rafmacalaba/datause-encoder-data huggingface dataset:
advantage of benchmarking Accra’s experience with those of a number of other similarly-sized large cities in Sub-Saharan Africa. We compare the housing situation of inner city low-income neighborhoods in Accra with similar areas in Nairobi, Dar es Salaam, and Addis Ababa.<sup>10</sup> As shown in Table 1, the mean and median expenditure on housing and services in Accra is much higher than that of similar expenditures in the other cities, yet the housing conditions in Accra are of lower quality and quantity. That is, while the residents of Accra pay much more for housing services, they get considerably less, paying, for example, a much higher share of the household income for some services, such as sanitation. Moreover, not only are costs high, the payment form is onerous. It is not unusual, for instance, for renters in Accra, who account for almost two-thirds of all households in the city, to have to make up-front payments of three years of rent, suggesting an extremely tight housing market.<sup>11</sup> Our survey results are also consistent with more general Census findings on urban housing conditions in Ghana, and a large body of literature on housing conditions in the city. These findings indicate that urban residents in Ghana live in highly dense areas: average room occupancy is high at more than three persons per room, with more than 45 percent of families living in a single room. Each plot houses several families (more than one-third of the plots covered in our study contain five to nine separate households) often—more than 40 percent of the time—living in what is termed compound housing,

> 10 The data were gathered across the four cities between 2004 and 2006 (see Mathema, 2004, 2005, 2006a, 2006b, 2007). In each case, the surveyed households were located in contiguous, low-income central city communities. The differences in the income levels of the surveyed households were similar to the differences in the national per capita income levels across countries, suggesting that the surveys covered the same general part of the income distribution. The four cities have populations from 2 to 3 million people, making them 4 of the 10 largest cities in Sub-Saharan Africa. The populations also tend to rely on similar levels of population served by basic services such as water and sanitation. Finally, the modal split in transportation—e.g., the share of the population using public transport, and walking—is quite similar. 11 Gulyani (2006) shows that rent levels in Nairobi’s slums while expensive are considerably lower than Accra’s.

7
Galal & Nauriyal 21

**Table 8. Quality of service indicators, before and after reform.**

|Country|<br>A|% of Unsuc<br>Year|cessful<br>B|Calls<br>Year|Ave<br>A|rage pend<br>Period|ing de<br>B|mand' (%)<br>Period|
|---|---|---|---|---|---|---|---|---|
|Argentina|19|1990|13|1992|32|1981-90|10|1991-92|
|Chile|--|--|1|1992|33|1981-86|35|1987-92|
|Jamaica|**--**|**--**|**--**|**--**|72|1986<sup>-</sup>87<sup>b</sup>|81|1989-92|
|Malaysia|**--**|**--**|**-**|**--**|24|1981-86|7|1987-92|
|Mexico|**I1**|1988|9|1992|22|1981-89|16|1990-92|
|Philippines|--|--|18|1992|46|1980-85|79|1986-92|
|Venezuela|43|1990|37|1992|25|1981-90|35|1991-93|

For each indicator column A **refers** to pre reform and column B to post reform; -- Information not available. a. Ratio of waiting list to main lines in operation.

b. No information was available for 1981-85.

Source: Intemational Telecommunications Union (various issues).

Besides benefitting from expansion in the system, consumers in all countries for which we have data also benefitted from better service, as indicated by improvements in the call completion rate (table 8). But levels of quality varied widely; in particular the sharp contrast between Chile and the Philippines persists; in 1992 only 1 percent of the calls were unsuccessful in Chile, while in the Philippines 37 percent of the calls were unsuccessful.

