rafmacalaba/datause-encoder-data huggingface dataset:
remain hard to measure comparatively and can be captured more adequately in qualitative research. But it also points to the importance of generating better comparative data on public administration systems in developing countries—data that may not only serve to track reform progress but also fuel future comparative research on administrative reform.

# **REFERENCES**

Abadie, Alberto, David Drukker, Jane Leber Herr, and Guido W. Imbens. 2004. “Implementing Matching Estimators for Average Treatment Effects in Stata.” _The Stata Journal_ 4 (3): 290–311.

- Acemoglu, Daron, and James Robinson. 2012. _Why Nations Fail: The Origins of Power, Prosperity, and Poverty_ . New York: Crown Publishing.

- Aldrich, John. 1995. _Why Parties? The Origin and Transformation of Party Politics in America_ . Chicago: University of Chicago Press.

Andrews, Matthew. 2006. “Beyond ‘Best Practice’ and ‘Basics First’ in Adopting Performance Budgeting Reform.” _Public Administration and Development_ 26: 147–61.

- Andrews, Matthew. 2009. “Isomorphism and the Limits to African Public Financial Management Reform.” HKS Faculty Research Working Paper RWP09-012, Harvard Kennedy School, Cambridge, Massachusetts.

- ———. 2010. “How Far Have Public Financial Management Reforms Come in Africa?” HKS Faculty Research Working Paper Series RWP10-018, Harvard Kennedy School, Cambridge, Massachusetts.

- ———. 2011. “Does Change Space Influence Public Sector Management Project Success?” Note −

- prepared for PRMPS as an input to the Public Sector Management Approach 2011 20, unpublished.

- Andrews, Matthew, Lant Pritchett, and Michael Woolcock. 2010. “Capability Traps? The Mechanisms of Persistent Implementation Failure.” Center for Global Development Working Paper No. 234, Center for Global Development, Washington, DC.

- ———. 2012. “Escaping Capability Traps through Problem-Driven Iterative Adaptation.” Center for Global Development Working Paper No. 299, Center for Global Development, Washington, DC.

- Andrews, Matthew, Roger Hay, and Jerrett Myers. 2010. “Governance Indicators Can Make Sense: Under-five Mortality Rates are an Example.” HKS Faculty Research Working Paper RWP10015, Harvard Kennedy School, Cambridge, Massachusetts.

41
Seven carriers connected to Haiti in 2011 via 12 routes, mostly focused on connectivity with the US and Caribbean: eight of the 12 routes were dedicated Caribbean routes (including the US, and other Caribbean hubs), one route was dedicated to the Far East, one route to South America (also with a Caribbean component), and one was dedicated to U.S. and Haiti only. Five of the carriers also provide service to the smaller Caribbean islands.

The market concentration of the freight cargo for the Caribbean presents a very diverse story.

**Figure 36: Herfindahl-Hirschman Index for Caribbean Ports (2012)**

<!-- Start of picture text --> Haiti,Port au Prince 2,218<br>,<br>Suriname,Paramaribo 2,006<br>Guyana,Georgetown 2,287<br>Belize,Belize City 5,489<br>,<br>Jamaica,Kingston 1,253<br>Dominican Republic,Caucedo 1,687<br>Barbados,Bridgetown 1,984<br>Trinidad and Tobago,Port of Spain 2,124<br>Trinidad and Tobago,Point Lisas 2,347<br>Dominican Republic,Puerto Plata 5,114<br>Jamaica,Montego Bay 5,236<br>Dominican Republic,Boca Chica 5,492<br>Bahamas,Nassau 6,536<br>Dominican Republic,Manzanillo 6,941<br>Bahamas,Freeport 7,477<br>,<br>St. Lucia,Castries 2,287<br>St. Kitts and Nevis,Basseterre 2,369<br>St. Vincent and the... 2,393<br>Grenada,St. George's 2,416<br>Dominica,Roseau 2,621<br>Antigua and Barbuda,St. John's 2,825<br>St. Lucia,Vieux Fort 3,059<br>0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000<br>TEU per Hour<br>Port<br><!-- End of picture text --> _Source: Authors’ calculation on data found in the Containerization International Yearbook (2012), and Tropical Shipping (2014)._ The story of the Caribbean intercontinental hub countries has three parts. First, the inter-continental transshipment hubs such as Kingston, Caucedo, and Port of Spain present the lowest HH levels indicating highest level of competition and less concentration of the cargo market, they capture high levels of traffic and many shipping lines dock there for their transshipment. Second, the smaller, more touristic ports of the hub countries present a highly concentrated market, as expected, given that their main functions are not for transshipment and perhaps not even for cargo. Finally, the big outlier is Freeport. Freeport is no doubt a big transshipment center but it predominantly serves the U.S.A., which likely explains the very low level of competition seen there (Figure 36).

