rafmacalaba/datause-encoder-data huggingface dataset:
**Small Is Beautiful, at Least in High-Income Democracies: The Distribution of Policy-Making Responsibility, Electoral Accountability, and Incentives for Rent Extraction**

Alexander Hamilton<sup>1</sup> _Key words: career concerns, corruption, rent extraction, electoral accountability, and political economy._ _JEL Codes: D02, D72, D73, D78, H11, Z18_ _Sector Board: Public Sector Governance (PSM)_

> 1 This article summarizes some of the key theoretical and empirical findings emanating from my doctoral thesis undertaken at the University of Oxford (2008-2012). The completion of this work would not have been possible without the professional and personal support I received from the following: my supervisors, Raymond Duch, Indridi Indridason, and Desmond King; my examiners Pablo Beramendi, David Soskice, and Jane Green as well as the funding provided by the ESRC. Invaluable feedback was also provided by Cristina Corduneanu-Huci, Varvara Lalioti, the participants of Nuffield College’s GRIPS Workshop and a seminar at the World Bank in Washington DC. Finally, I would like to thank the MIT University Press for their kind permission to reproduce some of their published materials. Comments and feedback can be sent to: a.hamilton@oxon.org

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Figure 3.5: Proportion of Argentine Youth who Drinks Frequently <u>by Age and Gender, 2005</u> Figure 3.6: Proportion of Argentine Youth who <u>Smokes Frequently by Age and Gender, 2005</u> <!-- Start of picture text --> 40.0 18.0<br>35.0 16.0 Females<br>14.0 Males<br>30.0<br>12.0<br>25.0<br>10.0<br>20.0<br>8.0<br>15.0 6.0<br>10.0 4.0<br>Females<br>5.0 Males 2.0<br>0.0<br>0.0<br>Source : Own calculations on ESCVJA (four urban areas) Source : Own calculations on ESCVJA (four urban areas)<br>15 years16 years17 years18 years19 years20 years21 years22 years23 years24 years<br>15 years16 years17 years18 years19 years20 years21 years22 years23 years24 years<br>Percent Percent<br><!-- End of picture text --> Abuse of illegal drugs, such as marihuana or cocaine, is a behavior of more concern than smoking and drinking, but also less prevalent. These drugs causes more concern both because the physical and mental effects are much stronger than those of alcohol and normal tobacco, and because of the association with crime and violence. Some 9 percent of all youth have tried drugs. At the age of 15, practically no youth have tried drugs, but with a steady increase, 16 percent of female youth and 19 percent of male youth have tried drugs at the age of 24 (Figure 3.7).

Figure 3.7: Proportion of Argentine Youth who have tried Drugs, by Gender, 2005 <!-- Start of picture text --> 25.0<br>Females<br>20.0 Males<br>15.0<br>10.0<br>5.0<br>0.0<br>15 years 16 years 17 years 18 years 19 years 20 years 21 years 22 years 23 years 24 years<br>Percent<br><!-- End of picture text --> _Source_ : Own calculations on ESCVJA (four urban areas) Lack of parental support and a safe and stable home affect the likelihood of youth to drink, to smoke or to illegal drug abuse. Figure 3.8 shows that for both female and male youth, it is always those living with both parents who are less likely to use any of these substances, compared to youth living with no parents. Naturally, a part of this is explained by age differences, because older youth are more likely to live without a 13
<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>0 1 2 3<br>Number of national elections<br>Renegotiations initiated by government<br>Renegotiations initiated by concessionaire<br>Number of renegotiations<br><!-- End of picture text --> Figure 3: Elections and the timing of renegotiations We confirm these stylized facts in the probit analysis. Indeed, results not shown here indicate that a lagged dummy for the first election is positive and significant at the 5% level on the probability of government-led renegotiation, with a marginal effect slightly above 10%, while it is negative and not significant for firm-led renegotiation. Overall, this gives a picture consistent with our model assumption, namely that concessionaire-led renegotiations tend to occur mainly for contractual reasons or in response to adverse unexpected shocks, while government-led renegotiations are generally politically motivated and are likely to follow elections. A more accurate analysis would need to take into account political changes occurring through elections, both at the national and the local level. The specificity of Latin American politics, however, makes it difficult to identify political changes. For example, newly elected governments or parliamentary majorities responding to the same party as their predecessor very often come from a different faction of this party, with very different political agenda, and should thus be considered as representing a political change. A striking example is the assassination of Donald Collosio, the Mexican officially designated presidential candidate of the Institutional Revolutionary Party (PRI), in December 1994, which is thought to have been ordered by members of its own party. Thus, while the PRI remained in power after Mexico’s 1995 elections and we would not formally classify this transition as a political change, there is some indication that a different political group actually became dominant at that time.

