rafmacalaba/datause-encoder-data huggingface dataset:
|Years in primary and secondary school|-0.006<br>|0.001<br>|-0.001<br>|-0.001<br>|
|---|---|---|---|---|
||(0.016)|(0.021)|(0.021)|(0.021)|
|Is a university graduate|<br>-0.024|<br>0.010|<br>0.018|<br>0.022|
||(0.200)|(0.219)|(0.219)|(0.219)|
|Is a college graduate|0.112|0.124|0.126|0.128|
||(0.229)|(0.264)|(0.265)|(0.264)|
|Years mostly self-employed|0.045*<br>|0.045<br>|0.056*<br>|0.037<br>|
||(0.026)|(0.033)|(0.033)|(0.035)|
|Years mostly in government employment|0.080***|0.085**|0.096***|0.078**|
||(0.027)|(0.034)|(0.034)|(0.036)|
|Years mostly in private sector employment|0.044*|0.050|0.060*|0.041|
||(0.026)|(0.033)|(0.033)|(0.035)|
|Industry: Mining and quarrying||-0.218|-0.203|-0.210|
|||(0.310)|(0.313)|(0.313)|
|Industry: Manufacturing||<br>0.278**|<br>0.267**|<br>0.257**|
|||(0.122)|(0.124)|(0.124)|
|Industry: Electricity, gas, water||0.466|0.475|0.456|
|||(0.405)|(0.406)|(0.406)|
|Industry: Construction||0.234*|0.214|0.200|
|||(0.134)|(0.135)|(0.135)|
|Industry: Wholesale, retail, restaurants, hotels||0.168|0.153|0.144|
|||(0.116)|(0.119)|(0.119)|
|Industry: Transport, storage, communications||0.224|0.221|0.203|
|||(0.147)|(0.150)|(0.150)|
|Industry: Finance, insurance, real estate, business|||||
|services||0.267|0.230|0.214|
|||(0.417)|(0.419)|(0.419)|
|Industry: Social services||0.016|0.006|0.001|
|||(0.110)|(0.112)|(0.112)|
|government worker/employee||0.030|0.006|0.003|
|||(0.160)|(0.161)|(0.161)|
|private worker/employee||<br>0.047|<br>0.029|<br>0.034|
|||(0.075)|(0.079)|(0.079)|
|In urban area|||-0.032|-0.032|
||||(0.105)|(0.105)|
|Status as household head: 1=yes; 0=no|||0.195|0.197|
||||(0.212)|(0.212)|
|Married: 1=yes; 0=no|||0.027|0.023|
||||(0.134)|(0.134)|
|Muslim: 1=yes; 0=no|||-0.072|-0.086|
||||(0.278)|(0.278)|
|House ownership: 1=owned; 0=not owned|||0.010|0.011|
||||(0.089)|(0.089)|
|Electricity in house: 1=yes; 0=no|||0.121|0.117|
||||(0.141)|(0.140)|
|Toilet in house: 1=yes; 0=no|||0.129**|0.132**|
||||(0.065)|(0.065)|
|Farm business: 1=hh with farming business; 0=no|||<br>-0.026|<br>-0.028|
||||(0.088)|(0.088)|
|Non farm business:1=hh with farming business; 0=no|||-0.052|-0.057|
||||(0.065)|(0.065)|
|Work history data incomplete||||<br>-0.165|
|||||(0.101)|

31
encourage people to consume cheaper calories, and so the FEI poverty line will fall.<sup>13</sup> The potential utility inconsistencies in FEI poverty lines are also worrying when there is mobility across the subgroups of the poverty profile, such as due to inter-regional migration. For example, Ravallion (1994) notes that it is possible that a process of economic development through urban sector enlargement, in which none of the poor are any worse off and at least some are better off, would result in a measured _increase_ in poverty.