87
Table 8a: PISA Reading Scores Decomposition for Poland, PISA 2000-2006

||b2000|b2006|X2000|X2006|Deter|minants of Test scor|es Differentials||
|---|---|---|---|---|---|---|---|---|
|Test Scores|||||||_as % of total_|_test score diff_|
||||||_Endowments_|_Unexplained_||_Unexplaine_|
||||||_b2006(X2006-X2000)_|_X2006(b2006-b2000)_|_Endowments_|_d_|
|Constant|296.47|161.49|1.00|1.00|0.00|-134.98|0.0|-205.2|
|_Schools_|||||||||
|Student - teacher ratio|2.08|-0.14|12.01|11.33|0.09|-26.61|0.1|-40.5|
|% of certified teachers<br>achievement data used|-23.92|18.85|0.90|0.97|1.21|38.57|1.8|58.6|
|to evaluated teachers|||||||||
|and principal|||||||||
|performance|51.94|7.00|0.98|0.92|-0.43|-44.01|-0.7|-66.9|
|More than 4 hours per|||||||||
|week of language class|3.27|42.77|0.01|0.76|32.09|0.41|48.8|0.6|
|attend topublic school|13.89|-22.18|0.98|0.98|-0.13|-35.25|-0.2|-53.6|
|_Student characteristics_|||||||||
|Age|0.28|12.85|15.73|15.71|-0.23|197.76|-0.4|300.6|
|Female|36.12|32.53|0.51|0.51|-0.05|-1.83|-0.1|-2.8|
|_Family background_|||||||||
|Mother - Upper|||||||||
|secondary|4.68|27.11|0.74|0.77|0.70|16.65|1.1|25.3|
|Mother -University|41.49|63.09|0.17|0.15|-1.52|3.65|-2.3|5.6|
|11–100 books|31.38|30.58|0.39|0.54|4.75|-0.31|7.2|-0.5|
|101-500 books|52.90|67.39|0.47|0.35|-8.03|6.87|-12.2|10.4|
|Computer at home|22.74|33.89|0.47|0.80|11.22|5.19|17.1|7.9|
|Total|||||39.7|26.1|60.3|39.7|
|Overall|||||65.8||100.0||

Source: Program for International Student Assessment ( PISA) 2000 and 2006 Table <u>8b: Determinants of PISA Differentials, Reading 2000-2006</u>

||_as %_<br>_Endowme_|_of total te_<br>_nts_|_st score diff_<br>_Unexplained_|
|---|---|---|---|
|Constant|0.0||-205.2|
|Schools|49.9||-101.7|
|Family|10.8||48.7|
|Student|-0.4||297.9|
|Total|60.3||39.7|
|Overall||100||

Source: Program for International Student Assessment ( PISA) 2000 and 2006 We also present a modified decomposition (Table 9). The results are very similar. That is, most of the differential is explained, and most is due to school characteristics. Most of the differences in school characteristics are the increased hours of math and language class instructions that students were subject to because of the reform.

25
**Figure 4.6: Prevalence of restructuring reform measures across country groupings**

|Percentage of|Vertically|Some degree of||Of which:|||
|---|---|---|---|---|---|---|
|countries adopted as<br>of 2015|integrated|vertical<br>unbundling|Partial<br>vertical<br>unbundling|Full vertical<br>unbundling|Full vertical<br>and<br>horizontal||
||||||unbundling||
|**By incomegroup**|||||||
|LIC|60**|40**|13|4|23*|100|
|LMIC|50|50|11|7|32|100|
|UMIC|33**|67**|17|0|50*|100|
|**By system size**|||||||
|<1GW|69**|31**|10|3|18*|100|
|1‐5GW|50|50|10|5|35*|100|
|5‐10GW|20**|80**|20|0|60*|100|
|10‐20GW|67**|33**|11|11|11**|100|
|>20GW|23**|77**|23|8|46*|100|
|**By political economy **<br>|||||||
|Dominant|55|45|18*|3|24**|100|
|Intermediate|52|48|18*|6|24|100|
|Competitive|44|56|4*|4|48**|100|
|**By rule of law**|||||||
|High rule of law|61|39|0*|8|31|100|
|Low rule of law|54|46|15*|4|27|100|
|**By fragility status**|||||||
|FCS|50|50|17|2|31|100|
|Non‐FCS|58|42|9|7|26|100|
|**By oil‐exporting statu**|**s**||||||
|Importer|36**|64**|15|7|42|100|
|Exporter|50**|50**|19|6|25|100|
|**Total**|**57**|**43**|**11**|**14**|**18**|**100**|

