rafmacalaba/datause-encoder-data huggingface dataset:
OECD and FAO (2018). _OECD-FAO Agricultural Outlook 2018-2027_ , OECD Publishing, Paris and Food and Agriculture Organization (FAO) of the United Nations, Rome. <u>https://doi.org/10.1787/agr_outlook-2018-en</u> Opoku, R., Edwin, I. A., & Agyarko, K. A. (2019). Energy efficiency and cost saving opportunities in public and commercial buildings in developing countries – The case of air-conditioners in Ghana. _Journal of Cleaner Production_ , 230, 937-944. <u>https://doi.org/https://doi.org/10.1016/j.jclepro.2019.05.067</u>

- Ouyang, D., Zhou, S., & Ou, X. (2021). The total cost of electric vehicle ownership: A consumeroriented study of China's post-subsidy era. _Energy Policy_ , 149, 112023. <u>https://doi.org/10.1016/j.enpol.2020.112023</u>

- Palčič, I., & Prester, J. (2020). Impact of advanced manufacturing technologies on green innovation. _Sustainability (Switzerland)_ , _12_ (8), 3499. <u>https://doi.org/10.3390/SU12083499</u>

- Palmer, K., Tate, J. E., Wadud, Z., & Nellthorp, J. (2018). Total cost of ownership and market share for hybrid and electric vehicles in the UK, US and Japan. _Applied Energy_ , 209, 108119. https://doi.org/10.1016/j.apenergy.2017.10.089

Palmer, M., & Truong, Y. (2017). The impact of technological green new product introductions on firm profitability. _Ecological Economics_ , _136_ , 86-93. <u>https://doi.org/10.1016/j.ecolecon.2017.01.025</u>

- Pan, X., Ai, B., Li, C., Pan, X., & Yan, Y. (2019). Dynamic relationship among environmental regulation, technological innovation and energy efficiency based on large scale provincial panel data in China. _Technological Forecasting and Social Change_ , _144_ , 428-435. <u>https://doi.org/10.1016/j.techfore.2017.12.012</u>

- Papadopoulos, S., Kontokosta, C. E., Vlachokostas, A., & Azar, E. (2019). Rethinking HVAC temperature setpoints in commercial buildings: The potential for zero-cost energy savings and comfort improvement in different climates. _Building and Environment_ , 155, 350-359. <u>https://doi.org/https://doi.org/10.1016/j.buildenv.2019.03.062</u>

Park, W. Y., Shah, N., & Phadke, A. (2019). Enabling access to household refrigeration services through cost reductions from energy efficiency improvements. _Energy Efficiency_ , 12(7), 1795-1819. https://doi.org/10.1007/s12053-019-09807-w 51
Table 2 also shows that the coefficient for initial level of policy quality is significantly negative, implying a regression toward the mean effect. This suggests that countries with strong initial social policies do not necessarily sustain them over time, while countries with weak policies tend improve them. Furthermore, high average growth rates are associated with improvements in social policy: countries that grow more rapidly find it easier to effect improvements in social policy. However, caution is required as causality could run both ways. Finally, Table 2 shows that democratization is positively related to social policy change: countries that expanded political freedoms tend also to have better social policies.<sup>10</sup> Again caution is required as omitted variables might drive changes in both social policies and political freedoms.

**Table 2: OLS**

|VARIABLES|Dependent variable:<br>changeinCPIAscore|
|---|---|
|initial level of policy quality|-0.336***<br>(0.0653)|
|average aid/GDP 2006-2014|-0.207<br>(0.539)|
|average per capita growth 2006-2014|0.0313***<br>(0.0100)|
|change in political freedom 2006-2014|-0.0633**<br>(0.0287)|
|social policy prior actions 2006-2014|0.00427**<br>(0.00207)|
|Observations|67|
|R-squared|0.421|

<u>Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Constant</u> included in the regression but not reported.

Table 3 presents the findings from estimating equation (1), instrumenting social policy conditions with the logarithm of population and the number of FPS prior actions. Population and FPS conditions are strong predictors of social policy prior actions (Fstat=12.900, p-value=0.0001). Furthermore, Wooldridge’s (1995) robust score test of overidentifying restrictions does not reject the null that the excluded instruments are exogenous to changes in the quality of social policy (test score = 0.325, p-value = 0.568).

