rafmacalaba/datause-encoder-data huggingface dataset:
Supplementary Table 2. Directory of variables in the plot-crop-level dataset

|**Variable name**|**Label**<br>|**Data processing notes**<br>|**Additional notes**|
|---|---|---|---|
|_crop_name_|Crop name|Absent of a harmonised list of<br>codes for crops, this variable<br>is coded a string.||
|_plot_id_obs_|Plot ID (panel<br>identificator)|Variable created by assigning<br>a numeric code to each unique<br>plot. The first number of the<br>code determines the country.<br>UGA: plot is equal to the<br>parcel ID in the**Plot-level**<br>**dataset**||
|_plot_id_merge_|Plot ID (to<br>merge with<br>raw data)|ID to merge with raw LSMS<br>data|Can be merged with the following<br>variables in the raw data<br>(concatenated variables are separated<br>by hyphens):<br>ETH: concatenation of_holder_id_,<br>_parcel_id_and_field_id_(wave 1)_,_<br>concatenation of_holder_id_,_parcel_id_<br>and_field_id_(wave 2)_,_concatenation<br>of_holder_id_,_parcel_id_and_field_id_<br>(wave 3)_,_concatenation of_holder_id_,<br>_parcel_id_and_field_id_(wave 4)_,_<br>concatenation of_holder_id_,_parcel_id_<br>and_field_id,_OR concatenation of<br>_holder_id_, and_field_id_for<br>households answering section 12c<br>(wave 5)<br>MWI: concatenation of_case_id_and<br>_plot ID_(wave 1), concatenation of<br>_y2_hhid_and_plot ID_(wave 2)_,_<br>concatenation of_y3_hhid_,_garden ID_<br>and_plot ID_(wave 3)_,_concatenation<br>of_y4_hhid_,_garden ID_and_plot ID_<br>(wave 4)<br>MLI: concatenation of_grappe_<br>_menage, bloc_and_parcelle_(wave 1<br>concatenation of_grappe_ _menage,_<br>_bloc_and_parcelle_(wave 2)<br>NER: concatenation of_hid, field_<br>_number_and_parcel number_(wave 1)_,_<br>concatenation of_GRAPPE,_<br>_MENAGE, EXTENSION, champ_and<br>_parcelle_(wave 2)<br>NGA: concatenation of_hhid_and_plot_<br>_ID_(wave 1)_,_concatenation of_hhid_<br>and_plot ID_(wave 2)_,_concatenation<br>of_hhid_and_plot ID_(wave 3)_,_<br>concatenation of_hhid_and_plot ID_<br>(wave 4)<br>TZA: concatenation of_hhid_and_plot_<br>_number_(wave 1)_,_concatenation of|