These problems can be seen to stem from the fact that the FEI poverty lines are anchored to attainments of only a sub-set of the functionings that we would normally identify as relevant to defining poverty, even in poor countries. Nor is it likely that calorific requirements would be the last valued functioning to be met as incomes rise. So this method is unlikely to be even weakly utility-consistent either within or between countries.

# **_The cost-of-basic needs method_**

The CBN method stipulates a consumption bundle deemed to be adequate for certain basic consumption needs, and then estimates its cost for each of the subgroups being compared in the poverty profile. This is essentially the approach of Rowntree (1901) in his seminal study of poverty in York England in 1899, and there have been numerous examples since, including the official poverty lines for the United States (Orshansky, 1965; Citro and Michael, 1995). As noted in section 2, the poverty bundle of goods can be interpreted in theory as the utilitycompensated demand vector at the reference level of utility needed to not be considered poor. In practice, however, the food bundle is chosen to be consistent with another functioning, namely adequate nutritional status. In particular, it is anchored to pre-determined food-energy requirements consistent with common diets in the specific context. However, allowances for non-food goods are also included, to assure that basic non-nutritional functionings are assured. Most lines in Figure 2(b) use some version of the CBN method.

In principle, this method can deliver utility consistent absolute poverty lines, but this will only be true in practice if the commodity bundle is the utility-compensated demand vector. As with the FEI method, consistency with nutritional norms alone does not assure utility consistency, since there are multiple commodity bundles that can attain any given nutritional level, and some of these bundles will undoubtedly be preferred to others. Thus it is useful to have

> 13 Wodon (1997) gives an example of this problem in data for Bangladesh. The FEI poverty line fell over time even though prices generally increased.

10
ASIA DEPARTMENT IV

# 1. Estimates and Projections

|_vow_|Population|Dop.<br>**rtio**|Priod|Growth<br>rate|Ca|CDR|TFt|*0**0**<br>**Mats**|0<br>**Fmles**|Io|qS<br>xtO00|
|---|---|---|---|---|---|---|---|---|---|---|---|
|195|765147|72.1|1985-89|2.10|32.4|11.4|4.26|57.8|57.9|99|128|
|1990|849651|70.3|1990-94|1.82|28.5|10.2|3.74|59.3|60.2|**88**|113|
|1995|930639|67.8|IM5-9|1.58|25.1|9.2|3.22|61.0|62.6|77|96|
|**2000**|1007122|62.1|2000-04|1.44|23.0|8.5|2.88|62.8|65.0|65|79|
|**2005**|1082479|56.1|2005-09|1.29|21.1|8.1|2.57|64.2|66.7|57|69|
|2010|1154J41|51.6|2010-14|1.13|19.1|7.8|2.29|65.6|68.4|48|58|
|2015|1221692|48.5|2014-19|1.04|18.1|7.7|2.19|67.0|70.2|39|48|
|2020|128660|46.7|2020-24|0.97|17.3|7.6|2.15|68.4|72.0|31|37|
|2025|1350430|6.2|2025-49|0.69|15.0|8.2|2.11|72.3|76.5|16|19|
|20^0|1603593|53.2|2050-74|0.31|13.1|10.0|2.08|76.8|81.6|5|7|
|207|132544|64.0|2075-W|0.15|12.4|11.0|2.07|79.3|84.8|3|5|
|2100|1797519|68.3|2100-24|0.10|12.0|11.1|2.06|81.2|87.4|2|4|
|2125<br>2150|**1841296**<br>1862220|71.4<br>73.0|2125-49|0.05|11.8|11.4|2.06|81.9|88.4|2|3|

# 2. Age structure (percent): 1990 and 2025

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# 3. Distribution of population (percent): 1990 and 2025

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As Table 7 shows, none of the explanatory variables discussed earlier contribute significantly to explaining urbanization. In particular, initial education does not explain differences across countries in the rate of urbanization. Nor do our urban bias measures (CEREAL or BMP), the crop shock variables, or the food aid data. We do find that SIVARD is significantly negative, indicating that civil strife reduces the growth rate in urbanization (the opposite sign from what we expected). The coefficient on the democracy variable is also significantly negative, suggesting that democracies tend to urbanize less rapidly than non-democracies, all else constant.

