rafmacalaba/datause-encoder-data huggingface dataset:
We find that even in this case (red “09” and “20”), the relative differences between reported and composite estimates are small. Thus, the NO2 composite estimate is completely reasonable.

# **6 Applications**

� We now turn to a number of applications of the NO2 composite estimate. The difference _y − z_ � between the composite estimate _y_ and reported GDP _z_ is the amount of _misstatement_ in GDP. When misstatement is positive, this indicates GDP understates true economic activity; when it is negative, this indicates overstatement. The standard deviation of misstatement is, where RMSE is the root mean square error from the regression to obtain the proxy. An ordering of all world countries based on this standard deviation provides a natural objective ranking of GDP accuracy. Such a world ranking is uniquely made possible by NO2, which both has data for all world countries, and explanatory power for fluctuations in each.

Table 7 presents estimates of this standard deviation over 2005-2020, by-data quality grade, ranked from highest variability to lowest. Thus, those at the top of the list have the most erroneous GDP data. At the bottom of the table, the overall means of standard deviations increase from grade A to E, indicating that our grades corroborate the grades on average, and are therefore not unreasonable. Specifically, the errors range from 0.25 to 5 percentage points on average, in line with Henderson et al. (2012)’s estimate of trend growth errors up to 3 percentage points. But at the same time, there are individual instances where our objective ordering represents a significant reordering of the existing subjective rankings. For example, Algeria, the most accurate of grade D countries, would be ranked as more accurate than Ireland, the most inaccurate of grade A countries. Overall, we judge that the Republic of Yemen had the most inaccurate GDP data over this period, and Australia the most accurate.

One potential issue with the Penn World Tables A through E grades is that they may not be reflective of GDP data accuracy. To further explore the implications of our rankings, we also considered the World Bank’s subjective ranking of GDP accuracy for the 114 countries in our sample of 178 which it grades. The analysis is presented in Online Appendix B.3. While there is a positive correlation between the Penn World Tables and World Bank subjective rankings, there are important differences. Regardless, our ordering entails a significant reordering of the World Bank, rankings as well.

In the following section, we consider some of the countries with the most inaccurate GDP data at the top of Table 7, in each grade. In many cases, the ranking is due in part to a single, cyclical episode of especially erroneous data. As we have previously shown, being able 26
regulatory governance as result of power sector reforms have brought enhanced service penetration, generation capacity expansion, utilization of generation capacity and reserve margin in some developing countries.

The effects of reforms on quality and access have differed across different development regions as showed by Nagayama (2010) based on econometric methods for panel data analysis. The introduction of foreign IPPs when coexistent with independent regulators and unbundling on its own increased per capita generation capacity in Asian developing countries while the establishment of an independent regulator produced an opposite effect. The per capita generation capacity increased among the LACs with the introduction of wholesale power market and power exchange. The reform impacts have triggered different impacts on electricity transmission and distribution (T&D) losses (Nagayama, 2010). The introduction of foreign IPPs decreased T&D losses in Asian developing countries while it had the opposite effect in Latin America along with the establishment of an independent sector regulator. The introduction of wholesale power market increased T&D losses in Asian developing countries as the amount of power traded increased.

On the other hand, private sector investments in the transmission and distribution networks contributed to a decline in electricity losses in Latin America (Balza, Jimenez and Mercado, 2013). Technical and non-technical losses fell sharply from above 20% in 1992 to just above 10% in 2007 in Argentina (Pollitt, 2008). The number of minutes of supply interruption per year fell to 2.1 in 2003 from 9.6 in 1997 in Chile while distribution losses fell from 19.8% in 1987 to 5.6% in 2003 (Pollitt, 2004). Generation capacity also increased in many LACs except Brazil post reforms (Millan, 2005).