> 10 We measure political freedom using Freedomhouse’s Political Rights variable that ranges from 1 through 7, with 1 representing the greatest degree of freedom and 7 the smallest degree of freedom.

11
Policy Research Working Paper 8249

# **Abstract**

Trade facilitation projects often assume indirect benefits for small-scale, cross-border traders. Recent studies have shown the challenges faced in Africa by this population, especially women, but it remains unknown in Cambodia and the Lao People’s Democratic Republic, despite large trade facilitation investments. This paper fills this gap, thanks to an innovative mix of original qualitative and quantitative data from various checkpoints on the borders with Thailand and Vietnam. The quantitative data, collected in 2014, consist of an exhaustive list of trade-related border crossings during two to three days and a survey of 158 randomly selected small-scale, cross-border traders and brokers. The paper combines qualitative data and statistical techniques to shed light on the structure of the small-scale, cross-border trade economy, traders’ and brokers’ profiles, the challenges they face, and potential solutions, with a particular emphasis on gender. Key challenges pertain to taxation and poor infrastructures. Narrow roads, insufficient parking space, and restrictive border regulations on transportation means cause traffic jams and delays. These disproportionately affect women, who are more time constrained. Despite a rather moderate tax pressure, widespread informal payments erode traders’ and brokers’ willingness to comply with taxes. Women suffer from a higher tax rate and a tax schedule that deters them from upgrading to more profitable cross-border trade activities. Along with capital constraints, this finding may explain the lower share of women in small-scale, cross-border trade than among own-account workers and the self-employed, as evidenced by nationally representative data. The paper delineates policy implications and puts forward concrete steps.

This paper is a product of the Macro Trade and Investment Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted atmarlonseror@gmail.com, rrecord@worldbank.org and jclarke1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|3|East Timor|0|0|0|0|0|0|1|2|0|0|-1|-2|6.7|1.8|-13.1|-4.8|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|3|Eritrea|..|..|0|1|..|..|25|35|..|..|-24|-35|..|..|-14.1|-11.3|
|3|Haiti|7|10|8|38|37|67|140|175|-30|-57|-132|-137|-6.5|-8.9|-13.4|-11.8|
|3|Liberia|1|0|3|1|46|32|24|61|-45|-32|-21|-60|-1.4|-0.8|-0.5|-1.2|
|3|Sierra Leone|1|0|0|1|16|13|19|26|-15|-13|-19|-25|-6.4|-7.6|-5.8|-5.1|
|3|Somalia|129|74|67|82|58|25|18|48|71|49|48|34|19.1|27.1|17.1|9.8|
|4|Cape Verde|1|2|0|0|7|14|13|31|-6|-12|-12|-31|-9.0|-9.4|-4.9|-6.9|
|4|Comoros|0|0|0|0|4|7|17|18|-4|-7|-17|-18|-6.1|-6.5|-9.9|-19.6|
|4|Kiribati|0|0|0|0|2|1|3|6|-2|-1|-3|-6|-8.2|-1.8|-7.0|-10.0|
|4|Maldives|0|0|0|0|1|7|17|22|-1|-7|-17|-22|-2.4|-4.1|-5.5|-4.0|
|4|Samoa|3|3|0|1|4|7|10|14|-1|-4|-10|-14|-1.1|-4.4|-5.0|-6.7|
|4|Sao Tome and Principe|..|0|0|0|2|1|2|3|..|-1|-2|-3|..|-3.3|-4.5|-4.4|
|4|Solomon Islands|2|0|0|0|3|6|4|3|-1|-6|-4|-3|-2.5|-6.6|-4.0|-2.7|
|4|Tuvalu|0|0|0|4|0|0|1|3|0|0|-1|1|-9.1|-3.4|-9.6|6.7|
|4|Vanuatu|2|3|2|5|3|1|2|4|-1|2|0|1|-2.9|1.0|0.2|0.4|
||Memo Items:|||||||||||||||||
|1-4|All LDCs (50)|724|529|739|1,106|1,281|1,546|2,061|3,913|-557|-1,017|-1,323|-2,807|-2.7|-4.0|-3.0|-3.9|
|2|Oil exporters (4)|191|58|111|223|291|391|542|970|-100|-334|-431|-747|-2.5|-8.7|-6.2|-4.7|
|3|Civil conflict (8)|160|101|108|160|203|208|353|559|-43|-107|-245|-399|-0.8|-1.6|-3.2|-3.6|
|4|Small Islanders (9)|8|9|3|10|25|45|68|105|-17|-36|-65|-95|-4.8|-4.2|-5.2|-5.3|
|1|Other LDCs(29)|365|361|516|713|762|902|1,097|2,278|-398|-541|-581|-1,565|-3.8|-3.9|-2.0|-3.6|

