rafmacalaba/datause-encoder-data huggingface dataset:
**page** 21

Table 6. EsCimation of If in Abengourou, Ivory Coast Under Constanc and Increasing Returns to Scale

|Constant<br>Returns<br>Incre<br>to Scale<br>to|asing Ret<br>Scale|urns|
|---|---|---|
|**(s-l.0)**<br>**(su.9)**|(s. 8)|**(su.7)**|
|1. Population covered by V8Ws attached<br>to a sngle fixed center.<br>7,900<br>5,750|3,860|2,320|
|2. Number of fixed centers "needed" to srve<br>the entire region of Abengourou:<br>17<br>24|36|60|
|3. Eimated<br>number of encounters by VHWs attached<br>to a. single flxed center with budget given under<br>Asumption F2 below (ie. pf V*):<br>37,100<br>27,000|18,200|10,900|
|4. Estimate of the parameter pf.<br>10.6<br>7.7<br>Data and Asumptions Used:|5.2|<br>3.1|
|Abengourou Mobile Team:<br>Fixed Centers as VaW Super|visors:||
|Ml. Estimated rural population<br>F1. Number of vaccinations<br>of Abengourou:<br>138,000<br>with no encounters,<br>V*:||3500|
|42. Total cost of mobile<br>F2. Budget for V* vaccinatio<br>team for one year. 5,293,814<br>F3. Assumed number of supe<br>M43. Cost of vaccine:<br>1,009,600<br>trips per year to each V|ns,<br>**C*:**<br>rvision<br>aW:|735,700<br>3|
|44. Cost to reach rural pop.<br>_F4._Maximum cost per spvsn<br>w/o vaccinating:<br>4,284,210<br>that stays within budget,|trip<br>C*:|245,233|
|M5. Average Cost per Cap<br>F5. Assumed number of enco<br>to reach rural pop.:<br>31.0'<br>with VHW per capita per|unters<br>year|4.7|

NOTES: Row 1: Asume the dLmple model of transport and supervision cost C - A Qs, where a is the returns to scale parameter with a similar interpretation to the s introduced in equatLon (2) of section m of the text, Q is the quantity of rural residents to vhom the traveling health professionals come sufficiently close to either vaccinate almost all of the target group among them or to supervise the VHW who **treats** them, and C represents all costs except drugs and/or vaccines. Then the ratio of two values of Q is equal to the ratio of the two corresponding costs to the power l/s. The entries in this row are thus equal to: (item MDl)xCtem F4/Item M4)(1/a) Row 2: Item Mli/Row 1.

Row 3: Row 1 x Item _F5._ Row 4: Row 3/Item F1 Ite Ml: The rural population **is estimated at** about 69% of the total population of Abengourou given by Sanoh as 200,000 in 1981. Items M2, M3, M4 ae from Table 3 of a draft &nal report on a cost-effectiveness study by L. Sanoh of CIRES, Abidjan and the Boston University Strengthening Health Delivery Services Project, and are measured in 1981 CFA francs. (Approx. 260 CPA francs/dollar in 1981). (Sanoh, 1983) Item M5: Item M4/item Ml.

(Notes continued on next **page.)**
20 Figure 8: Rural Africa derives a much larger share of income from agriculture, a similar share from non‐farm self‐employment, but less from wages (in and outside agriculture) <!-- Start of picture text --> 100% 1 3<br>7<br>90% 15<br>15<br>80%<br>14 Others<br>8<br>70%<br>5<br>Transfers<br>60%<br>21<br>Non‐farm self empl<br>50%<br>40% 13 Non‐farm wage income<br>30% 64 Ag wage income<br>20% On farm income<br>34<br>10%<br>0%<br>SSA ROW<br><!-- End of picture text --> Note: ROW=Rest of the World Source: Davis, Di Giuseppe, and Zezza, 2017.

Of course, there are differences across African countries, and within countries, partly driven by agro‐ ecological potential and market access, which are discussed in more detail by the authors. Yet the overall employment and income patterns discerned here correspond to the picture emerging from the macro‐ data. There has been structural transformation away from agriculture in SSA over the past two decades— the agricultural share in GDP fell from 23 percent in 1995 to 17 percent in 2013, and the share of agricultural employment likewise fell by an estimated 10 percentage points (Barrett et al., 2017). Yet, it has been towards self‐employment in (non‐tradable) services, not wage employment in tradable manufacturing (Rodrik, 2016). This is partly because of Africa’s commodity boom, which fueled the emergence of consumption cities, characterized by higher shares of imports and employment in non‐tradable services (Gollin, Jedwab, and Vollrath, 2016). Given their prevalence, this raises the question of whether informal rural household enterprises can serve as pathways out of poverty.