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**Table 4: The Transition Matrix**

|||**ce**<br>|**ll in t+1 (2000**<br>|**)**<br>||
|---|---|---|---|---|---|
||**5**<br>**(smallest)**|**4**|**3**|**2**|**1**<br>**(largest)**|
||**5**<br>0.987|0.013|0.000|0.000|0.000|
|**n t**<br>**0)**|**4**<br>0.183|0.720|0.098|0.000|0.000|
|**ll i**<br>**97**|**3**<br>0.000|0.091|0.800|0.109|0.000|
|**Ce**<br>**(1**|**2**<br>0.000|0.000|0.029|0.882|0.088|
||**1**<br>0.000|0.000|0.000|0.000|1.000|

The probability of staying in the same state is the highest at 100 percent for the cities at the top of the hierarchy (cell 1), which implies no downward mobility for the largest agglomerations. Also the mobility is extremely low for the smallest agglomerations in cell five. This extremely high probability of smallest cities in cell five staying in the same state (98.7 percent) is quite different from the finding of Henderson and Wang (2005).<sup>3</sup> The cities in the middle portions of the hierarchy have a relatively high degree of mobility moving up and down in response to changing demands for their products, product readjustment, and local entrepreneurship or lack thereof. In particular the lower-medium size cities in cell 4 have only 72.0 percent probability of staying in the same state and the probability of moving down a state exceeds that of moving up (18.3 percent versus 9.8 percent). However the upper-medium size agglomerations in cell two have a higher probability of moving up a state than moving down (8.8 percent versus 2.9 percent). The stationarity of the transition matrices is just accepted,<sup>4</sup> implying it is reasonable to assume the city size distribution evolves over time according to a homogeneous stationary first-order Markov process. Figure 5 provides a continuous view of the dynamics of city rankings between 1970 and 2000. Points below the 45 degree line represent cities that have increased their population and

> 3 For the metro areas of the world with population over 100,000, the probability of the smallest cities staying in the same state was 78 percent, and that of the largest cities 96 percent (Henderson and Wang 2005).

> 4 The χ 2 statistic is 27.07 with 40 degrees of freedom (p-value 0.059).

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# **5. Conclusion**

We build on the existing advantages of the World Bank’s widely-used SCI, both conceptually and empirically to offer a new SPI that can better measure a country’s statistical performance. The SPI offers the most complete coverage of country statistical capacity to date. In particular, we present clearer conceptual motivation, employ a stronger mathematical foundation, and significantly expand the number of indicators (including adding new indicators) and countries covered by this index. We also provide some brief empirical illustrations of the strong correlation of this new index with other commonly used development indicators. The indicators can be aggregated into overall scores not just for an index, but also for 22 dimensions and five pillars, which can offer a “dashboard” roadmap that visually describes a country’s statistical performance. Our framework proposes future directions to further improve the SPI as the global data landscape evolves.

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CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 8. Countries’ spending on rehabilitation and maintenance, relative to norms**

<!-- Start of picture text --> 500%<br>400%<br>300%<br>200%<br>100%<br>0%<br>-100%<br>-200%<br>Maintenance Rehabilitation<br>Percentage deviation from norm C had iaNi r e g ndaUg a i N er g Sengal e iMala w i Ethiopa Lesoth o Rwanda Ghana Knya e Tanzania inBe n mibiaN a n o Zambia frica A<br>Camero<br>Madagascar Mozambique South<br><!-- End of picture text --> Source: Gwilliam and others 2008.

# **Challenges**

In spite of adequate levels of road financing, maintenance activities are poorly planned and ineffective in optimizing the life cycle of road assets. A recent audit of the maintenance contracts financed by the country’s road maintenance fund found that only about 45 percent of civil works scored good or fair in technical quality (World Bank 2011c).

These deficiencies in maintenance have left Cameroon’s road network in relatively poor condition. The condition of the country’s classified paved road network is below the level of peer countries, with only 52 percent of the classified paved network in good or fair condition versus 68 percent in Africa’s resource-rich countries and 82 percent in middle-income countries (table 3). The quality of the roads hobbles the private sector. About one in three firms in Cameroon identified roads as a major constraint for doing business, slightly above the average for similar resource-rich countries (at 30 percent), but almost twice the level in middle-income countries (at 18 percent).

The quality of Cameroon’s sections of critical regional corridors is also poor, especially when compared with sections in neighboring countries. That weakness prevents landlocked countries from moving goods and people efficiently. Cameroon has sections in four regional corridors: Douala-Bangui, Douala-Ndjamena, Pointe Noire-Brazzaville-Bangui, and Nouakchott-Ndjamena. The Cameroon sections of the Douala-Bangui, Pointe Noire-Brazzaville-Bangui, and Douala-Ndjamena corridors are still not completely paved. Although some sections of the Douala-Bangui corridor are being upgraded as part of the CEMAC Transport Transit program, about 250 kilometers in Cameroon (and 210 kilometers in the Central African Republic) are surface pavement treated. All of Cameroon’s section in the Pointe NoireBrazzaville-Bangui corridor (308 kilometers) is unpaved (as are 1,000 kilometers in the Congolese side).

