rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 2. Business Climate for FDI: ranking of 29 African countries, average 1995-1997**

Country FDI/business climate **a** ICRG political riskb Institutional Investor

|Namibia|1|1|NA|
|---|---|---|---|
|Mali|2|12|13|
|Mozambique|3|11|18|
|Zambia|4|4|14|
|Chad|5|NA|NA|
|Senegal|6|13|6|
|Angola|7|18|20|
|Benin|8|NA|12|
|Mauritius|9|NA|I|
|Cote d'Ivoire|10|8|8|
|Tanzania|11|5|10|
|Uganda|12|15|11|
|Central African Republic|13|NA|NA|
|Ghana|14|7|4|
|Madagascar|15|9|NA|
|Burundi|16|NA|NA|
|Rwanda|17|NA|NA|
|Zimbabwe|18|4|3|
|Congo, Rep.|19|14|19|
|Nigeria|20|17|15|
|Niger|21|20|NA|
|Guinea|22|19|17|
|Malawi|23|6|7|
|Cameroon|24|16|9|
|Kenya|25|5|5|
|South Africa|26|2|2|
|Ethiopia|27|10|15|
|Sierra Leone|28|21|22|
|Congo, Dem. Rep.|29|22|21|

Sources: Author's own calculations; Pigato (1999).

**a** The business climate index is defined as net FDI inflows normalized by GDP and the total value of natural resources in each host country.

b Political risk rating based on the opinion of banks, TNCs and other institutional investors indicating corruption, political and judicial institutions.

**c** Institutional Investor rating measures a country's creditworthiness, which is mostly determined by economic and financial variables.

The ranking of 29 African countries according to the indicator proposed above is presented in table 2 (first column). In 1995-1997, the most attractive country was Namibia, followed by Mali, Mozambique, Zambia, Chad and Senegal.<sup>6</sup> The least attractive were Congo, Sierra Leone and Ethiopia. Preliminary findings for 1998 indicate

**6** The good ranking of Chad and Zambia reflects that the first country offers great oil reserves (not reflected in our indicator of natural resources) that have attracted companies interested to explore those

6
# _Which technologies and infrastructure sharing policies should governments encourage to enable universal broadband?_

Of the four different technology options tested, the cheapest deployment option across all scenarios and strategies was the combination of 4G with a wireless backhaul. This provides substantial evidence for those countries currently aiming to cycle through the cellular generations sequentially, to instead ‘leapfrog’ to providing 4G in underserved areas. Most of the investment saving comes from operating fewer sites, thanks to 4G being a more spectrally efficient technology deployed in frequencies with better propagation characteristics. However, there are also numerous benefits from having a more flexible IP-enabled core network making the 4G case even more convincing. The analysis demonstrates that the technology strategy has a very large impact on the viability of universal service broadband, and that using 4G with a wireless backhaul is the most cost-efficient way to push coverage out to rural and remote areas with low ARPU. This is particularly true when considering the different capacity targets, given that this technology option performs well when focusing on universal broadband of 10 GB/month per user.

Reflecting on the hypothetical modeling approach presented here, governments need to be aware that by deploying a higher capacity target or a more superior technology (e.g., 4G with fiber), there is ultimately less capital available to redeploy to unviable areas via user cross-subsidy, increasing the required state subsidy. Moreover, least-cost strategies (e.g., wireless-based 4G) can also be considered as having less risk overall, because higher cost strategies (e.g., fiber-based 3G) may be subject to greater cost overruns or miscalculations.

Infrastructure sharing has a very large impact on the cost of delivery, especially in helping to serve hard-to-reach areas. The caveat to these results is that governments must balance the desire to push out service to unviable areas (by reducing supply-side costs), against the benefits of competitive infrastructure markets. From decades of economic research, dynamic competition has demonstrated positive outcomes for consumers and the wider macroeconomy. However, issues arise in areas of 35
The database was created various approaches, including:

- Field-visits to different geographical locations in the city with varying economic conditions

- Visits to property tax payment centers

- Contacting panel members previously engaged by local consultants

- Reaching out to other connections in the cities, particularly in lower socioeconomic communities

- Word-of-mouth/snowballing effect following the above approaches

From the databases created, a diverse set of eight females was invited to take part in the discussion from each city, based on a range within the criteria outlined in the participant profiles section. Where more than one female had the same demographics, a random selection was made from among them. A matched reserve list was also created in case replacements were needed.