_Households in rural Africa diversify into non‐farm activities mainly for survival_ . While the dominant form of off‐farm income, there is little systematic evidence on the prevalence, patterns and performance of Africa’s rural non‐farm enterprises. Nagler and Naudé (2017) are the first to exploit the enterprise modules of the LSMS‐ISA survey to characterize systematically the rural household enterprise landscape in SSA. They find that non‐farm self‐employment activities in the African household are indeed mostly oriented around survival. The evidence lies in the nature of these activities: most are small, unproductive, informal household enterprises, operated from home, without any non‐household employees and often operating only for a portion of the year. They are concentrated in easy‐to‐enter activities, such as sales and trade, rather than in activities that require higher starting costs, such as transport services, or educational investment, such as professional services.
Table 18 summarizes our adjustment results for 1,306 Functional Urban Areas with populations greater than 500,000. It tabulates percent deviations by range for urban areas with higher- and lowerthan-expected emissions. The table reveals the frequency of relatively large deviations in both categories, with the representation of higher-percent ranges significantly greater for urban areas with lower-than-expected emissions.

|Table 18:<br>|FUAs: Distribution of Percent Dev<br>by Performance Category<br>Emissions Relative to<br>Expected Value|iations|
|---|---|---|
|% Deviat|ion<br>Lower<br>Higher|Total|
|0-1|64<br>52|116|
||9.28<br>8.44|8.88|
|1-5|120<br>150|270|
||17.39<br>24.35|20.67|
|5-10|91<br>104|195|
||13.19<br>16.88|14.93|
|10-20|123<br>123|246|
||17.83<br>19.97|18.84|
|20-50|151<br>98|249|
||21.88<br>15.91|19.07|
|50+|141<br>89|230|
||20.43<br>14.45|17.61|
|Total|690<br>616|1,306|
# **Table 1: Characteristics of Civil Servants**

## **Means and standard deviations**

||**(1) All**|**(2) Federal**|**(3) Regional**|**(4) Local**|
|---|---|---|---|---|
|**Organizational Characteristics**|||||
|**Number of organizations**|382|5|54|323|
|**Number of bureaucrats per organization**|5.71|56.60|8.67|4.43|
|**Number of managers per organization**|1.22|6.80|2.57|0.90|
|**Number of employees per organization**|3.58|49.20|5.41|2.56|
|**Span of control (employees per manager)**|7.79|27.28|15.98|5.97|
|**Official's Characteristics**|||||
|**Number of bureaucrats**|1831|280|431|1120|
|**Age**|35.30|35.87|38.10|34.07|
||[8.85]|[9.10]|[8.74]|[8.58]|
|**Years in position**|2.70|2.29|2.98|2.69|
||[2.74]|[2.88]|[2.87]|[2.64]|
|**Years in organization**|7.34|5.80|7.97|7.48|
||[7.13]|[6.47]|[7.27]|[7.18]|
|**Years in civil service**|13.13|12.87|15.49|12.29|
||[8.88]|[8.98]|[9.00]|[8.65]|
|**Number of service organizations worked in**|2.78|3.02|3.08|2.55|
||[2.01]|[2.07]|[2.40]|[1.73]|
|**Grade**|7.93|7.53|10.89|6.90|
||[13.06]|[9.92]|[17.97]|[11.21]|
|**Education [undergraduate degree=1]**|0.82|0.96|0.90|0.76|
||[0.38]|[0.19]|[0.30]|[0.43]|
|**Education [masters degree=1]**|0.11|0.35|0.22|0.01|
||[0.32]|[0.48]|[0.42]|[0.12]|
|**Gender [female=1]**|0.20|0.27|0.23|0.18|
||[0.40]|[0.44]|[0.42]|[0.38]|
|**Manager**|0.25|0.12|0.32|0.26|
||[0.44]|[0.33]|[0.47]|[0.44]|

