rafmacalaba/datause-encoder-data huggingface dataset:
# **5. Some rough efficiency measures**

This section provides some illustrative estimates of the efficiency levels achieved by the central government in delivering sectoral services. We report only the results that are reasonably robust. We tried many combinations of sectors. In particular, we tried to assess the multiple output responsibilities of government (education, health, and infrastructure). Unfortunately, the data problems prevented us from producing the multisector analysis we had hoped for.

We ended up focusing on four sector specific panels of data (table 5). For each panel, we estimated two models. The first is the simplest possible distance function. The second is a model corrected to account for institutional variables such as the quality of the bureaucracy and the level of corruption in the country. The models generate a measure of efficiency at the country level, but because we believe that country-specific data often are not reliable, we only report the aggregated data at the country-group level.

**Table 5 Panel used in the estimation for each sector**

|Panel|Sector|Number of<br>countries|Number of<br>observations|Number of<br>outputs/outcomes|Number of<br>inputs <sup>a</sup>|
|---|---|---|---|---|---|
|4|Transport|81|547|2|1(4)|
|5|Energy|66|481|2|1(3)|
|7|Health|60|146|2|1(1)|
|9|Education|36|103|3|1(1)|

_Note_ : Any difference with table 1 is due to the fact that table 1 is based on a matching of outputs and inputs, whereas table 5 adds matching for the availability of data on corruption and bureaucratic quality.

a. The first number is the number of expenditure categories; the second (in parentheses) is the number of control variables used in a second group of regressions (i.e., corruption, quality of bureaucracy, GDP, population, GDP/capita.) These datasets are used to estimate a distance function, which is then used to generate the efficiency estimates for each sector. The empirical application of a distance function requires the specification of an appropriate functional form. We rely on a translogarithmic functional form (hereinafter _translog_ ) because it is flexible, easy to calculate, and allows the imposition of the homogeneity condition for outputs (homogeneity of level 1 in outputs). It offers a flexible functional form providing a local second-order approximation to an unknown functional form. In other words, we made no assumptions regarding _a priori_ restrictions about production technology. Finally, we specify an output orientation in the results reported here. In other words, we try to assess the extent to which with the current level of public expenditures (the input), we can achieve more outputs.

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# **References**

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- Arrow, K., C. Panosian, and H. Gelband (Eds.) (2004). _Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance_ . Washington, DC: Institute of Medicine: National Academies Press.

- Arrow, K. J. (1963). Uncertainty and the welfare economics of medical care. _The American Economic Review 53_ (5), 941–973.

- Baker, A. (2001). _Crossing the quality chasm: a new health system for the 21st century_ , Volume 323. British Medical Journal Publishing Group.

- Banerjee, A., J. Das, J. Hammer, R. Hussam, and A. Mohpal (2020). The market for healthcare in low income countries. Mimeo.

- Banerjee, A. V., E. Duflo, and R. Glennerster (2008). Putting a band-aid on a corpse: Incentives for nurses in the Indian public health care system. _Journal of the European Economic Association 6_ (2-3), 487–500.

- Bariş, E., R. Silverman, H. Wang, and F. Zhao (2022). _Walking the talk: reimagining primary health care after COVID-19_ . World Bank Publications.

- Belloni, A., V. Chernozhukov, and C. Hansen (2014, May). High-dimensional methods and inference on structural and treatment effects. _Journal of Economic Perspectives 28_ (2), 29–50.

- Berzosa, P., A. de Lucio, M. Romay-Barja, Z. Herrador, V. González, L. García, A. Fernández-Martínez, M. Santana-Morales, P. Ncogo, B. Valladares, et al. (2018). Comparison of three diagnostic methods (microscopy, RDT, and PCR) for the detection of malaria parasites in representative samples from Equatorial Guinea. _Malaria Journal 17_ (1), 1–12.

- Bisoffi, Z., B. S. Sirima, A. Angheben, C. Lodesani, F. Gobbi, H. Tinto, and J. Van den

23
CRED defines a disaster as “a situation or event which overwhelms local capacity, necessitating a request to a national or international level for external assistance; an unforeseen and often sudden event that causes great damage, destruction and human suffering.” For a disaster to be entered into the database, at least one of the following criteria must be fulfilled: 10 or more people reported killed; 100 or more people reported affected; declaration of a state of emergency; or call for international assistance.