- Note: Food is defined as raw food, excluding all cash crops, processed food products and seafood (see detailed classifications in Appendix Table 1). 1 = All other LDCs; 2 = Oil exporters; 3 = Civil conflict countries; 4 = Small Islanders (see detailed classifications in Appendix table 3).

Source: Based on partner data from UN COMTRADE Statistics.

49
**<u>Table 8: Certification impact on rental market participation: marginal effects from probit models</u>**

|||**Rented-out**<br>**Chamb**|**erlain**||**Rented-in**<br>**Cham**|**berlain**|
|---|---|---|---|---|---|---|
|Land use certificates issued|0.097*|0.134**|0.126**|-0.002|-0.014|-0.010|
||(1.82)|(2.35)|(2.38)|(-0.04)|(-0.32)|(-0.23)|
|Total owned land in hectares|0.062***|0.065***|0.055***|-0.099***|-0.103***|-0.091***|
||(4.98)|(5.30)|(4.77)|(-5.75)|(-5.88)|(-5.81)|
|Share of good quality land|-0.001|-0.016|-0.032|-0.061|-0.050|-0.053|
||(-0.03)|(-0.37)|(-0.74)|(-1.34)|(-1.14)|(-1.19)|
|Number of dependents|-0.007|-0.006|0.001|0.016|-0.029|-0.019|
||(-0.66)|(-0.25)|(0.04)|(1.31)|(-1.17)|(-0.79)|
|Number of adult male|-0.025|-0.028|-0.031|-0.010|-0.061*|-0.044|
||(-1.63)|(-0.78)|(-0.87)|(-0.62)|(-1.71)|(-1.24)|
|Number of adult female|<br>0.024|<br>0.006|<br>0.021|<br>-0.001|<br>-0.015|<br>-0.009|
||(1.48)|(0.19)|(0.68)|(-0.05)|(-0.49)|(-0.27)|
|Number of oxen|<br>-0.181***|<br>-0.080***|<br>-0.086***|<br>0.134***|<br>0.077***|<br>0.089***|
||(-9.93)|(-3.54)|(-3.87)|(7.76)|(3.39)|(3.91)|
|Value of other animals x 10<sup>-3</sup>(Birr)|<br>-0.018**|<br>-0.002|<br>0.003|<br>0.014***|<br>0.011|<br>0.008|
||(-2.50)|(-0.24)|(0.35)|(2.65)|(1.61)|(1.01)|
|Roof corrugated iron sheet|-0.088**|-0.057|-0.078|0.113***|0.104**|0.114**|
||(-2.00)|(-0.87)|(-1.17)|(3.24)|(2.10)|(2.15)|
|Age of household head (years)|-0.005|-0.012*|-0.011*|-0.001|0.004|0.007|
||(-0.75)|(-1.69)|(-1.65)|(-0.15)|(0.41)|(0.85)|
|Age of household head squared|0.000*|0.000*|0.000*|-0.000|0.000|-0.000|
||(1.70)|(1.72)|(1.69)|(-0.92)|(0.02)|(-0.52)|
|Female headed household|0.373***|0.143|0.225**|-0.204***|-0.148**|-0.177***|
||(5.87)|(1.39)|(2.19)|(-7.70)|(-2.53)|(-2.61)|
|Household head can read and write|0.067*|0.082|0.090|-0.019|0.019|0.038|
||(1.81)|(1.41)|(1.54)|(-0.57)|(0.37)|(0.74)|
|Initial year participation as landlord|||<br>0.124***<br>||||
||||(2.83)||||
|Lagged participation as landlord|||<br>0.332***<br>(7.51)||||
|Initial year participation as tenant||||||0.174***<br>(5.30)|
|Lagged participation as tenant||||||<br>0.273***<br>|
|||||||(7.44)|
|Year = 2007|0.086***|0.067**|0.032|0.163***|0.163***|<br>0.171***|
||(2.74)|(2.09)|(0.90)|(5.09)|(4.76)|(4.65)|
|Number of observations|1424|1424|1302|1424|1424|1302|
|Number of households|736|736|736|736|736|736|
|Log lik.|-579.050|-554.254|-462.474|-657.789|-645.424|-553.642|
|<br>Chi-squared|231.821|212.860|408.046|160.423|157.457|348.339|
|<br>Rho|0.395|0.424|0.038|0.502|0.502|0.038|
|sigma_u|0.807|0.857|0.199|1.003|1.005|0.200|
|<br>Lik.-ratio test of rho=0|-591.925|-568.202|-461.949|-680.949|-668.285|-553.291|
|Chibar2|25.749|27.896|1.050|46.319|45.722|0.701|