With respect to prices, this is one area where compiling comparable data proved to be the most difficult. Even where it was feasible to distinguish calls by customers and peak and off peak periods, apportioning fixed costs to different services and exchange rate manipulations reduced the value and comparability of the data. Accordingly, we found it useful to simply estimate the changes in consumer surplus using real revenue per line as a proxy for the price and the number of operating lines as a proxy for quantity. The changes in consumer surplus are then approximated using Slutsky compensation. 12 To facilitate inter country comparisons, we normalized the average annual changes in consumer surplus by the sector's corresponding average annual revenues. The results are reported in table 9, which shows that consumers were better off in Argentina, Chile and Jamaica. They were worse off in the remaining countries, especially in Mexico, Venezuela, and the Philippines.

> 12 According to Slutsky's compensation, changes in real consumer surplus in year t are estimated as **(P,** - **Pt.,).Q,,** where, P is the real price and **Q** is the quantity sold.
Table 3: Summary statistics of children under 5 at time of the survey in 2016

||Mother's age <br>|at start of the <br>|war in 1996<br>||
|---|---|---|---|---|
||0 to 29|0 to 15|16 to 29||
|Variables||||Dierence|
||(All women)|(Treatment)|(Control)||
|Panel A: Outcomes|||||
|Height/age sd|-1.55|-1.52|-1.85|0.33***|
||[1.34]<br>|[1.33]<br>|[1.39]<br>|(0.11)<br>|
|Weight/age sd|-1.35|-1.33|-1.50|0.17*|
||[1.07]|[1.06]|[1.16]|(0.09)|
|Weight/height sd|-0.65<br>|-0.65<br>|-0.62<br>|-0.03<br>|
||[1.10]|[1.09]|[1.15]|(0.09)|
|Body mass index sd|-0.51|-0.51|-0.45|-0.06|
||[1.11]|[1.11]|[1.14]|(0.09)|
|Panel B: Mother's exposure to conict|||||
|B1: During 0-15 years|||||
|Months of war|3.17<br>|3.43<br>|0.00<br>|3.43***<br>|
||[6.01]|[6.17]|[0.00]|(0.49)|
|Number of casualties|5.83|6.29|0.00|6.29***|
||[14.28]|[14.74]|[0.00]|(1.16)<br>|
|Months inc. neighboring villages|11.79|12.73|0.00|12.73***|
||[13.10]|[13.16]|[0.00]|(1.04)|
|B2: Whole life|||||
|Months of war|4.69|4.73|4.10|0.63|
||[7.42]|[7.41]|[7.64]|(0.61)|
|Number of casualties|8.85<br>|8.92<br>|8.09<br>|0.82<br>|
||[18.74]|[18.55]|[20.92]|(1.53)|
|Months inc. neighboring villages|17.15|17.20|16.54|0.66|
||[13.73]|[13.73]|[13.83]|(1.12)|
|Panel C: Controls|||||
|Child sex (girl=1)|0.47|0.48|0.42|0.06|
||[0.50]|[0.50]|[0.49]|(0.04)|
|Child birth order number|2.36<br>|2.16<br>|4.97<br>|-2.81***<br>|
||[1.57]|[1.25]|[2.53]|(0.11)|
|Child is a twin (yes =1)|0.01|0.01|0.02|-0.01|
||[0.11]|[0.11]|[0.14]|(0.01)|
|Female headed HH (yes=1)|0.33|0.33|0.30|0.03|
||[0.47]|[0.47]|[0.46]|(0.04)|
|Hight caste (yes=1)|0.36|0.37|0.32|0.04|
||[0.48]|[0.48]|[0.47]|(0.04)|
|Rural (yes =1)|0.89|0.89|0.93|-0.03|
||[0.31]|[0.31]|[0.26]|(0.03)|
|Eastern region|0.18|0.18|0.24|-0.06*|
||[0.39]|[0.38]|[0.43]|(0.03)|
|Central region|0.27|0.27|0.28|-0.01|
||[0.44]|[0.44]|[0.45]|(0.04)|
|Western region|0.19|0.20|0.16|0.04|
||[0.40]|[0.40]|[0.37]|(0.03)|
|Mid-western region|0.22|0.22|0.22|0.00|
||[0.42]|[0.42]|[0.41]|(0.03)|
|Far-western region|0.13|0.14|0.11|0.03|
||[0.34]|[0.34]|[0.31]|(0.03)|
|Number of children|2,168|2,007|161|2,168|

Note: Standard deviations are in brackets and standard errors are in parentheses and signicance levels are denoted as follows: * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.