CRED divides disasters according to type (for example: drought, flood, etc), and provides the dates when the disaster occurred and ended; the number of casualties (people confirmed dead and number missing and presumed dead); the number of people injured (suffering from physical injuries, trauma or an illness requiring immediate medical treatment as a direct result of a disaster), and the number of people affected. People affected are those requiring immediate assistance during a period of emergency (i.e. requiring basic survival assistance such as food, water, shelter, sanitation and immediate medical help). People reported injured or homeless are aggregated with those affected to produce the “total number of people affected”.

Finally, EM-DAT also provides an estimate of “economic damage”. Although “economic damage” could be a good indicator of the gravity of a disaster, it has important drawbacks both from a measurement and estimation perspective. First, CRED admits that there is no standard procedure to determine economic impact. Second, economic losses are reported for only one third of the disasters, with the proportion differing substantially across the types of disasters.<sup>13</sup> Third, such a measure would make the exogeneity assumption tenuous, as the amount of damage may be correlated with the growth during the period under consideration.

> 13 For example, economic losses are reported for nearly 50% of all the windstorms entered in EM-DAT and 40% of the earthquakes. This is most likely due to the infrastructure damage that is directly and clearly attributable to these events. Floods are the third largest category, with losses reported for about one-third of the total events. For droughts, on the other hand, less than 25% of the events have losses reported. There may be several factors for this. In particular, CRED recognizes that droughts may only draw the international attention in terms of lives lost, with little consideration for economic costs. Droughts do not result in infrastructure or shelter damage but in heavy crop and livestock losses, therefore, most economic losses are of an indirect or secondary nature and difficult to quantify.

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# **II. Developments in World Trade in the Early 1990s-Decoupling of Trade and Output Growth?**

Chart 5. Growth of World Exports and GDP, 1971-1997 <!-- Start of picture text --> Percent<br>12.<br>Exports (Merhandise) _ X<br>0 7v Gross Domestic Product<br>-4<br>72 74 76 78 80 82 84 86 88 90 92 94 96<br>Source: Irtemagonal Economics Department, Wodd Bank, August 1995.<br><!-- End of picture text --> The growth of world trade relative to world income has been much higher in the 1990s than in the 1970s or 1980s (see Chart 5). In 1994, world merchandise trade volume is estimated to have grown by 9.2 percent, which, in relation to world GDP growth of 2.8 percent, implies an elasticity of world trade of 3.3 with respect to GDP.<sup>4</sup> That is more than twice the trade elasticity of 1.5 that prevailed in the 1970s and 1980s. It is also much higher than in any single year in that period, including 1976 and 1984, which, like 1994, were years of cyclical upswing from world recession. Even more remarkable, although world trade growth slowed from a buoyant 6.1 percent in the late 1980s, when world output expanded by an average of 3.3 percent, it maintained an average of 4.1 percent in

- **4** Growth in world export volume soared from 4.4 percent in 1993 to 9.2 percent in 1994, as the surge in European import demand associated with economic recovery was added to already strong import demand in the United States, East Asia, Latin America, and even Japan (where a strengthening yen and continued market opening and deregulation have overridden the effects of domestic recession). The data suggest that these patterns have persisted for the most part into 1995, with import demand continuing to grow rapidly in almost all regions and the stimulus from Europe remaining especially notable. The only exception is Latin America, where recession in Mexico and accelerated balance of payments adjustment in Argentina are expected to generate a 3-4 percent fall in regional import volumes.

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**Figure 2.** Trends in the categories of body mass index in adult persons between 20 to 24 years of age of the lowest income quintile by gender. Mexico 2000, 2006 and 2012.

<!-- Start of picture text --> Females Males<br>100% 100% 0.03<br>0.08 0.1<br>90% 0.14 0.17 90% 0.19<br>0.26<br>80% 80%<br>0.31<br>0.33<br>70% 0.3 70% 0.23<br>0.32<br>60% 60%<br>50% 50%<br>40% 40%<br>0.71<br>30% 0.61 0.56 30% 0.57 0.58<br>0.51<br>20% 20%<br>10% 10%<br>0% 0%<br>2000 2006 2012 2000 2006 2012<br>Normal Sobrepeso Obesidad Normal Sobrepeso Obesidad<br><!-- End of picture text --> <!-- Start of picture text --> Normal Sobrepeso Obesidad<br><!-- End of picture text --> <!-- Start of picture text --> Normal Sobrepeso Obesidad<br><!-- End of picture text --> Source: Authors’ estimates based on data from the National Health Survey 2000 (ENSA 2000), National Health and Nutrition Survey 2006 (ENSANUT 2006) and the National Health and Nutrition Survey 2012 (ENSANUT, 2012).

