rafmacalaba/datause-encoder-data huggingface dataset:
_Table 1.01 The Individual Measures of Limited Government (EDD/NEDD Ratio) and Rent Extraction_

||**(1)**|**Corruptio**<br>**(2)**|**n Perceptions In**<br>**(3)**|**dex (CPI)**<br>**(4)**|**(5)**|
|---|---|---|---|---|---|
||<br>**(GLM)**|<br>**(GLM)**|<br>**(GLM)**|<br>**(GLM)**|<br>**(GLM)**|
||**Raw Score**|**Raw Score**|**Raw Score**|**Raw Score**|**Raw Score**|
|**Regulatory**<br>**Density*High-**<br>**Income Dem **||||-3.80*<br>(2.16)||
|**Small Govt* High-**<br>**Income Dem**|||||-4.49**<br>(2.22)|
|**Regulatory Density**|-19.37***<br>(4.00)|||-1.00***<br>(0.12)||
|**Non-EU**||-1.01***<br>(0.36)||||
|**Small Govt**|||-7.00***<br>(2.00)||1.31<br>(1.33)|
|**High-Income**<br>**Democracy**||||-0.057<br>(0.35)|1.92<br>(1.40)|
|**Coordinated**<br>**Capitalist**<br>**Economies**|-0.43<br>(0.32)|-0.61*<br>(0.34)|-0.52<br>(0.38)|-0.27*<br>(0.16)|-0.096<br>(0.32)|
|**Economic Openness**<br>**(Long-Term)**|-0.43<br>(0.84)|2.01***<br>(0.59)|2.83***<br>(0.70)|0.0094<br>(0.0036)|-.095<br>(0.30)|
|**Latitude**|-1.73<br>(1.53)|-3.39**<br>(1.54)|-4.75**<br>(1.91)|-0.44<br>(0.57)|-1.50<br>(1.19)|
|**(log) Per capita**<br>**income**|-0.42<br>(0.41)|-0.73<br>(0.48)|-0.93**<br>(0.45)|-0.37**<br>(0.11)|-0.47**<br>(0.20)|
|**School Enrolment**|0.014<br>(0.12)|-0.014<br>(0.020)|-0.02<br>(0.01)|-0.0038<br>(0.0038)|-0.020**<br>(0.0089)|
|**Additional Controls**||||√|√|
|**Sample**|High-Income<br>Dem|High-Income<br>Dem|High-Income<br>Dem|All|All|
|**Number of**<br>**Observations**|21|21|21|54|52|
|**Log-Likelihood**|-6.69|-6.42|-6.97|-20.41|-19.90|

Note: robust standard errors in parentheses. * Denotes significant at the 10% level, ** significant at the 5% level, ***significant at the 1% level. CPI score divided by 10 to aid interpretation. OLS results are -0.36** (0.08); 0.14* (0.07); 0.01*** (0.005); -0.0031 (0.067),0.013* (0.0068); for models (1), (2), (3), (4) and (5) respectively. Additional controls: Ethno-linguistic fragmentation, income inequality, (log) population, size of extractive industries, democratization (current), demographic dummy variables, economic openness (current), religious make-up variables, regional dummies (see Appendix A for details of variable descriptions). _Source: The Author_ However, while the results in Table 1.01 are consistent with the model’s theoretical expectations, they remain a relatively limited test of its robustness. Firstly, if the model is correct, it should be the case that (1) these three measures of the EDD/NEDD ratio are measuring the same underlying latent variable (extensiveness of government); and 2) while the most robust control variables were utilized, it is essential to include other control variables that may affect these results. Given the small sample size, it is not possible to introduce all the potential control variables using the OECD country sub-set alone (due to the problem of model saturation/lack of degrees of freedom), and therefore it becomes essential to utilize a larger dataset.

22
-2-

# **1.** Introduction: **Thi,** issues

## 1.1. Intrdugtion

The economic literatwire on investment has been characterized<sup>by considerable controversy,</sup> even by the standards of economists. A number of different,<sup>often overlapping, models of</sup> investment determination have been hypothesized and the<sup>empirical evidence has done little to</sup> clarify which, if any, are accurate representations of the way in<sup>which capital formation occurs</sup> in the economy. This is particularly true for developing countries<sup>where there has been less</sup> empirical work, the data are less reliable, and the appropriateness<sup>of existing theoretical models</sup> is debatable.

