rafmacalaba/datause-encoder-data huggingface dataset:
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE over a number of years. Safaricom, a mobile operator in Kenya, offered some of its shares on the Nairobi Stock Exchange in 2008, the largest initial public offering in East Africa.<sup>8</sup> Foreign investment has contributed to connectivity and service enhancement, particularly when the same investor is involved in different countries. For example, Zain offers attractive mobile roaming prices for its subscribers roaming between Kenya, Tanzania, and Uganda. Collaboration over mobile roaming and fiber optic connectivity between Rwandatel and Uganda Telecom has been facilitated by common ownership. These multi-country networks have provided the basis for the regional roaming arrangements detailed in table 4.4 and box 4.2.

**Table 4.4 . Foreign investors in the East Africa’s telecom sector**

||Econet (Zimbabwe)|ESSAR (India)|France Telecom|Libyan African<br>Portfolio|Orascom (Egypt)|Millicom (Luxembourg)|MTN (South Africa)|Vodacom (South<br>Africa)|Vodafone (UK)|VTEL (100%)|Zain|TOTAL|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Burundi|100%||||100%|||||100%||3|
|Kenya|36%|34%|51%||||||40%|||5|
|Rwanda||||80%||88%|55%||||80%|4|
|Tanzania||||||100%||65%|||60%|2|
|Uganda|||53%|51%|||95%||||100%|4|
|EAC|2|1|2|2||2|2|1|1|1|3||
|Ethiopia||||||||||||0|
|Sudan|||||||85%||||100%|2|
|East Africa|0||0|0||0|1||||1||
|TOTAL|2|1|2|2||2|3|1|1|1|4||

Source: Derived from Ampah and others 2009.

Ethiopia and Sudan have for some time enjoyed access to international fiber optic systems. In the case of Sudan, the 333 km SAS-1 submarine cable system links Port Sudan to Jeddah. Ethiopia has a fiber optic link between Addis Ababa and Djibouti, which provides access to submarine cables landing in Djibouti. There is also a fiber optic cable linking Metema in the country’s north into Sudan, which allows Ethiopia to access other cables landing in Sudan.

> 8 http://www.bloomberg.com/apps/news?pid=20601116&sid=aqOUSqNggEf4&refer=africa#

51
# 1. INTRODUCTION

Since Vietnam started its transition from a centrally planned to a market oriented economy under its _doi moi_ (“renovation”) policy in 1986, Vietnam has been among the fastest growing economies with an average annual growth rate of 6.9 percent.<sup>2</sup> Vietnam’s trade, measured by its sum of imports and exports, grew even faster than its Gross Domestic Product (GDP), as the share of trade relative to GDP increased from 23.2 percent in 1986 to 112.5 percent in 2000, 139.0 percent in 2004 and 169.6 percent in 2007 (WDI, the World Bank). However, Vietnam’s industrial employment share initially remained unchanged at around 12 percent of total employment through the end of the 1990s (WDI), suggesting that expansion of trade does not necessarily lead to industrial job creation. In contrast, since the recent millennium, Vietnam’s industrial employment share in total employment grew substantially, rising from 12.4 percent in 2000 to 17.4 percent of total employment in 2004 (WDI). The U.S.-Vietnam Bilateral Trade Agreement (BTA) of 2001, which led to a dramatic expansion of labor-intensive manufacturing exports to the U.S., appears to have contributed to the surge.

The standard Heckscher-Ohlin (H-O) theory predicts that, as developing countries are abundant in unskilled labor and scarce in skilled labor, freer trade would lead a developing country to specialize in a sector which uses its unskilled labor intensively, raising labor demand in the latter sector. Its companion theory, the Stolper-Samuelson (S-S) theorem (1941), suggests that the increase in the relative output prices of unskilled-labor-intensive goods relative to skilled-labor-intensive goods would translate into a rise in the relative wages of unskilled labor, narrowing the wage gap between skilled and unskilled workers. However, the validity of the Heckscher-Ohlin-Samuelson (H-O-S) theory has been challenged since, contrary to the prediction of the theory, many developing countries experienced an _increase_ rather than a 2 The average for the period 1986-2011 (the World Development Indicators (WDI), the World Bank).