24
|**Table 1: East Asi**|**a and Pacifi**<br>**Be**|**c sample su**<br>**nchmark ye**|**mmary sta**<br>**ar**|**tistics**||**Total**|
|---|---|---|---|---|---|---|
|**1988**|**1992**|**1997**|**2002**|**2007**|**2012**||
|**Number of surveys**<br>5|8|9|11|16|11|60|
|**Years between survey year and benchmark year**|**(%, by benc**|**hmark year**|**)**||||
|‐2<br>0.00|0.00|0.00|0.00|6.25|0.00||
|‐1<br>20.00|12.50|22.22|9.09|12.50|22.22||
|0<br>40.00|37.50|33.33|63.64|31.25|33.33||
|1<br>20.00|37.50|33.33|9.09|37.50|33.33||
|2<br>20.00|12.50|11.11|18.18|12.50|11.11||
|Within +/‐ 1 of benchmark<br>80.00|87.50|88.88|81.82|81.25|88.88||
|**Income vs. Consumption surveys (%, by benchm**|**ark year)**||||||
|**Consumption**<br>80|87.5|88.89|90.91|93.75|100.00|91.67|
|**Income**<br>20|12.5|11.11|9.09|6.25|0.00|8.33|
|**GDP (in 2011 PPP‐adjusted USD) (% of regional**|**GDP represe**|**nted in the**|**database)**||||
|**EAP**<br>95.00|99.02|99.55|99.38|99.48|96.30|98.12|
|**East Asia**<br>95.78|99.80|99.79|99.61|99.50|96.56|98.51|
|**CHN, IDN, PHL only**<br>100.00|100.00|100.00|100.00|100.00|100.00|100.00|
|**Other East Asia**<br>76.92|99.00|98.79|97.19|95.83|60.71|88.07|
|**Pacific islands**<br>0.00|0.00|55.88|32.71|92.36|3.98|30.82|
|**LIC**<br>0.00|100.00|0.00|100.00|100.00|100.00|66.67|
|**LMIC**<br>84.08|97.82|99.66|96.49|96.73|94.43|94.87|
|**UMIC**<br>98.81|99.45|99.79|99.97|99.99|96.59|99.10|
|**Population (% of regional population represent**|**ed in the dat**|**abase)**|||||
|**EAP**<br>94.39|99.47|99.21|96.92|97.30|95.31|97.10|
|**East Asia**<br>94.75|99.86|99.32|97.28|97.31|95.81|97.39|
|**CHN, IDN, PHL only**<br>100.00|100.00|100.00|100.00|100.00|100.00|100.00|
|**Other East Asia**<br>48.10|98.61|93.49|79.28|79.69|68.68|77.97|
|**Pacific islands**<br>0.00|0.00|72.53|20.06|95.73|3.41|31.95|
|**LIC**<br>0.00|100.00|0.00|100.00|100.00|100.00|66.67|
|**LMIC**<br>76.25|98.42|99.74|87.23|89.10|87.64|89.73|
|**UMIC**<br>99.75<br>_Notes: The last column is the(unweighted) average ov_|99.75<br>_er the bench_|99.93<br>_markyears 19_|99.99<br>_88 to 2012._|99.99|97.91|99.55|

6
ILO (2003): Guidelines concerning a statistical definition of informal employment, in: _Seventeenth International Conference of Labour Statisticians_ , Report of the Conference; Doc. ICLS/17/2003/R. Geneva: ILO Jonasson, E. (2011). Government Effectiveness and Regional Variation in Informal Employment.

(forthcoming in _Journal of Development Studies_ ).

Kahanec, M. and A. Zaiceva (2009). Labor market outcomes of immigrants and non-citizens in the EU: An East-West comparison. _International Journal of Manpower_ , 30 (1/2), 97−115.

Kahanec, M., A. Zaiceva and K. F. Zimmermann (2010). Ethnic Minorities in the European Union: An Overview. _IZA Discussion Paper_ No. 5397.

Kahanec, M. and K. F. Zimmermann (eds) (2011). _Ethnic diversity in European labor markets:_ _Challenges and solutions_ , Cheltenham: Edward Elgar Publishing (forthcoming).

Latvian State Labor Inspectorate (2011). Number of caught shadow workers almost doubled. Press release, 11.07.2011 (in Latvian), http://www.vdi.lv/lv/jaunumi/?print=5862 Le, A. T. (1999), Empirical Studies of Self-Employment, _Journal of Economic Surveys_ 13(4):381-416.

Le, A.T., P. W. Miller, W. S. Slutske, and N. G. Martin (2010), Attitudes towards Economic Risk and the Gender Pay Gap, _IZA Discussion Paper_ No. 5393.

Leoni, T. and M. Falk (2010). Gender and field of study as determinants of self-employment. _Small_ _Business Economics,_ 34:167–185 Liang, K.-Y. and S. L. Zeger (1986). Longitudinal data analysis using generalized linear models. _Biometrika_ 73 (1): 13–22.

Loayza, N.V., L. Servén and N. Sugawara (2009). Informality in Latin America and the Caribbean.

_World Bank Policy Research Working Paper_ No. 4888.

Macieira, M.H.C. (2009). _The determinants of self-employment_ . Dissertation. Lissabon: Instituto Superior Tecnico, Universided Tecnica de Lisboa.

Maloney, W. (1999). Does Informality Imply Segmentation in Urban Labor Markets? Evidence from Sectoral Transitions in Mexico. _The World Bank Economic Review_ , 13 (2): 275 – 302. Maloney, W. F. (2004). Informality revisited. _World Development_ , 32 (7): 1159-1178.

Muravyev, A. (2010). Evolution of Employment Protection Legislation in the USSR, CIS and Baltic States, 1985-2009, _IZA Discussion Paper_ No. 5365.