41
consumption is 3.7 percent among the bottom 50 percent and about 3 percent among deciles 6–8, and it declines to 2 percent among the top decile.

**Figure 2. Tobacco Consumption, by Per Capita Consumption Decile**

- **a. Share of households consuming b. Tobacco consumption in total tobacco household consumption**

<!-- Start of picture text --> 6% 6% 5% 5% 5% 5%<br>5% 5%<br>5% 5% 5%<br>68% 69% 69% 71% 69% 66% 65% 64% 65% 4%<br>60% 4% 4% 4% 4% 4% 4%<br>55% 3%<br>3% 3%<br>3% 3%<br>3% 2%<br>2%<br>2%<br>1%<br>0%<br>1 2 3 4 5 6 7 8 9 10 All<br>1 2 3 4 5 6 7 8 9 10 All<br>Per capita household consumption decile<br>Deciles of household per capita consumption<br>All households Conditional on positive consumption<br><!-- End of picture text --> _Source:_ Calculations based on data of HIES 2016/17.

_Note:_ Tobacco includes cigarettes, bidis, tobacco leaf, gul, betel leaf, betel nut, zorda, lime, khoer, rolled betel leaf, and other smoked or smokeless products. Consumption deciles are defined based on household consumption per capita deflated spatially to control for differences in prices across areas.

To obtain a sense of how different the consumption patterns are relative to the patterns in other countries in the region, one may compare Bangladesh with India, a neighboring country with similar tobacco products, particularly bidis. Information from India Consumption Expenditure Survey, 2011– 12 reveals that the socioeconomic gradient for consuming tobacco is steeper in India than in Bangladesh (figure 3). Among the poorest deciles, the share of households consuming tobacco is similar, but, among the richest decile, a higher share of households consume tobacco in Bangladesh (55 percent) than in India (36 percent). The share of total consumption assigned to tobacco products is also substantially lower in India than in Bangladesh. However, similar to Bangladesh, there is a small gradient in the share of the budget assigned to tobacco products across deciles. These differences become smaller between Bangladesh and the state of West Bengal, highlighting the greater similarity of cultural and consumption patterns in the latter region of India.

6
Zimbabwe and after controlling for firm size and age, various personal characteristics of the entrepreneurs, as well as sector and country differences – found that Asian and European firms start larger and grow faster than indigenously-owned African firms. By the same token, Hewitt and Wield (1997) show that Asian businesses in the Tanzanian manufacturing sector have a better access to sources of technology than indigenous companies. Consistently, in the previously-cited study by Goedhuys and Sleuwaegen (2000), the dummy variable ‘non-African’ significantly and positively affects the likelihood of becoming an entrepreneur in Côte d’Ivoire. Similarly, when analyzing a randomly-selected sample of 296 Ethiopian SMEs, Mengistae (2001) finds that companies owned by the indigenous minority group of the Gurage perform better than average in the country; in particular, new businesses start larger and then grow faster. More recently, Goedhuys and Sleuwaegen (2010; see above) show that the Asian dummy (equal to 1 for entrepreneurs of Lebanese, Indian, Middle Eastern or other Asian origin) turns out to be positive and significant in affecting firms’ growth in Sub-Saharan Africa.