## _Male property owners_

The same approach was used to select male property owners (save for differences in demographics required), and the aim was to broadly match the male and female participants wherever possible on the following characteristics:

- Age

- Socioeconomic category (as defined by household income)

- Length of ownership of property

- Property value at time of purchase

- Property value at the time of the research

Details on male owners were collected in the same way and at the same time as female owners and saved into the database; selections were then made from among them as appropriate. Eight female owners and eight male owners were invited to participate in their city’s FGD. A matched reserve list of male candidates was made for each city to allow for replacements if needed.

# Conduct of FGDs

All the FGDs were conducted between September 2 and September 17, 2021, and lasted between 60 and 90 minutes. Moderators of the FGD sessions were all experienced field executives who speak a range of languages including English, Hindi, Kannada, and other Indian languages. All were trained on the particulars of the project (including the overall topic, questions, and methodology) and on the required conduct of the FGDs. Moderators were informed about gender-based discounts in the different cities to allow them to accurately pose questions in each city, to lead the discussion by contextualizing participants’ responses, and to encourage the flow of questions and suitable responses and discussion. Moderators were not made aware of the hypotheses of the research to avoid any bias.

Due to the COVID-19 pandemic, all FGDs were conducted online, with each participant logging on individually. Where necessary, participants received help accessing the digital meeting room. Participants were requested to sit in a room separate from family members/colleagues/others, and cameras were on 42
**Table 2:** Baseline Characteristics of PSI-PMI Pilot Senior Secondary Schools and Other Senior Secondary Schools before Matching, 2014

|Variable|PS<br>s|I-PMI pil<br>econdary|ot senior<br>schools|O<br>seco|ther seni<br>ndary sc|or<br>hools|Diff|P-value|
|---|---|---|---|---|---|---|---|---|
||_N_|_Mean_|_Std. dev._|_N_|_Mean_|_Std. dev._|||
|**_School hardware characteristics_**|||||||||
|Good desks (%)|16|0.92|0.34|55|0.91|0.26|0.96|0.82|
|Has library (%)|16|1.00|0.00|55|0.85|0.05|0.15|0.00|
|Has computer lab (%)|16|1.00|0.00|55|0.76|0.06|0.24|0.00|
|Has water tap (%)|16|0.88|0.09|55|0.89|0.04|-0.01|0.86|
|Has electricity (%)|16|0.88|0.09|55|0.69|0.06|0.19|0.08|
|**_School software characteristics_**|||||||||
|Total enrollment|16|868.36|168.06|55|509.09|51.41|359.29|0.04|
|Student-teacher ratio|16|34.53|2.87|55|26.10|1.75|8.43|0.01|
|Qualified teachers (%)|16|0.97|0.55|55|0.95|0.75|2.14|0.21|
|Female teachers (%)|16|0.92|0.19|55|0.95|0.13|-0.24|0.91|
|**_Students’ performance_**|||||||||
|_Mathematics_|||||||||
|Students passing the test (score 1-8) (%)|16|0.16|0.05|55|0.13|0.03|0.03|0.72|
|Students with excellent or good scores (1-3) (%)|16|0.01|0.01|55|0.01|0.01|0.01|0.41|
|Students earning credit (score 4-6) (%)|16|0.05|0.02|55|0.03|0.01|0.02|0.34|
|Students with passing scores (7-8) (%)|16|0.09|0.03|55|0.10|0.02|-0.01|0.80|
|_English_|||||||||
|Students passing the test (score 1-8) (%)|16|0.37|0.06|55|0.27|0.03|0.01|0.11|
|Students with excellent or good scores (1-3) (%)|16|0.01|0.00|55|0.00|0.00|0.004|0.17|
|Students earning credit (score 4-6) (%)|16|0.11|0.03|55|0.06|0.01|0.05|0.17|
|Students with passing scores (7-8) (%)|16|0.26|0.04|55|0.21|0.02|0.05|0.20|
|Students taking science subjects in the WASSCE<br>2014(%)|16|0.34|0.34|55|0.18|0.30|0.16|0.09|

_Note:_ The WASSCE is graded on a score of 1 to 9, with lower values being better. The P-value is based on using the t-test to examine whether these two groups (program versus non-program schools) have equal means for the WASSCE scores, by clustering at the school level. Robust standard errors are presented. PSI-PMI = Progressive Science Initiative and Progressive Mathematics Initiative; WASSCE = West African Secondary School Certificate Examination.

