rafmacalaba/datause-encoder-data huggingface dataset:
Indonesia: Afkar, Rythia, Jon Jellema, and Matthew Wai-Poi. 2015. “CEQ Master Workbook: Indonesia. Version: February 26, 2015,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University and the World Bank).

Inchauste, Gabriela and Ivica Rubil. 2017. “The Distributional Impact of Taxes and Social Spending in Croatia”. Mimeo.

Iran: Enami, Ali, Nora Lustig and Alireza Taqdiri. 2017. “CEQ Master Workbook: Iran. Version: May 5, 2017,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University and Economic Research Forum).

Jordan: Abdel-Halim, Morad, Shamma A. Alam, Yusuf Mansur, Umar Serajuddin and Paolo Verme. 2016. “CEQ Master Workbook: Jordan. Version: March 8, 2016,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University and the World Bank).

Montenegro: Younger, Stephen and Dajana Draganic (2017). “Fiscal Incidence in Montenegro.” World Bank: Washington DC.

Mexico: Scott, John. 2013. “CEQ Master Workbook: Mexico. Version: September 2, 2013,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University).

Nicaragua: Cabrera, Maynor and Hilcias E. Moran. 2015. “CEQ Master Workbook: Nicaragua. Version: October 14, 2015,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University, Instituto Centroamericano de Estudios Fiscales (Icefi) and International Fund for Agricultural Development (IFAD).

Paraguay: Galeano, Juan Jose, Maria A. Lugo, Lea Gimenez, Carolina Paredes, Flavia Sacco, Miguel A. Vega and Fatima Franco. 2017. “CEQ Master Workbook: Paraguay. Version: 2017,” CEQ Data Center (CEQ Institute, Tulane University).

Peru: Jaramillo, Miguel. 2015. “CEQ Master Workbook: Peru. Version: August 7, 2015,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University).

Russia: Lopez-Calva Luis F., Nora Lustig, Mikhail Matytsin, and Daria Popova (2017). “Who Benefits From Fiscal Redistribution In The Russian Federation?” Working Paper 39, CEQ Institute, Tulane University.

South Africa: Inchauste, Gabriela, Nora Lustig, Mashekwa Maboshe, Catriona Purfield, Ingrid Woolard and Precious Zikhali. 2016. “CEQ Master Workbook: South Africa. Version: March 6, 2016,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University and the World Bank).

Sri Lanka: Arunatilake, Nisha, Gabriela Inchauste and Nora Lustig. 2016. “CEQ Master Workbook: Sri Lanka. Version: March 10, 2016,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University).

Tanzania: Younger, Stephen, Flora Myamba and Kenneth Mdadila. 2016. “CEQ Master Workbook: Tanzania. Version: June 1, 2016,” CEQ Data Center on Fiscal Redistribution (CEQ Institute, Tulane University).

35
**<u>Table 6: The Kitagawa-Oaxaca-Blinder Decomposition of Harvest Value and Crop Sales</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|||Harvest Value|||Crop Sales||
|VARIABLES|Overall|Endow.|Coeff.|Overall|Endow.|Coeff.|
|Male|11.981***|||11.778***|||
||(0.019)|||(0.027)|||
|Female|11.047***|||10.816***|||
||(0.026)|||(0.046)|||
|Raw Difference|0.934***|||0.962***|||
||(0.032)|||(0.054)|||
|Difference due to|0.526***|||0.587***|||
|Endowments|(0.031)|||(0.049)|||
|Difference due to|0.408***|||0.375***|||
|Coefficients|(0.040)|||(0.064)|||
|Individual Age||0.015|-0.415||0.077*|-1.471|
|||(0.030)|(0.671)||(0.042)|(1.067)|
|Individual Age Squared||-0.018|0.069||-0.087**|0.763|
|||(0.027)|(0.341)||(0.039)|(0.543)|
|Number of Adults in HH||0.022***|0.148***||0.024***|0.099|
|||(0.006)|(0.050)||(0.006)|(0.081)|
|Number of Children Aged 0-5||0.019***|-0.032||0.004|0.068|
|In HH||(0.004)|(0.030)||(0.003)|(0.048)|
|Individual owns a Financial||0.010|0.000||0.032**|-0.067***|
|Account||(0.008)|(0.011)||(0.014)|(0.015)|
|Individual is Married||0.007|0.356*||0.031*|0.836***|
|||(0.016)|(0.204)||(0.017)|(0.226)|
|Individual is Divorced/Married||-0.008|0.090*||-0.002|0.159***|
|/Separated||(0.022)|(0.050)||(0.024)|(0.045)|
|Individual Completed||-0.000|-0.003*||0.002*|-0.001|
|Secondary Schooling||(0.001)|(0.002)||(0.001)|(0.001)|
|Individual has a Vehicle||0.011|0.050||-0.035|-0.137|
|(bicycle, motorcycle, car)||(0.016)|(0.053)||(0.025)|(0.087)|
|Household owns Mechanized||0.052***|0.029||0.127***|0.014|
|Agriculture Equipment||(0.007)|(0.022)||(0.014)|(0.046)|
|Area of Plot of Land||0.003|0.006||0.013|0.056*|
|||(0.003)|(0.012)||(0.013)|(0.029)|
|Area of Plot of Land Squared||-0.001|-0.002||0.004|-0.021|
|||(0.002)|(0.003)||(0.012)|(0.019)|
|Land Distance from House||0.000|-0.100***||-0.000|-0.073**|
|||(0.000)|(0.016)||(0.000)|(0.028)|
|Crop Grown for Export||0.161***|-0.003||0.106***|-0.005|
|||(0.009)|(0.003)||(0.011)|(0.005)|
|Manager of Farm is the Owner||-0.003|0.019||-0.019|-0.018|
|||(0.010)|(0.028)||(0.015)|(0.046)|

