empirically identify that change by picking useful instruments.

2. Questionnaire Design:

- Include only one idea or dimension per question. Do not ask in one question about, for example, the "quality, integrity, and efficiency" of services.

- Consider the costs and benefits of numeric versus adjectival scales. Scales in which each point is labeled may be more precise than numeric scales in which only the endpoints are labeled. On the other hand, responses are very sensitive to the exact adjective chosen, and it may be impossible to translate adjectives precisely across languages, making it impossible to compare responses across countries.

- Recognize that the share of respondents expressing opinions will be biased upwards if the survey does not include a middle (i.e., "indifferent" or "don't know") category, and biased downward if the survey does include the middle category.

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rely on FDIs alone as a source of new knowledge for initiating the new industry.

In addition, the results of international comparative study conducted by Eifert, Gelb, and Ramachandran (2008) indicates that firms in SSA suffer from the poor quality of basic infrastructure, such as roads, electricity, and communication networks. Thus, basic infrastructure should be provided to areas designated for industries in or near each major city. Also, the central government or local governments can assist the formation of industrial clusters by zoning areas of land for light manufacturing.<sup>14</sup> In so doing, considerable attention should be given to the prevention of fraud and corruption.

# **III. Promotion of Market Transactions**

In cases 1 to 10 in Table 1, the pioneer of a new industry exerted great efforts to find new production methods, sources of materials, and marketing channels. Once the new business was established, the pioneer received sizable entrepreneurial profits, despite the low quality of his or her products, because of the large demand for inexpensive products from poor domestic consumers. This profit attracted a swarm of imitators to the industry. Many of them were spin-offs, i.e., the former employees of the pioneer, and produced the same low-quality products by using the same low-quality inputs and sold their products on the same local markets as the pioneer. In this way, an industrial cluster is formed, as illustrated in Figure 1. Almost the same path was followed by industries in which the initiation of the industry was based on the technology brought by FDIs, SOEs, and international organizations (cases 11 to 17 and

> 14 For example, a part of the Kariobangi area in Nairobi was designated by the local government as an area for artisans in the late 1980s.  The skilled workers who lost jobs at large factories as a consequence of the Structural Adjustment Program cleared the area to construct roads and to start their own businesses in the area, which is now a cluster of metalwork enterprises.  See Sonobe, Akoten, and Otsuka (2011) for details.

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countries. Almost all countries experience declines in aggregate demand for maize imports. However, since a country’s decision to import Tanzanian maize depends on both the substitution and the expansion effect in equation 1, a key factor is the change in relative prices of maize from Tanzania versus other exporters. For example, in the case of Tanzanian maize exports to Latin America and the Caribbean – an extremely minor destination for Tanzanian maize exports in the benchmark database – the substitution effect is positive and dominates the demand effect, and Latin America increases its imports of Tanzanian maize.

Let us now turn to the impacts of the Export Restriction Regime – reported in the second group of columns in Table 4 as well as the price effects reported in Table 5.

In the years when production is above trend and an export restriction is in place, the maize price may drop below the price that would have prevailed if there was no restriction. This can be seen in column III of Table 5. In the case of the years with positive shocks to Tanzanian maize production (Scenarios 1995 and 1971), the domestic price of maize falls even more under the export restriction. With the export restriction in place, no increases in exports are possible (columns VI and VII in Table 4). In the case of Scenario 1982, when Tanzanian maize production was below-trend, and maize exports decreased for all countries (since the modeling of the restriction allows for decreases from 2001 benchmark flows).

The intuition behind these results can be illustrated by Figure 1, which considers the case of a simple net-trade model (not modeled in this paper) and the strong restriction of an export ban. In the base period, domestic grain demand and domestic grain supply are characterized by D and S0. If the domestic price of grain is P0 then the quantity demanded is Q0, with there being no imports or exports of grain. PM and PX are the import and export parity prices respectively, within which prices normally vary in an open economy. In a small open economy, if the maize supply increases or decreases, then in the short run, the impact on price will be forced to remain within the import parity upper bound and the export parity lower bound. For example, if there is a drought that reduces supply, then the grain price will rise and consumption would fall. However, the price increase would not be greater than the import parity price. Similarly, in the event of a large increase in grain supply – due to a year of exceptionally good weather, for example – then the grain price cannot decrease below that of the export parity price. If there is a year of favorable climate, the supply schedule shifts outwards, from S0 to S’. Domestic consumers now demand Q’ grain at price PX, and the country exports a quantity of Grains equal to Q’’ minus Q’. However, in the presence of an export ban, the price would continue to fall to PEB. This 18
co-nationals with a focus on high-income countries. It has been widely documented that the forcibly displaced seek a settlement in communities where there already is a sizeable population that shares common social attributes such as religion, ethnicity, or language. This finding is quite general and has been documented in both developed countries and developing countries. Based on the work of Altonji and Card (1991); Card (2001), many authors have used that feature to predict where refugees and IDPs would settle either across countries or within countries (Altındag et al., 2020; Caruso et al., 2019; Morales, 2018).

The strategic location decision has been shown to yield some positive results in terms of integration into the local labor market, highlighting the importance of social networks in determining refugees’ labor market outcomes. Only a few studies focus on networks with hosts, all of which find an association with positive labor market outcomes (Cheung and Phillimore, 2014; De Vroome and Tubergen, 2010). The remaining studies all focus on networks with co-nationals, usually defined as those residing in the same area. Exploiting random dispersal policies, several studies for high-income countries find positive impacts of co-nationals on earnings or employment (Damm, 2009; Damm and Rosholm, 2010; Mart ́en et al., 2019; Edin et al., 2003).

However, to the extent that labor markets are thin, a large inflow of refugees or IDPs over a short period of time might end up crowding out labor markets, adversely affecting those arriving at the same time, as well as natives and previous immigrants, and hence dampening the positive social network externality (Beaman, 2011). More generally, the quality of the network matters for labor market outcomes (Battisti et al., 2022; Dagnelie et al., 2019; Parsons et al., 2020; Stips and Kis-Katos, 2020).

# **4.1.3 Attitudes and perceptions of hosts**

Besides labor market conditions and networks with co-nationals, the economic and social integration of refugees and IDPs can be hindered or facilitated by the host community perceptions of and attitudes towards the forcibly displaced (Aksoy et al., 2020; Bailey et al., 2022). These attitudes and perceptions affect the ability of refugees and IDPs to build up social networks with hosts that are important for integration. Discriminatory hiring practices (Carlsson, 2010; Loiacono and Silva Vargas, 2019) and teacher biases in grading (Alesina et 9
# **_Using CDRs in Social and Economic Research_**

Although CDRs may appear to present a narrow and technical dataset, due to the dramatic expansion of mobile phone use over the past several decades these records can provide a rich source of information about human behavior and the characteristics of communities. CDRs can be used to infer certain personal attributes about a cellular user, such as their home location, and they can be used to analyze social networks, as each call or message can be viewed as a link between MNO customers. This approach enables researchers to map social interactions, identify community nexus points and examine how information is transmitted across groups and regions.<sup>5</sup>

> 5 These applications are described in greater detail in Blondel et al. (2015).

**5**