Nikolovova, P., F. Pertold and M. Vozar (2010). Informal Economy in the Czech and Slovak Republics Microeconometric Analysis of Labor Force Transitions. Background paper for ― _In_ _from the Shadow: Integrating Europe’s Informal Labor_ ‖. World Bank, Washington DC, mimeo. Novinite (2011). 1/5 of Bulgarian Employees Work in Gray Economy.

http://www.novinite.com/view_news.php?id=125106 OECD (2009). _Is Informal Normal? Towards More and Better Jobs_ . Paris: OECD.

Packard, T. G. (2000) ―Do Workers in Chile Choose Informal Employment? A Dynamic Analysis of Sector Choice‖. Washington D.C.: The World Bank (mimeo).

Pedersen, S. (2003). The Shadow Economy in Germany, Great Britain and Scandinavia: 48
(Table 2, first row). The negative effect of climate change is lower for large firms in low and lower-middle income countries but still sizable and statistically significant at 14%, suggesting that large firms in LMCs are better able to adapt but cannot fully mitigate the adverse impact of higher temperatures on firm revenues.

The negative effects of higher temperatures disappear among firms in upper-middle income (UMCs) and high-income countries (HCs). The result may reflect that countries in both income groups face substantially lower average temperature levels. That is, while the average temperature is 23<sup>_◦_</sup> C in LMCs, it is only 17<sup>_◦_</sup> C in UMCs and 15<sup>_◦_</sup> C in HCs. In other words, although it is possible that firms in higher income countries face fewer constraints to adapt to rising temperatures, the results may also indicate that the costs are larger in hotter regions, in line with earlier studies using macroeconomic data (Dell, Jones, and Olken 2012; Burke, Solomon M Hsiang, and Miguel 2015). Notably, all sizes of firms in high income countries—which face relatively low average temperatures of 15<sup>_◦_</sup> C in our sample— benefit from positive temperature deviations, which is consistent with the finding of R. S. Tol (2021). Our results remain stable when we include all covariates that displayed statistically significant correlations with temperature differences as additional controls (see Table A1).

The baseline results establish that temperatures exceeding location-specific historical means significantly slow private sector growth in LMCs, disproportionally affecting small and medium firms. The findings thus highlight substantial heterogeneity in the effects of rising temperatures depending on firm-level and location-specific characteristics, emphasizing the importance of using granular data to assess the economic impact of climate change.

To isolate the effect of high temperatures, we estimate the impact of extremely hot days on firm performance. This also serves as a robustness check for our baseline results, using an alternative measure of heat. We re-estimate Equation 1 using an alternative climate shock reflecting the impact of hot days, i.e., the difference in the total number of days in a fiscal year with temperatures reaching at least 35<sup>_◦_</sup> C for at least 1 hour in the fiscal year in the WBES compared to the historical mean. As with average temperatures, there is a significant 17
standard fi xed or random e ff ect approach is not su ffi cient to ensure correct inference; clustering standard errors by individual is necessary. This is what we do.

Having described how the dependent variable is constructed, we turn to regressors. We begin � by describing how we construct an estimate of E[ys<sup>h</sup> |z<sup>h</sup> ], the level of income (or consumption) ys<sup>hthatamigrantwithcharacteristicszhcanexpecttoearnindistricts.Toconstructsuch</sup> estimate, we use the 1995/96 NLSS data. The reason for using the 1995/96 data instead of the 2002/3 NLSS survey is to avoid reverse causation, i.e., migration causing a change in income patterns. Migrants are unlikely to be able to accurately predict the evolution of incomes in each district over time. Income and consumption levels observable before migration are thus a reasonable starting point.

Using the NLSS data we begin by estimating a regression of the form: where ys<sup>kisthelogofincome(orconsumption)ofhouseholdkresidingindistricts,coefficients</sup> δ s, β s and χ s vary by district, a<sup>k</sup> s<sup>standsfortheageandagesquaredofthehouseholdhead,</sup> Es<sup>kistheeducationleveloftheheadmeasuredinyearsofcompletededucation,andH</sup> s<sup>k=1</sup> if the head belongs to what we have earlier classi fi ed as a high caste (i.e., Brahmin, Chhetri or Newar). Since income or consumption are expressed in logs, β s and χ s can be thought of as education and high caste premia, respectively. Female headed households are excluded from the regression since the focus is on migrant males. Vector <u>a denotes the average age and age squared</u> of observations across the sample. Variables E and Hs denote the district-speci fi c averages of Es<sup>kandH</sup> s<sup>k.Bydemeaningregressors,weensurethat�δsmeasurestheunconditional,district-</sup> speci fi c average of ys<sup>k.Maritalstatus,householdsize,andotherhouseholdcharacteristicsare</sup> 15
# **Apppendix: Detailed Tables**

