rafmacalaba/datause-encoder-data huggingface dataset:
- 25 -

be determined using comprehensive household survey data, as done here.

The figures in the top half of Table 7 give the amount of money which can be saved from new information, starting from a base of 10 million CFA Francs. These figures apply to the sample only, and to obtain figures for all Cote d'Ivoire they should be multiplied by 769 (cf. footnote. 17). Taking the initial case of no information (i.e. completely untarge-ed transfers), about 6-9 million ef the original 10 million CFA Francs (5-7 billion CFA Francs for all Cote d'Ivoire) can be saved using the various targeting models. This is a substantial drop in costs and one would think that the cost of gathering the information is likely to be much less. In many cases much of the information required may already be available from national census data (e.g. housing characteristics) and if not the marginal cost of amending the census to collect it may be rather small. It is also interesting to note that, for the case of no information, differences in the value of a do not seem to make a big difference in terms of cost reductions. Thus, one could go ahead with a targeting scheme even if ones value judgment regarding the proper value of a is not fully thought out.

Turning to the situation where initial information consists of the variables in Model 1, the marginal cost reduction gains of new information are relatively low, but still substantial. There is also more variation across a, especially for Model 2. It is useful to take one example, the value of knowing household size, which allows one to create the per capita floor area variable. Turning to Model 4, between 1.8 and 2.6 million CFA Francs could be saved if accurate information on household size were collected to supplement data used in Model 1. Extrapolating for C8te d'Ivoire as a whole,
|PB|0.18**<br>(0.03)|-0.91**<br>(0.23)|
|---|---|---|
|Years of PB (8 or|0.32*|-7.07**|
|More)|(0.16)|(1.41)|
|N: Policy Councils|62,954|60,361|
|PB|7,005|6,990|

We find that voluntary policy management councils in areas surrounding health care are associated with high levels of health and sanitation spending relative to municipalities that lack these councils, all else equal. Similarly, we find that PB is associated with still higher levels of health and sanitation spending. Voluntary policy management councils related to health care are also related to infant mortality levels, which are systematically lower in municipalities with these councils.

Another possibility in the causal chain is that participatory institutions might lead to very visible public spending in areas that are desirable to the taxpayer. This is also similar to Bodea and LeBas’ survey results in Nigeria: citizens satisfied with government services support universal tax obligations (Bodea and LeBas 2016). In this case, we exploit variation in PB spending decisions across municipalities and assess the extent to which spending patterns are associated with subsequent tax outcomes. Specifically, we asked PB administrators whether their programs supported more social programs and projects, such as those in health care and education, more capital projects, such as roads, buildings, or other facilities, or a mixture of both. Previous scholarship suggests that capital projects tend to appeal more to middle- and upper-class residents than social programs that target the poor (Wampler 2015). Roads and buildings are also theoretically more visible than social programs in the sense that new roads and buildings are potentially more apparent than new programs that use existing infrastructure or additional funding for existing programs– especially those that are geographically concentrated in poor communities. Only a handful of municipalities selected capital spending as their primary use of PB funds, but PB programs are relatively split between supporting a mix of capital and social spending alone. We therefore expect a mix of projects and programs to increase projects’ visibility and, subsequently, improve broader perceptions of government performance and service delivery for tax compliance purposes.

24
(―dysfunctions by default‖). The latter probably persist as a result of inertia, distrust, indifference and neglect or are, in some cases, the unintended consequences of previous reform efforts. Section 3 translates the findings from the ̳prospective‘ PE analysis into operational practice and discusses how the design of the P&PP is expected to address some of the current dysfunctions and thereby provide ̳best-fit‘ solutions for civil service reform in Sierra Leone. Section 4 evaluates the risks associated with the suggested approach and discusses why there are greater chances of success today than in the past. Section 5 concludes by suggesting potential implications of this ̳live experiment‘ for ongoing efforts to develop a ̳theory of change‘ in Public Sector Management and adopt ̳best-fit‘ reforms solutions in fragile and institutionally challenging environments.