15
recognized for its potential to mitigate gender bias in taxation (see Coelho et al.,2022). Table 1 outlines the business income tax rates applicable to individuals based on their income levels. The statutory tax system exhibits progressivity by requiring individuals with higher taxable income to contribute a larger portion of their earnings. However, it is important to note that the actual tax paid as a percentage of revenue may deviate from what is anticipated under tax policy. Our dataset provides the opportunity to examine whether the tax paid as a percentage of sales revenue aligns with the progressive tax structure, and this will be explored in detail later in the study.

Table 1: Individual Business Income Tax rates in Ethiopia

|**Taxable Income (Per Year)**|**Income Tax Rate**|
|---|---|
|0-7,200|0%|
|7,201-19,800|10%|
|19,801-38,400|15%|
|38,401-63,000|20%|
|63,001-93,600|25%|
|93,601-130,800|30%|
|Over 130,8000|35%|

Source: FDRE (2016a) Another gender-relevant structural aspect of the business tax system pertains to whether the tax system is global or schedular. In a global system, taxpayers aggregate their income from all sources and apply a uniform set of tax rates. For instance, if a taxpayer has income from two different business sources, that income is combined, and a uniform tax rate is applied. Consequently, this approach may result in a higher tax rate if there are exemptions at lower income levels. In contrast, a schedular system taxes various income sources separately or at distinct rates. Both systems have their own advantages and disadvantages.

8
quartiles can be largely ignored in any discussion of national resource mobilization. This is true for the United States and other advanced economies, and likely holds with even more force for developing countries.

Wealthy households in many developing countries prefer to hold a relatively large fraction of their assets in safer, more transparent, more diversified and more efficient financial markets offshore.<sup>1</sup> Since such holdings may be illegal because of remaining exchange controls, or because taxes were not paid, offshore holdings would not show up in survey data, and this is likely one of the reasons that most developing countries have not conducted comprehensive surveys of financial asset holdings of households.

The share of financial assets in the average household’s domestic portfolio increases with the national level of development (and in advanced economies it increases also with the wealth of the household). Within financial assets, holdings of risky assets are even more concentrated: for these, holdings below the top 5 percent of wealth-holders are nationally negligible.

Curiously, even though the financial assets of the poor are in aggregate negligible, it appears that countries with deeper financial systems have less absolute poverty, even after taking account of mean national income. This suggests that a deep financial system also affects the structure of the economy in subtle ways that are relevant for the inclusiveness of economic development.

# _Access_

The main focus of current policy concern with regard to household finance in developing countries is the perceived need to increase the access of poor households to basic financial services; deposits, payments, insurance and credit. In other words, policydriven research for such countries has focused not on allocation decisions between

> 1 Identified offshore banking deposits as a share of GDP are statistically uncorrelated across countries with per capita GDP (Hanson, 2003).  Hence, since the elasticity of total bank deposits with respect to income is well over one, we may conclude that poorer countries tend to hold a higher share of their financial assets offshore.

3
into the relationship between land tenure and investment, but the existing evidence is largely inconclusive (see Brasselle et al., 2002 for the survey of empirical studies in Africa). In spite of the conventional belief, only a few studies have confirmed that tenure insecurity is a serious impediment to land-related investments, largely confined to Asia (Feder 1988) or Latin America (Carter and Olinto, 2002); Besley (1995) provides evidence of this nature for Ghana. Some recent studies, however, show that land rights have little effect on land improving investments and planting tree crops, not least in Africa (e.g. Migot-Adholla et al. 1994, Pinckney and Kimuyu 1994). In fact, some affirm the existence of reverse causality, i.e., that farmers may undertake land investments in order to enhance tenure security (Sjaastad and Bromley, 1997; Place and Otsuka, 2002; Brasselle et al., 2002). These findings have cast considerable doubt on the need for embarking on ambitious land registration and titling policies. Some authors have even argued that the current traditional tenure systems in Africa have the necessary elements to stimulate smallscale investments. Consequently, they have underscored that developing land rights alone might not be a panacea for problems of low agricultural investment and land productivity. Thus, there still remains a need for proper understanding of the evolution of property rights along with a careful empirical investigation of the links between land rights and investment (Besley, 1995).

In view of this, unbundling the institutions of property rights is necessary to ensure a careful interpretation of findings. Following North (1981), Acemoglu and Johnson (2003) define property rights as the rules and regulations that protect citizens against the power of the government and elites. Contrary to papers that have to rely on interpreting customary laws and the protection they entail, we can rely on two related but empirically distinguishable concepts: transfer rights and the threat of expropriation (‘security’). The former are measured at the plot-level, and simply consider whether the household head thinks that a plot can be transferred to someone else. We also asked whether households perceived that land reform and redistribution would result in land been taken away from them in the next five years. Both measures are used in the paper.

