rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 2: Tax revenues as a share of GDP.**

<!-- Start of picture text --> Venezuela<br>Uruguay<br>Peru<br>Paraguay<br>Panama<br>Nicaragua<br>Mexico<br>Honduras<br>Haiti<br>1990<br>Guatemala<br>2004<br>El Salvador<br>Ecuador<br>Dominican Republic<br>Costa Rica<br>Colombia<br>Chile<br>Brazil<br>Bolivia<br>Argentina<br>0% 5% 10% 15% 20% 25% 30% 35%<br><!-- End of picture text --> Source: Gomez Sabaini (2005).

When looking at joint patterns of social spending and taxation, we can only observe a moderate relationship between social spending, and the level of taxation: although no country finds itself completely off the diagonal in Figure 3 (i.e., high spending and low taxation, or vice-versa), most countries do not exhibit a strong link between revenues and social spending. This may be due to some countries having different priorities than social spending; another likely cause is that commodities inflate government revenues in many countries (Tanzi, 2000).

**Figure 3: Patterns of social spending and taxation.**

<!-- Start of picture text --> Low Spending Medium Spending High Spending<br>Guatemala Mexico<br>Venezuela<br>Low Taxation<br>Paraguay<br>Dominican Republic Peru Chile<br>El Salvador Colombia<br>Medium Taxation<br>Honduras<br>Nicaragua<br>Argentina<br>Brazil<br>Hi g h Taxation<br>Uruguay<br><!-- End of picture text --> Source: Lindert _et al._ (2006) for social spending; Gomez Sabaini (2006) for taxation.

5
example replacement of gas water heaters with solar water heaters would provide higher potential for GHG reduction than through increased efficiency of gas water heaters. Therefore, the study does not include efficiency improvements in ranges and water heaters.

Other measures we considered in reducing GHG mitigation from the residential sector include lightbulbs, TV, refrigerator and washing machines. The average number of bulbs per household is 8, and they operate around 5.26 hours daily. Almost 70% of households use incandescent bulbs, 8% efficient bulbs, and 22% have both. Almost all households have TV, from which around 11% is new LED type TVs. 84% of household own refrigerator and among them 48% state that their refrigerator is energy efficient. Around 68% have the washing machine, 43% of which is purchased after 2010. As for the cooling systems the typical cooling system in Georgia is local split system, used to cool one or two rooms. The central cooling systems are very rare in country, and would result in increase of consumption.

The GHG mitigation potential is sensitive to the assumptions made on their future penetration. Historical rate of penetration could be a guidance. Government policies and incentive mechanism available also affect the rates of penetration. Based on these factors we have made assumptions on the penetration rates of measures and options with potential to reduce GHG emissions.

Like in the residential buildings, space heating is the main energy end-user in the commercial and public buildings. Therefore, improved building insulation provides the highest potential to save building energy consumption and GHG mitigation. The selection of these options in Georgia are based on energy audits (Sustainable Development Centre Remissia, 2015) and Sustainable Energy Action Plans (SEAP) of cities (Zugdidi SEAP 2014, Batumi SEAP 2014, Kutaisi SEAP 2014). Other technologies, such as refrigerators, cooling systems, etc. could also be considered but required data were not available.

The discount rate used in the analysis is a social discount rate, which is assumed to be 7.5%, which is commonly used for infrastructure project evaluation in Armenia and Georgia. Since the results are sensitive to discount rates, we have carried out sensitivity analysis.

# **4. Results**

The marginal abatement cost curve consisting of various GHG mitigation options related to energy efficiency improvement is presented in Figure 1. The residential and commercial sector energy efficiency measures considered in this study are estimated to reduce 16.4 million tons of CO2 in 10
Varghese 2012). And in Kenya, M-Shwari – a combined savings and loan product offered in partnership between CBA, a bank, and Safaricom, the mobile money operator behind M-Pesa – offers small, short-term loans to its customers based on their transaction and payments history with Safaricom and M-Pesa (Cook and McKay, 2015).

Incorporating payments data into credit files not only has benefits for potential borrowers but also helps financial institutions in better being able to assess credit risks and thus minimize their nonperforming loan (NPL) portfolio. Using the payments and transaction history of its customers with Safaricom to assess their credit worthiness and assign individual credit limits, CBA in Kenya was able to significantly reduce NPLs of its M-Shwari portfolio (Cook and McKay, 2015). While a reduction in nonperforming loans can contribute to overall financial sector stability, for most financial institutions loans extended to adults solely based on payments data will likely make up a very small share of their overall loan portfolio.