It is against this background that this paper suggests some methodological innovations<sup>in</sup> modelling aggregate investment behavior. A theoretical<sup>framework for analyzing investment</sup> decisions is presented in Section 2 that takes into account some of the<sup>structural features of a</sup> developing economy. Starting from the firm's optimization problem,<sup>an aggregate investment</sup> function is derived Lhat reflects the results of a survey of decision-making<sup>in fifty private sector</sup> firms in Egypt. The mo&ol is then tested at the macroeconomic<sup>level in Section 3 using new</sup> econometric techniques that have emerged in the recent literature on stationarity<sup>testing and</sup> cointegration. The relationship between investment and an array of government policies<sup>is</sup> highlighted in the economnetric analysis. The conclusions, both methodological<sup>and empirical, are</sup> presented in Section 4.
# **Summary Statistics (Country-year Panel)**

||**Source**|**N**|**Mean**|**SD**|**P50**|**Min**|**Max**|
|---|---|---|---|---|---|---|---|
|Disputes|PPI and UNCTAD|4200|0.086|0.280|0|0|1|
|# of disputes|PPI and UNCTAD|4200|0.159|0.902|0|0|34|
|**Macro shock variables**||||||||
|Real per capita GDP growth|WDI|3601|0.044|0.067|0.047|-0.484|0.963|
|Change in terms of trade (TOT)|WDI|2462|0.009|0.124|0|-0.826|1.316|
|Natural disaster|EM-DAT|4200|0.315|0.465|0|0|1|
|**Political institution**||||||||
|Democracy|Polity IV|3300|0.555|0.497|1|0|1|
|Democratization|Polity IV|2993|0.056|0.230|0|0|1|
|Resource rents|WDI|4116|9.981|15.740|3.541|0|89.511|
|High state fragility|Marshall and Cole (2009)|3500|0.448|0.497|0|0|1|
|Aid/GNI in 2000|Besley and Persson (2011)|3864|0.086|0.099|0.048|0.000|0.483|
|**Other variable**||||||||
|Log income per capita in 2000|Besley and Persson (2011)|3948|8.247|0.942|8.386|5.790|10.561|
|Gini index|World Bank (PovCal net)|3360|41.545|9.200|39.995|25.620|65.770|
|**Region dummies**||||||||
|EAP|PPI and UNCTAD|4200|0.133|0.340|0|0|1|
|ECA|PPI and UNCTAD|4200|0.207|0.405|0|0|1|
|LAC|PPI and UNCTAD|4200|0.207|0.405|0|0|1|
|MENA|PPI and UNCTAD|4200|0.093|0.291|0|0|1|
|South Asia|PPI and UNCTAD|4200|0.053|0.225|0|0|1|
|AFR|PPI and UNCTAD|4200|0.307|0.461|0|0|1|

26
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 22. Hidden costs in power generation in Cameroon and comparator countries**

<!-- Start of picture text --> a. AES Sonel b. Selected power utilities in Central and West Africa<br>180 350<br>160<br>300<br>140<br>250<br>120<br>100 200<br>80 150<br>60<br>100<br>40<br>20 50<br>0 0<br>Collection Inefficiencies Underpricing Losses Collection Inefficiencies Underpricing Losses<br>Source: Briceño-Garmendia, Smits, and Foster 2009.<br>Percentage of revenues Percentage of revenues<br><!-- End of picture text --> The power supply in Cameroon is unreliable. Investment climate surveys suggest that firms encountered around 128 outages in 2009, almost as twice the average for Africa’s middle-income countries, enduring blackouts of four hours each time. On average, at least 16 days a year are spent without power due to outages (World Bank 2009c). To combat erratic power supply, many firms generate their own power. It is estimated that as much as 31 percent of the country’s installed capacity is selfgeneration. Suppressed demand for power is around 241 GWh and is expected to progressively increase with growing domestic demand (World Bank and IFC 2010).

Investment climate surveys from 2007 determined that unreliable power was one of the top five constraints to business activity. Around 67 percent of firms reported that power was a major constraint to business, similar to other fragile states but worse than the averages for Africa’s low- and middle-income countries and resource-rich countries.

Firms that generate their own power face high costs. Self-generation is estimated to cost the private sector $0.46 per kilowatt-hour, four times the tariff charged by the power utility, which pushes up the prices of the firms’ products. If the price premium of running backup generators is included assuming that they run 10 percent of the time, firms pay a markup of around $0.04 per kilowatt-hour to power costs.