2
**Table 4.3. Government budget in 2012 and by simulation in 2030 (Percent of nominal GDP)**

|||**2012**|**base**|**min+**|**inf+**|**hd+**|**geff+**|
|---|---|---|---|---|---|---|---|
|**Receipts**|**Domestic transfers**|1.5|1.5|1.5|1.5|1.5|1.5|
||**Foreign transfers**|21.5|12.6|10.7|12.3|12.5|12.5|
||**Direct taxes**|4.5|9.9|12.6|9.7|9.8|9.8|
||**Import tariffs**|6.9|5.2|4.8|5.4|5.3|5.4|
||**Other indirect taxes**|4.4|3.4|3.3|3.5|3.4|3.5|
||**Domestic borrowing**|-0.2|1.0|1.0|1.0|1.0|1.0|
||**Foreign borrowing**|0.8|1.5|1.3|1.5|1.5|1.5|
||**Total**|39.4|35.2|35.3|34.8|35.1|35.2|
|**Spending**|**Education**|5.0|5.0|5.4|4.8|7.1|5.2|
||**Health**|4.5|4.4|4.7|4.2|6.3|4.8|
||**Water-sanitation**|2.1|0.9|1.1|0.9|1.4|1.0|
||**Energy**|3.7|3.6|3.9|5.6|3.6|3.8|
||**Roads**|4.9|3.1|3.4|5.0|3.0|3.3|
||**Other infrastructure**|2.0|1.1|1.3|2.0|1.1|1.2|
||**Other**|13.8|13.5|12.1|8.8|9.0|12.3|
||**Domestic transfers**|2.9|2.9|2.9|2.9|2.9|2.9|
||**Domestic interest**|0.4|0.4|0.3|0.4|0.4|0.4|
||**Foreign interest**|0.1|0.2|0.2|0.2|0.2|0.2|
||**Total**|39.4|35.2|35.3|34.8|35.1|35.2|

**Note:**

**Columns other than 2012 show averages for 2013-2030 by simulation.**

26
**Table 2: Robustness Checks: Fiscal Multipliers**

|||**Impact**|**1year**<br>_Fiscal po_|**2years**<br>_sition only_|**Long run**|**Impact**<br>_Re_|**1year**<br>_cessions an_|**2years**<br>_d fiscal posit_|**Long run**<br>_ion_|
|---|---|---|---|---|---|---|---|---|---|
|**Alternative fiscal position**||||||||||
|Fiscal balances|_Strong_<br>_Weak_|[0.16, 0.31]<br>[-0.08,0.08]|[0.29, 0.63]<br> [0.16,0.54]|[0.43, 1.10]<br>[0.19,0.76]|[0.39, 1.38]<br> [-0.05,0.97]|[0.37, 0.57]<br> [-0.02,0.24]|[1.34, 2.00]<br> [1.04,1.78]|[1.76, 2.66]<br>[1.25,2.16]|[1.09, 2.35]<br>[0.65,1.74]|
|**Alternative business cycle dates**||||||||||
|Auberch and Gorodnichenko (2012b)|_Strong_<br>_Weak_|...<br>...|...<br>...|...<br>...|...<br>...|[0.54, 0.85]<br>[0.48,0.80]|[1.23, 1.75]<br>[1.36,1.96]|[1.13, 1.78]<br>[0.85,1.52]|[0.61, 1.48]<br> [-0.56,0.54]|
|||_Flexible_|_exchange ra_|_te and fiscal_|_position_|_Fixed e_|_xchange rat_|_e and fiscal_|_position_|
|<br>**Controlling for exchange rate regime**|<br>_Strong_<br>_Weak_|<br>[0.02, 0.23] <br>[-0.14,0.07]|<br> [-0.06, 0.44]<br> [-0.23,0.15]|<br> [-0.13, 0.62]<br> [-0.63,-0.08|<br> [-0.37, 0.81] <br>][-2.28,-0.90]|<br> [0.72, 1.01]<br> [0.53,0.90]|<br>[2.11, 2.84]<br>[1.57,2.32]|<br>[2.26, 3.29]<br>[1.10,2.20]|<br>[1.44, 3.18]<br> [-7.81,-0.72]|