# **5.** **<u>Conclusions and policy implications</u>**

‘Entrepreneurship’ is an extremely complex, and somewhat controversial phenomenon. From a microeconomic point of view, far from being solely the result of the entrepreneurial ‘creative destruction’ process proposed by Schumpeterian advocates, any set of entrepreneurial ventures can be seen as a rather heterogeneous aggregate where real and innovative entrepreneurs are to be found together with passive followers, overoptimistic gamblers and even escapees from unemployment. From a macroeconomic point of view, progressive new firm formation can generate permanent economic growth, while defensive and regressive start-ups originate only temporary positive effects, and ultimately market turbulence.

Therefore, both scholars and policy makers should bear some important caveats in mind. Firstly, the evidence discussed in this study calls for a more rigorous definition of the terminology adopted, since the generic term ‘entrepreneur’ may include a population of 28
<u>Table 9. Impact on S2 student composition</u>

||Check 3||
|---|---|---|
|Control Mean<br>and SD<br>|Treatment-<br>Control<br>|Treatment-<br>Control<br>|
|(1)|(2)|(3)|
|A. Characteristics of Students: Demographics|||
|Students' age<br>16.15|-0.449**|-0.281**|
|(1.81)|(0.18)|(0.14)|
|Gender (1 male)<br>0.5|-0.024|-0.017|
|(0.50)|(0.03)|(0.03)|
|B. Characteristics of Students: Education|||
|Inverse PLE Score (4=lowest; 36=highest)<br>17.72|1.046*|1.227**|
|(5.51)|(0.57)|(0.58)|
|Absence, last week<br>0.47|-0.068*|-0.075**|
|(0.50)|(0.04)|(0.04)|
|Repeated a grade<br>0.46|0.027|0.026|
|(0.50)|(0.06)|(0.05)|
|Walks to school<br>0.88|-0.017|-0.012|
|(0.33)|(0.03)|(0.03)|
|Help on homework<br>3.04|0.029|0.014|
|(1.23)|(0.12)|(0.12)|
|<br>Hhold visits school<br>0.63|<br>0.113***|<br>0.108***|
|(0.48)|(0.03)|(0.03)|
|C. Characteristics of Students: Household|||
|Hhold Assets<br>2.31|0.118|0.037|
|(1.06)|(0.11)|(0.09)|
|Hhold service<br>2.6|0.041|-0.016|
|(0.83)|(0.09)|(0.07)|
|Hhold size<br>9.32|-0.765**|-0.887***|
|(4.80)|(0.37)|(0.32)|
|Mother's education<br>2.79|0.2|0.105|
|(1.36)|(0.12)|(0.10)|
|Father's education<br>3.37|0.297**|0.220*|
|(1.44)|(0.12)|(0.12)|
|D. School characteristics according to student|||
|Answered question<br>0.95|-0.014|-0.01|
|(0.22)|(0.02)|(0.02)|
|Noise in classroom<br>0.44|0.013|0.016|
|(0.50)|(0.05)|(0.05)|
|Shares desk<br>2.16|0.037|0.005|
|(0.90)|(0.12)|(0.13)|
|<br>Fixed Effects Region level|<br>No|<br>Yes|
|Number of obs.<br>585|1299|1299|
|Test for jointly significance|||
|Chi2||39.816|
|Prob>Chi2||0.001|

Note. Column (1) report mean and standard deviation for the control group. Columns (2) and (3) report the estimate of effects of a regression of each outcome variable against the treatment indicator, controlling for regional fixed effects. Standard errors are cluster at the school level.

26
# **Appendix III: Natural-historical path**

Since the early arrival of the Portuguese Mariner Diego Cão in 1483, the Congolese (Kingdom of Kongo at that time) has had cultural, social and economic connections with Europe. Western religions, literacy, the wheel, the plow, the gun and many other technologies were quickly adopted by the Congolese (Acemoglu and Robinson 2012).<sup>27</sup> All these came at very large expense: one of the principally traded goods in exchange were slaves<sup>28</sup> .