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# **TABLE 1. SUMMARY STATISTICS: MEANS OF VARIABLES BY GROUP PRIOR TO TREATMENT**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Full<br>Sample|Control<br>Group (C)|Treatment<br>Group (T)|Unadjusted<br>_p-_ values|Adjusted<br>_p-_ values|
|**PANEL A: IVV DETERMINANTS**||||||
|Student is male|0.49|0.51|0.49|0.659|0.990|
|Student’s age|11.94|11.86|11.97|0.247|0.825|
|Student lives in urban area|0.73|0.72|0.74|0.502|0.995|
|Student’s household composition||||||
|Student lives with both parents|0.53|0.47|0.55|0.073|0.465|
|Student lives only with one parent|0.32|0.36|0.31|0.125|0.745|
|Student lives with one parent and stepparent|0.06|0.07|0.06|0.658|0.995|
|Student lives with other relatives/adults|0.09|0.10|0.08|0.551|0.990|
|Student’s mother’s level of education:||||||
|Basic education (1–6 years)|0.31|0.34|0.30|0.283|0.935|
|Intermediate education (7–12 years)|0.62|0.59|0.63|0.379|0.985|
|University or higher (13 years +)|0.07|0.07|0.07|0.775|0.995|
|Student’s travel time from house to school (min.)|17.64|16.98|17.85|0.459|0.985|
|Student is alone at home after school|0.05|0.05|0.05|0.805|0.995|
|Student’s school year|5.75|5.67|5.78|0.072|0.450|
|Student is enrolled in the morning shift|0.71|0.71|0.72|0.667|0.995|
|Student’s violence index|0.04|0.04|0.04|0.790|0.995|
|**PANEL B: ACADEMIC OUTCOMES**||||||
|Academic scores Q1 2016 (Baseline)||||||
|Reading scores|6.66|6.48|6.73|0.034|0.110|
|Math scores|6.50|6.44|6.51|0.628|0.565|
|Science scores|6.61|6.47|6.66|0.222|0.185|
|Behavior scores|7.39|7.28|7.43|0.085|0.185|
|Absenteeism Q1 2016|1.69|2.21|1.52|0.010|0.110|
|**PANEL C: CLUBS’ CHARACTERISTICS**||||||
|Average club size|-|-|13.4|-|-|
|Average club take-up|-|-|0.57|-|-|
|Community tutors|-|-|0.31|-|-|
|Club category||||||
|Leadership|-|-|0.29|-|-|
|Art and Culture|-|-|0.16|-|-|
|Sports|-|-|0.26|-|-|
|Science|-|-|0.29|-|-|
|Share of treated by course|-|-|0.42|-|-|
|Observations|1056|258|798|||

Table 1 shows descriptive statistics of the available variables at baseline for the sample of enrolled children. Panel A summarizes information obtained from the enrollment form that was used as determinants in the IVV estimation. Panel B presents administrative data provided by schools. This data is from the first quarter of academic year 2016, before the clubs were implemented. We imputed average course grade for missing observations. For the absenteeism data, we have no records for some courses; thus we imputed the average school absenteeism. The scale of grades in El Salvador is 0–10 points. Panel C presents club characteristics. Take-up is estimated as number of hours attended by student _i_ /total hours in each club. _p_ -values in column (5) have been adjusted for multiple hypothesis testing of means and FWER using a command developed by Jones et al. (2018), which follows the free step-down resampling methodology of Westfall and Young (1993). In the estimations of adjusted _p_ -values, we separated the variables into the two main outcomes families (Panel A and Panel B).

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# **2. Survey Design and Sample Characteristics**

The firm survey was conducted between the second half of 2007 and the first quarter of 2008 in 16 European Union countries, 12 of which included the questions on alternative margins of labour cost adjustment analysed here: Belgium, Czech Republic, Estonia, France, Greece, Hungary, Ireland, Italy, Lithuania, Poland, Portugal and Slovenia.<sup>2</sup> The survey was carried out by the National Central Banks and all countries used a harmonised questionnaire as the basis for the survey. This questionnaire was developed in the context of the Eurosystem Wage Dynamics Network, a research network analysing wage and labour cost dynamics. The collection of information varied across countries, the survey being conducted in most cases by traditional mail, but phone and face-to-face interviews were also used. The survey was directed at the company’s CEO, or to senior-level human resources management employee(s).