# **1. The Civil Service in Sierra Leone: Defining the functional problem**

Despite strong economic growth of 4.57 % between 2008 and 2010, Sierra Leone still faces serious development challenges: over 62 percent of the population lives below the poverty line of $1.25 a day, and almost two-thirds of the population is illiterate. Life expectancy is 49 years, well below the regional average of 54 years. Available evidence suggests that the Sierra Leone‘s performance on the delivery of services and the provision of basic infrastructure – such as road and power - is below par with respect to other similarly placed countries in the region (table 1), and not commensurate with the resources being expended (World Bank 2010).

4
**Table 3: Comparison of Means Before and After the Adoption of Performance-Related Pay**

|**Befo**|**re**<br>**Afte**|**r**<br>**Di**|**fference**|
|---|---|---|---|
|**Pernambuco**||||
|Child Mortality (per 1,000 born alive), 2007–2011|33.98|19.84|‐14.14***|
|Homicide Rate (per 100,000 inhabitants), 2007–2011|52.78|45.52|‐7.26***|
|IDEB Public Secondary School Score, 2007–2011|2.70|3.10|0.40|
|IDEB Public Secondary School Score, 2008–2013|2.70|3.23|0.53**|
|**Minas Gerais**||||
|Child Mortality (per 1,000 born alive), 2004–2011|20.4|17.38|‐3.03***|
|Homicide Rate (per 100,000 inhabitants), 2004–2011|18.6|20.68|2.08*|
|Child Mortality (per 1,000 born alive), 2008–2011|18.95|16.53|‐2.43***|
|Homicide Rate (per 100,000 inhabitants), 2008–2011|20.68|19.45|‐1.06|
|IDEB Public Secondary School Score, 2008–2011|3.45|3.63|0.18***|
|**Rio de Janeiro**||||
|Homicide Rate (per 100,000 inhabitants), 2008–2011|49.87|34.45|‐16.29***|
|IDEB Public Secondary School Score, 2010‐2013|2.80|3.20|0.60***|

_Source_ : Authors’ calculations, based on data from IBGE and MEC. _Note_ : *** Significant at the 0.01 level (2-tailed); ** Significant at the 0.05 level (2-tailed).

In the case of Pernambuco, there are substantive and statistically significant reductions in child mortality and homicide rates, and an increase in the IDEB score for public secondary schools after 2007. For Minas Gerais, there are two cut-off points, after 2004 when the first stage performance agreements were introduced and after 2007 when an additional agreement was added between Secretaries and teams. In both cases, there are statistically significant reductions in child mortality vis-à-vis the average from the previous period. For the second comparison, there is a significant difference in the secondary school outcome measure.

For the state of Rio de Janeiro, there is a substantial and statistically significant reduction in the average homicide rate when comparing the period before and after the adoption of a resultsbased model and the introduction of a performance-related bonus for the state security forces. The negative trend in homicide rates had started prior to the adoption of the results-based approach and the UPPs. In secondary education outcomes, the IDEB results for the state show a statistically significant increase equivalent to a fifth of the prior score.

This method is, however, vulnerable to threats to internal validity, in particular, the threat of external events affecting outcome scores. Given that there are, in fact, reasons to think that the after series might be different from the before series even without the treatment, the results could be spurious. To address this risk, the results are also compared with those of the other states in the respective regions. For each of the groups, the differences between changes over time 21
increment to the correlation. Another increment is added for low SES households. In contrast, high SES subtracts a significant increment.

Given the complexity of collective actions in the commons, it is widely acknowledged that systems of governance need to be flexible to allow adaptation of management regimes to local conditions (Adhikari and Lovett 2006). In the literature on the environmental commons, attention focuses primarily on class-related interests that affect the prospects for local or regional action. Our results suggest that socioeconomic status may also operate through perceptions of environmental change that become more varied as socioeconomic status increases. While our results suggest that the strongest perceptual foundations for action in the commons are local, they also indicate that disagreement about priorities may undermine the leadership potential of local elites. By implication, village-level governance based on widespread participation in decisionmaking seems likely to promote the most effective environmental measures in the Indian Sundarbans. This is a key message for development partners, policy makers and practitioners working on management of environmental resources in the Indian Sundarbans.