As their contact deepened other types of goods were introduced such as ivory, rubber, copper, diamonds, raffia cloth, and pottery among other natural resources. The European trade was based in the coastal cities of Sonyo and Pinda so it required an extensive trade network toward the eastern part of the country (primarily near present day Kivu and Katanga provinces) where much of the mineral deposits and other natural resources were mined. Fueled by the industrial revolution and new inventions such as the inflatable rubber tubes, the demand for goods increased dramatically.<sup>29</sup> By the end of the 19th century the Congo was a personal possession of King Leopold II (not an official Belgian Colony). The king was engaged in a vigorous publicity campaign aimed at convincing the other European powers to recognize the legitimacy of his rule, a difficult task in view of the notorious brutality of his administration in Africa. One of the products of King Leopold’s “Office of Publicity” is a very detailed “Carte du Congo Belge”<sup>30</sup> (Map of the Belgian Congo), which includes caravan routes and existing and projected railways (see figure A4). This map, which shows the transport network constructed to move slaves, ivory and mineral resources between the interior of Congo and the coastal harbors, is the main input used to construct the natural-historical instrumental variable.

The natural-historical IV is constructed by merging two sources: the historical caravan route map described above, and a natural walking path map calculated for this study. The natural walking path, or natural path, is created by estimating the time minimizing route a pedestrian would travel over land, absent the benefits of a road network. The subsequent section details how the natural path network was calculated.

## Natural Path walking time calculation

The first step is creating a GIS cost surface model that accounts for all the traffic off-road or outside the caravan network for the year 1900. For that purpose, this report followed a similar approach to that which was used to construct a global map of accessibility in the World Bank's World Development

> 27 Why Nations Fail. Acemoglu and Robinson 2012

> 28 “The wealth of Africa, The kingdom of Kongo”. The British Museum 2011.

> 29 Congo Free State, 1885-1908 (http://www.yale.edu/gsp/colonial/belgian_congo/)

> 30 Carte du Congo Belge / éditée par l'Office de Publicité, anciens établissements J. Lebègue & Cie. - Éditeurs, Bruxelles (1896). Stored at the Library of Congress and downloaded from http://www.wdl.org/en/item/59/

45
Scheinkman (1996) sense, namely by privatizing first firms that required flexible management.<sup>11</sup>

# **_3. Privatization and Growth_**

A number of theoretical models provided competing predictions about the effects of privatization on macro-economic performance and growth. In Thorvaldur Gylfason (1998), privatization is shown to increase national economic output in a two-sector fullemployment general-equilibrium model by enhancing efficiency as if a relative price distortion were being removed through price reform, trade liberalization, or stabilization. Nico Hansen (1997) uses a general equilibrium imperfect competition model to show that a broad distribution of ownership rights can have favorable influence on micro-economic efficiency.

Several studies use aggregate data to assess the effect of privatization on economic performance. Using data from thirty five developing market economies Patrick Plane (1997) finds that privatization (through divestiture) has a significant positive effect on economic growth and that the effect is stronger when privatization takes place in industry or infrastructure rather than in other sectors. Daniel Berkowitz and David De Jong (2001) find that regions with more large-scale privatization exhibit greater formation of new (legally registered) enterprises, which in turn exhibits a strong positive correspondence with growth. Steven Barnett (2000) uses macroeconomic and privatization data from 18 countries to find that privatization proceeds transferred to the budget tend to be saved and used to reduce domestic financing. His other main finding is that total privatization, as opposed to just the proceeds being transferred to the budget, is correlated with an improvement in macroeconomic performance as manifested by higher real GDP growth and lower unemployment. In a cross-country aggregate study, Clifford Zinnes, Yair Eilat, and Sachs (2001) use a panel data set from 25 transition countries to find that privatization does not by itself increase GDP growth, but they suggest that a positive

> 11 Glaeser and Scheinkman (1996) examine sequencing strategies that would increase efficiency via informational gains. In their model private firms respond to demand and cost shocks, but this information is ignored by public firms. The Glaeser-Scheinkman model predicts that privatization should begin where demand or cost volatility is the greatest and where it maximizes the flow of information. Thus when demand uncertainty is greater than cost uncertainty, the authors argue that downstream firms should be privatized before upstream firms because downstream firms are better positioned to transmit information between the retail and upstream sectors.