41
_-4-_

# B. **Independent Variables**

There are three sets of explanatory variables used in this study. They reflect the model's implications, implications from the previous empirical work, and basic intuition regarding factors affecting infrastructure spending. In a nutshell, we postulate that infrastructure spending per capita depends on factors related to the domestic economy, external financial assistance and the government's preferences, as revealed by various spending policies. Thus, we organized the three sets of variables as ones that define:

* Characteristics of the economy * Level and mix of external funding * Government objectives

## _1. Characteristics of the Economy_

There are five variables, implied by the structure of the underlying theoretical model, which define the characteristics of the domestic economy. These are: (i) the existing stock of infrastructure, (ii) population density, (iii) urbanization, (iv) urban-rural balance, and (v) labor force participation rate.

1.1. **Existing Stock of Infrastructure:** Two opposing forces dictate the influence of the existing stock of infrastructure on current expenditures on infrastructure. First, it is hypothesized that there are diminishing returns to infrastructure expenditures such that, _ceteris paribus,_ countries with high stocks of infrastructure are expected to derive less benefit from additional expenditures on infrastructure and accordingly spend less. Second, the greater the stock of infrastructure, the greater the expenditure required to offset depreciation of the existing stock. Further, previously high levels of infrastructure expenditure may reflect a greater degree of complementarity between infrastructure and the existing productive structure of the economy, dictating higher optimal levels of current infrastructure expenditure. Overall then, the direction of influence of the existing stock of infrastructure is uncertain and reflects the relative strength of the opposing forces.

Sensitivity analysis is performed to determine the sensitivity of the results to alternative measures of the stock of infrastructure. Two different classes of variables are used to measure the existing stock of infrastructure. The first is the total kilometers of roads plus railways relative to GDP per capita in constant 1980 US dollars (FCINFGDP) or per thousand square kilometers land area (FCINFPA) or per person (FCINFPP). The data on kilometers of roads and railways are those compiled by Canning and Fay and used as the basis for their 1993 paper. The Canning and Fay data are available for five year intervals. Linear and nonlinear methods were used to interpolate the data across five-year intervals. The data source for kilometers of land area and population is the _World Development Report 1991: Supplementary Data_ (World Bank, 1991). The second class of variable is an estimate of the total capital stock (public plus private) constructed using the perpetual inventory
The SWIFT methodology has been tested extensively, not only using _within-sample tests_ —that is, testing how well it predicts poverty using the same data on which the model is trained—but also using _betweensample tests—_ that is, using two rounds of comparable household surveys with complete consumption data and using one round for model training and another round for model testing. Among other countries, the methodology was tested using data from Afghanistan in its development stages. The SWIFT-plus model, trained on NRVA 2011-12 data, predicted a poverty rate of 53.5 percent in 2016-17, outperforming traditional Proxy Means Testing (PMT) approaches (see Table 1). To date, the methodology has been applied in more than 75 countries, where SWIFT-plus predictions are proven to be within +/- 2 percentage points of those from actual consumption data, even when large changes in the poverty rate have occurred. The results are available in the annex A.