Reforms triggered different impacts on the plant load factor, T&D losses and gross electricity generation among the Indian states (Sen and Jamasb, 2012). Unbundling and tariff orders had a positive and significant effect on plant load factors. Introduction of independent regulation, unbundling and privatisation of distribution segment contributed to increases in T&D losses. Gross electricity generation in India increased with the introduction of the IPPs while privatisation of the distribution segment led to decline in electricity distribution losses. The average level of T&D losses in Sub-Saharan Africa was around 27.5% in 2009 although the system losses substantially ranges from 14.5% in Angola to 68% in Swaziland (ESMAP, 2009). Reforms have also been unable to reduce electricity theft in most regions of the developing world considering that the quality of governance such as effective accountability, political 20
9 differencing nan induce a simultaneity problem is well known from the conventiona literature on time series and has been explored in a panel data context (see Chamberlai 1983). The usual solution is to employ an instrumental variables estimator. Holtz Eakin and others (1988) have considered this problem in the context of estimating an testing vector auto-regression coefficients using panel data. Thei- results sugges that the instrumental variable approach is the appropriate one, but it should h implemented in a different fashion because the variables that are legitimate candidates for use as instrumental variables change ove- time. To derive their instrumental variables estimator, which has a generalized least squares (GLS) interpretation, one needs to assume that the error term uit(61<sup>t), is uncorrelated with all past</sup> values of P and A and the individual effect: (and a similar condition for Est). The orthogonality conditions (4) are used to identify the parameters of (3), since the distulrbance term Mit (-uit -uit.1) and Oit (-eit - Cit 1) will be uncorrelated with Pit-s and Ait.6<sup>for s S 2. The equation for each time</sup> period t has 2m right-hand side variables. To ideritify the parameters, there must be at least this many instrumental variables. The 2(t-2) variables [Pit-.2z. pi . .AAt-2 Ai11] are available as instrumental variables to estimate the equation for the time period t. Thus to have at least as many instrumental variables as r'1⁄2ht-hand side variables, it must be true that 2(t-2) < 2m or t 5 m+2.

Holtz-Eakin and ochers (1988) demonstrate that in the absence of crosssection heteroshedasticity in the forecast errors uit and est, and if stationarity is assumed in the individual effect coefficients and in the lag coefficients as well, than
14

# C) Testing for asymmetry in kurtotic<sup>distributions</sup>

Before moving to exami.e the implications<sup>of asymmetry in cross countLy</sup> comparisons a final statistical point needs<sup>to be addressed. The critical levels</sup> used in the previous sections for the<sup>skewness and kurtosis statistics are</sup> generated under the null that the parent<sup>distribution is normal. However, given</sup> that the distribution is kurtotic (thick<sup>tailed), the critical levels for the</sup> skewness statistic generated under the<sup>null of normality will not be correct.</sup> In the small samples available it is difficult<sup>to statistically distinguish</sup> kurtosis (relatively large probabilities<sup>of extreme observations) from skewness</sup> (tendency for extreme observations in one<sup>tail). Deviations from normality due</sup> to skewness, implying an asymmetric<sup>process, are of greater economic interest</sup> than fat tails. To test the hypothesis of skewness independently<sup>we need to</sup> determine the distribution of the skewness<sup>coefficient under the null of a more</sup> general symmetric, but kurtotic distribution.

The "student's" t distribution commonly<sup>used in hypothesis tests is a</sup> symmetric distribution, with a level of<sup>kurtosis that decreases with the degrees</sup> of freedom (v): 8) _b4 =_ 6 Rejection levels for each of the 1:wo<sup>skewness statistics were generated using</sup> Monte Carlo simulations from t disvributions<sup>with varying degrees of freedom.</sup> The rejections levels are reported in<sup>Table 6.</sup> For instance, if the parent distribution is assumed to be normal then<sup>values of the skewness statistic (b</sup> 3<sup>)</sup> greater (in absolute value) than .84<sup>with a sample size of 23 indicate a</sup> rejection of the null of no skewness at<sup>the 5% level. However, if the parent</sup> distribution is a t(5), with excess kurtosis<sup>of 6, then the absolute value of b</sup> 3 needs to exceed 1.87 to reject at the<sup>same confidence level.</sup> T;ie critical values in table 6 can be<sup>compared with the skewness statistics</sup> in table 2 to check the robustness of the<sup>symmetry tests to kurtosis. The values</sup> of the skewness statistics are so high<sup>that many countries are still able to</sup> reject symmetry. Using the critical values<sup>generated from a t(6) distribution</sup>
Table 1B: Harnessing Entrepreneurship and Job Creation in Cities-Informal Manufacturing