14
Figure 13. Financial downturns: duration, amplitude and slope Duration (in quarters) <!-- Start of picture text --> 21<br>18 Downturns ***<br>Crunches/Busts<br>15<br>12 *** ***<br>9<br>6<br>3<br>0<br>Credit House Price Equity Price<br>Amplitude<br>(in percent)<br>0<br>-10<br>-20<br>-30<br>***<br>***<br>-40<br>-50 Downturns<br>-60 Crunches/Busts ***<br>-70<br>Credit House Price Equity Price<br>Slope<br>(in percent)<br>0<br>-2<br>***<br>-4 ***<br>-6<br>***<br>Downturns<br>-8<br>Crunches/Busts<br>-10<br>Credit House Price Equity Price<br><!-- End of picture text --> Source: Claessens et al (2012a).

Note: the amplitude and slope statistics correspond to sample medians. For duration, the means are shown. Duration is the number of quarters between peak and trough. Amplitude is based on the decline in each variable during the downturn. Slope is the amplitude divided by the duration. Busts (crunches) are the worst 25% of downturns calculated by the amplitude. ***, ** and * imply significance at the 1%, 5% and 10% levels, respectively. Significance refers to the difference between busts (crunches) and other financial downturns.

73
chemical applications took place prior to its introduction. In some respects, these two scenarios can be mapped to developed and developing countries where the input intensity may roughly correspond to the numbers used in this hypothetical experiment. Thus, the difference in incremental profit under the two scenarios ($100 _versus_ $600), which reflects productivity increases, can be seen as the driving force behind China’s and India’s adoption of biotechnology and subsequent increase in cotton production.

The pros and cons of biotech cotton (and biotechnology in general) have been discussed extensively in a broad context and in terms of specific costs and benefits, the latter mostly from survey-based research. Despite early signs regarding the benefits of biotechnology, institutions were at first reluctant to engage in the debate (or take an ‚official position‛), not only in terms of policy or financial assistance but also in terms of a general policy discussion. Such reluctance reflected, most likely, the controversial nature of the subject.

Perhaps, the first institutional study to discuss and explicitly acknowledge the broader benefits of biotechnology in developing countries was FAO’s 2004 _The State of Food and Agriculture_ report, which showed that on balance, biotech cotton growers were better off than growers of conventional seed varieties. Individual authors followed suit. Baffes (2005) argued that in addition to subsidy elimination and domestic policy reforms, adoption of biotech varieties should have been a priority among policymakers in low-income cotton producing countries. Falck-Zepeda, Horna, and Smale (2007) and Anderson, Valenzuela, and Jackson (2008) warned that the downward pressure on world cotton prices caused by the large-scale adoption of biotech cotton is likely to force other countries to adopt the technology in order to compete in the global market.

Numerous survey-based country-specific papers have evaluated the costs and benefits of biotech cotton. An earlier review by Smale, Zambrano, and Cartel (2006) surveyed 47 peer-reviewed articles published between 1996 and mid-2006. While they concluded that the evidence is promising in the sense that biotechnology is beneficial to producers, they also noted that it was too early to reach definite conclusions, in part due to methodological limitations and in part because the longer term economic impact is often shaped by institutional and political considerations the effects of which cannot be discerned within a limited timeframe.

Later reviews, however, reached more definite conclusions. Qaim (2009) summarized the evidence from 11 studies representing seven countries (table 4). The results show that, on average, introduction of biotech cotton varieties is consistent with a 50 percent reduction in insecticide use, 19 percent increase in effective yield and 160 percent increase in gross margin (measured in $US/hectare). Although insecticide reduction varies little among the cases reviewed, there was _— 5 —_