**Note:** The Chamberlain specification includes the mean value of the time-varying household level variables (Chamberlain 1980), but not reported. A constant term is included in all the regressions. Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%.

25
# A Empirical evidence on VFI and debt

Figure 1 Panel B illustrates the relationship between a country’s vertical fiscal imbalances (VFI) and changes in total government debt over time, for Spain, Germany, and Portugal. Figure A.1 in this appendix supplements those figures and shows the same general pattern among 25 other OECD countries.

In addition, we run a fixed effect panel regression on these 28 countries to establish positive correlation between VFI and change in total government debt. We follow the regression specification in Eyraud and Lusinyan (2013), but instead of primary deficit, we use change in total government debt-to-GDP ratio as the dependent variable: where ∆ _dit_ is the change in country _i_ ’s debt-to-GDP ratio between years _t −_ 1 and _t_ ; VFI _t_ is the country’s VFI in year _t_ ; _Xit_ is a set of control variables, which include expenditure decentralization, lag of debt-to-GDP ratio, lag of output gap, a measure of trade openness, population, and rule of law; _αi_ are country fixed effects, _θt_ are time dummies, and _εit_ is the error term. We follow Eyraud and Lusinyan (2013) and Aldasoro and Seiferling (2014) in deciding the control variables. Both papers empirically study the relationship between a country’s VFI and primary deficit or change of government debt. Table A.1 shows that a 10 percentage point increase in VFI is associated with a 1.2 to 1.3 percentage point acceleration in the growth of total government debt as percentage of GDP.

**Table A.1:** Dependent Variable: Change of total government debt (% GDP) ∆ _dit_ = _<u>β</u> ×_ VFI _<u>it</u>_ <u>+</u> _<u>γ</u> × Xit_ <u>+</u> _αi_ <u>+</u> _θt_ <u>+</u> _εit_

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|VFI|0.12**|0.13***|0.13***|0.12**|
|Expenditure decentralization||-0.32***|-0.38***|-0.31***|
|Lag of debt-to-GDP ratio|||-0.11***|-0.13***|
|Lag of output gap|||-0.20|0.1|
|Openness (trade as % GDP)||||-0.02|
|Population (million)||||0.04|
|Rule of law||||-4.5***|
|Observations|356|356|327|273|
|Countries|28|28|25|25|
|Overall R<sup>2</sup>|0.40|0.45|0.52|0.51|
|Within R<sup>2</sup>|0.23|0.29|0.38|0.32|

Note: Fixed effects estimation with robust standard errors clustered at the country level. *, **, *** for significance at the 10, 5, and 1 percent level, respectively. Sample restricted to 1985–2007 if available.

3
**Table 6. Impact evaluation on capability indicators for participants with SHG time above average**