**Notes:** Standard deviations are in parentheses.  The unit of observation for the first panel is the civil servant and for the second panel the organization.  Only employees and managers responded to the information module and hence only these individuals are used for the statistics in the first panel.  These statistics are based on the survey sample and not on administrative data.  The span of control is taken from the survey question "How many personnel do you manage?"; the average is taken for an organization if there is more than one manager; and the organization-level statistics are reported (each organization has an equal weight in the `By Organization' statistics).  `Manager' refers to managers within the organization.  Grade is the official civil service grade of the professional civil servant, ranging from 1 to 17 in the sample. The 2013-14 National Civil Service Human Resource Statistics Abstract (Ministry of Civil Service) records the overall female employee percentage to be 35%; this statistic includes frontline staff.  Figures are rounded to two decimal places.
the ED measure.<sup>69</sup> The positive, large, and statistically significant coefficient on the SFD-DFD-ED interaction term suggests that the financial systems of the source and destination countries are not substitute sources of external finance. Instead, they seem to work in synergy. In column (8), we find the same result when we include a triple interaction term involving both financial development variables and the TANG measure. Higher DFD (SFD) appears to have a larger effect on relative bilateral FDI in financially vulnerable sectors when SFD (DFD) is high. This finding is new in the literature and may be explained by the possibility that borrowing by firms to engage in FDI becomes easier when financial institutions of both source and destination countries are involved. In each country, banks may be more willing to lend if the presence of a foreign lender guarantees better monitoring of the activities of both the parent firm and its foreign affiliate.

_Relative effects vs. absolute effects of financial development_ Our identification of the effects of financial development is purely based on the differential effects of financial development across sectors varying in their financial vulnerability. In that way, we can establish with more certainty the causal impacts of financial development on FDI. However, a drawback of this approach is that our estimates are only informative about the direction of the effects of SFD and DFD on the _relative_ volume of bilateral FDI in financially vulnerable sectors. They do not say anything about the direction of the effects of SFD and DFD on the _absolute_ volume of bilateral FDI in financially vulnerable sectors. We can gain some insights on their signs by replacing in column (8) the time-varying country-pair fixed effects by time-invariant countrypair fixed effects. This allows us to identify the coefficients on the SFD and DFD variables when included on their own and by extension to calculate the absolute effects of financial development on FDI. We limit the risk of an omitted variable bias by controlling on both sides for GDP, GDP per capita, and institutional quality; these variables have been previously defined. We also rescale the ED and TANG variables so that the coefficients on the financial development variables reflect the effects of financial development on a (hypothetical) sector with the lowest value of ED and the highest value of TANG.

> 69For ease of interpretation, we subtract the sample mean from the financial development variables before constructing the interaction term.

32
Over a longer term perspective the ISSER calculation suggested that the proportion went up from 25 per cent in 1960 to its 1990 level of 45 per cent.

# **6.4.2 The Formal-Informal Earnings Difference in Urban Ghana**

The crucial factor here seems to be the formal-informal earnings divide which has already been analyzed in Sections 3 and 5 above. Real wage recovered quite strongly in the public sector after the ERP (and in fact there has been a further large increase in July 1992). Mean wages of these workers were above the earnings of private formal sector employees in large firms, and considerably above those finding employment in micro and small enterprises. The earnings of some of the self-employed in petty businesses could be as low as and, indeed below the low income of informal sector wage earners. It is clear that the self-employed sector must be characterized by a wide dispersion of earnings and it is this which has contributed to the increased inequality of income in the urban areas relatively to the rural.

# **6.4.3 Income Growth and Poverty Reduction in the Rural Economy**

The analysis of the GLSS surveys show that poverty measured by the headcount ratio fell sharply for both cocoa farmers and food producing farmers between 1987-8 and 1991-2-- from 42.7 percent to 32.0 percent for the former and from 42.8 percent to 37.2 percent for the latter (World Bank 1995a, Table 3.4,p.28). The work also suggests that the poverty reduction is dominated by the growth of income. Distributional changes by themselves would have, if anything, increased the poverty ratio in most regions of rural Ghana (Jones and Xiao, 1995, Table 2, p.6). However, it has been noted that this widespread reduction in poverty ( as measured by the proportions in the distribution of expenditure below the poverty line) has not been accompanied by matching increase in agricultural production or producer income. National accounts estimates suggest that agriculture performed below its potential after the initial spurt at the beginning of the ERP, and has been a constraint on accelerated growth since then (see table 5.1 World Bank 1995b, p.39 ).

The slow-down in the recovery of cocoa production is clearly an effect of a downturn in the producer price of cocoa-and hence accentuates the deceleration in the growth of income of cocoa producers. Alderman (1994) estimated that payments to cocoa farmers increased from 5 billion cedis in 1984 to 13 billion in 1987-8 (in constant prices), but this was a one-time shift largely deriving from the depreciation of the cedi in the early years of adjustment. Since then world prices of cocoa have been declining, and have not been matched any further exchange rate depreciation.