Finally, since land is such a central concern in the policy debate, this issue has attracted precedents in terms of research in Ethiopia. The very few rigorous empirical studies have produced the issue of endogeneity in the relationship between land rights and tree investment alluded to in Bruce (1988) and Besley (1995).

5
- 24 -

**<u>Table 7. Separated GMM estimation by country group</u>**

|Growing e<br>|conomies<br>|Other countries<br> <br>|
|---|---|---|
|Coef.|Std.Err.|Coef.<br>Std.Err.|
|ln_N_t-1<br>0.622|<br>(0.066) ***|1.679<br>(0.092) ***|
|ln_N_t-2<br>0.172|<br>(0.049) ***|-0.609<br>(0.108) ***|
|ln_N_t-3<br>0.169|<br>(0.047) ***|-0.071<br>(0.015) ***|
|ln_HRREGION_<br>0.010|<br>(0.023)|-0.002<br>(0.002)|
|ln_HRLOCAL_<br>-0.123|<br>(0.022) ***|-0.001<br>(0.003)|
|ln_POPD_<br>-0.079|<br>(0.018) ***|-0.002<br>(0.002)|
|_t_=2014<br>0.074|<br>(0.032) **|-0.008<br>(0.010)|
|_t_=2015<br>-0.048|<br>(0.035)|-0.026<br>(0.010) ***|
|_t_=2016<br>-0.043|<br>(0.028)|-0.048<br>(0.007) ***|
|_t_=2017<br>0.086|<br>(0.029) ***|0.010<br>(0.010)|
|_t_=2018<br>0.424|<br>(0.041) ***|-0.015<br>(0.003) ***|
|_t_=2019<br>0.344|<br>(0.072) ***|-0.013<br>(0.002) ***|
|_t_=2020<br>0.329|<br>(0.061) ***|-0.021<br>(0.004) ***|
|constant<br>0.475|<br>(0.079)***|0.046<br>(0.012)***|
|Obs.<br>1,374|||
|Wald stat<br>.|||
|No. of groups<br>255|||
|No. of instruments<br>209|||
|Arellano-Bond test:|||
|AR(1)<br>-5.71|***||
|AR(2)<br>0.02|||
|<br>Hansen overidentifying restriction<br> <br>|test:<br>||
|Chi2<br>133.18|||
|p-value<br>0.997|||
|Structural test (Ho: All coef. are t|he same):||
|Chi2<br>816.7|***||

Note: The dependent variable is ln _N_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

# **VI. Conclusion**

49. In this paper, the impact of transport connectivity on agglomeration economies was reexamined with unique data from the Caucasus and Central Asian countries. Transport connectivity is considered to be an important policy variable to facilitate agglomeration economies, which are one of the most important determinants of firm productivity, urban formation and economic growth. However, the potential impacts of transport connectivity remain more complex than we believe, especially when long-term causality is considered. One of the most important empirical challenges is potential endogeneity between economic outcomes and
- [17] Rauch, James R. (1999), “Networks versus markets in international trade”, _Journal of International Economics_ 48, 7-37

- [18] Rose, Andrew K. (2007), “The Foreign Service and Foreign Trade: Embassies as Export Promotion”, _World Economy_ 30(1), 22-38.

- [19] Santos-Silva, Joao, and Silvana Tenreyro (2006), “The log of gravity”, _Review of Economics and Statistics_ 88(4), 641-658.

- [20] Tenreyro, Silvana (2007), “On the Trade Impact of Nominal Exchange Rate Volatility”, _Journal of Development Economics_ 82, 485-508.

21
productivity per hectare has increased from 4.7 to 7.7 kg per hectare per year (a 63% increase)<sup>18</sup> . This new estimate – plus the sustained high prices of coca leaf in Bolivia (above $5/kg) and Peru (above $2.5/kg), which likely created an incentive for farmers in these two countries to increase coca cultivation – have resulted in a large increase in the estimated potential cocaine production between 2003 and 2004. While in Bolivia, potential cocaine production increased by 35% in 2004 and by 23% in Peru (in the same year), in Colombia it increased by 16%, despite the reduction in the number of coca cultivated hectares.