# _B. Teenage pregnancy_

Another subject of specific relevance is pregnancy during adolescence. According to the ENSA and ENSANUT, the probability of teenage pregnancy increased from 9.3% to 10.7% between 2000 and 2012. The increase in teenage pregnancy occurred despite the fact that the prevalence of contraceptive use also increased and is thus attributed to the fact that an even greater proportion of teens reported having initiated sexual activity.

The analysis controlling for socioeconomic status showed that in 2000 the probability of pregnancy among adolescents who had initiated sexual activity was 20 percentage points higher for adolescents in the lowest income quintile compared to those in the highest income quintile. Moreover, this gap increased to 33 percentage points in 2012. Although the likelihood of having sex was higher among adolescents who live in higher income households, this group was also more likely to report the use of protection to prevent pregnancies (Gutiérrez et al., 2016a). Despite these disparities, the 2012 ENSANUT analysis suggests that the increase in the fertility rate for teens aged 15 to 19 occurred to a lesser extent among adolescents in PROSPERA households compared to adolescents in the two poorest income quantiles in households that did not receive aid from PROSPERA.

# **III. PROSPERA’s theory of change from the social mobility perspective**

The starting point for intergenerational mobility analysis in health and education attributable to PROSPERA is understanding the degree to which mobility is part of the program’s 7
**Figure 2: Stunting under 2‐year olds**

<!-- Start of picture text --> Stunting in children 0 to 23 months<br>Based on official DHS estimates when available<br>44<br>42<br>37<br>32<br>31<br>30 30 30<br>29<br>27<br>26 26<br>25<br>22<br>20<br>17<br>Bangladesh (HK) Bolivia Ethiopia Nepal Zimbabwe<br>Bangladesh (IFPRI) Cambodia Indonesia (RKD) Peru<br>Stunting year 1 Stunting year 2<br>Source: Author estimates. Data for Bangladesh from 2010 (HK) and 2011 (HK, IFPRI); Bolivia 2003, 2008; Cambodia 2005, 2010;<br>Ethiopia 2000, 2011; Indonesia 2010 (RKD); Nepal 2000, 2011; Peru 2005, 2012; Zimbabwe 2005, 2010<br>40<br>30<br>20<br>10<br>Percentage of children with HAZ < -2 SD<br>0<br><!-- End of picture text -->

**Figure 3: Stunting under 5‐year olds**

<!-- Start of picture text --> Stunting in children 0 to 59 months<br>Based on official DHS estimates when available<br>58 57<br>43 43 44<br>40 41<br>36 33 32 34 35 32<br>29<br>27<br>18<br>Bangladesh (HK) Bolivia Ethiopia Nepal Zimbabwe<br>Bangladesh (IFPRI) Cambodia Indonesia (RKD) Peru<br>Stunting year 1 Stunting year 2<br>Source: Author estimates. Data for Bangladesh from 2010 (HK) and 2011 (HK, IFPRI); Bolivia 2003, 2008; Cambodia 2005, 2010;<br>Ethiopia 2000, 2011; Indonesia 2010 (RKD); Nepal 2000, 2011; Peru 2005, 2012; Zimbabwe 2005, 2010<br>60<br>40<br>20<br>Percentage of children with HAZ < -2 SD<br>0<br><!-- End of picture text --> 20
# **Table 2: Descriptive statistics**

|**Panel A**||**Full Sa**<br>|**mple**<br>||
|---|---|---|---|---|
||Mean|Std.Dev.|Min|Max|
|Total wealth|163,582|215,857|2,152|902,960|
|Produced capital|30,625|40,887|132|183,078|
|Natural capital|11,925|20,730|2|169,150|
|Net foreign assets|-24|12,515|-49,818|74,280|
|Intangible capital|121,056|170,061|278|799,123|
|Intangible capital/|||||
|total wealth|0.64|0.18|0.05|0.90|
|Years of schooling|6.52|2.87|0.76|12.59|
|Human capital|3.06|0.91|1.46|5.34|
|Rule of law|56.17|23.19|6.96|100.00|