We. have, however, already noted that cocoa is only a small part of the sources of agricultural income in Ghana. For the non-export agricultural sector producer prices are not affected by the exchange rate, or by other aspects of government price policy. Unlike many other African countries food prices are not subsidized in Ghana. Supplyand-demand conditions in the domestic market determine real producer prices-and in 46
# **Table 23: DID Regression (9)**

||No. of stud<br>Total|ents|Boys||Girls||Preschool||
|---|---|---|---|---|---|---|---|---|
|D|0.104|(0.131)|0.143|(0.132)|-0.234|(0.130) *|-0.007|(0.099)|
|T|-1.201|(0.157) ***|-0.719|(0.147) ***|-0.924|(0.142) ***|-0.105|(0.104)|
|D*T|0.430|(0.190) **|0.163|(0.179)|0.588|(0.176) ***|0.154|(0.131)|
|lnHH size|1.044|(0.124) ***|0.861|(0.111) ***|0.786|(0.108) ***|0.292|(0.072) ***|
|Adult share|-3.885|(0.278) ***|-2.938|(0.245) ***|-2.837|(0.237) ***|-1.420|(0.156) ***|
|HH head sex: Male|-0.188|(0.148)|-0.281|(0.134) **|-0.058|(0.134)|0.129|(0.102)|
|HH head education: Elementary|-0.032|(0.100)|-0.012|(0.096)|-0.026|(0.094)|0.069|(0.066)|
|HH head education: Secondary|-0.655|(0.213) ***|-0.432|(0.188) **|-0.411|(0.185) **|0.230|(0.147) *|
|HH head education: University|-0.694|(0.367) *|-0.617|(0.304) **|-0.317|(0.355)|0.406|(0.330)|
|Appliance ownership: Refrigerator|-0.052|(0.146)|-0.075|(0.144)|0.014|(0.142)|-0.140|(0.113)|
|Appliance ownership: Color TV|0.113|(0.132)|-0.031|(0.131)|0.110|(0.130)|-0.006|(0.100)|
|Appliance ownership: Radio|-0.028|(0.097)|0.106|(0.091)|-0.087|(0.091)|0.003|(0.067)|
|Appliance ownership: Gas stove|0.272|(0.129) **|0.237|(0.122) **|0.042|(0.120)|0.019|(0.095)|
|Appliance ownership: Washing|||||||||
|machine|0.072|(0.135)|0.149|(0.127)|-0.009|(0.125)|0.031|(0.091)|
|Gas use for cooking|0.263|(0.123) **|0.088|(0.116)|0.151|(0.114)|0.057|(0.084)|
|Power use for lighting|0.232|(0.141) *|-0.003|(0.134)|0.264|(0.135) **|0.060|(0.103)|
|Tap water for cooking|-0.093|(0.098)|0.006|(0.094)|-0.206|(0.095) **|0.028|(0.071)|
|Constant|-0.692|(0.414)*|-1.705|(0.371)***|-1.808|(0.360)***|-3.639|(0.245)***|
|Obs.|2,064||2,064||2,064||2,064||
|R-squared|0.391||0.298||0.278||0.114||
|F-stat|116.01||66.91||58.63||15.05||

Note: Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at 10%, 5% and 1%, respectively.

42
compared to an average of 0.57 lines for Sub-Saharan Africa (SSA), 1.97 for South Asia, 5.99 for North Africa, 7.06 for East Asia and 12.14 for Latin America. Low coverage rates were further aggravated by the recurrence of services outages due to poor system maintenance. At<sup>this</sup> time, it was estimated that it could take up to six months to have a simple telephone fault repaired.

Despite the entry of Telekom Networks Malawi (TNM) in 1995, cellular penetration remained very low. By 1998, there were only 10,500 cellular subscribers in Malawi, representing about 0.1 per 100 inhabitants. In part this was the result of TNM's policy of offering only post-paid services, which precluded people who couldn't pay the required deposit and satisfy other conditions designed to ensure that they wouldn't be a credit risk from obtaining services. It also reflected the low level of interconnection capacity between the fixed line<sup>and</sup> cellular networks.