# **References**

Adhikari, B. and Lovett, J.C., 2006. Institutions and collective action: does heterogeneity matter in community-based resource management?. _The Journal of Development Studies_ , _42_ (3), pp.426445.

Alesina, A., Baqir, R. and Easterly, W., 1999. Public goods and ethnic divisions. _The Quarterly Journal of Economics_ , _114_ (4), pp.1243-1284.

Araral Jr, E., 2009. What explains collective action in the commons? Theory and evidence from the Philippines. _World development_ , _37_ (3), pp.687-697.

Baland, J.M. and JP Platteau, 1993. _A multidisciplinary analysis of local-level management of environmental resources_ . Facultés Universitaires Notre-Dame de la Paix.

13
# Table 2: Baseline Summary Statistics and Take-Up Analysis

# Mean and Standard Deviations

||Treatment|Control|Orthogonality<br>Verification<br>(1)-(2)<br>Difference<br>(p-value)|Took-up|Did Not<br>Take-up|(4)-(5)<br>Difference<br>(std. err.)|
|---|---|---|---|---|---|---|
|Panel A: Stratification variables|(1)|(2)|(3)|(4)|(5)|(6)|
|Manufacturing sector dummy|0.300|0.323|-0.023|0.363|0.229|0.134*|
||(0.460)|(0.468)|(0.630)|(0.484)|(0.423)|(0.075)|
|Commerce sector dummy|0.253|0.230|0.023|0.225|0.286|-0.061|
||(0.436)|(0.422)|(0.597)|(0.420)|(0.455)|(0.397)|
|Services sector dummy|0.447|0.447|0.000|0.413|0.486|-0.073|
||(0.499)|(0.498)|(0.998)|(0.495)|(0.503)|(0.372)|
|Full-time paid employees|14.400|13.684|0.716|18.825|9.343|9.482*|
||(30.887)|(31.479)|(0.821)|(36.288)|(22.444)|(0.060)|
|Panel B: Re-randomization variables|||||||
|Principal decision maker's age (years)|42.561|42.876|-0.315|42.443|42.696|-0.253|
||(10.212)|(9.878)|(0.756)|(9.540)|(10.999)|(0.881)|
|Male principal decision maker dummy|0.727|0.720|0.007|0.800|0.643|0.157**|
||(0.447)|(0.450)|(0.881)|(0.403)|(0.483)|(0.031)|
|Principal decision maker's yrs of schooling|15.630|15.932|-0.302|16.138|15.050|1.088|
||(4.919)|(5.196)|(0.559)|(4.472)|(5.358)|(0.177)|
|Business age (years)|11.053|13.652|-2.599|12.825|9.029|3.796**|
||(10.330)|(28.120)|(0.275)|(11.501)|(8.437)|(0.024)|
|N|150|282|432|80|70|150|

Note: Columns 1, 2, 4 and 5 present means and standard deviations (in parentheses). Column 3 shows the difference in means across the treatment and control group with the corresponding p-value in parentheses. Column 6 shows the difference in means across treatment enterprises that did and did not take-up the program with the corresponding p-value in parentheses. Significance levels: *10 percent, **5 percent, ***1 percent.