||Base<br>estimation|District<br>traits|Full<br>estimation|Adding<br> consumptio|Using log<br>n entry count|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||_DV i_|_s log entry e_|_mployment b_|_y district-in_|_dustry_|
|Log of incumbent employment in district-industry|0.163+++<br>(0.031)|0.123+++<br>(0.029)|-0.075++<br>(0.029)|-0.078+++<br>(0.029)|-0.040<br>(0.026)|
|Log of district population|1.051+++<br>(0.161)|0.878+++<br>(0.157)|1.010+++<br>(0.160)|1.025+++<br>(0.153)|0.866+++<br>(0.138)|
|District Traits:||||||
|Log of district population density||-0.019<br>(0.070)|-0.044<br>(0.068)|-0.042<br>(0.073)|-0.044<br>(0.057)|
|Share of population with graduate education||-0.002<br>(0.080)|-0.026<br>(0.084)|-0.079<br>(0.087)|-0.046<br>(0.074)|
|Demographic dividend for district (age profiles)||0.954+++<br>(0.326)|1.053+++<br>(0.330)|0.770++<br>(0.326)|0.798+++<br>(0.285)|
|Index of infrastructure quality for district||0.386+++<br>(0.096)|0.365+++<br>(0.097)|0.259++<br>(0.104)|0.325+++<br>(0.086)|
|Strength of household banking environment||0.222+++<br>(0.080)|0.211+++<br>(0.080)|0.152+<br>(0.082)|0.193+++<br>(0.071)|
|Stringency of labor laws in district's state||-0.007<br>(0.069)|0.000<br>(0.069)|0.020<br>(0.066)|0.030<br>(0.062)|
|Log travel time to closest large city||-0.004<br>(0.069)|0.009<br>(0.074)|0.029<br>(0.074)|0.017<br>(0.065)|
|Log per capita consumption||||1.191+++<br>(0.365)||
|Local Industrial Conditions by Incumbent Firms:||||||
|Labor market strength for district-industry|||0.263+++<br>(0.075)|0.271+++<br>(0.075)|0.228+++<br>(0.067)|
|Inputs / supplier strength for district-industry|||0.553+++<br>(0.107)|0.542+++<br>(0.108)|0.504+++<br>(0.096)|
|Outputs / customer strength for district-industry|||0.291+++<br>(0.050)|0.292+++<br>(0.051)|0.246+++<br>(0.044)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|
|Observations|6451|6451|6451|6451|6451|
|Adjusted R-squared|0.195|0.233|0.264|0.267|0.294|

Notes: See Table 1A.

15
(7) We find the following relationship between the two ticket prices, _pe_ and _pb_ : We now introduce a measure of passenger weight as fraction of total aircraft weight; this is an important measure in determining how the carbon footprint of passengers depends on passenger weight. When not correcting for this factor, there will be a certain relative over-allocation of carbon emissions units to business-class passengers, and under-allocation to coach-class passengers. In (3), assume that we have approximately where α and 1-α are total aircraft passenger and non-passenger weight shares, and where we now take both W and α as fixed. This can hold only approximately as the total number of passengers, N, is to some degree variable as it depends on the passenger shares in economy and business classes), but is a good approximation as long as the weight share of passengers is low, and the fractions of economy- and business-class passengers not relatively stable. The overall passenger weight share in total aircraft weight, while depending in aircraft type and whether or not fuel tanks are filled up, is relatively small in most cases (e g around 12% for a fully-fueled Boeing 747).<sup>14</sup> We now consider long-run industry equilibrium in the aviation sector, given zero profits and endogenous seating arrangements by travel class, and where overall passenger capacity of airlines is endogenous. In the long run, competitive airlines will add capacity as long as profits per flight are positive, and reduce capacity when profits per flight are negative; and will do so for each of the two travel classes. In a long-run steady state, profits are zero, in total and related to passengers on each of the two travel classes. We represent this by setting Π = 0 in (4), and let this relationship, together with (2), (6) and (7), determine the endogenous variables pe, pb, λ, and one relationship between Ne and Nb.<sup>15</sup> Consider now, in (4), total aircraft weight W as (approximately) constant (and independent of the passenger number), and insert from (8) (with П = 0) into (4), to yield