Our sample includes developed countries with a mature urban system, which have essentially stopped urbanizing. We checked whether any of our results changed if we excluded them. The results were generally robust to the change in samples (although the coefficient on cereal food-aid became significantly negative).

# Conclusion

Urbanization levels are closely correlated with levels of income. But changes in income do not explain changes in urbanization. Urbanization continues even during periods of negative growth, carried by its own momentum, largely a function of the level of urbanization. From that point of view, Africa's urbanization without growth is not a puzzle.

The finding of a strong positive association in levels between urbanization and income, combined with the absence of such a relationship in changes should be related to similar results obtained by Easterly (1999) on the relation between income and quality of life indicators. Easterly speculates on this patterns of results as (i) the long and variable lag that comes between growth and changes in the quality of life, (2) the possibility that global socio-economic progress is more important than home country growth for many quality of life indicators. Similar explanations could be applied to urbanization, notably the possibility that with globalization comes and inherent push for urbanization.

Factors other than income that help predict differences in _levels_ of urbanization across countries include: income structure, education, rural-urban wage differentials, ethnic tensions and civil disturbances. Also, the relationship between economic incentives and urbanization is weaker in countries with less civil or political liberties.

Factors other than the initial urbanization level that help explain the _speed_ of urbanization include: the sector from which income growth is derived, ethnic tensions, civil disturbances and democracy (the latter two contributing to slowing the pace of urbanization, all else constant.) Rural-urban wage differentials, whether they represent an urban bias, or simply lower productivity in agriculture than in other sectors are also significant determinants of the rate of growth of urbanization. Furthermore, at least in the pre-1980 period, the inclusion of rural-urban wage differentials in regressions reduces the significance of the Africa dummy - in other words, they explain the otherwise "extraordinary" pace of urbanization in Africa in the post-colonial period.