_Note:_ The table presents estimates of fiscal multipliers from alternative specifications of the IPVAR model for the strong and the weak fiscal positions. The top panel presents the multipliers using an alternative measure of fiscal position. The middle panel considers an alternative business cycle dating scheme. The bottom panel presents estimates of fiscal position-dependent multipliers for flexible and fixed exchange rate regimes. Fiscal position is strong (weak) when government debt is high (low) or when fiscal balances are low (high). When fiscal position is measured in terms of government debt, the strong position corresponds to the 10<sup>th</sup> percentile and the weak position corresponds to the 90<sup>th</sup> percentile. When fiscal balances are taken as the measure of fiscal position, the strong position corresponds to the 90<sup>th</sup> percentile and the weak position corresponds to the 10<sup>th</sup> percentile. Numbers reported in square brackets are the 16-84 percent confidence range.

24
hand, some firms may have more flexible production processes that can adapt to temporary power supply interruptions. Finally, firms may adopt different mechanisms to cope with power constraints. For instance, some firms may adjust their production schedules, switch to off-peak hours or implement energy-saving measures during outages, helping them to minimize the negative impact of power disruptions.

In a fully competitive final output market, there is a single final product _Y_ , and it is aggregated from the output _Ys_ of _S_ manufacturing industries (or branches) by the following production function: where _θs_ is an output elasticity parameter for industry _s_ . Minimizing production costs implies: _Ps_ is the price of the output _Ys_ of the industry _s_ , and the price of the final product is _P ≡_ ∏<sup>_S_</sup> _s_ =1 � _θPss_ � _θs_ . The output _Ys_ of the industry _s_ is the CES aggregate of _Ms_ types of differentiated products: where _σ_ is the elasticity of substitution between the differentiated products within the industry _s_ .

The production function of each differentiated product is given by a Cobb-Douglas function of the firm’s TFP and three production inputs, including capital, labor and energy: where _αs_ denotes the capital share of industry _s_ , _βs_ denotes the labor share of industry _s_ , (1 _− αs − βs_ ) denotes the share of intermediate products or the share of energy of industry _s_ , and _Asi_ denotes the level of TFP of firm _i_ . Note also that this framework allows input shares to differ between different industries but not between firms in the same industry.

The profit maximization program for monopolistically competitive firms with respect to _Ksi_ , _Lsi_ , and _Esi_ is as follows: where _r_ represents the price of capital, _w_ represents the labour cost, _PE_ represents the cost of intermediate inputs, i.e. energy. _τEsi_ represents the energy constraints (increase in the cost of energy, power outages leading to additional costs for back-up generators, increase in losses for firms, etc.) facing a representative firm _i_ .

The maximization of the profit (5) using the Lagrangian method of optimization combined with the first order conditions yields: 5
**<u>Table 4: Plot Characteristics for Pairs of Operator-cum-Landlords and Owner-cum-Tenants</u>**

||**Total**|**Only own**|**ed plots**|
|---|---|---|---|
||**Sample**|**Landlords**|**Tenants**|
|**Output and input use:**<br>||||
|Crop output/ha (birr)|3060.91|2709.98|3268.03|
|Plot size (ha)|0.33|0.35|0.32|
|Used fertilizer|0.37|0.30|0.41|
|Fertilized used per ha (kg)|50.75|41.28|56.34|
|Manure used|0.28|0.34|0.25|
|Manure used per ha (kg)|371.55|421.17|342.22|
|Used pesticides|0.07|0.04|0.09|
|Pesticides used per ha (kg)|0.22|0.09|0.30|
|Pair of oxen days per ha|25.50|24.75|25.95|
|Male family labor/ha (days)|113.54|109.00|116.23|
|Female family labor/ha (days)|66.18|76.72|59.94|
|Total family labor/ha (days)|153.66|145.65|158.38|
|Used hired labor|0.84|0.86|0.83|
|Hired labor/ha (days)|14.22|7.53|18.17|
|**Plot characteristics:**||||
|Years of possession|20.48|23.62|18.63|
|Good soil quality|0.51|0.56|0.47|
|Medium soil quality|0.34|0.32|0.35|
|Flat land|0.73|0.74|0.73|
|Gently sloped land|0.22|0.21|0.23|
|Irrigated land|0.08|0.06|0.08|
|**Crop choice:**||||
|Teff|0.29|0.24|0.32|
|Wheat/barely|0.20|0.19|0.21|
|Sorghum/maize|0.42|0.51|0.37|
|Pulses|0.12|0.12|0.13|
|Oilseeds|0.05|0.04|0.06|
|Number of observations|2213|822|1391|

_Source:_ Own computation from AU/WB/UG land tenure survey.