Source: Own elaboration based on RISE 2015 database ****** Differences are statistically significant at the 5% level ***** Differences are statistically significant at the 10% level Once embarked on a restructuring reform, only about half the countries seem likely to pursue it to the fullest extent of vertical and horizontal unbundling. Figure 4.7 illustrates the evolution of countries adopting different degrees of restructuring in 2005 and looking at the extent of their further progress on restructuring reform ten years later, by 2015. Just over half the countries simply retained a vertically integrated structure over this period with no movement on reform. Among those embarking on restructuring reform since 2005, about half went all the way to full vertical and horizontal unbundling, while the other half remained in an intermediate stage. None of the countries that had undertaken partial vertical unbundling by 2005 took this any further during the following decade. By contrast, the majority of countries that had reached full vertical unbundling in 2005 went on to introduce horizontal unbundling during the following decade. The evidence suggests that taking preliminary steps towards unbundling is no guarantee that the process will be completed. However, countries are unlikely to undo restructuring 17
# **Appendix B: Equations of the Model**

## **1 Notation:**

### **Sets:**

Regions are indexed by _r ∈ R_ or _s ∈ R_ . Goods and services are indexed by _i ∈ I_ or _j ∈ I_ , with subsets _k ∈ K ⊆ I_ for Krugman treatments, and _m ∈ M ⊆ I_ for Melitz treatments. Factors are indexed by _f ∈ F_ , with the subset _F_<sup> ̃</sup> _⊆ F_ indicating a sector-specific factor.

### **Variables:**

_Dr_ Full consumption index<sup>1</sup>

- _Yir_ Output index<sup>2</sup>

- _Air_ Supply index<sup>3</sup>

- _er_ Unit-expenditure index (true-cost-of-living index)

- _cir_ Price of domestic output (marginal cost)

- _Pir_ Price of goods and services

- _w_ Price of factors _fr_

- _w_  ̃ _fir_ Price of sector-specific factors

_Mir_ Number of entered firms ( _∀i ∈ K ∪ M_ ) _pkrs_ Gross firm-level price (Krugman firms) _qkrs_ Firm-level quantity (Krugman firms)

- _Nmrs_ Number of operating Melitz firms

- _p_  ̃ _krs_ Gross firm-level price (Melitz representative firms)

- _q_  ̃ _krs_ Firm-level quantity (Melitz representative firms)

- _φ_  ̃ _krs_ Firm-level productivity (Melitz representative firms)

> 1In the model variation where we have an aggregate intermediate good this real quantity is gross of intermediates inputs. Consumption includes all final demand: household consumption and investment, and government spending.

> 2Under monopolistic competition treatments _Yir_ indicates the composite (of value added and intermediate) input that firms in industry _i_ use for real fixed and variables costs.

> 3 Under an Armington treatment this is the Armington aggregate, and under monopolistic competition this is the Dixit-Stiglitz aggregate.

1
of households from their villages. Moreover, comparison with a hypothetical pure-PMT demonstrates the two methods to be roughly equivalent in their ability to identify poorer households within a village.

Further, we shed light on what information village heads use to assess the welfare status of households. We asked village heads to rank households by different welfare criteria and then compared these data to household survey data. We find that village heads seem to base their assessments of specific criteria like household land ownership, likelihood of being hungry and likelihood of needing assistance on a holistic impression of household welfare, rather than specific indicators like actual land ownership, nutrition or economic shocks. Using random forest analysis, we find that their rankings are best explained by easily observable indicators of welfare like household characteristics and asset ownership.

In this program, updating poverty ranks by village heads provided a much quicker and less expensive alternative to a full PMT plus community-ranking and validation process, without compromising the targeting efficacy of the program and produced outcomes acceptable for the community. The results suggest that the performance of village heads compared to alternative methods may be particularly strong in remote areas with varying poverty levels. There, the village heads’ familiarity with residents and local circumstances can help them distinguish among community members’ relative welfare statuses. In these cases, a flexible targeting led by village heads may be successful at achieving pro-poor targeting objectives.

There are caveats about the applicability of these findings in other contexts. First, village heads only updated prior, community-supported poverty rankings in this program. This reduced the reliance on village heads for determination of eligibility and may have also improved community acceptability, since applicants may have perceived the updated rankings as a revision of a system they had already endorsed. Other populations without a comparable experience may view village head targeting as less legitimate. Second, the selection for the RMG program also relied on self-selection into the program by the neediest households. This may explain some of the targeting success of the program. Third, our analysis suggests that village heads hold a holistic impression of household welfare built on easily observable household and individual characteristics. This type of assessment may be less useful when targeting households with specific, less observable needs, such as the need for nutrition support.