|**Table**|**Al. Inclusion in D**<br>(43 projects with a m<br>(|**iagnosis of Tax Structure Analysis**<br>ajor administration component)<br>% of projects)||
|---|---|---|---|
||TALs, LILs, SILs, R|ILs|SALs and SECALs|
|Major Taxes in Country|78.9|Tax to GDP ratio|100.0|
|Tax to GDP ratio|75.0|Major Taxes in Country|95.2|
|Recent Reform History: rates|63.6|Recent Reform History: base and<br>exemptions|90.0|
|Nature of Exemptions|61.1|Nature of Exemptions|85.7|
|Tax shares|58.8|Recent Reform History: rates|85.0|
|Rates|57.9|Base indicators|76.2|
|Base indicators|52.6|Tax shares|66.7|
|Other Analysis|47.4|Rates|66.7|
|Recent Reform History: base<br>and exemptions|45.5|Other Analysis|66.7|
|Effective Rates|15.8|Buoyancies/ Elasticities|28.6|
|Other Formal Analysis|15.8|Effective Rates|23.8|
|Buoyancies/ Elasticities|11.8|Other Formal Analysis|10.0|
|Tax to base ratio: other|10.5|Tax to base ratio: other|9.5|

Notes: 1. Projects are included if they examine an area, however sketchily and whether qualitatively or quantitatively.

2. Projects where the diagnosis category is not relevant are excluded from the denominator in calculating percentages.

|**Table A2. In**|**clusion in Diagnosis of**<br>(43 projects with a major<br>**(%/**of|**Effectiveness Indicators and**<br>administration component)<br>projects)|**Analysis**<br>|
|---|---|---|---|
||TALs, LILs, SILs, RILs||SALs and SECALs|
|Number of taxpayers|52.4|International comparison|38.1|
|Taxpayer service|38.1|Corruption|23.8|
|Tax Gap: Specific activities|33.3|Number of taxpayers|19.0|
|Overall Tax gap|25.0|Tax gap: specific taxes|14.3|
|International comparison|25.0|Overall Tax gap|9.5|
|Corruption|22.7|Tax Gap: Specific activities|9.5|
|Tax gap: specific taxes|20.0|Taxpayer service|9.5|
|Note: See the notes to Table 4|.3.|||

|**Table A3. Inclusion**<br>(43 proj|**in Diagnosis o**<br>ects with a major<br>(%of|**f Efficiency Indicators and Analys**<br>administration component)<br>projects)|**is**|
|---|---|---|---|
||TALs, LILs,<br>SILs, RILs|<br>_|SALs and<br>SECALs|
|Technology deployment|85.7|Activity wise efficiency|40.0|
|Activity wise efficiency|66.7|Technology deployment|33.3|
|Manpower|57.1|Extent of Outsourcing/ Privatization|33.3|
|Extent of Outsourcing/ Privatization|47.6|Manpower|28.6|

31
**THE COMPOSITION OF GROWTH MATTERS FOR POVERTY ALLEVIATION**

# **I. Introduction**

There is little doubt that economic growth contributes significantly to poverty alleviation. The evidence is mounting and coming from various sources: cross-country analyses (Besley and Burgess, 2003; Dollar and Kraay, 2005; Kraay, 2005; and López, 2004), cross-regional and time-series comparisons (Ravallion and Chen, 2004; Ravallion and Datt, 2002), and the evaluation of poverty evolution using household data (Bibi, 2005; Contreras, 2001; Menezes-Filho and Vasconcellos, 2004). At the same time, it is clear that the effect of economic growth on poverty reduction is not always the same. In fact, most studies point to considerable heterogeneity in the poverty-growth relationship, and understanding the sources of this divergence is a growing area of investigation (Bourguignon, 2003; Kakwani, Khandker, and Son, 2004; Lucas and Timmer, 2005, Ravallion, 2004). Most of the received literature focuses on socio-economic conditions of the population as determinants of the relationship between growth and poverty reduction. Thus, wealth and income inequality, literacy rates, urbanization levels, and morbidity and mortality rates, among others, have been found to influence the degree to which output growth helps reduce poverty.