20
**Table 4. Difference (in percentage points) between statistics computed before and after winsorizing the target variable**

|**Country**|**Gini**|**MLD**|**Theil**|**Atkinson**|**H**|**PG**|**PG2**|
|---|---|---|---|---|---|---|---|
|Armenia|0.0|0.1|0.0|0.2|0.0|0.0|0.0|
|Bangladesh|0.5|0.7|1.6|0.6|0.0|0.0|0.0|
|Bolivia|1.7|3.7|7.8|5.5|0.0|0.0|0.0|
|Bulgaria|0.3|0.5|0.5|1.4|0.0|0.1|0.1|
|Burkina Faso|0.0|0.1|0.2|0.1|0.0|0.0|0.0|
|Cambodia|0.1|0.1|0.2|0.2|0.0|0.0|0.0|
|Cameroon|0.4|0.7|1.6|0.4|0.0|0.0|0.0|
|Côte d’Ivoire|0.8|1.3|2.8|1.0|0.0|0.0|0.0|
|Ecuador|0.2|0.4|0.7|0.4|0.0|0.0|0.0|
|Ethiopia|0.4|0.6|1.3|0.5|0.0|0.0|0.0|
|Georgia|0.6|0.9|1.9|0.9|0.0|0.0|0.0|
|Ghana|0.4|0.7|1.5|0.6|0.0|0.0|0.0|
|Guatemala|0.2|0.4|0.7|0.6|0.0|0.0|0.0|
|India|0.7|1.2|2.8|0.9|0.0|0.0|0.0|
|Iraq|0.3|0.4|0.7|0.4|0.0|0.0|0.0|
|Kyrgyzstan|0.4|0.4|0.6|0.8|0.0|0.1|0.1|
|Malawi|3.9|6.2|26.2|4.2|0.0|0.0|0.0|
|Mali|2.5|4.3|11.3|2.6|0.0|0.0|0.0|
|Mexico|2.2|4.0|8.1|4.8|0.0|0.0|0.1|
|Mongolia|0.2|0.2|0.3|0.9|0.0|0.0|0.0|
|Mozambique|2.4|4.5|10.4|4.8|0.0|0.1|0.1|
|Nepal|0.4|0.5|1.1|0.4|0.0|0.0|0.0|
|Nicaragua|0.2|0.4|0.6|0.6|0.0|0.0|0.0|
|Niger|0.2|0.3|0.7|0.3|0.0|0.0|0.0|
|Nigeria|4.5|7.5|24.4|5.0|0.0|0.0|0.1|
|Pakistan|0.2|0.3|0.6|0.3|0.0|0.0|0.0|
|Peru|0.2|0.4|0.7|0.5|0.0|0.0|0.0|
|Rwanda|1.4|2.5|6.3|1.8|0.0|0.0|0.0|
|Senegal|0.5|0.9|1.5|1.8|0.0|0.0|0.1|
|Serbia|0.3|0.4|0.7|0.6|0.0|0.0|0.0|
|Sierra Leone|0.1|0.2|0.2|0.4|0.0|0.0|0.0|
|Tanzania|0.3|0.4|0.9|0.4|0.0|0.0|0.0|
|Uganda|1.5|2.7|8.0|1.8|0.0|0.0|0.0|
|Vietnam|1.4|2.0|4.4|1.6|0.0|0.0|0.0|
|_Average_|0.9|1.5|3.9|1.4|0.0|0.0|0.0|
|_SD_|1.1|1.9|6.3|1.6|0.0|0.0|0.0|
|_Min_|0.0|0.1|0.0|0.1|0.0|0.0|0.0|
|_Max_|4.5|7.5|26.2|5.5|0.0|0.1|0.1|

Note: MLD is for Mean Logarithmic Deviation; ‘Atkinson’ is for the Atkinson index, where the inequality aversion parameter epsilon is set equal to 2; H is for the poverty headcount ratio; PG is for the poverty gap; PG2 is for the poverty gap squared. Poverty indices are computed using a relative poverty line, equal to 60% of median per capita consumption in the original distribution.