> 14 For a fully-tanked Boeing 747, fuel constitutes about half of total aircraft weight. Passenger weight is a larger share of aircraft weight, closer to 20%, with “close to empty” fuel tanks. See Wickpedia (2011). 15 Note that the levels of N1 and N2 are not determined by our model set-up. The reason is that these are given by the relative demand for economy-class and business-class aviation services. No such demand relations are specified here; our model is valid for any such relative demands .

8
<!-- Start of picture text --> Loglinear and quadratic model Loglinear and quadratic model Loglinear and quadratic model<br>AIDS AIDS AIDS<br>QUAIDS QUAIDS QUAIDS<br>3.5 4.0 4.5 5.0 5.5 6.0 3.5 4.0 4.5 5.0 5.5 6.0 3.5 4.0 4.5 5.0 5.5 6.0<br>Income per capita Income per capita Income per capita<br>Loglinear and quadratic model Loglinear and quadratic model Loglinear and quadratic model<br>AIDS AIDS AIDS<br>QUAIDS QUAIDS QUAIDS<br>3.5 4.0 4.5 5.0 5.5 6.0 3.5 4.0 4.5 5.0 5.5 6.0 3.5 4.0 4.5 5.0 5.5 6.0<br>Income per capita Income per capita Income per capita<br>Loglinear and quadratic model<br>AIDS<br>QUAIDS<br>3.5 4.0 4.5 5.0 5.5 6.0<br>Income per capita<br>0.45 0.05<br>0.40 0.04 0.010<br>0.35<br>0.03 0.008<br>0.30<br>0.02<br>Exp. share food 0.25 0.006<br>Exp. share education<br>Exp. share school material<br>0.20 0.01<br>0.004<br>0.15 0.00<br>0.10 −0.01 0.002<br>0.10<br>0.04<br>0.04<br>0.03 0.08<br>0.03 0.02 0.06<br>Exp. share health care Exp. share medicine 0.01<br>Constrained Health Services 0.04<br>0.02 0.00<br>−0.01 0.02<br>0.01<br>0.6<br>0.4<br>0.2<br>0.0<br>Constrained Medication<br>−0.2<br>−0.4<br><!-- End of picture text --> Figure 4: Engel curves estimated on subsample of untreated households (OLS).
# A Proxy Means Test for Sri Lanka

Ashwini Sebastian Shivapragasam Shivakumaran Ani Rudra Silwal David Newhouse Thomas Walker<sup>1</sup> Nobuo Yoshida Keywords: Proxy means test; poverty; welfare; targeting JEL codes: H53, I32

> 1 Corresponding author, email: twalker@worldbank.org. All authors are affiliated with The World Bank. The authors are grateful to officials from the Sri Lanka Ministry of Finance, National Planning Department, Ministry of Social Empowerment and Welfare, Department of Census and Statistics, Ambar Narayan, Phillippe Leite, and Emil Tesliuc for their feedback and suggestions on an earlier drafts. All remaining errors and omissions are the responsibility of the authors.
# **3. METHODS OF ANALYSIS**

For each of the 20 included countries, the following questions were posed:

- 1) Is modeled UNAIDS incidence rising/falling, and at what relative rate of change?