|||Nearest-n<br>|eighbours<br>||Kerne<br>|l-based mat<br>|ching<br>|
|---|---|---|---|---|---|---|---|
|Outcome|A|E|A|TT|ATE|A|TT|
||Coef.|z-stat|Coef.|z-stat|Coef.|Coef.|t-stat|
|Health limits activities***|0.1424|4.48|0.1314|2.84|0.1089|0.1085|3.91|
|Able to meet socially***|0.0765|3.68|0.0797|2.34|0.0734|0.0781|4.26|
|Lost sleep from worry (mental health)|0.0205|0.96|0.0423|2.01|0.0056|0.0171|0.77|
|Able to enjoy recreation***|0.0903|3.86|0.1248|5.08|0.0908|0.1239|5.01|
|Own home**|0.0618|2.52|0.0482|1.45|0.0897|0.0962|4.39|
|Accommodation suitable|0.0411|1.18|0.0581|1.20|0.0953|0.1171|4.20|
|Feel safe walking***|0.0991|3.01|0.1253|2.71|0.0792|0.0868|3.23|
|Risk of future assault*|0.0593|1.96|0.0518|1.73|0.0412|0.0336|1.39|
|Risk of future discrimination*|0.0407|1.88|0.0579|2.63|0.0251|0.031|1.40|
|Influence local decisions***|0.1209|3.78|0.1152|3.79|0.1248|0.1161|4.17|
|Freedom of political and religious<br>expression***|0.1046|3.21|0.0772|1.83|0.1241|0.1209|4.34|
|Support of family and friends|-0.0206|-0.81|0.0239|0.71|0.0067|0.0289|1.06|
|Free to live life***|0.1299|4.55|0.0973|3.26|0.1476|0.1366|4.92|
|Freedom of creative expression***|0.1537|5.39|0.1166|4.03|0.1828|0.1527|5.54|
|Interesting work***|0.1235|3.92|0.1009|2.29|0.1585|0.1598|5.96|
|Note: See notes for Table 5; n=2943||||||||

46
<!-- Start of picture text --> 80 eeeor 15<br>S aeetecnn ©<br>wo 60 al 10 5<br>s “— =<br>27) we =o<br>o<br>ro 5 3&<br>E a =<br>20 Lo<br>oj rs—a0<br>020 40 60 80 100<br>t<br>A M --®- Sh S| —*- Welfare/Real Income<br><!-- End of picture text -->
**Figure 4: Cement Production Capacity and Consumption.** Green markers indicate economies in continental Africa. Consumption is total consumption including imports and local production. Observations with zero installed production capacity and non-zero consumption are reported on the bottom line of the plot. Data from 2017 are shown.

<!-- Start of picture text --> CHN<br>16<br>45° line<br>4 IND<br>1 BRARUSVNMIDN USA<br>SAU<br>THAPAK<br>AREIRQNGAMYSDEUPHL<br>1/4 BGD<br>TWNZAFMAR<br>PER<br>GBR<br>MMR<br>PRCIVJOR T ZA<br>OMN<br>1/16 TGOZMBMNGBFABGR UGA CMR SEN BOLLAOKWTKHM ECU LKA BELNPL<br>PAN<br>NLD<br>1/64 NAM URYCOGMRTZWEGINPRYHND NORFINBHR MLIBEN<br>SVN<br>MWIGABMDG<br>NERTCD<br>1/256 FJI BRN LBRSLERWA BWA<br>DJI<br>SGP<br>LSO SWZ<br>1/1024 GNQ<br>BDI<br>SUR<br>0 COMGNBCPV MUSGMB<br>1/1024 1/256 1/64 1/16 1/4 1 4 16<br>Cement consumption (USA=1)<br>Installed cement production capacity (USA = 1)<br><!-- End of picture text -->
GHANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 6. Benchmarking water and sanitation indicators**

||Unit<br>Low-income<br>countries|**Gha**|**na**|Middle-income<br>countries|
|---|---|---|---|---|
||Mid 2000s<br>**Mid 2**|**000s**|**Late 2000s**|Mid 2000s|
|Access to piped water|% pop<br>10.1|**15.1**|**13.1**|56.4|
|Access to stand posts|% pop<br>16.1|**20.5**|**27.5**|20.4|
|Access to wells/boreholes|% pop<br>38.3|**42.1**|**40.1**|6.3|
|Access to surface water|% pop<br>33.8|**20.1**|**11.1**|13.9|
|Access to septic tanks|% pop<br>5.3|**10.3**|**14.1**|44.0|
|Access to latrines|% pop<br>57.2|**63.1**|**62.5**|33.9|
|Open defecation|% pop<br>37.1|**24.6**|**23.1**|15.8|
|Domestic water consumption|liter/capita/day<br>72.4|||NA|
|Urban water assets in need of rehabilitation|%<br>35.5|**42.0**||25.0|
|Revenue collection|% sales<br>96.0|**75.0**|**95.0**|99.2|
|Distribution losses|% production<br>33.0|**53.0**|**50.8**|23.1|
|Cost recovery|% total costs<br>56.0|**48.4**|**61.8**|80.6|
|Total hidden costs as % of revenue|%<br>130.0|**183.7**|**128.9**|84.9|
|U.S. cents per m<sup>3</sup>|**Ghana**<br>**Mid 2000s**<br>**Late 2000s**|Scarce<br>resour|water<br>ces<br>Ot|her developing<br>regions|
|Residential tariff|**41.7**<br>**46.2**||60.26||
|Nonresidential tariff|**219.8**<br>**142.0**||120.74|3.0 – 60.0|