32
12 How does the performance of CEEC as a region and individual countries compare with that of our group of comparators? The increase in CEEC exports over 1993-97 of 185% was larger than corresponding increases in the value of Mexican exports (91%) and Turkish exports (127%), but lower than the increase in Malaysian exports (227%). Note that Hungary and Poland recorded larger increases than Malaysia. On the import side, Turkey (129%) recorded the largest increase-CEEC imports grew 98%.

# 3.3. **Composition of trade in parts**

Table 4 reports values of CEEC-10 imports and exports of parts sorted in descending order according to the value of imports in 1998. Table 4 does not include all parts but only those with the value of either exports or imports above US $50 million. Parts not included accounted for 5 percent and 3 percent of exports and 4 and 2 percent of imports of all parts in 1993 and 1998 respectively.

Trade in parts is highly concentrated in few items in both exports and imports. Top four product groups accounted for 69 (71% in 1997) percent of imports of all parts and 45 percent (53% in 1997) of exports in 1998. Moreover, there is a high degree of correspondence between imports and exports-their rankings only marginally diverge. In other words, large exports require large imports, which appears to suggest than within product groups production sharing takes place. Trade in motor vehicle parts (SITC 784) is the largest item in both exports and imports. Their turnover increased from US $1.7 billion to US $7.3 billion over this period. Telecommunication parts, the second largest item in CEEC imports of parts, rank third in CEEC exports after parts of switchgear.

One should note that the ranking of parts in terms of values of exports and imports is very similar to that for OECD countries (excluding Mexico and new members from Central Europe). Parts to motor vehicles (784), office and computing machines (759), and telecommunication equipment (764) account for two-thirds of trade turnover of these countries in parts (Yeats 1998). Hence, it appears overall that CEEC have become incorporated into this vastly expanding trade.

CEEC import more parts than they export, although the gap has been on the decline. While in 1993 export earnings accounted for 48 percent of import expenditure on parts, this ratio of exports to imports slightly increased to 53 percent in 1997 and surge to 60 percent in 1998. But trade deficit in parts is hardly surprising. Consider that highly developed countries run trade surplus in parts, especially high in such large items as parts of motor vehicles and parts of telecommunication equipment (Yeats 1998). If anything, this demonstrates that fragmentation of
Table 2: ITT effects on primary outcomes at 2 and 6-months

|||2-month<br>Endli|ne||6-month<br>Endlin|e||
|---|---|---|---|---|---|---|---|
|Dependent Variable|N|Control Mean|ITT|N|Control Mean|ITT|Diff.|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Index of personal initiative|1,592|4.36|0.027<br>[0.035]|||||
|Score on mock test|1,592|0.668|0.024<br>[0.012]**|||||
|Index of planning practices|1,592|0.417|0.117<br>[0.019]***|1,613|0.518|0.009<br>[0.020]|-0.109<br>[0.023]***|
|Index of accounting practices|1,592|0.541|0.069|1,613|0.590|0.019|-0.050|
||||[0.015]***|||[0.016]|[0.017]***|
|Index of marketing practices|1,592|0.487|0.016|1,613|0.503|-0.006|-0.022|
||||[0.014]|||[0.014]|[0.016]|
|Index of business practices|1,592|0.502|0.054|1,613|0.545|0.007|-0.047|
||||[0.013]***|||[0.014]|[0.015]***|
|Sales in past month|1,591|17,023|4,113<br>[1,461]***|1,607|15,365|-1,136<br>[1,263]|-5,248<br>[1,439]***|
|Log sales in past month|1,372|9.19|0.112<br>[0.058]*|1,379|8.79|-0.057<br>[0.074]|-0.169<br>[0.077]**|
|Profts in past month|1,591|6,309|648<br>[506]|1,607|4,896|-512<br>[348]|-1,160<br>[502]**|