|**Panel B**|**OECD**|**Non-**<br>**OECD**|**Year 1995**|**Year**<br>**2000**|**Year**<br>**2005**|
|---|---|---|---|---|---|
|||Means (stan<br>|dard deviationsin<br>|parentheses)<br>||
|Total wealth|486,177<br>(183,435)|61,000<br>(84,088)|157,722<br>(204,314)|162,365<br>(213,149)|169,751<br>(229,400)|
|Produced capital|90,562|11,566|30,153|30,237|31,410|
||(34,932)|(17,580)|(40,364)|(40,081)|(42,447)|
|Natural capital|17,656|10,102|12,050|12,947|10,806|
||(19,540)|(20,805)|(21,709)|(22,212)|(18,360)|
|Net foreign assets|-3,469|1,072|273|-165|-135|
||(15,814)|(11,083)|(11,537)|(12,799)|(13,120)|
|Intangible capital|381,427|38,260|115,246|119,347|127,669|
||(147,274)|(52,810)|(158,600)|(167,351)|(182,924)|
|Intangible capital/|0.78|0.59|0.63|0.63|0.66|
|total wealth|(0.06)|(0.18)|(0.17)|(0.19)|(0.19)|
|Years of schooling|9.64|5.53|5.99|6.61|6.88|
||(1.59)|(2.44)|(2.83)|(2.84)|(2.88)|
|Human capital|4.14|2.71|2.92|3.08|3.16|
||(0.57)|(0.71)|(0.88)|(0.90)|(0.95)|
|Rule of law|86.64|46.48|57.74|56.04|54.96|
||(10.12)|(16.92)|(24.25)|(22.52)|(23.06)|

Notes: Panel A: full sample includes an unbalanced panel of 115 developed and developing countries for years 1995, 2000 and 2005 (total of 315 observations), except for human capital for which sample is 112 countries and 309 observations. All variables except years of schooling, human capital and rule of law are expressed in per capita terms in constant 2005 US$. Years of schooling in population aged 15 and over from Barro and Lee (2001); human capital _h_  _Ah_<sup>_e_</sup>  ( _s_ ) (see text for details); rule of law index (1-100 scale) from Kaufmann, Kraay and Mastruzzi 23
# Global income distribution:

from the fall of the Berlin Wall to the Great Recession<sup>1</sup> _Christoph Lakner and Branko Milanovic_ JEL codes: D31 Keywords: Income distribution, globalization, top incomes Sector board: social protection 1 Both authors are with the World Bank Research Department. The authors would like to thank Statistics Finland, Statistics Portugal and Eurostat for providing tabulated micro data; Maria Ana Lugo and Philippe van Kerm for help with the empirical implementation of the non-anonymous GICs; Shaohua Chen for help with PovcalNet; Tony Atkinson and La-Bhus Jirasavetakul for many helpful discussions. The BHPS data were accessed via the UK Data Service. The paper was in part funded by the World Bank grant under the Knowledge for Change Project (KCP) “Changing inequalities: facts, perceptions and policies” TF012968. Parts of the paper were presented at workshops held at the World Bank and OECD and we thank the participants for useful comments. The opinions expressed in the paper are the authors’, and should not be attributed to the World Bank and its affiliated organizations.

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makes each annual payment larger. It is ESCOs, not governments, who specify the investment plans with all these conditions taken into consideration.

**Table 1. Major Energy** **<u>Savings Technologies Adopted in Japanese ESCO Projects</u>**

|Adopted technology|Share of ESCO<br>contracts using this<br>technology (%)|Avg installed<br>capacity if<br>adopted (kW)|
|---|---|---|
|Lighting|79|4,479|
|Air conditioning|84|1,109|
|Hot water boiler|31|335|
|Cogeneration system|27|515|

_Source_ : Author’s survey data.

# **III.  Empirical model**

ESCO projects are procured under the multidimensional auction system. Scoring auctions are a commonly used format. The bidder whose score is highest wins the public contract (i.e., first-score auctions). In scoring auctions, the bid evaluation rule is disclosed to bidders prior to calling for proposals. In addition, scoring auctions allow each bidder to submit a single offer. By contrast, in menu auctions, bidders can submit a schedule composed of various combinations of solutions.

Multidimensional auction theory tells that economic efficiency is achieved if the auctioneer preannounces his true utility function determining the scoring rule and if there is no commitment problem (Milgrom, 2000; Asker and Cantillon, 2008). A simple scoring auction model suggests that the bidding strategy can be written by a single parameter if the private information is one-dimensional (Che 1993; Branco 1997; Naegelen 2002).<sup>4</sup> Under the private value paradigm, the firm’s bidding strategy essentially depends on three factors: (i) the scoring rule, i.e., weights, (ii) the number of bidders, and (iii) bidders’ private information, i.e., underlying cost parameter.<sup>5</sup>