21
- 24 -

# Table 2.3. Percent of Manufacturing Establishments Without Own Vehicles for Shipment, Workers, or Garbage Disposal In Each Employment Size Category

|||ii<br>hieria|<br>|<br>_|____|Indorn|esia||Thailan|d|
|---|---|---|---|---|---|---|---|---|---|
|EmpLaymnt|-||||||--|||
|size|For<br>Ship-<br>emnt|For<br>Worker|For<br>Garbage<br>Disposal|For<br>Ship-<br>mnt|For<br>Workar<br>-|For<br>Garbage<br>Disposal|For<br>Ship-<br>ment|For<br>Worker|For<br>Garbage<br>Disposal|
|I- 19|50.0|87.5|100.0|60.0|80.0|100.0|27.8|97.0|100.0|
|20 - 49|40.0|94.3|100.0|28.7|89.4|98.9|13.5|100.0|100.0|
|50- 99|40.9|81.8|88.6|30.4|75.0|100.0|10.3|97.1|98.5|
|100 - 199|28.6|85.7|68.6|22.6|53.2|95.2|24.1|85.2|98.2|
|200- 499|30.8|80.8|a8.5|34.1|47.7|86.4|23.4|83.0|100.0|
|S00 - 99|26.7|80.0|80.0|42.1|36.8|94.7|18.8|50.0|93.8|
|1000&<br>over|50.0|87.5|75.0|40.0|50.0|100.0|37.5|75.0|100.0|
|All|36.9|85.5|86.6|30.3|67.6|96.2|18.7|90.3|9.0|

NOTE: Fro Appendix Ublab 35, 36, 99, CS .C6, C9, D5, D6, and D9.

Source: World Bank Nigeria Infrastructure Project EstabLishmnt Survey 1988. World Rank Research ProJect (RPO 676-71) Establishment Survey for Indonesia and Thailand 1°92.
How does hydropower compare with other sources of electricity generation in terms of installed capacity? Figure 2 answers to this question. As of January 1, 2017, the world’s total installed capacity for power generation stands at 6,665 GW, of which hydropower accounts for 19%. As expected, coal and natural gas fired electricity generation technologies have more installed capacity than that of hydropower. While coal accounts for 30% of the global installed power generation capacity, natural gas accounts for 25%. Other renewable energy, including wind, solar and biomass, accounts for 13%.

<!-- Start of picture text --> Solar PV Other renewables<br>4% 2%<br>Wind<br>7%<br>Coal<br>30%<br>Hydropower<br>19%<br>Nuclear<br>6%<br>Gas<br>25%<br>Oil<br>7%<br><!-- End of picture text -->

**Figure 2. Electricity generation capacity mix, 2016 (%)**

Source: IEA (2017b) A comparison of power generation sources or technologies in terms of their installed capacities could be misleading for two reasons. First, different types of electricity generation capacities are not equally available to contribute to meeting the demand, or load. Some 6
24 <!-- Start of picture text --> TABLE 2<br><!-- End of picture text --> <!-- Start of picture text --> SUMMARY RESULTS FOR TOTAL PUBLIC EXPFNDlTURE BENEFITS<br>IN FOUR DEVELOPING COUNTRIES<br><!-- End of picture text --> <!-- Start of picture text --> Author, Country, Year of Data Total Expenditures<br>Mecaman, Malayda, 1974 -s<br>Bahl et aL, South Korea<br>Urban 1965 +<br>1968 +<br>1971 +<br>1974 ++<br>Rural 1965 +<br>1968 +<br>1971 +<br>1974<br>Foxley et aL, Chile<br>I ~~~~~~~~~+<br>U ~~~~~~~~~+<br>Riboud, Cost Rima 1986 +<br>'A "plus indicates that public ex_diue ae pro-poor on equality<br>enhancing, while a "mius" indicates that public expenditures reduce equality on<br>a pro-rich Pogressvity rsults are based on expenditure as a percentage of<br>income compared across income gOups. BahI et aL also examines the percent<br>of total expenditure spent in each income class. They find expenditues<br>distributed mofe equally than income and with more progressivity over tume.<br>bA "4e" indicates that public expenditures are pro-poor and have become<br>inceasingly pro-poor in that region of the country during the 1965 to 1974 time<br>period. A "+-" indicates that public expenditures are pro-poor but have become<br>less pro-poor in that region of the country dudng the 1965 to 1974 time period.<br>SOURCES: Meennan, 1979, p. 321; Dabl et aL, 1986, pp. 150-215; Foxley<br>et aL, 1979, p. 119, Riboud, 1990, Table IL 4.<br><!-- End of picture text -->
anomalies. Although these relationships are monotonic, they are significantly nonlinear across the range of weather anomalies. The relationship between temperature anomalies and the log monthly per capita consumption expenditure (MPCE) is close to a quadratic fit. There is a sharp increase in consumption with more negative temperature anomalies (z-score<-2). However, consumption does not decrease much as positive anomalies become more extreme (z-score>0). On the other hand, the decrease in consumption is steep with adverse vegetation anomalies (z-score<-1). Moreover, we find some evidence of households decreasing non-food consumption to save themselves from food insecurity. While both food and non-food consumption decrease with adverse vegetation shocks (zscore,-1), households were able to maintain their food consumption by reducing non-food consumption when temperature anomalies were positive in 2018 (z-scores).