**Notes: Personal initiative** is an index of 7 questions measuring personal initiative, with a higher score denoting more initiative. It was only asked in the 2-month survey; **Score on mock test** is the proportion right on an 11-question knowledge measure, only measured at the 2-month survey; **Index of planning practices** is the proportion of 3 planning practices used; **Index of accounting practices** is the proportion of 10 accounting practices used; textbfIndex of marketing practices is the proportion of 9 marketing practices used; **Index of business practices** is the proportion of all 22 business practices used; **Sales in past month** is sales measured in Mexican pesos (winsorized at the 99th percentile); **Log sales** is log of sales in the past month for firms with positive sales; **Profits in past month** is profits in Mexican pesos (winsorized at the 1st and 99th percentiles). Regressions control for randomization strata, baseline value of outcome where available, and additional controls selected by pdslasso. Robust standard errors in parentheses. *, **, and *** denote significance at the 10, 5, and 1 percent levels respectively.
budgetary revenues are largely collected at the sub-national level and remain outside the government budget management systems. Officially reported at 3-5% of GDP, the total extra-budgetary revenues may have reached nearly 7% of GDP in China in 2007 according to some estimates.<sup>16</sup>

**_In public service delivery, the reliance on fees has differed widely by sector_** .  In education, fees have accounted for 10% of total spending, although the share has been declining for the 9-year compulsory education.  In health, the National Health Accounts show that the reliance on out-of-pocket payment has declined in recent years, from about 60% of total health spending in 2001 to 45% in 2007.  But this level is still considered excessive by international standards, with multiple negative consequences, including the risk of impoverishment and care avoidance.  Moreover, in terms of provider payment mechanisms, user fees still account for more than 70% of providers’ income.  This is because social health expenditures are largely in the form of ex-post reimbursements by insurance schemes, such as the Basic Medical Insurance, to patients for fees they paid at the time of service.

**_International experience and studies in China suggest that the reliance on user fees, accompanied by a pressure on and autonomy of service providers to generate revenues, has negative consequences particularly in the presence of information asymmetry and weak monitoring_** .  Namely, it may divert the incentives of providers toward revenue maximization and away from the citizens’ needs and public interest.  More broadly, for instance in health, the reliance on fees has the following negative consequences:

- Significant challenges in the implementation of national policies (incentives to raise revenues differ from the incentives to implement national policy priorities and standards)

- Exclusion (financial barriers in access to and low utilization of both preventive and cost-effective clinical care with grave impact on neonatal and maternal mortality in poor localities and among poor disadvantaged population groups on one hand, and “catastrophic” expenditure and impoverishment due to healthcare spending on the other)

- Inequalities (within as well as between urban and rural areas, high within provinces)

- Inefficiency (being outside the formal budget management framework, fees and other extra-budgetary revenues are largely used for wages and operating / administrative cost and account on average for 37% of total revenues in public sector units and government agencies)

- Quality and safety problems (due to information asymmetries discussed above, citizens are not able to act as well-informed purchasers and monitor the appropriateness, quality and safety of services received)

- Citizens’ dissatisfaction (as the previous chapter illustrated).

> 16  The Brookings-Tsinghua-World Bank China Public Policy Forum, March 2008.

34
14 characteristics. In both countries, a variety of policies—especially those associated with land reform—are argued to have been especially beneficial to the poor (World Bank 2002, Ravallion and Chen 2004), and presumably had much to do with the large negative _V_ in both countries. At the same time, it is clear that market-oriented reforms have made for a closer link in both countries between incomes and individual characteristics. For example, the rate of return to human capital appears to have increased considerably in both China and Vietnam in the transition from planned to market economy (Gallup 2003, Heckman and Li 2003). This closer link between incomes and individual characteristics has presumably had much to do with the large positive value of _H_ + _R_ .

A second question is whether more should be—or should have been—done to assist those who fare less well in the move from a planned to a market economy. In both countries social safety net issues have indeed played a big part in recent policy discussions (cf. e.g World Bank 2002).

A third question is whether the analysis of the past in this paper sheds any light on the future. One possibility is that the same pattern of changing income inequality—a large negative _V_ being offset by a positive _R_ + _H_ —will continue. An alternative and possibly more plausible scenario is that that the inequality-reducing policy measures that have helped generate the large negative _V_ in the past have run their course, and that future policies will be less inequalityreducing and may even be inequality-increasing (cf. World Bank 2002, Ravallion and Chen 2004). It also seems possible that the link between incomes and individual characteristics will become even stronger, as the economies of China and Vietnam become more market-oriented; the rate of return to human capital in Vietnam, for example, is still low by market-economy standards (Gallup 2003). In which case, the future may well be one of _positive_ (or at least