23
- 30 -

price elasticity (together with the budget constraint) to<sup>work out the</sup> effect of a price change on the total demand for all other goods and then to multiply that effect by the average tax rate on all other goods to obtain an approximate revenue effect from substitution into other goods. In many cases it will be possible to do better than this and obtain estimates (perhaps from other countries) of how the increased expenditure<sup>on all</sup> other goods will be divided between important categories of<sup>goods.</sup> 3.42 Moving to the pricing of sales to firms, an analysis of the fiscal and distortionary effects requires the use of the technique described by Newbery and mentioned above to calculate the effects of intermediate good prices on factor and consumer goods prices. This technique requires an input-output table and a knowledge of which goods are internationally traded. This technique allows the calculation of the effects on factor price and consumer prices, and their fiscal and distortionary effects can be calculated usit,, the data discussed above. However, the pricing of intermediate goods above marginal cost also has a distortionary effect on production (measured as the revenue lost by substitution away from taxed inputs), which can only be estimated with some knowledge of substitution possibilities in the relevant industries. This may well be the most difficult data to obtain.

3.43 The introduction of distributional considerations is the final complication to be introduced. This requires a household budget survey in order to allocate the losses from price increases between households on different income levels. The distributional weights are the other requirement, but these come from the government's objectives rather than empirical data.
the World Bank, which has a long history of producing high-quality household survey data. The first wave was administered in 2011/2012 to 3,969 rural households, the second in 2013/2014 to 5,262 households – 3,776 panel households and 1,486 new, mainly urban, households. Our data set covers only households that were interviewed in both waves, which are all rural. The survey collected extensive information on household characteristics, consumption, living conditions, and health.

The unique feature of the survey is its focus on agriculture. To gather detailed and accurate agricultural data at the plot level, households were visited three times during the agricultural year. The first visit, in September-October, collected data on planting activities. During this visit, the area of most plots was measured with GPS.<sup>7</sup> The second visit, in November, implemented the livestock module. The final visit, between January and April, collected data on agricultural production and executed the household questionnaire. The first and final visits include detailed information on labor inputs at the plot level which we also use in our analysis.

In this paper, we exploit the fact that yields from the same plots were measured with two different methods: crop cuts and self-reported production. The crop cuts in the ESS data are a sub-sample of crop cuts that are done for the Government of Ethiopia’s (GoE) Annual Agricultural Sample Survey (AgSS) by the same trained technicians, following the same protocols as used by the AgSS. The AgSS data are used by the GoE to measure core agriculture statistics including total volume of crop production, yield of crops and also crop forecasts.

In both survey waves, crop cuts were carried out on 23 major crops: five plots per crop were randomly selected from a list of all plots (stratified on crops) cultivated by the sampled households within an enumeration area. In most cases, the plot selected for crop cutting was mono-cropped, but in those cases where the crop was cultivated on fewer than five plots in pure stand (that is, mono-cropped) within an enumeration area, the crop cut was made on mixedstand plots.

Once a plot was identified, a rectangular subplot within it was randomly sampled and delineated. In the first wave, the subplot was 2m by 2m square; in the second it was 4m by 4m square. Within the subplot, the crop was harvested by a trained enumerator and weighed. If logistical constraints allowed, crop-cutting occurred simultaneously with the farmer’s harvest of the main crop. In both waves, nearly 40 percent of the crop cuts were executed in November

> 7 The smallest plots were also measured with tape and compass—considered as a gold standard for measuring small areas.

6
<!-- Start of picture text --> Ireland U.K.<br>5<br>20 15<br>070614<br>101315<br>15 1 111289 1 67<br>0 08<br>10<br>1 1784<br>060719<br>09<br>101620<br>0 12 11 13 -5<br>08<br>09<br>20<br>-10 20<br>-10<br>-10 0 10 20 -10 -5 0 5<br>Reported % Chg. Real GDP Reported % Chg. Real GDP<br>Ireland UK<br>Reported Composite Difference Reported Composite Difference<br>2015 22.46 20.80 1.65<br>* 15.15 7.31<br>2020 -10.37 -9.96 -0.41<br>* -8.56 -1.81<br>Composite Estimate Composite Estimate<br><!-- End of picture text --> Figure 12: Multinationals.