- 2) How well does empirical data (ANC, DHS/AIS, clinical trials and observational studies, including ALPHA Network) match modeled incidence trends?

- 3) How have these incidence trends changed prior to and during ART scale-up?

To address these questions, multiple forms and sources of incidence data have been used here: 1) Nationally reported prevalence data from ANC reports and DHS/AIS, focusing on females 15-24, 2) HIV prevalence and incidence data within young females obtained from ALPHA Network studies, 3) Directly measured incidence data from published clinical trials and/or observational studies (unrelated to ALPHA network), and 4) Modeled incidence and ART coverage data obtained from Spectrum.

Nationally reported HIV prevalence data in females 15-24 were obtained directly through ANC sentinel surveillance reports available online or from DHS/AIS reports found through MeasureDHS website’s, http://www.measuredhs.com/ and http://www.statcompiler.com/. UNAIDS/GARPR Indicator 1.6 (HIV prevalence in young people) data were accessed from the AIDSinfo Online Database (http://www.aidsinfoonline.org/ ) and used to supplement when ANC or DHS/AIS reports were not available for later years. Although Indicator 1.6 data guidelines request antenatal clinic attendees data (aged 15-24) be used, the inclusion of population based survey data, for both sexes, is also allowed.

Directly measured HIV prevalence and incidence data were identified through PubMed and Google Scholar searches of published HIV trials or observational studies using keywords “HIV epidemiology” or “HIV incidence” paired with the name of the included countries. For ALPHA Network site data, the names of the various ALPHA sites combined with “HIV” were used in searches, and recent publications from the ALPHA Network were used to find older publications as per their citation lists.

Modeled incidence estimates from Spectrum were downloaded directly from the UNAIDS AIDSinfo website (http://www.unaids.org/en/dataanalysis/datatools/aidsinfo/ ). Estimated ART coverage among those eligible for treatment was obtained from a variety of sources: for the years 2004-2007 - UNAIDS Report on the global HIV/AIDS epidemic 2008 (based on ART eligibility CD4 at or <200); for the years 2009-2011 - UNAIDS Report on the global AIDS epidemic 2012 (based on ART eligibility CD4 at or <350) and http://aidsinfo.unaids.org (accessed June 2013); for 2012 – WHO Global update on HIV treatment 2013: results, impact and opportunities and <u>http://aidsinfo.unaids.org (accessed October 2013) based on ART eligibility CD4 at or</u> <350).

20
Policy Research Working Paper 6254

# **Abstract**

This paper develops a framework for the quantitative analysis of individual income dynamics, mobility and welfare. Individual income is assumed to follow a stochastic process with two (unobserved) components, component representing measurement error or transitory income shocks and an Autoregressive (AR(1)) component representing persistent changes in income. The analysis uses a tractable consumption-saving model with labor income risk and incomplete markets to relate income dynamics to consumption and welfare, and derive analytical expressions for income mobility and welfare as a function of the various parameters of the underlying income process. The empirical application of the framework using data on individual incomes from Mexico provides striking results. Much of measured income mobility is driven by measurement error or transitory income shocks and therefore (almost) welfare-neutral. A smaller part of measured income mobility is due to either welfare-reducing income risk or welfare-enhancing catching-up of low-income individuals with high-income individuals, both of which have economically significant effects on social welfare. Decomposing mobility into its fundamental components is thus seen to be crucial from the standpoint of welfare evaluation.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at Wmaloney@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
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# Per capita expenditures

||Percentiles|Smallest|||
|---|---|---|---|---|
|1%|170|45|||
|5%|253|58|||
|10%|314|61|Obs|12,447|
|25%|441|70|Sum of wgt.|16307879.5|
|50%|649||Mean|898|
|||Largest|Std.<br>dev.|3,926|
|75%|991|15,678|||
|90%|1,513|18,704|Variance|15414949|
|95%|2,080|147,453|Skewness|81|
|99%|3,954|340,687|Kurtosis|6,877|