> 4 Asker and Cantillon (2008) show that a single pseudotype of contractor is sufficient to characterize the equilibrium outcome in any multidimensional auction with more than one dimension of private information. 5 See Annex II for a formal multidimensional auction model.
Table 1. Sociodemographic Characteristics of Migrants Who Registered in the RAMV

|_Panel A. Age Groups_||||||
|---|---|---|---|---|---|
|Variable|Total Obs.|Observations|Percentage|Average|STD|
|0-18 years|440,869|116,529|26.43%|7.64|4.94|
|18 - 30 years|440,869|167,743|38.05%|24.23|3.51|
|31 - 40 years|440,869|88,392|20.05%|35.07|2.84|
|41 - 50 years|440,869|44,568|10.11%|44.91|2.83|
|51 - 60 years|440,869|17,602|3.99%|54.6|2.76|
|61 - 70 years|440,869|4,948|1.12%|64.34|2.66|
|71 - 80 years|440,869|880|0.20%|74.2|2.7|
|_≥_80years|440,869|207|0.05%|87.27|8.9|
|_Panel B. Individual Covariates_||||||
|Variable|Total Obs.|Observations|Percentage|95%|CI|
|_Gender_||||||
|Female|442,616|219,847|49.67%|49.52%|49.82%|
|Male|442,616|222,435|50.25%|50.11%|50.40%|
|Transgender|442,616|334|0.08%|0.07%|0.08%|
|_Household role_||||||
|Head of Household|442,616|231,915|52.40%|52.25%|52.54%|
|_Marital Status_||||||
|Single|442,617|279,956|63.25%|63.11%|63.39%|
|Married / Cohabitating|442,617|153,659|34.72%|34.58%|34.86|
|Divorced / Separated / Widowed|442,617|9,002|2.03%|1.99%|2.08%|
|_Level of Education_||||||
|Preschool / Primary|442,617|150,884|34.09%|33.95%|34.23%|
|Secondary|442,617|221,319|50.00%|49.86%|50.15%|
|Tech School / University/ Post Grad|442,617|70,414|15.91%|15.80%|16.02%|
|_Panel C. Household Covariates_||||||
|Variable|Observations|Mean|STD|Min|Max|
|Number of family members|437,059|3.51|2.06|0|10|
|Number of relatives in Venezuela|435,758|2.56|2.72|0|14|

_Notes:_ The table depicts the descriptive statistics of Venezuelan migrants who registered in the Registro Administrativo de Migrantes Venezolanos (RAMV) Census.

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countries, among which 145 are developing and 24 developed, using the World<sup>Bankdefinition</sup> of developed and developing.<sup>3</sup> The 11 representative variables are: gross<sup>domesticproduct(Q,</sup> line 99b in IFS<sup>4</sup> ), unemployment (Q, line 67c in IFS), the consumer price<sup>index (M,IFSline64),</sup> exports (M, line 70 in IFS), imports (M, line 71 in IFS), foreign direct<sup>investment(Q,line</sup> 78bed), the exchange rate (M, exchange rate at the end of period national currency<sup>units, line ae</sup> in IFS), government revenue (Y, IFS line 81, central government<sup>fiscal revenue), government</sup> expenditure (Y, IFS line 82, central government fiscal expenditure),<sup>moneysupply-M2(M, sum</sup> of IFS line 34 and 35) and the deposit interest rate<sup>5</sup> (M, IFS<sup>line601).Theseindicatorsare</sup> certainly not an exhaustive list of economic data that might<sup>beconsidered importantfor</sup> monitoring and judging economic policy outcomes, but they do represent<sup>theindicatorsthat all</sup> countries should have to some degree.

For each of these variables, I determined the "desirable" frequency level. This level<sup>was</sup> determined by observing the actual frequency level with which the<sup>dataarepublished inmost of</sup> the industrialized/high income countries and taking the most frequent<sup>levelasbeingsomething</sup> that is both achievable and desirable. A "Q" indicates that the data is<sup>expectedto beavailableon</sup> a quarterly basis, the "M" indicates its availability on a monthly basis and<sup>a"Y" itsavailability</sup> on a yearly basis. In other words GDP numbers can be and are produced<sup>on aquarterly basisin</sup> some, mostly richer countries. These countries are assigned the highest<sup>score(ora1) in terms of</sup> "transparency" with respect to GDP as long as they are also available<sup>onatimely basis.As</sup> Table I explains, both the frequency and the date for which<sup>thelatest dataareavailableare</sup> counted in formulating the index.

> **3** Developed countries are those classified as "high income" or having<sup>gross national income equal to or greater than</sup> US$9,206 per capita. Countries with lower per capita income<sup>areclassifiedas developing.</sup>

> 4 IFS refers to the International Financial Statistics - a publication<sup>of theInternationalMonetary Fund.</sup>

> 5 Generally this is a 3-month deposit rate.

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