**Table 1: Past implementation of SWIFT-plus in Afghanistan to predict poverty changes, 2011 to 2016**

|||Afghanistan(2011 - 2016)|
|---|---|---|
||Official Estimates|Original PMT<br>SWIFT Plus|
|2011|38.3%||
|2016|54.5%|39.4%<br>53.5%|

**Note:** _PMT estimates exclude fast-changing consumption dummies._ **Source:** _Modified from_ Yoshida et al (2022).

# Model training in the IE-LFS 2019/20

The SWIFT-plus identifies variables that best predict household expenditure. Candidate variables include household characteristics, education levels, asset ownership, dwelling conditions, and fast-changing consumption variables. The SWIFT-plus approach relies on a regression model to estimate a welfaregenerating function on the data where both a welfare vector and household characteristics are available. In contrast with traditional PMT approaches, it implements several steps to control for the correlation and stability of the variables used to predict welfare, cross-validation of the variable selection process to account for potential model overfitting, and multiple imputations to account for the model’s error (see annex B for details on the SWIFT-plus algorithm). A key innovation of the SWIFT-plus approach compared to traditional PMT is that it includes fast-changing consumption variables to better capture welfare changes during shocks. This is important because evidence shows that changes in household assets and dwelling conditions lag changes in household expenditure. Table 2 shows the full list of variables we used to train the SWIFT-plus module.<sup>5,6</sup>

> 5 Not all the variables were included as part of the SWIFT-plus module. For instance, household composition variables were already part of previous AWMS rounds. Additionally, models from the IE-LFS 2019/20 and 2021 differ. 6 Questions on shocks were excluded from the training steps since they referred to the past year and not only to the current quarter used in the prediction. Additionally, employment variables were excluded as evidence suggested that employment status was not a good proxy for household generating capacity. Robustness tests show that excluding these variables has no impact on model performance.

6
TABLE VI Intent-To-Treat Effects on Student Test Scores, by Target Status of Teachers

||(1)<br>_Y_2|(2)<br>_Y_3|
|---|---|---|
|_Conditional treatment efect for students with:_|||
|Target teacher in _Y_2|-0.004<br>(0.026)||
|Non-target teacher in _Y_2|-0.001<br>(0.030)||
|Target in _Y_2 and _Y_3||-0.023<br>(0.033)|
|Target in _Y_2 and non-target in _Y_3||0.055<br>(0.041)|
|Non-target in _Y_2 and target in _Y_3||0.020<br>(0.039)|
|Non-target in _Y_2 and _Y_3||0.029<br>(0.036)|
|_no match between student and teacher_|-0.092|-0.045|
||(0.056)|(0.049)|
|_H_0 : causal parameters are the same (_p_-value)|0.91|0.26|
|_H_0 : causal parameters are the same and equal to zero (_p_-value)|0.99|0.37|
|Total student-subject observations|279,066|274,993|

_∗ ∗∗ ∗∗∗ Notes: p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01. The table reports intent-to-treat effects on student-level test scores, conditional on the target-status of teachers. The regression model used is the same as the regression model used for the table (V) results, except that the dummy variable indicating a treatment school is interacted with a variable measuring the target status of a student’s teacher. For _Y_ 2 outcome data (column (1)) we present causal parameters for students with target teachers in _Y_ 2, and for students who do not have target teachers in _Y_ 2. For _Y_ 3 outcome data (column (2))there are four categories, as indicated. For a minority of students we could not match students to teachers (for example when teachers were absent during the field visit). For column (1) results, about 5.6% of the student-test-level observations were not matched to teachers in _Y_ 2. For column (2) results, about 12.2% of the student-level observations were not matched to teachers in both _Y_ 2 and _Y_ 3. The treatment effects for the subgroup of students that are not matched to teachers are not statistically significant, suggesting that the inability to match students to teachers does not cause biases. School-level clustered standard errors reported in parentheses.
today accounts for some 5 billion people. In 1870, the gap between the richest countries (Australia and Great Britain) and the poorest (Nepal and Ghana) was 8 to 1; in 2007, it is 31 to 1 (United States and Norway vs. Nepal, North Korea and Ghana).

Moreover, the very logic of widening income disparities alluded to before shows that the effect of the number of countries cannot be decisive. Because the logic is that some people, living in specific geographical locations, earn today approximately the same—very low--level of income as their ancestors did more than a century and a half ago, while other people, at other locations, live at income levels that are manifold greater than in the past and some 100 times above the subsistence. Thus, the ratio between top and bottom must rise, almost regardless of how we decide to ―slice‖ the population of the world (that is, in how many countries they live).<sup>15</sup> We obtain the same conclusion if we compare real wages in rich and poor countries. Table 1 shows wages for three occupations in five selected countries (or rather their major cities): two rich (London and New York) and three poor (Beijing, Delhi and Nairobi). The data refer to after-tax nominal wages which are then deflated by the food cost of living in the same cities to obtain real food wages. The advantage of these data, collected for 74 cities and 14 occupations and published by Union de Banques Suisses, UBS (2009), is their almost full comparability. In effect, the wage data refer to net wages earned by the same, very narrowly defined, professions (see Notes to Table 1); they are adjusted for the effective number of hours worked, and are geographically limited to large cities for which UBS publication also calculates a food cost of living index.