<!-- Start of picture text --> 35%<br>30%<br>25%<br>20%<br>20% -<br>10% _<br>5%<br>0) c, o, c -E°<br>ME1985-90 * 1990-95 131995-1998 1<br>l 1985-90 U 1990-95 01995-1998 l<br><!-- End of picture text --> Figure 2: Average Annual Growth of Main Lines (In %) Source: International Telecommunication Union (2001). ' Sub-Saharan Africa excludtng South Africa MPTC's corporatization in 1995 failed to have an appreciable impact on access. Annual growth of main lines had averaged 6.1 percent in TU Malawi between 1985 and 1990, compared to 6.5 percent for SubSaharan Africa (SSA). However, over the 1990s, growth slowed in Malawi. =E Following corporatization, growth of main lines fell to an annual rate of 2.9 percent in Malawi, as growth dramatically accelerated in other African countries, especially in early reformers such as Ghana (see Figure 2). Although MPTC had intended to connect 40,000 additional main lines following commercialization, it only managed to achieve 8 percent of its target by the time of reform in 1998. This slow growth meant that the number of mainlines per 100 inhabitants declined<sup>by3</sup> percent between 1995 and 1998, compared to an increase of 35 percent in SSA as a whole<sup>(see</sup> 9
Fortunately, this lack of homogeneity in our sample is not a problem in our fxed effect regressions because we use country dummies which capture unobserved country-specific differences in economic performance.'

# Estimation Results

The fixed effect estimation results show that increased openness has had a favorable impact on economic performance of Sub-Saharan Africa.' Differences in openness account for differences in cross-country economic performance. What is even more interesting is that the "coefficients" of openness for Sub-Saharan Africa are not significantly different from those for Non-Sub-Saharan Africa in most cases. The results on openness are also surprising robust to different sample sizes and different measures. All four measures are significant and have the right sign. In fact the results also hold when we control for macro-economic policy.

# Cross Section Estimation

Cross-section and fixed effect equations are estimated in both level" and 'differencew forms involving GDP level and GDP growth rate as performance variables. Cross-section estimates use period-average data for "level" and period-growth data for "change," the latter being the difference between the base and terminal years of the period; the other uses year-to-year difference.

The cross-section estimates using period-average data, which is in the tradition of most of the existing literature, yield poor results for Sub-Saharan Africa (SSA). Period averages were computed

> 2 This is also confirmed by the similarity of the coefficient estimates from regressions using the 12 countries (not reported in the paper) and that using the whole sample.

'rhe openness variable performs poorly in the traditional cross-section equations using periodaverage data.

19
P. is the population of five provinces, B1j is province i's base for revenue sources j, t, is the: national average tax rate for revenue source j, or: = 1jTRij/X1<sup>Bij</sup> where TRj; is actual revenues under revenue source j in province i. The total entitlement of province i, TE1, equals the sum of all the entitlement under different revenue sources: 'FE, = EjEij.

This program equalizes have-not provinces up to the national average--only those provinces that were beJlow the national average are affected by the program--and is paid for out of general federal revenues. Provinces whose tax capacities are above the national average--the have provinces---are not equalized down. Thus, the system does not fully equalize tax capacities across all provinces.

'Currently, there are 30 revenue sources for this program. The main sources include personal income taxes, corporate income tax, secession duties, general sales taxes, gasoline taxes, motor vehicle license fees, alcoholic beverage taxes, forestry taxes, oil royalties, natural gas royalties, sales of Crown leases and reservations on oil and gas lands, other oil and gas revenues, metallic and non-metallic mineral revenues, water power rentals, other provincial taxes, and miscellaneous provincial revenues.

Table 2.2. Erovincial Per Capita Notional Revenues Before and After Equalization, 1990-91

|Provinces|Notional<br>revenue yield'|Equalization|Index of<br>Tax capacityb|Index of<br>fiscal capacity'|
|---|---|---|---|---|
|Newfoundland|2,898|1,686|0.63|0.93|
|Prince Edward Island|2,988|1,595|0.65|0.93|
|Nova Scotia|3,517|1,066|0.76|0.93|
|New Brunswick|3,295|1,288|0.71|0.93|
|Quebec|3,973|610|0.86|0.93|
|Ontario|5,085|...|1.10|1.03|
|Manitoba|3,737|847|0.81|0.93|
|Saskatchewan|4,058|525|0.88|0.93|
|Alberta|6,306|...|1.36|1.2S|
|British Columbia|4,808|...|1.04|0.97|

a/ Per capita yield of tax bases at national average tax rates.

b/ Notional revenue before equalization relative to the national average.

c/ Notional revenue yield after equalization relative to the national average. Source: Broadway and Hubson (1993), p.59.

In most cases the determination of tax bases is relatively straight forward, based on provincial data. The most complex calculation involves the determination of the property tax base. Because assessment practices vary markedly from province to province, a standardized base cannot be inferred from provincial data. Instead, the value of land and capital in residential property, commercial, industrial and federal property, and farm property must be calculated by province. In the case of residential property, the value of buildings in residential use is calculated as a percentage of the value of the total residential capital stock. T1he value of land in residential use is calculated as a percentage of personal disposable income (net 7