We also show that the distribution of rainfall anomalies has little variation across clusters in 2018. Moreover, we observe less variation in the distributions of rainfall shocks over time: rainfall distributions are tight, with a significant overlap between 2018 (a declared drought year) and 2019 (a normal weather year). This finding is not surprising given the scarcity of rainfall in most parts of the country due to its arid climate. Hence, we use NDVI and temperature anomalies as measures of drought in our analysis.

We explore several potential sources of heterogeneity. Given the complex security situation at the time in Afghanistan, we find suggestive evidence that adverse vegetation shocks disproportionally affected the consumption of households in districts under Taliban control. This may be because these districts had limited access to humanitarian assistance. We find that households who owned agricultural land could mitigate the adverse effects of these weather shocks. This aligns well with extensive evidence that these assets allow households to ‘self-insure’ and smooth their consumption (see, for instance, Dercon 2002). We find no evidence that livestock owners are better insulated from shocks, in line with evidence that livestock ownership is an imperfect coping mechanisms as their sale under duress cannot fully compensate for income shortfalls (Fafchamps et al., 1998). Taliban-managed districts were more remote, with restricted movements of people and goods, including limited access to food and humanitarian aid, which may explain these differential effects. When faced with extremely hot temperatures, we also see a divergence in consumption expenditures between rural and urban households.

We also estimate the vulnerability to poverty of Afghan people due to future droughts. We follow Hill and Porter (2017), who use the historical distribution of covariate weather shocks in Ethiopia to estimate vulnerability to poverty by simulating log monthly per capita consumption with varying weather conditions. We find that 62.5 percent of households have a greater than one in four chance of falling into poverty in a given year, 3
16 that is, very imprecisely _(intermediate information case 1)._ We have implemented this possible scenario using Census data for the second largest city of Argentina"<sup>3</sup> at the level of _the.ftacci6n,_ which is a collection of _radios,_ which in turn is a collection of _manzanas,_ the smallest reporting unit available.'<sup>4</sup> We have also computed costs when information is available at the level of the _radio._ The results of this thought experiment can be seen in Table 1.

Another intermediate information example _(intermediate information case 2)_ might be one, as in Argentina, in which the wire center locations are known, line totals are known, and location data are known at the level of the _manzana._ The relatively disaggregated location data are used to impute a more uneven customer location distribution by assigning the lines in the wire center proportionally to the populations of each _manzana._ The results of the computations for this particular case can also be seen in Table 1 below.

**Table 1**

||**Total I**|**nvestment (in**|**dollars)**|
|---|---|---|---|
|**[Total lines**|**Manzana**|**Radio**|**Fracci6n**|
|**1**320,403|**215,032,106**|**216,802,535**|233,801,440|

The total investment shown in the table is the sum of feeder costs, distribution costs, drop costs and some other costs (e.g., terminals, interfaces).<sup>15</sup> As can be readily seen from the table, the use of relatively aggregated Census data does not markedly bias the results with respect to what we take to be the best approximation to the true cost, i.e. interrnediate inforrnation case 2 _(manzana)._ Note that using _radio_ there are no significant differences in total investment (less than 1% higher), and using _fracci6n,_ total investment is only 9% higher than with _manzana_ (our best estimate of the true cost, at least with Census data).

The best situation regarding data availability would be one in which costs calculations could be performed having nearly perfect information available on precise individual

> 13 A brief description of the database used for these exercises can be found in the appendix.

> 14 Those familiar with the U.S. Census data reporting conventions can think of the _manzana_ as analogous to the Census block, and the _radio_ as equivalent to the Census block group, or CBG.

> **15** Total investment does not include maintenance, depreciation and cost of capital charges, among others.