**Multinationals.** In Figure 12, we finally turn to countries in the highest data quality grade A. At the top of the list in Table 7 was Ireland, which in 2015 reported an outlier 22.46% growth rate in GDP. This was due to the counting of intellectual property from multinational corporations attracted by relatively low tax rates. In this case, the reported figure causes us to pause and reflect whether GDP, which is inflated by such intangible capital, measures what we want it to: economic activity. The composite measure allows us to measure this more directly, as only economic activities cause NO2. Because Ireland is a grade A country, there is only a small weight on the composite estimate, so our initial estimate of 20.80% does not differ much from the reported figure. However, if we treat Ireland as a grade C country, we obtain a smaller composite estimate of 15.15%, fully 7.31% less than the reported rate. In this instance, we conclude that GDP may not be an accurate reflection of true economic activity.

For comparison, we place Ireland side-by-side with the UK., which did not have the same experience pertaining to multinationals, but is similar in grade and other characteristics. During the 2020 COVID-19 crisis, the UK reported an astounding -10.45% change in GDP over 2020. The composite estimate suggests the actual change may have been somewhere in the range of -9.96% to -8.56%, depending on how we treat the weighting on GDP. To interpret this finding, it would be useful to compare it with differences elsewhere. We now create a country ranking of the difference between reported GDP and the composite estimate for context.

32
Financial Regulation and Government Revenue rejected and therefore it cannot be dismissed that compliance indeed took place. In Appendix A, we also present two other estimations in which we slightly change the main regression model, and the results are indeed unchanged. To verify also graphically that compliance occurred, in Appendix B we provide a figure showing the average balance-sheet composition across months and we verify the NBE bill purchase after April 2011.

Table 1: Regulation and Compliance: Monthly Variation (Million Real Birr)

|Variable||NB|E Bills (Real B|irr)||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Lending _×_ Policy|0.263***|0.274***|0.251***|0.248***|0.254***|
||(0.00752)|(0.00689)|(0.00868)|(0.00902)|(0.00976)|
|Bank FE||Yes|Yes|Yes|Yes|
|Year FE|||Yes|Yes||
|Month FE||||Yes||
|Non-Param. Month FE|||||Yes|
|Obs.|538|538|538|538|538|
|Number of banks|14|14|14|14|14|
|Adjusted _R_<sup>2</sup>|0.889|0.950|0.959|0.959|0.959|
|Mean Dep. Var.|501.9|501.9|501.9|501.9|501.9|
|SD Dep. Var.|616.6|616.6|616.6|616.6|616.6|

_Note:_ This table reports OLS estimates. The unit of observation is bank level. Robust standard errors are in parentheses. NBE Bills is the amount of bills issued by the NBE in million birr, and is a continuous variable. Its mean and standard deviation are reported in the final two rows. The quantity of NBE Bills is regressed on “Lending _×_ Policy”, which is the interaction between Private Lending (in million birr) and a continuous variable, and the Policy dummy, taking unit value from April 2011 onward. In Tables A1 and A2, we also include the respective variables Lending and Policy as well as their interaction, without statistically detectable changes in the coefficient. The null hypothesis is that this coefficient is 0.27, as prescribed by the NBE directive, and we cannot reject this. In column (1) we exploit all variation. In column (2) we exploit only within-bank by introducing bank fixed effects. In columns (3) and (4) we also add year and month fixed effects, respectively. In column (5) we remove parametric month and year fixed effects and replace them with non-parametric effects for each month of each year (non-param. month FE). Appendix A reports an additional test of this null hypothesis including more indicators. All quantities are in millions of real birr, at their 2010 value. The adjusted _R_<sup>2</sup> of these regressions is also reported. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

# **Safety and Profitability**

In this short subsection, we show that the regulation had two consequences: 1) an increase in the purchase of NBE bills; 2) an increase in the overall quantity of safe assets held by banks (i.e., cash, other liquid assets, government bonds, bank-to-bank deposits, central bank reserves). This is important, because unless banks change their actual asset allocation, then it may be argued that this policy overall generates only a change in safe asset composition (i.e., less cash and more government 8