Looking ahead, simulations suggest that with development of regional trade, made possible by further development of Cameroon’s hydropower resources, AES Sonel’s long-run marginal costs could fall to $0.07 per kilowatt-hour, less than half the current value, and would be better aligned with current tariffs levels (figure 23). The cost reductions would come from further development of

**Figure 23. Long-run prospects for power in Cameroon**

<!-- Start of picture text --> 20<br>15<br>10<br>5<br>0<br>Total costs Average Effective tariff LRMC (trade LRMC (trade<br>revenue stagnation) expansion)<br>Source: Rosnes and Vennemo 2009; World Bank 2008<br>Note : LRMC = long-run marginal costs.<br>US cents per kWh<br><!-- End of picture text --> 35
the studies that did include CEA covered DHIs for clients (n=119 IEs and n=11 SRs) and health care providers (n=45 IEs and n=8 SRs). Only three IEs and no SR covering DHIs for health system managers reported economic outcomes. Moreover, none of the studies on DHIs for data services included any cost data (See Figure 4).

The economic outcomes reported by the studies are further organized into simple, intermediate and summary/impact based on the DHI’s TOC causal pathway. The simple economic outcomes refer to reporting of cost data without any reference to health or wellbeing outcomes (e.g., cost incurred/saved). However, some refer to outputs such as cost per user or persons reached. Studies reporting simple economic outcomes are generally variations on forms of costing analyses. Intermediate and summary economic outcomes link intervention cost data to select health outcomes. The difference between the two is the level of aggregation and finality of the health outcome. Intermediate economic outcomes focus on immediate or natural outcomes such as cost per infection averted whereas the summary outcomes invoke ultimate health outcomes such as cost per life saved or cost per quality adjusted life-year gained. Highly aggregated outcomes, such as net benefits calculated under a cost-benefit analysis framework, would also be classified as a summary outcome, however no cost-benefit analyses were identified in the included studies.

<!-- Start of picture text --> Cost only/Cost analysis 85 8<br>Cost minimization analysis 1<br>Cost consequence analysis 2<br>Cost effectiveness analysis 55 8<br>Cost utility analysis 26 2<br>Cost benefit analysis 0<br>0 10 20 30 40 50 60 70 80 90 100<br>Impact evaluation Systematic review<br><!-- End of picture text -->

**Figure 4 Economic Outcomes reporting from the DHI Evidence base**

# 3.1.7 Evaluation methods

Most studies (n=583, 91%) evaluated the interventions through an RCT framework. The remainder of the studies used quasi-experimental designs. Among the studies with an explicit economic evaluation framework, the majority did not adopt any specific economic evaluation method but instead simply reported the cost incurred or saved. Cost-effectiveness analysis was the most frequently used economic evaluation method (n=63), followed by cost utility analysis (n=28). Cost minimization and cost consequence analyses were reported in one and two studies, respectively. None of the included studies used cost-benefit analysis.
(0) ) Table 2. **Change** of Central-Local Relative Fiscal Status, 1981-1992

|Year||Reven|ue||||Expe|nditure|||
|---|---|---|---|---|---|---|---|---|---|---|
||Total|Central|Local|Central|Local|Total|Central|Local|Central|Local|
||(Y bil)|(Y bil)|(Y bil)|<br>I%)|(0)|(Y bil)|(Y bil)|(Ybil)|(%)|(0)|
|1978<br>|<br>112.1<br>|||||111.1|51.0|60.1|||
|1979|<br>110.3|15.8|94.6|14.3|85.7|127.4|64.7|62.7|50.8|49.2|
|1980<br>|<br>108.5<br>|17.4|91.2|16.0|84.0|121.3|65.1|56.2|53.7|46.3|
|1981<br>|<br>108.9<br>|22.5|86.5|20.6|79.4|111.5|60.2|51.3|54.0|46.0|
|1982<br>|<br>112.4<br>|25.8|86.5|23.0|77.0|115.3|57.5|57.8|49.9|50.1|
|1983<br>|<br>124.9<br> <br>|37.2|87.7|29.8|70.2|129.2|64.3|65.0|49.7|50.3|
|1984<br>|<br>150.2<br>|52.4|97.7|34.9|65.1|154.6|73.9|80.8|47.8|52.2|
|1985|<br>186.6|70.8|115.9|37.9|62.1|184.5|83.7|100.8|45.3|54.7|
|1986<br>|<br>226.0<br>|91.7|134.4|40.6|59.4|233.1|96.2|136.9|41.3|58.7|
|1987<br>|<br>236.9<br> <br>|90.6|146.3|38.2|61.8|244.8|103.2|141.7|42.1|57.9|
|1988<br>|<br>262.8<br>|104.6|158.2|39.8|60.2|270.7|106.0|164.6|39.2|60.8|
|1989|<br>294.8|110.5|184.2|37.5|62.5|304.0|110.5|193.5|36.4|63.6|
|1990|331.3|136.8|194.5|41.3|58.7|345.2|137.3|207.9|39.8|60.2|
|1991<br>|<br>358.3<br> <br>|137.0|221.3|38.2|61.8|379.4|149.7|229.6|39.5|60.5|
|1992|<br>415.3|164.9|250.4|39.7|60.3|439.0|181.8|257.2|41.4|58.6|

s Revenues collected by the center or localities (before tax-uharLng), not revenues available to the the localLtLes after tax-sharing.

center and Sources China Btatistical Yearbook, various issues.