In this paper we take a different, albeit complementary, perspective on the sources of heterogeneity in the poverty-growth relationship. We focus on the characteristics of output growth itself, rather than the demographic, social, or economic conditions of the population. We study how the production structure of the economy and, specifically, the sectoral composition of growth affect its capacity to reduce poverty. Our conjecture is that growth in certain sectors is more poverty reducing than growth in others and that a sector’s poverty-reducing capacity is related to its intensity in the employment of unskilled labor.

There are important studies that precede and motivate our work. Thorbecke and Jung (1996) develop a social-accounting method to estimate the impact of various production activities on poverty reduction. The method requires knowledge of complex elasticities connecting the distribution of households with eight employment and production sectors. The authors apply the method to Indonesia in the 1980s and find that 1
on seven of those sub-periods, selected on the basis of a minimum duration (specifically, those comprising at least 249 daily observations, amounting to approximately one year). The relevant parameters characterizing these sub-periods are summarized in Table La. The first three sub-periods involve a peg to the **US** dollar with a band, while the last four involve a basket peg with a band.

Figure **1** displays the observed exchange rate in terms of the weighted basket numeraire, along with the announced bands. The figure shows that the trend of the peso has been to depreciate over time, with significant appreciations and depreciations on several occasions, and highlights the fluctuations of the exchange rate within the band, as well as the gradual widening of the latter. In some periods, like **1991-92,** the exchange rate is close to the lower band. In other periods, like **1994-95,** the exchange rate fluctuates farther inside the band. After suffering pressure on the peso, the authorities decided to narrow the band from 12% to **3.5%** in September **1998** to show their commitment to the value of the peso. The band was widened back to **8%** in December **1998.** Table **1.b** reports the results of the verifiability tests using the Chilean exchange rate data, based on first-difference **OLS** estimates of the basic equation. For each of the seven sub-periods under consideration, the table presents the cumulative rejection frequencies of the null hypotheses of Test **1** and Test 2 at increasing sample sizes **-** **_50,_ 100** and 200 observations. For example, a rejection frequency of **100** for **50** observations in Test 2 means that in **100** percent of the estimations with sample sizes smaller than **50** we can reject the null hypothesis that the weights are equal to **0.2** In addition, the table also reports point estimates and standard errors of the weight of the **US$** in the basket

**23**
# **Figure 9A: Unskilled Worker Migration Corridors in East Asia and the Pacific that Declined the Most between 2007 and 2050.**<sup>**a**</sup>

<!-- Start of picture text --> 0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>Malaysia / Philippines Malaysia / Thailand Singapore / Malaysia<br>Singapore / China Japan / South Korea<br>Source: Authors’ results<br>Millions of People<br>2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049<br><!-- End of picture text --> Source: Authors’ results Note: a. Number of unskilled migrant workers by location/home region

**Figure 9B: Skilled Worker Migration Corridors in East Asia and the Pacific that Declined the Most between 2007 and 2050 (excluding Singapore / China).**<sup>**a**</sup>

<!-- Start of picture text --> 0.14<br>0.12<br>0.1<br>0.08<br>0.06<br>0.04<br>0.02<br>0<br>Japan / Philippines Indonesia / China Malaysia / Philippines<br>Malaysia / Thailand Singapore / Malaysia<br>Millions of people<br>2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049<br><!-- End of picture text --> Source: Authors’ results Note: a. Number of unskilled migrant workers by location/home region 28
|**Region**|**2004**|**2005**|**2006**|**2007**|**2008**|**2009**|**2010**|**2011**|
|---|---|---|---|---|---|---|---|---|
|North|71.6|75.0|96.9|111.6|120.5|154.2|155.1|198.4|
|Northeast|444.8|446.7|495.7|549.1|574.0|696.5|690.0|823.7|
|Southeast|204.8|215.6|228.2|244.7|243.7|304.5|305.5|379.2|
|South|79.8|87.7|80.2|81.0|78.0|103.5|100.1|118.1|
|Center-West|33.3|39.1|45.6|49.2|49.8|65.9|68.9|82.7|
|Total|834.3|864.1|946.6|1,035.6|1,065.9|1,324.5|1,319.6|1,602.1|