In Table 1, I have selected three occupations with increasing level of skills: building laborer, skilled industrial worker, and engineer. The real wage gaps are the greatest in the case of unskilled workers: for them, the ratio of real wages in rich and poor countries is almost 11 to 1.

> 15 The caveat ―almost‖ is necessary  because in the extreme case, when the world population were divided into (say) two countries and in such a way that each half would contain the same proportion  of locations that have not seen any progress since 1850 and locations that are today much richer, the gap between these two halves need not be greater, and may be smaller,  than it was in 1850. But obviously,  this is an extreme case which has nothing in common with the real processes of state-formation and real state of affairs  both now and in  the past.

13
Table 2.1. Base Year SAM for the Two-Sector Model

||Export D|omestic|Final|Intermediate Labor|Capital|Rural Urban|World|Total|
|---|---|---|---|---|---|---|---|---|
|Export|||||||30.00|30.00|
|Domestic|||73.00|2.00||||75.00|
|Final||||||40.00 60.00||100.00|
|Intermediate|5.00|||||||5.00|
|Labor|20.00|30.00||||||50.00|
|Capital|5.00|45.00||||||50.00|
|Rural Household||||35.00|5.00|||40.00|
|Urban Household||||15.00|45.00|||60.00|
|World|||27.00|3.00||||30.00|
|Total|30.00|75.00|100.00|5.00 50.00|50.00|40.00 60.00|30.00||

Source: Adapted from Devarajan, Lewis and Robinson (1990) To make the general equilibrium model fully computable, we must find numerical estimates for the structural parameters characterizing technology, the distribution of income among households and import aggregation functions. When these parameters cannot be estimated econometrically, one can resort to model _calibration_ . This process solves relevant equations for values of the parameters that are consistent with base year data. For the parameters of the Cobb-Douglas production functions we have the following expressions: The parameters of the Armington function are given by the following: Based on the above expression and the base data presented in SAM (table 2.1) the calibration process led to the results presented in table 2.2. This information reveals that the export sector is labor intensive relative to the domestic sector. The latter is more capital intensive than the former. Thus this stylized model may be thought of as representing a developing country exporting a primary agricultural commodity produced 10
2. Coverage differs by subject: While math test scores are already available for a set of countries in the mid-1960s (by the IEA assessment First International Math Study (FIMS)), reading and science results are not available until the 1970s (First International Science Study (SISS) and First International Reading Study (FIRS)).

3. We have also different coverage by grade: Surveys that have assessed students in primary school are much scarcer than assessments carried out in secondary school. There is, for example, no primary math score for any country before the year 1995 (from TIMSS). Similarly, the first reading score for primary school students only becomes available in 1990 (from the Second International Reading Study (SIRS).

4. There exist many gaps by subject. While the first reading assessment took place during the early 1970s (FIRS), there is a 20-year vacancy until 1990 when the Second international Reading Study (SIRS) was conducted. In math, there is also a fifteen-year gap between the 1965 scores and the 1980 results.

5. Coverage by country and world regions differs considerably. In fact, African and Latin American countries did not participate in any surveys until the 1990s, when regional tests such as SACMEQ, PASEC and LLECE were first setup. Some pre-1990 test score estimates exist for outlier countries (for example FIRS scores in Chile and Malawi from 1970 or SIMS scores in Swaziland and Nigeria from 1980), but no broad coverage exists that could facilitate intra-regional comparisons or averaging of scores.

The facts described above can be studied in detail when looking at the graphs provided in the Annex. We provide coverage by grade level (for primary school, see Figures A1-A5; for secondary school see Figures B1-B5) for every country that has participated in any test from 1965 until 2010<sup>1</sup> . We also show coverage if scores are averaged over different grade levels (see Tables C1-C5).

The World Maps in Figure 1 and Figure 2 deliver an even more stylized overview. They show countries by whether they are part of our database or not, i.e. have at least one adjusted test score at any point in time in any subject at any grade or no test score at all (Figure 1). Moreover,

> 1 Coverage by subject is available on request. The coverage between 2003 and 2010 (stemming from countries’ participation in PISA 2009, PISA 2006, TIMMS 2007, PIRLS 2006 and SERCE 2006) is reported in the graphs. The adjusted test scores of the Altinok and Murseli (2007) database, however, only covers the period until 2003 (PISA and TIMSS 2003 are the most recent assessment integrated in their overview).