Source: Banerjee and others 2009; Morella and others 2009. Derived from AICD water and sanitation utilities database downloadable from http://www.infrastructureafrica.org/aicd/tools/data The performance of Ghana Water Company (GWC) has improved somewhat in recent years following institutional reforms and the contracting of a private operator. As of 2005, the utility was recovering only 48 percent of costs and collecting only 75 percent of revenue. Since 2007, tariff increases have substantially improved cost recovery. At the same time, the private operator’s stronger commercial incentives have improved revenue collection from 75 percent to 95 percent in a short period of time. As a result, the hidden costs associated with the water sector fell from $113 million per year in 2001 to $96 million per year in 2009 (table 7), and they now absorb a much smaller share of GDP (figure 7). Relative to sector revenue, hidden costs have fallen from 190 to 129 percent over the same period. Nonetheless, Ghana remains only a middling performer on utility efficiency by regional standards (figure 8).

18
sions. We establish that, for sufficiently low capital stocks, there is a negative relationship between investment and inflation, consistent with McKinnon’s original argument. However, our calibration of the model illustrates that this relationship is non-monotone, due to the feedback between investment and cash flows in inflationary environments.

At the same time, our calibration suggests that, especially for average levels of inflation in developing countries, McKinnon’s mechanism generates a substantial reduction in indivisible investment by small firms. Moving from 7.4% inflation (which is the average in our sample) to -3% inflation (which, in our calibration, corresponds to the Friedman rule) results in about a 10% increase in steady state capital stocks.

We show some suggestive empirical evidence, based on our finding that, in McKinnon’s environment, inflation creates a misallocation of investment between small and large firms, as lower-cash-flow firms must accumulate retained earnings for longer periods of time in order to finance a given size of investment. We find that small firms are likely to invest less than large firms in the presence of inflation; however, the magnitudes of the effects are substantially larger than those suggested by our calibration, implying perhaps that other mechanisms may be contributing to this wedge.

# **References**

- Cole, H. and A. Stockman (1992). Specialization, transactions technologies, and money growth. _International Economic Review 33_ (2), 283–298.

- Fama, E. F. and M. R. Gibbons (1982). Inflation, real returns and capital investment. _Journal of Monetary Economics 9_ (3), 297–323.

- Fischer, S. (1979, April). Anticipations and the nonneutrality of money. _Journal of Political Economy 87_ (2), 225–52.

23
> <sup>20</sup>

being. Approximately 47 percent of all respondents believe that individuals are responsible for their own welfare, while 49 percent believe that government has an important role to play. This variable is equal to one for respondents who believe that individuals are responsible for their own success and zero otherwise.

Opinions about democracy are relevant in two ways. First, some parties are associated with the transition to democracy; the appeal of such parties can be captured by controlling for respondents’ attitudes towards democracy. Second, a sense of civic responsibility may lead respondents to support some party, independent of the credibility of the party’s commitments, in order to fully participate in the democratic process. In question 37, respondents indicate whether they believe that democracy is preferable to any other kind of government, whether non-democratic governments might be preferable, or whether the type of government does not matter. More than 60 percent of respondents agree that it is the best form of government. The democracy variable is coded one for respondents who believe democracy is always better and zero otherwise.

In all democracies, partisan preference is strongly associated with _ex post_ evaluations of incumbent performance. On the one hand, good performance should inspire greater partisan attachment. On the other hand, respondents’ evaluation of incumbent performance could be influenced by whether the politician is from the party they favor. However, this second possibility is most likely when political parties can credibly represent the interests of broad groups of citizens. One indication that, unlike the United States, partisan preference does not drive incumbent approval, is the high percentage of African respondents – 34 percent – who approve of incumbent performance, but do _not_ express a partisan preference. The corresponding figure in the United States is only 8.6 percent (American National Election Studies, http://www.electionstudies.org//nesguide/toptable/tab2a_2.htm).