Finally, moving payments from cash into accounts might be especially valuable for women, who benefit from the greater confidentiality and control such payments offer. This is particularly true for payments women receive either as compensation for work, remittance payments, or government transfer payments. The arrival of a payment into an account is private information, allowing the recipient to at least temporarily conceal the payment from other household members or friends or relatives who may place inappropriate demands on the use of the money. A payment into an account thus gives women more control over their money and assets and makes it harder for family and friends to access these funds.

There is evidence from the earlier mentioned social cash transfer program in Niger, for instance, that the greater privacy and control of mobile transfers, compared to manual cash transfers, shifts intra-household decision making in favor of women who receive the transfers (Aker et al., 2013). And in Kenya, research has shown that the arrival of mobile money transfers has increased women’s economic empowerment in rural areas by making it easier for women to request remittances from their husbands who migrated to urban areas for work (Morawczynski and Pickens, 2009). Giving women more control over their money may also have larger societal and development benefits. A large body of research suggests that income in the hands of women, 10
Policy Research Working Paper 8545

# **Abstract**

This paper introduces, validates, and applies a Least Absolute Shrinkage and Selection Operator with multiple imputation by Predictive Mean Matching (LASSO-PMM) method to estimate intra-generational welfare dynamics using cross-sectional data. Compared to previous welfare dynamic prediction methods, the LASSO-PMM makes fewer and less restrictive assumptions and allows estimating poverty transitions and income changes. We validate the method using 36 harmonized panel data sets in four Latin American countries and then apply it to cross-section data from 43 countries across the world. To the best of our knowledge, this is the first paper that uses these many datasets to validate and estimate welfare and mobility predictions. Validation results indicate that LASSO-PMM predictions are in general statistically indistinguishable from actual household poverty rates, mobility indicators, and income or consumption changes; results which are further supported by a series of sensitivity tests and robustness checks. These findings are sufficiently encouraging to suggest that estimating economic mobility using a LASSO-PMM approach may accurately approximate actual welfare dynamics in settings where panel data are unavailable. This application provides useful policy information on the dynamics of individual welfare in countries where two or more rounds of cross-section data are available.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/research. The authors may be contacted at llucchetti@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
9. Output 10. Armington aggregate 11. Sector-specific energy aggregate 12. Private consumption 13. Public consumption 14. Investment 15. CO2 emissions 16. Balance of payment (market clearance for foreign exchange)

# **B.3 Income-expenditure balance**

17. Income balance of representative agent (household)

## **Table 5:** Sets and indexes

_i, j_ Indexes for sectors and goods _EG_ All energy goods: Coal, crude oil, natural gas, refined oil, and electricity _FF_ Primary fossil fuels: Coal, crude oil, natural gas 46
Table 1: Loan Characteristics by lender

||Obs.|_L_|_σ_(_L_)|Maturity|_r_|_rc_|Collat.|Z. Int.|
|---|---|---|---|---|---|---|---|---|
|_Com. Bank_|118|196|246|54|0.2208|0.2326|83.1|5.1|
|_BAAC_|1,293|41|80|20|0.2232|0.2239|29.4|0.3|
|Individual|380|75|130|30|0.1273|0.1273|100.0|0.0|
|Group|913|27|37|16|0.2631|0.2643|0.0|0.5|
|_Ag. Coop_|353|43|69|18|0.1373|0.1385|36.3|0.9|
|_Vil. Inst._|174|47|103|32|0.1036|0.1639|9.2|36.8|
|_Informal P_|553|51|157|21|0.4203|0.5176|20.1|18.8|
|_Informal R_|820|20|44|17|0.2736|0.5499|4.9|50.2|
|**Formal**|**1,928**|**51**|**106**|**23**|**0.1948**|**0.2041**|**31.9**|**4.6**|
|**Informal**|**1,373**|**33**|**106**|**18**|**0.3327**|**0.533**|**11.0**|**37.6**|

Note: Each loan is counted as an observation. Com. Bank includes Finance and Insurance Companies. Village-level Institutions include loans from Village Funds, Rice Banks, Buffalo Banks and Production and Credit Groups. “Informal P” includes Moneylenders, Store Owners, Landlords and traders. “Informal R” includes friends and relatives. Column _L_ reports the average loan size and column _σ_ ( _L_ ) its standard deviation in 1,000 Baht. The figures in both columns are in Baht. The length of the loan is in months. The interest rates _r_ and _rc_ are net and yearly compounded. Column “Collat.” reports the percentage of loans that required collateral. Finally, column “Z. Int.” reports the percentage of loans given interest-free. Source: Townsend-Thai data.