24
of a gradual impact of funding costs and cyclical movements in output on the demand for excess reserves.

Figure 1 displays the evolution of some of the variables included in the regression equation during the period 1992-98. The first panel shows the evolution of the ratio of excess liquid assets (obtained as the difference between actual and required liquid assets) to bank deposits.<sup>1 0</sup> The figure shows that excess liquid assets started to increase significantly well before the crisis (as early as late 1996), rising quite dramatically in the immediate aftermath of the currency and financial turmoil that began with the devaluation of the baht in July 1997. At the same time, the cyclical component of output turned sharply negative. The figure also shows that the money market rate fluctuated considerably during the period 1992-98--exceeding at times the discount rate and falling well below it at other times. Finally, both the ratio of credit to the private sector to deposits, and the ratio of currency in circulation to deposits, increased at first and subsequently fell in the aftermath of the crisis.

Table 1 presents the summary of the estimation results of the autoregression using ordinary least squares based on monthly data and using the entire sample ending in September 1998. The table reports the sum of the coefficients and their standard errors of the lags of the regressors included in the estimation since individual coefficients in the autoregression are difficult to interpret directly. Conceptually, the sum of the coefficients are obtained by estimating the dynamic model and then setting L _=_ 1. Thus, the sum of the coefficients reported in the table correspond to the long-run effects once they - have been appropriately scaled by: 1 al(1). In other words, Table 1 shows 10As noted in Sirivedhin (1998), prudential regulations in Thailand around the time of the crisis required commercial banks to hold liquid assets (averaged over a fortnight) of not less than seven percent of their deposit base; eligible assests included at least two percent non-interest-bearing deposits at the Bank of Thailand, a maximum of 2.5 percent cash in vault, and (making up the remainder) bonds issued by the government, approved state enterprises, specialized financial institutions or the Bank of Thailand.

12
Policy Research Working Paper 9773

# **Abstract**

The history of Iraq over the past decade is a stark example of increasing vertical and horizontal inequalities, preceding and following a period of violent conflict. This paper uses a cross-section of individual-level nationally representative survey data to study relationships around how adults in Iraq report confidence in national institutions, quality of life, and quality of service delivery. The findings show that positive perceptions across these categories declined during ISIS-control and have slowly picked up in recent years. Men are more likely than women in the post-ISIS period to report lower country leadership approval ratings, a weaker standard of living, and depressed job prospects. The analysis finds that self-identified Shias, Kurds, and adults living in Baghdad are significantly more likely to have a poorer quality of life, compared with Sunnis, in the post-ISIS period. Nearly all ethno-religious groups, in all periods, perceive the government to be corrupt.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at sansar1@worldbank.org; bceliku@worldbank.org; lklapper@worldbank.org; and wmansour@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
Ryan, Richard M. and Edward L. Deci. 2006. “Self-regulation and the problem of human autonomy: does psychology need choice, self-determination, and will?” _Journal of Personality_ 74:6.

Sajogyo, Pudjiwati, Endang L. Hastuti, Syarifah Surkati, Winati Wigna and Krisnawati Suryanata. 1979. _Studies in Family Planning_ , Vol. 10, No. 11/12, pp. 364-370.

Samman, Emma, and Maria E Santos. “Agency and empowerment: A review of concepts, indicators and empirical evidence.” Oxford Poverty and Human Development Initiative, 2009.

<mark>Scholz, U., Dona, B.G., Sud, S. and Schwarzer, R., 2002. Cross-Cultural Assessment of Coping Resources: The General Perceived Self-Efficacy Scale.</mark> _<mark>European Journal of Psychological Assessment</mark>_ <mark>,</mark> _<mark>18</mark>_ <mark>(3), pp.242251.</mark> <mark>Schultz, D. and Schultz, S., 2016.</mark> _<mark>Theories of personality</mark>_ <mark>. Cengage Learning.</mark> <mark>Schweigert, W.A., 2011.</mark> _<mark>Research methods in psychology: A handbook</mark>_ <mark>. Waveland Press.</mark> Sen, Amartya K. 1985. “Well-being, agency and freedom: The Dewey lectures 1984.” _Journal of Philosophy_ 82.