25
Table 3 Corruption levels in ECA, from 2005 BEEPS

||Means or p|roportions* (200|2, 2005 ranks in pa|rentheses)|
|---|---|---|---|---|
||Bribe tax<br>(Q40)|Bribe freq.<br>(Q39a)|Impact of<br>capture(Q44b)|Obstacle<br>(Q54q)|
|Albania|1.80 (25, 24)|0.46 (23, 26)|0.88 (24, 27)|0.32 (27, 25)|
|Armenia|1.17 (7, 20)|0.10 (5, 7)|0.47 (1, 17)|0.15 (7, 10)|
|Azerbaijan|2.89 (24, 26)|0.27 (16, 22)|0.57 (10, 23)|0.21 (13, 20)|
|Belarus|1.11 (15, 19)|0.22 (15, 16)|0.04 (2, 1)|0.07 (12, 3)|
|Bosnia|0.39 (9, 3)|0.20 (11, 13)|0.84 (22, 26)|0.25 (24, 22)|
|Bulgaria|1.58 (17, 23)|0.16 (18, 12)|0.51 (27, 19)|0.19 (19, 16)|
|Croatia|0.76 (2, 10)|0.11 (3, 9)|0.26 (14, 10)|0.18 (16, 15)|
|Czech. Rep.|0.63 (6, 6)|0.10 (4, 6)|0.38 (7, 12)|0.20 (6, 19)|
|Estonia|0.29 (1, 2)|0.06 (2, 2)|0.17 (8, 6)|0.04 (1, 2)|
|Georgia|0.46 (23, 4)|0.07 (25, 3)|0.16 (21, 4)|0.20 (26, 17)|
|Hungary|1.06 (10, 15)|1.06 (10, 15)|0.19 (5, 8)|0.11 (3, 7)|
|Kazakhstan|1.42 (19, 21)|0.10 (12, 5)|0.19 (3, 7)|0.12 (9, 8)|
|Kyrgyz Rep.|2.46 (26, 25)|0.53 (27, 27)|0.45 (16, 21)|0.33 (15, 26)|
|Latvia|0.71 (8, 9)|0.07 (7, 4)|0.46 (23, 20)|0.10 (5, 5)|
|Lithuania|0.87 (3, 12)|0.24 (10, 20)|0.46 (11, 22)|0.14 (10, 9)|
|Macedonia|0.62 (4, 5)|0.25 (13, 21)|0.73 (26, 25)|0.35 (23, 27)|
|Moldova|1.09 (18, 18)|0.22 (19, 17)|0.45 (13, 15)|0.20 (20, 18)|
|Poland|0.70 (11, 8)|0.15 (8, 11)|0.33 (6, 13)|0.16 (18, 12)|
|Romania|0.81 (21, 11)|0.23 (24, 18)|0.36 (15, 14)|0.29 (25, 24)|
|Russia|1.07 (12, 16)|0.39 (26, 25)|0.29 (4, 11)|0.17 (8, 13)|
|Serbia/Mont.|0.67 (16, 7)|0.33 (6, 24)|0.61 (20, 24)|0.26 (11, 23)|
|Slovak Rep.|0.93 (13, 13)|0.11 (22, 8)|0.22 (17, 5)|0.11 (21, 6)|
|Slovenia|0.17 (5, 1)|0.05 (1, 1)|0.16 (12, 3)|0.04 (2, 1)|
|Tajikistan|1.07 (22, 17)|0.21 (21, 14)|0.48 (19, 18)|0.16 (14, 14)|
|Turkey|--|0.13 (14, 10)|0.39 (25, 16)|0.17 (17, 14)|
|Ukraine|1.52 (20, 22)|0.28 (20, 23)|0.20 (9, 9)|0.23 (22, 21)|
|Uzbekistan|0.99 (14, 14)|0.21 (9, 15)|0.10 (18, 2)|0.09 (4, 4)|

*Proportions (as defined in Table 2) are reported for bribe frequency, state capture and corruption as an obstacle.

51
cation Regulators’ Council<sup>26</sup> (SATRC), Asia-Pacific Telecommunity<sup>27</sup> (PITA) and Pacific Island Telecommunication Association<sup>28</sup> (APT).

The second motivation behind the instrument is that the presence of regional associations of telecom operators and multinational telecom operators in multiple countries in the region can also facilitate learning and sharing of business ideas, and operational strategies that can stimulate convergence in telecom network expansion within the region.<sup>29</sup> These factors suggest that access to telecom services such as mobile phone networks is plausibly correlated within regions, as countries may anchor their access targets on current and projected access rates in other countries in the region. In other words, I argue that the average mobile phone penetration rate within a region is a strong predictor of the penetration rate in a given country. Leveraging this idea, I instrument mobile network coverage (of districts) in a given country using lagged (past) average mobile network coverage rates in other countries in the region.

To this end, I adopt the approach of Acemoglu et al. (2019) and Acemoglu et al. (2021), and _′ ′_ define _Ic =_ { _c_ : _c = c_ , _Rc ′ = Rc_ } as the set of countries whose mobile phone access (coverage) rates influence the penetration (coverage) rates in country _c_ in the same region _R_ . Using these sets, the instrument is constructed as follows: where _MobileCoveragec ′_ ( _j_ ) _t_ represents the (district level) mobile penetration rate in other countries in the same region in year _t_ . Essentially, _Zct_ , represents the predicted mobile penetration that a district, _j_ , in country _c_ at time _t_ would have faced if it was a different country in the same region in a given year. In simple terms, the _Zct_ is a "leave-one-out" type instrument where the mobile penetration rate in a district in country _c_ is instrumented with the average mobile penetration rate of all districts in other countries in the same region (i.e. excluding own country penetration rates).