14"
data are from 2009, a year in which the data include 930 institutions with a combined 80.1 million borrowers.

The largest sample we use contains data on 1,335 institutions: 90 for-profit banks, 235 credit unions and cooperatives, 465 NGOs, 401 non-bank financial institutions (NBFIs), and 102 rural banks. Non-bank financial institutions are a broad range of institutions that generally span the space between NGOs and banks, and we divide the sample between institutions with forprofit legal status (300 institutions) and those with not-for-profit status (101 institutions). In addition, we analyze two aggregate categories defined by the MIX Market: 826 institutions with not-for-profit legal status, and 499 institutions with for-profit legal status.<sup>9</sup> The key relationships are analyzed by comparing means and distributional parameters of subgroups within the sample. A series of LOWESS (non-parametric smoothed) bivariate regressions describe the distributions of the data, and multivariate regressions are used to control for relevant covariates.

A major focus is how key variables like profit, cost, interest rates, and subsidy vary with the average loan size of microfinance institutions. The average loan size variable is a proxy for the income level of customers, drawing on evidence that poorer customers tend to take smaller loans. The variable is measured at the institution-level and is an average of loan sizes that could vary broadly within the institution. To control for different levels of income and development across regions, we normalize the average loan size variable by dividing it by the country’s GNI (gross national income) per capita, measured at the 20<sup>th</sup> percentile. The step of dividing by GNI per capita is relatively standard, but it creates a potential distortion in countries in which there is

> 9 Fourteen institutions were dropped: one “bank” with not-for-profit status and 13 rural banks with notfor-profit status. Because all variables are not available for all institutions, sample sizes vary for some analyses. We have repeated the analysis in a balanced panel of 814 institutions and find very similar results to those reported here.

9
# **Figure 3.   Demand for Representative Business Service** **_s_ (Dixit-Stiglitz) Sectors in Regional Market (RM) m**

<!-- Start of picture text --> Business Service s<br>CES σ =1, 5<br>Rest of World<br>Own Regional Market Services<br>Cross Border Services<br>CES σ =3 = esub<br>Multinational Service Russian Service<br>Providers in RM m Providers in RM m<br>CES σ =3 = esub CES σ =3 = esub<br>Multinational firms in RMm Russian firms from RMm<br><!-- End of picture text --> 77
rNF _8kAL_ fXIHAN( RAIL AN14 Thf, _'tF_ t1 LFMPLt'Y:Nt (..rra1tlon c*ut;. * -0.50) Ri1ta ( litlOax <!-- Start of picture text --> 1 US1 80/1<br>dO/2<br>0 90 61<br>d8/2<br>* 4 230 ~~~d4/1<br>82 2 _<br>79 -- 84~~1 - 84/2 88/1 8 S/2<br>d3/1 85t<br>67/1 87/2. 7t 2<br>* 60 88/1<br>8812<br>40 45 5 6 5.5 6.0 6.5 7.8 7.5 8.6<br>Rate of Unmploy.ent<br>THE RSAL INTEREST RATE AND ThLE RATE of UNEMPLOYMFNT<br>(rjrreojctcln (00ff,. .35)<br>Real Interest<br>Rate (t 9onth1y)<br>67/1 87/2 A 86/1 88/2<br>88/1 , 86/2<br>5<br>d;)/l dl/1 81/2 , 85/2<br>63/1 82/1 82/2 10/2<br>85/1<br>f 63/2<br>- 5 j 684/1<br>84/2<br>4 8 4 5 5.O 5.5 6. 0 6.5 7 8 7.5 e.8<br>Pate of Inemplovrent<br>THE REAL WAGE AND THE RATE OF UltPWLOYp4EiT<br>(CorrelatLon Coeff. * 0.22)<br><!-- End of picture text --> <!-- Start of picture text --> Real ..age<br>(Index)<br>1 .48 -<br>88/1 ddi2<br>1. 30 *.87/2<br>03/1 87/1 84/2 .. AA<br>1 ZO2S3/2 85/1 _ , 86/2<br>1. 8 8 881/1 4/1 ,* - - .86/1<br>82/1 82'/2 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ .8/<br>_.18J 80/2<br>I OO- 80/1 d/<br>60/1 ~~~~~~~~~~~~~~~~~~~~~.85/2<br>4 8 4 5 5 .0 5.5 6 O 5 S 7 8 7.5 8.0<br>lote See note to Fltura 10.<br><!-- End of picture text -->