Table 2 reports the variables by source of credit that will be used in the estimation. From the sample of 2,612 households, 34 percent of the sample self-finance, 36 percent borrow from a formal institution, 17 percent borrow from an informal lender only and 13 percent of the sample borrow from both a formal and an informal lender. These numbers are large if compared to those of Aryeetey (1997) where only 16 percent of all households interviewed in the Ghana Living Standards Measurement Survey reported borrowing from the formal sector.

Observed loan size _k − b_ is large for clients of a formal institution that requires collateral and for those households who resort to both a formal and informal lender. This fits well with the prediction of the model that institutions with higher fixed costs should cater to households with higher financing requirements. A test of equal mean capital requirements across lending choices that is easily rejected by the data.

Those who borrow from a formal institution are also wealthier than those who borrow from an informal source or both sources. Those who self-finance are, on average, 16
state of Indian businesses and government agency monitoring of the Indian economy. The typical survey collects data from over 150,000 Indian establishments. In this respect, the surveys are comparable to the Annual Survey of Manufacturing conducted in the United States, with the Indian sampling frame being about three times larger.

Establishments are surveyed with state and four-digit National Industry Classification (NIC) stratification. The surveys provide sample weights that we use to construct populationlevel estimates of employment by district. Districts are administrative subdivisions of Indian states or territories that provide meaningful local economic conditions and policy choices. The average district size is roughly twice the size of a U.S. county (average size of ~5,500 square kilometers) and there is substantial variability in district size (standard deviation of ~5,500 square kilometers). Indian districts can be effectively considered as self-contained labor markets and, to some degree, economic units.

Our surveys record economic characteristics of plants like employment, output, and raw materials. Our analysis considers aggregated measures of manufacturing employment in locations and industries (e.g., state-industry). For this purpose, we sum the activity of plants up to the indicated level, combining the organized and unorganized sectors and using sample weights to prepare population estimates.

Much of our analysis considers tradable vs. non-tradable industries. We define tradable industries following Kothari (2014). Using data from the 2005/06 ASI and the Department of Commerce, Kothari calculates for each three-digit NIC industry the ratio of exports plus imports to gross production in that industry. Industries above the median index value are labeled tradable, and industries below the median index value are labeled non-tradable. We favor this definition of tradability over one that varies with each year of our cross-section due to the fact that tradability should relate more to the nature of the good than whether or not the good is traded in a specific time-period. For instance, in 1989, there were many policies and distortions in place that made it difficult to import/export goods that ideally would have been traded. Over time, these distortions have been lifted, so defining tradability using data from a recent year speaks more to the nature of the good itself than of the policy situation. Appendix Table 1 provides a list of three-digit NIC industries and their assignment. Appendix Table 2 describes the sizes of industries in formal and informal sectors in 2000.

6
# **TABLE AI0: THE COMPOSITION OF DOMESTIC DEMAND**

DOMEST : DOMESTIC SUPPLY TO DOMESTIC MARKET -- BILLIONS OF TL (1990) IMPORT : IMPORT SUPPLY TO DOMESTIC MARKET -- BILLIONS OF TL (1990) INTERMX : INTERMEDIATE DEMAND -- X OF TOTAL D HOUSEX: DOMESTIC HOUSEHOLD DEMAND -- % OF TOTAL D GOVTX GOVERNMENT DENAND -- % OF TOTAL INVESTX INVESTMENT DEMAND -- X OF TOTAL STOCKX STOCK CHANGE -- X OF TOTAL