Using lagged values of the predicted mobile coverage of all other countries in the region as instrument for mobile penetration, I estimate a two-stage least squares (2SLS) regression with the corresponding first stage and second stage equations specified as:

> 26https://www.apt.int/APTSATRC

> 27http://www.pita.org.fj/

> 28https://www.apt.int/

> 29Examples of such associations include the Asia Pacific Carriers’ Coalition, European Telecommunications Network Operators’ Association, Southern Africa Telecommunications Association (SATA), and the East African Communications Entities Organization (EACO).

13
# **5 Conclusion**

This paper presents evidence of the effects of electricity access and reliability on tax compliance attitudes of individuals and analyzes the fiscal implications of improving reliability in electricity supply in Sub-Saharan Africa.

Complementing data from the Afrobarometer survey with other relevant data sources, we find a robust positive impact of electricity access on tax compliance. More importantly, we find that reliability of electricity supply matters crucially in explaining the effect of access on citizens’ attitudes toward taxation in SSA. Additionally, we find strong positive spillover effects of reliable access to electricity. Extending grid connection to a community induces positive attitudes among residents toward honoring their tax obligations, even if the individual’s household is yet to be connected to electricity. The results further show that one mechanism through which access and reliability of electricity affect tax compliance attitudes is their effect on national identity. Access to electricity improves citizens’ sense of “belonging” to the state as opposed to ethnic groups, and therefore increases willingness to contribute resources (taxes) for the efficient functioning of the state.

Finally, the paper examines the fiscal implications of improving reliability in electricity supply by estimating tax-revenue gains from a complete elimination of power outages. Simulations on tax-revenue losses/gains reveal that countries in the region can generate more than $9.5 billion (4.5 percent of total tax revenue) every year by resolving reliability constraints in the electricity sector.

Policy prescriptions stemming from this analysis include the following: First, expanding access to electricity, particularly in rural communities, can produce significant economic and fiscal benefits for the country. Second, while efforts at expanding grid access are important, improving reliability in supply should be given equal attention, as outages have the potential to undermine the impact of electricity access through their negative impact on household welfare and firm performance. Citizens may therefore have less incentives to pay taxes, as they view such actions as a form of protest against government’s inability to provide reliable electricity. Third, the elimination of power outages has the potential to generate significant tax revenues that can, in turn, be used to refinance investments in the electricity sector.

21
to have an advantage in acquiring such finance.

We conclude our cross-country and firm-level analyses with some observations. The crosscountry approach allows us to measure the financial development gap between Africa and other developing economies and identify factors that are of particular relevance for Africa. However, we recognize the limitations with cross-country studies, and hence attempt to address them in three ways. First, we are cautious in drawing causation; we _do,_ however, view variables, such as population density and natural resources endowment, as exogenous variables for predicting financial sector development. Second, as noted above we complement our country-level tests with firm-level analyses, and the micro-level evidence is consistent with the country-level results. In particular, both the cross-country and firm-level studies are consistent with the importance of overcoming low population density to achieve financial development in Africa. Finally, we employ new data on the outreach of financial sectors, in particular bank branch penetration measures, to better understand the channels through which population density may affect different aspects of financial sector development. Moreover, we explore whether transportation infrastructure, urban agglomeration, and the share of arable land are driving the strong relationship between population density and banking sector development in Africa.

# **V. Within-country Study: Equity Bank and Financial Access in Kenya**

Our cross-country and firm-level analyses both indicate that sparsely populated regions pose a particularly severe problem for African financial development. What types of financial institutions are best suited to providing services in such areas? A satisfactory answer to this question is well beyond the scope of the current paper, but in this section we examine the effects of the recent expansion of bank branch networks in Kenya to get some sense of the factors that drove a large increase in use of bank accounts and loans within a relatively short time period.

32
and only 2 percent knew that lower incomes were not liable for business income taxes. In Bolivia, McKenzie and Sakho (2010) report that only one-third of informal owners know where the tax office is located (which is the registration location), and only 10 percent have even heard of the commerce registry.