Based on the most recent CNC cultivation estimates, along with the DEA’s coca yield and laboratory efficiency data, the US State Department also produces an estimate of potential cocaine production. According to this source, after reaching a peak of more than one thousand metric tons in 2001, total cocaine production in the Andean countries declined between 2001 and 2004 (see Figure 6 (b)). For 2005, potential cocaine production is not comparable to prior years due to the fact that ONDCP includes in its calculation those newly surveyed fields that were not included in previous years. In fact, our own calculations suggest that potential cocaine production would have been 431 metric tons (the same as in 2004) if one does not include the new surveyed areas. While potential cocaine production according to this source has remained relatively stable during the last three years in Bolivia and Peru (at 60 metric tons and 140 metric tons per year respectively), in Colombia it decreased very rapidly until 2004 and it remained stable during 2005. By 2004 and 2005 potential cocaine production in this country (430 MT) was about 50% of what it was in 2001 (840 MT).<sup>19</sup> According to an ONDCP report (ONDCP, 2005), the fact that potential cocaine production declined more rapidly

> 18 See UNODC (2006).

> 19 Again, without including in the calculations the 39,000 has of new surveyed fields of 2005 to make the data comparable to previous years.

14
**Table 2**

|**Data**|**Children**<br>**under 61**<br>**months of**<br>**age**|**Children**<br>**under 61**<br>**months of age**<br>**with height**<br>**measurement**|**Children under**<br>**61 months of age**<br>**with height**<br>**measurement &**<br>**biologically**<br>**plausible height**<br>**for age z-scores**|**Children under 61**<br>**months of age**<br>**with height**<br>**measurement &**<br>**biologically**<br>**plausible height**<br>**for age z-scores &**<br>**in the 2015 FIES**|
|---|---|---|---|---|
|Observations (unique individuals)|18,629|17,014|16,873|13,172|
|Females (%)|48.3|48.4|48.4|48.7|
|Males (%)|51.7|51.6|51.6|51.3|
|Urban (%)|33.6|33.4|33.4|31.5|
|Cases with missing age (%)|0|0|0|0|
|Age range (completed months)|0-60|0-60|0-60|0-60|
|Mean age (completed months)|32|32|32|32|
|Median age (completed months|33|33|33|33|
|Cases with missing height (%)|8.7|0|0|0|
|Height range (cm)|24.5-144.55|24.5-144.55|41.9-131.45|41.9-131.45|
|Mean height (cm)|85.2|85.2|85.3|85.3|
|Median height (cm)|86.9|86.9|87|87.1|

Given that different modules of the NNS focus on specific segments of the population, there is a tradeoff between the scope of nutrition drivers included and the size and age-composition of the sample considered in the analysis (Figure 1). For example, a total of 16,873 children have valid HAZ scores in the NNS and while all of these children are represented in each of the household-level modules of the NNS (Form 1.2, Form 1.5 and Form 1.6), only 10,188 (around 60 percent) are represented in Form 3.1, which captures variables on maternal health care, and only 5,945 (around 35 percent) are captured in Form 4.3, which captured data on infant and young child feeding practices for children under the age of 2. If data from the FIES are matched to the NNS sample, a total of 4,049 children with valid z- scores (all under the age of 2 years due to the inclusion criteria of Form 3.1) have information from all of the available NNS and FIES modules. There is no evidence that factors that influence stunting other than age are being affected as a result of inclusion/exclusion criteria of the different NNS modules, as the unweighted stunting rates for children under the age of 24 completed months do not vary with the sample size.

4
Figure 4: Italy 2002–2006–2010: Type Opportunity Growth Incidence Curve <!-- Start of picture text --> 0 5 10 15<br>types<br>type OGIC 2002−2006 (C1)<br>type OGIC 2006−2010 (C1)<br>6.00<br>4.00<br>2.00<br>0.00<br>% yearly growth<br>−2.00<br>−4.00<br><!-- End of picture text --> _Source: Authors’ calculation from SHIW (Bank of Italy)_ OGIC for 2002–06, reported in Figure 4, does not always lie above zero for the whole distribution; in particular, the types ranked 3 and 15 experience a loss. Most importantly, the shape of the type OGIC differs significantly from the shape of the individual OGIC. According to this perspective, growth can no longer be classified as regressive. For the Italian case, this is equivalent to saying that households whose heads were born in the South grow, on average, less than households with different geographical origins.<sup>29</sup>

> 29As reported in Table 1, the circumstance “head born in the South” appears in the five poorest types in 2002 and 2010 and in the four poorest types in 2006.

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