The harmonised questionnaire contained a core set of questions, referring to general firm characteristics, and the firms’ price and wage setting strategies that were included in all counties’ questionnaires.<sup>3</sup> An enlarged questionnaire, including the relevant questions for this study, was used in 12 countries. This harmonised questionnaire was further adapted by some countries to account for specific country characteristics and different institutional frameworks, but it retained its comparability in all the dimensions covered in this paper.

The sample frame in each country was based on firms with at least 5 employees. The sectors covered are manufacturing, energy, construction, market services, non-market services, trade and financial intermediation; there are however differences in the sectoral coverage of individual countries. The sample used here covers around 12,000 firms representing around 37.2 million employees.<sup>4</sup> A description of the distribution of the sample by country, sector and size is provided in Appendix 2.

In order to make the results representative of the total population the sample statistics presented in the following sections use employment adjusted weights. For each firm/observation these weights indicate the number of employees each observation represents in the population. They can be roughly calculated as the population employment divided by

> 2 Luxembourg is also conducting the survey and the data will be made available to the network’s researchers at a later stage.

> 3 Firms were instructed to answer the wage setting questions with reference to their main occupational group.

> 4 Appendix 1 provides detailed information on the survey characteristics.

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Central Intelligence Agency. Since the map originally had an image format, we used ArcMap to digitize it by hand and convert it to a shapefile format. Our variable takes the value of one if a census tract contains part of a road or railway.

- **Distance to the capital:** We calculated the Euclidean distance in kilometers from the centroid of each census tract to San Salvador, the capital city of El Salvador.

- **Distance to the coast:** We calculated the Euclidean distance in kilometers from the centroid of each census tract to the nearest coast.

- **Distance to departmental boundaries:** We calculated the Euclidean distance in kilometers from the centroid of each census tract to the nearest departmental boundary.

# **A.C** **_Population and Household Census of 2007 (PHC)_**

The PHC of 2007 is available at http://www.censos.gob.sv/censo/Default.aspx.

- **Census cartography:** DIGESTYC also provided maps of the 12,435 census tracts ( _segmentos censales_ ) in the 2007 census. Each census tract represents a small area with a fixed geographic perimeter. On average, they have an area of 1.7 _km_<sup>2</sup> , a perimeter of 5.5 _km_ , 131 households, and 473 individuals.

- **Wealth score:** we built a wealth score that represents the living conditions of each household using household characteristics and asset ownership such as the type of roof, access to water, television, etc. To construct the score, we used a principal component analysis following the steps recommended by the Demographic and Health Surveys program (DHS), which can be consulted at https://dhsprogram.com/topics/wealth-index/Wealth-Index-Construction. cfm. We calculate the average of this measure for each census tract.

- **Years of education:** The PHC asks each individual the total number of years of education in single years. However, our variable only takes into account individuals older than 18 years since most of this population already finished secondary school. We calculate the average of this variable for each census tract.

- **Literacy rate:** The PHC asks each individual if they can read and write. Thus, our literacy rate variable is the number of individuals older than 18 years who can read in each tract over the total population in the same age range in the same tract.

- **Public good provision rates:** The PHC asks each household if they have water access, sew-

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<!-- Start of picture text --> P 12<br>(c+D)Q<br>cQ<br>10 A‐BQ<br>8<br>6 t (c’+D)Q<br>4 t’ c’Q<br>2<br>0<br>0 1 2 3 4 5 6<br>Q<br><!-- End of picture text --> Figure 1: A downward rotation of the supply curve increases the optimal tax ages are convex in , which represent emissions, a flow. The usual challenge to that assumption is based on the fact that GHGs are a stock rather than a flow pollutant, and that emissions during a short period (relative to the half life of the stocks) are small relative to the stock size. This observation means that the change in damages arising from a change in emissions can be adequately approximated using a first order Taylor approximation to the damage function. If that claim is correct, then as noted above, green policies that lower future prices and thus lower the current rent have no effect on the current optimal emissions tax. There are, however, reasons to doubt that a constant marginal damage provides a good approximation to the true damage function.

First, there is considerable inertia in the climate system, which is associated with the potential for abrupt changes that are not reversible on a policy-relevant time scale. Consider two scenarios; in one, there is an additional 2 units of emissions over the next 25 years, and in the second there is an additional  units of emissions in both of the next two 25 year periods. In the absence of inertia or the potential for abrupt changes, the world 100 years from now is better off in the first scenario. Moving the  units of emissions from the second to the first quarter century gives it longer to decay, so the stock (and thus damages) 100 years from now is lower in the second scenario than in the first. The possibility of abrupt changes favors the second scenario, because there the stock of GHGs is (slightly) less likely to cross a threshold that triggers an abrupt change. If the second scenario is worse for the future than the first, then there is a plausible case that (the 16