||DOMEST|<br>IMPORT|<br>INTERM%|D HOUSE%|D_GOVTX|<br>INVEST%|<br>STOCK%|
|---|---|---|---|---|---|---|---|
|AGR|86.5|2.6|41.3|56.5|0.4|1.9|1.8|
|FRS|3.4|0.5|78.3|25.9|4.7|-8.9|-8.9|
|FIS<br>|2.5|0.0|9.4|90.9||-0.3|-0.3|
|COL<br>|2.8|0.8|58.5|32.1|8.0|1.4|1.4|
|CRU|1.9|9.9|95.1|||4.9|4.9|
|<br>MIN|<br>0.2|<br>0.2|<br>102.7|||<br>-2.7|-2.7|
|STO|2.5|0.3|72.4|||27.6|27.6|
|MEA|3.0|0.5|31.2|52.0|15.9|0.9|0.9|
|CAN|05|01|441|397|181|-19|-19|
|<br>VEG|.<br>3.5|.<br>0.8|.<br>44.4|.<br>61.5|.<br>1.2|.<br>-7.1|.<br>-7.1|
|<br>GRN|<br>5.9|<br>0.2|<br>52.5|<br>56.3|<br>1.0|<br>-9.8|-9.8|
|SUG|2.8|0.9|30.5|59.1|1.2|9.3|9.3|
|OFP|15.6|0.8|30.4|65.0|1.8|2.7|2.7|
|ALC|2.4|0.2|22.5|58.1||19.4|19.4|
|BEV|1.4|0.0|32.5|46.2|0.5|20.8|20.8|
|TOB|5.8|1.9|1.1|85.1||13.8|13.8|
|TXT|20.9|2.4|56.6|41.4|0.3|1.7|1.3|
|WAP|5.3|0.6|3.0|89.8|6.9|0.3|0.1|
|LEA|1.9|0.5|78.6|13.5|0.0|7.9|7.9|
|SHO|1.6|0.1|5.7|73.2|10.7|10.4|10.4|
|Woo|8.1|0.2|74.0|27.8|0.2|-2.0|-2.0|
|FRN|2.2|0.1|39.2|48.0||12.8|0.8|
|PAP|4.8|1.0|79.6|15.7|2.3|2.4|2.4|
|PRN|3.2|0.1|24.1|71.0|0.9|4.1|4.1|
|FRT|1.8|0.8|100.2|||-0.2|-0.2|
|DRG|3.6|0.9|34.8|63.7|1.6|-0.2|-0.2|
|OCP|11.2|8.8|60.8|40.1|0.5|-1.4|-1.4|
|<br>REF|<br>21.6|<br>3.4|<br>88.5|<br>21.8|<br>5.5|<br>-15.8|-15.8|
|PET|2.1|0.2|91.2|31.3|5.3|-27.8|-27.8|
|RBR|4.8|0.6|65.3|34.6|0.7|-0.5|-0.5|
|PLA|2.7|0.4|58.0|41.4|0.3|0.3|0.3|
|GLA|1.9|0.2|45.6|55.4|1.7|-2.7|-2.7|
|<br>CEM|<br>4.5|<br>0.1|<br>100.2||0.6|<br>-0.8|-0.8|
|NMM|3.5|0.6|94.2|14.0|1.0|-9.3|-9.3|
|IRO<br>|12.8<br>|4.9<br>|98.3<br>||0.1<br>|1.6<br>|1.6<br>|
|NFM|4.1|5.4|840||00|160|160|
|<br>FAB|<br>9.2|<br>1.4|.<br>36.7|50.0|.<br>0.7|.<br>12.6|.<br>3.1|
|MAC|7.0|10.6|14.6|19.2|3.3|62.9|2.1|
|AGN|1.9|0.2|31.5|||68.5|10.7|
|ELM|8.5|5.6|39.5|27.3|1.4|31.7|9.1|
|SHP|0.3|0.6|28.9|||71.1|9.5|
|RRE|0.3|0.1|30.3|||69.7|-33.9|
|VEH|12.0|5.4|37.2|21.3|0.2|41.3|10.1|
|OTE|0.1|0.9|9.3|||90.7|-2.6|
|_OMN_|5.1|2.5|12.6|58.2|1.3|27.9|4.8|
|ELE|10.6|0.0|73.8|12.0|3.8|10.4|10.4|
|GAS|2.4|0.0|51.7|33.9|14.2|0.2|0.2|
|CON|57.0||||0.4|99.6|-0.9|
|TRD|67.4||38.6|47.7|1.6|12.1|3.7|
|RES|17.6|1.2|7.4|90.3|1.5|0.8|0.8|
|TRN|63.6|2.1|38.6|54.7|1.7|5.0|2.6|
|COM<br>|6.8<br>|0.1<br>|53.1<br>|31.2|6.6|9.1|9.1|
|FIN|160|00|842|30|40|88|88|
|<br>SRV|.<br>65.8|.<br>0.7|.<br>20.4|.<br>33.2|.<br>49.6|.<br>-3.2|.<br>-3.2|

A-23
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weights. A zero weight is of course equivalent to omission of that observation from the sample. An iterative procedure is required because the weights affect the estimated coefficients and therefore the residuals on which the weighting formula is based. Table 3 shows the results of reestimating the regressions in table 1 by robust regression.