Sen, Amartya K. 1999. _Development as Freedom_ . New York: Oxford University Press.

Sen, Amartya K. 2002. _Rationality and Freedom_ (Cambridge, Mass, Balknap Press), ix.

Seymour, Greg, and Peterman, Amber. 2017. “Understanding Differences Between Sole and Joint Decision-making: Evidence on Women’s Autonomy from Bangladesh and Ghana.” IFPRI, working paper in progress.

Seymour, Greg, Malapit, Hazel, and Quisumbing, Agnes. 2017 _._ “Measuring time use in development settings.” Policy Research Working Paper, World Bank.

<mark>Shankar, A.V., Onyura, M. and Alderman, J., 2015. Agency-based empowerment training enhances sales capacity of female energy entrepreneurs in Kenya.</mark> _<mark>Journal of health communication</mark>_ <mark>,</mark> _<mark>20</mark>_ <mark>(sup1), pp.67-75.</mark> Shroff Monal, Paula Griffiths, Linda Adair, Chirayath Suchindran, and Margaret Bentley. 2008. “Maternal autonomy is inversely related to child stunting in Andhra Pradesh, India”. _Maternal and Child Nutrition_ 5(1): 64-74.

<mark>Skinner, E.A., 1996. A guide to constructs of control.</mark> _<mark>Journal of personality and social psychology</mark>_ <mark>,</mark> _<mark>71</mark>_ <mark>(3), p.549.</mark> <mark>Smith, T.W., 2003. Developing comparable questions in cross-national surveys.</mark> _<mark>Cross-cultural survey methods</mark>_ <mark>, pp.69-92.</mark> Smith, Lisa C., Usha Ramakrishnan, Aida Ndiaye, Lawrence Haddad, and Reynaldo Martorell. 2003. “The importance of women’s status for child nutrition in developing countries.” IFPRI Research Report 131.

Sproule, Katie and Kovarik, Chiara. 2014. “Cognitive Testing and vignettes.” Gender Methods Seminar, IFRPI. December 12, 2014 65
its voting share in the legislature. A greater number implies greater polarization. This method of measuring polarization has been used by Franzese (2008) and Eslava and Nupia (2010). The data used to calculate government polarization are from the Database of Political Institutions (2010).<sup>6</sup> The second important variable of interest is the quality of institutions. Ideally this would require a measure that captures how the interests of powerful groups are translated into central government policies. Such powerful groups can be political leaders, provincial governments, labor unions, parastatal enterprises, and financial backers of the ruling government. This measure at its lowest would describe the case of a central government that is weak and surrenders to all the demands of the interest groups. At its highest value, it captures the case when the central government is strong and makes optimal decisions for the aggregate economy. As far as I know, there is no cross-country indicator of institutions or governments that measures that. Instead I used two proxies. The first is the standard market assessment measure of institutional quality: the International County Risk Guide (ICRG) institutional index. The index from the PRS Group is based on investors’ evaluation regarding the rule of law, bureaucratic quality, corruption, expropriation risk, and government repudiation of contracts. It ranges from 0 and 1, with high values representing better institutions. This measure of institutional quality includes several aspects of government structure. There are two potential disadvantages: 1) ICRG, as any market assessment measure of institutions, tends to improve with income level rather than reflect durable institutional constraints in the government; and 2) ICGR might already contain information about past defaults,<sup>7</sup> hence there would be an endogeneity problem.

The second proxy that I use is regulation of participation (parreg) from POLITY IV. It measures the degree of institutionalization or “regulation” applied regarding how political preferences are expressed by subordinates.<sup>8</sup> It captures the extent to which the political system enables non-elites to influence political elites in regular ways. A five-category scale is used: 1- Unregulated, 2- Multiple identity, 3- Sectarian, 4- Restricted, and 5- Regulated. Details of each

> 6 A broader concept of polarization is social polarization. We can think of different interest groups of the society represented in the government by governing parties. If we use income inequality (Gini coefficient) as a proxy for social polarization as it has been suggested by Woo **(** 2003) and Keefer and Stephen (2002), results are similar to those using polar.

> 7 Reinhart, Rogoff and Savastano (2003) show that history of non-repayment is an important driver of the market perceptions of default risk.

> 8 Marshall & Jaggers (2000), p. 23

7