A natural policy response to this lack of information is to try and provide information to firms about how to register, and about the possible benefits of formalizing. However, two randomized experiments which have done this have found zero resulting increase in formalization. In Belo Horizonte, Brazil, Andrade et al. (2013) gave a glossy brochure prepared by a marketing team of the State Government to 208 informal firms, and find that firms receiving this brochure were no more likely to register over the following year. In Bangladesh, de Giorgi and Rahman (2013) had field staff deliver information and brochures to 1500 informal firms, and find that fewer than 5 percent registered, with this number being no greater than control group firms not given the information.

Two randomized experiments also investigate the impact of lowering the cost of formalizing along with this information. In Sri Lanka, de Mel et al. (2013) provide information and agree to reimburse the full cost of registering for taxes (approximately US$10) to 104 firms, with only one firm taking up the offer and formalizing. In Belo Horizonte, Brazil, Andrade et al. (2013) provide information, have the government waive all registration costs and the sanitary and inspection taxes for the first year (approximately US$200), and provide free mandatory accounting services for the first year (approximately US$1800). This package of information and reduced costs is offered to 255 informal firms, with again only one single firm taking up the offer. Lowering the cost of registering for taxes thus appears to have very little impact on registration of existing firms. One reason for this finding may be that the ongoing costs of being registered, including tax payments, accountant costs, and other fees, still deter firms from registering even if the process of doing so is simplified.

In many countries, the formalization process requires registration with more than one government agency, implying different ongoing costs of being registered with each agency. A municipal license is often the first license that informal firms receive, and because it typically then requires only a small annual license fee, but no sizeable local tax payments, it is less expensive for firms than registering with the national (and state) governments for business taxes. We therefore might expect to see firms being more willing to obtain this license if its cost is lowered. Evidence along these lines comes from a randomized experiment conducted by Alcázar et al. (2010) in Lima, Peru. They follow-up on a reform which had lowered the time to get a municipal license from 160 days to 1.6 days by trying to induce 8
# **Table 11. Aggregate Impact of the Internationalization Events**

This table shows the aggregate implications of the 2013-20 foreign internationalization events for firm equity financing and investment activity publicly listed firms with residence and operations in mainland China. We compute the aggregate impact for each variable using estimates from the difference-in-differences regressions in Appendix Table 5 and Appendix Table 6. Columns 1 to 4 show the actual cumulative aggregate outcomes (2013-20) for each variable and group of firms. For market capitalization and cash, which are stock variables, the columns report the aggregate outcomes in 2020. Columns 5 to 8 show the aggregate effect of the internationalization events as a percentage of the actual aggregate outcomes. Values are expressed in trillions of 2011 U.S. dollars (USD). PSM = propensity score matched.

|||**Aggregat**<br>(USD, T<br>|**e Values**<br>rillions)||**Sha**|**re Attributed to I**<br>(Percentage of A<br>|**nternationaliza**<br>ggregate Values)|<br>**tion**|
|---|---|---|---|---|---|---|---|---|
|Sample:||Full Sample||PSM Sample||Full Sample||PSM Sample|
|Comparison:|Connected|Domestic Listed|All Listed|Connected|% of<br>Connected|% of Domestic<br>Listed<br>|% of All Listed|% of<br>Connected|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Equity Raised (2013-20 cumulative)|0.41|0.48|0.66|0.12|33.1|28.4|20.4|59.3|
|Market Cap. (2020)|4.42|4.87|8.15|0.89|32.5|29.9|17.8|46.8|
|Capex (2013-20 cumulative)|1.17|1.35|2.72|0.23|12.4|10.7|5.3|37.3|
|Acquisitions (2013-20 cumulative)|0.18|0.20|0.34|0.05|13.1|12.3|7.2|38.7|
|Cash and ST. Investments (2020)|1.06|1.17|1.89|0.18|26.6|25.2|15.6|35.6|
|R&D (2013-20 cumulative)|0.27|0.31|0.47|0.07|27.7|23.9|16.0|37.0|