**Table 2. A Breakdown of Expenditures**

|Dependent variable:|Consumption<br>expenditure/GDP (%)|Capital<br>expenditure/GDP (%)|Wage<br>expenditure/GDP (%)|
|---|---|---|---|
|Constant|-7.21***<br>(-4.75)|6.17***<br>(8.05)|0.749<br>(1.32)|
|Dummy fuel > 25%|2.87***|2.37***|0.387*|
|exports|(5.47)|(7.21)|(1.78)|
|Dummy ores and<br>metals > 10%<br>exports|-1.50***<br>(-2.91)|0.896***<br>(3.51)|-0.312<br>(-1.49)|
|Hard peg dummy|-4.46***|0.597*|-1.00***|
||(-8.82)|(1.78)|(-6.70)|
|Political stability|0.452***|-0.0266**|0.0977***|
|(0 to 100)|(19.15)|(-2.37)|(11.45)|
|Time trend|0.274***|0.0273|0.0280|
|(=0 in 2000)|(5.61)|(1.17)|(1.37)|
|n|1,286|1,232|1,128|
|R-squared|0.279|0.080|0.102|
|RMSE|7.73|3.90|3.05|

_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries. RMSE = root mean square error, or sample standard deviation.

8
**Table 7: Mean Hourly Labor Earnings (Constant 1994 pesos)**

|**Type of Occupation**|**Mean**<br>**Labor E**<br>**(Main O**|**Hourly**<br>**arnings**<br>**ccup Hrs) **|**Mean Ad**<br>**Labor E**<br> **(Main O**|**.**<sup>**1/ **</sup>**Hourly**<br>**arnings**<br>**ccup Hrs)**|**Mean**<br>**Labor E**<br>**(Tota**|**Hourly**<br>**arnings**<br>**l Hrs)**|**Mean Ad**<br>**Labor E**<br>**(Tota**|**.**<sup>**1/ **</sup>**Hourly**<br>**arnings**<br>**l Hrs)**|
|---|---|---|---|---|---|---|---|---|
||**Urban**|**Rural**|**Urban**|**Rural**|**Urban**|**Rural**|**Urban**|**Rural**|
|Primary Teacher in Pub. School|12.5|13.8|15.8|17.5|11.7|12.0|12.6|16.3|
|Primary Teacher in Priv. School|9.5||12.0||9.2||17.4||
|Lower Secondary Teacher in Pub. School|13.3|14.4|16.9|18.3|12.2|13.6|16.6|14.6|
|Lower Secondary Teacher in Priv. School|10.0||12.7||9.7||17.0||
|Upper Secondary Teacher in Pub. School|15.9|11.3|20.1|14.4|14.4|11.3|15.8||
|Upper Secondary Teacher in Priv. School|12.4||15.7||9.3||8.6||
|University Teacher in Pub. School|15.6||19.8||14.4||12.7||
|University Teacher in Priv. School|29.8||37.8||21.8||29.0||
|**The mixed-skilled group**|**4.4**|**1.8**|**4.7**|**2.0**|**4.2**|**1.7**|**4.0**|**1.0**|
|The agricultural group|1.2|0.6|1.3|0.6|1.1|0.5|0.8|0.4|
|The low-skilledgroup|2.4|1.5|2.5|1.6|2.2|1.3|1.8|0.8|

Source: Own calculations based on ENIGH96 survey 1/ Following OECD, it is assumed that teachers work on average 41 weeks per year and non teachers work on an average of 49 weeks per year.

As shown in Table 8, real monthly labor earnings have increased substantially for primary public school teachers. Such earnings almost doubled from 1988 to 1994. In real terms, the teachers' salary increase was significantly above the increase obtained by other groups. _The mixed-skilled_ group has lost purchasing power; losses have been even more severe for people employed in _the agricultural_ group and _the low-skilled_ group. There is considerable variation in teacher’s labor earnings but significantly less than variation in earnings from other occupations. Basic public school teachers clearly earn higher earnings than _the agricultural_ group, _the low skilled group_ and _the mixed-skilled_ group. In 1988, moreover, teachers were underpaid with respect to other _Professionals with an University degree in Education not teaching_ ; after 1994 this trend changed. In fact, _University Teachers in Public School_ were earning a higher salary.

14
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