Table 3: Amount of transfer from BFP by region (in million of R$ of 2011) Source: Ministry of Social Development (2012). Values in R$ of 2011 based on IPCA (Portuguese acronym for Extended National Consumer Price Index elaborated IBGE). This is the official inflation index used by the Central Bank for inflation targeting. The average nominal exchange rate in 2011 was R$ 1.00 = US$ 0.59.

<!-- Start of picture text --> Bolsa Família’s indicators, by region (2004 − 2009)<br>Share of transfers from Bolsa Família on GDP Share of Bolsa Família’s treated Households<br>North Northeast Southeast South Center−West Brazil North Northeast Southeast South Center−West Brazil<br>.15 .4<br>.3<br>.1<br>.2<br>Share on GDP<br>.05<br>.1<br>Share on Total Brazilian’s Households<br>0 0<br>2004 2005 2006 2007 2008 2009 2004 2005 2006 2007 2008 2009 2004 2005 2006 2007 2008 2009 2004 2005 2006 2007 2008 2009 2004 2005 2006 2007 2008 2009 2004 2005 2006 2007 2008 2009 2004 2005 2006 2007 2008 2009 2004 2005 2006 2007 2008 2009 2004 2005 2006 2007 2008 2009 2004 2005 2006 2007 2008 2009 2004 2005 2006 2007 2008 2009 2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> Figure 1: Bolsa Fam ́ılia beneficiaries and transfers, by region (2004 - 2009) Source: Ministry of Social Development (2012) for total number of BFP’s beneficiaries and total amount of transfer; IBGE (2012) for total population and GDP.

It is noticeable that the Southeast, South and Center-West regions are relatively more homogeneous in terms of GDP per capita, amount of BFP’s transfers received and share of treated households. The North and Northeast regions’ GDP per capita are dramatically lower, these regions receive a larger amount of transfers proportional to their GDP and have a larger share of treated households. The Northeast is the second most populous region with approximately 53.5 million people, which represents almost 28% of the Brazilian population.<sup>54</sup> Section 8.6 in the appendix provides further descriptives on regional heterogeneity in Brazil.

> 54This further illustrates the importance of BFP; BFP plays a significant role in the public discourse and elections (See Bohn [2011] and Zucco [2011].

21
**Figure 9: Comparison of QFD at current vs. benchmark performance**

<!-- Start of picture text --> Botswana<br>South Africa<br>Zimbabwe<br>Sudan<br>Gambia, The<br>Malawi<br>Ethiopia<br>Senegal<br>Guinea<br>Comoros<br>Zambia<br>São Tomé and Príncipe<br>Swaziland<br>Côte d'Ivoire<br>Mali<br>Burkina Faso<br>Rwanda<br>Burundi<br>Madagascar<br>Mauritania<br>Kenya<br>Lesotho<br>Nigeria<br>Mozambique<br>Sierra Leone<br>Liberia<br>Current performance<br>Mauritius<br>Benchmark performance<br>Cameroon<br>Tanzania<br>Niger<br>Congo, Rep.<br>Central African Republic<br>Ghana<br>Gabon<br>Benin<br>Cape Verde<br>Uganda<br>Togo<br>Seychelles<br>-0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5<br>% of GDP in the reference year<br><!-- End of picture text --> _Source_ : World Bank staff calculations based on utility financial statements, annual reports, and other documents.

Is there a trade-off between the QFD and access? That is, could it be that utilities run deficits to take on more ambitious electrification programs? Is a country’s QFD in the power sector affected by the income level or the depth of poverty? Examination of QFDs with economic parameters found that none of the six measures of QFDs—as shares of GDP, revenue, and cash collected at current as well as benchmark performance—were correlated with household access to electricity in 2012 (World Bank and IEA 2015), the poverty gap at $3.10 per person per day at purchasing power parity in 2011 international dollars (in 2012, or if the poverty gap is not available in 2012, within two years of 2012), per capita GDP at the market exchange rate, and per capita GDP at purchasing power parity using a 5-percent significance 41