rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 1**

# **The main variables during 1997-2010 for the whole sample**

<!-- Start of picture text --> 7.00% Gross Savings 10% Gross Capital Formation<br>6.50% 9%<br>6.00% 8%<br>5.50% 7%<br>5.00% 6%<br>4.50% 5%<br>4%<br>4.00%<br>3%<br>3.50%<br>2%<br>3.00%<br>1%<br>2.50%<br>0%<br>2.00%<br>-1%<br>4% Excess Savings 25% Non-Operating Assets<br>3%<br>20%<br>2%<br>15%<br>1%<br>0% 10%<br>-1%<br>5%<br>-2%<br>0%<br>-3%<br>Leverage Liquidity Holdings<br>75% 16%<br>15%<br>70%<br>14%<br>65%<br>13%<br>60%<br>12%<br>55%<br>11%<br>50% 10%<br>45% 9%<br>40% 8%<br>Median<br>Asset-Weighted Average<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Excess Savings, Gross Savings, Gross Capital Formation, Non-Operating Assets, Leverage, and Liquidity Holdings, during the period 1997-2010, according to median and asset-weighted average, for the sample of manufacturing firms from France, Germany, Italy, Japan and United Kingdom.
# **Annex A2. Validation exercise: synthetic panel methodology of Dang et al (2011)**

# **The methodology of “synthetic panel” is tested and validated using panel data for Moldova for the**

**periods.** The analysis creates two different subsamples: subsample 0 is used as a reference point whereas results from the synthetic panel model build only on subsample 1. Estimated bounds generated in the synthetic panel exercise from subpanel 1 are compared to the true transitions observed from the descriptive statistics of subsample 0.

**The basic consumption model uses the welfare aggregate as dependent variable.** The household consumption aggregate, consistent with the regional standardized aggregate for ECA, excludes rent, health and durables. The poverty line is fixed to 2.5 PPP USD.

**The set of independent variables only includes time- invariant characteristics regarding the household and the household head.** The test uses four different consumption models to test for sensitivity and performance. This way results illustrate how the explanatory power and especially regional characteristics have an impact on the estimated bounds from the synthetic panel exercise. The following variables are included in each model:

- **Model 1:** Dummy for female headed households, birth cohort dummies from cohort 1945-1950 to cohort 1970-1975, education categories and dummy for rural areas.

- **Model 2:** in addition to variables in model 1, eleven different region dummies which capture _any_ heterogeneity on the regional level.

- **Model 3:** in addition to variables in model 1, this specification includes detailed information calculated on the region level, such as the mean consumption level, the standard deviation of consumption, the share of HH with at least secondary education, the employment rate, old-age and young-age dependency ratios and an interaction term between education in the region and educational outcomes for the household head (all calculated on the region level using all individuals in the sample).

- **Model 4:** in addition to variables in model 1, this specification includes information on the number of children (defined in terms of age cohort), a squared term on the number of children, interaction term of children with rural areas and an interaction term between educational categories and rural areas. Furthermore, the specification is flexible enough to allow for returns to education to vary with the birth cohort by including a large set of interaction terms between educational categories and birth cohorts.

The sample selection and the estimation procedure (using bootstrapping to generate bounds) require a random number generator which uses a common seed (seed 123456) for all calculations. Here the number of repetitions is set to 200.<sup>9</sup>

> 9 Instead of using one sample we could instead estimate bounds several times and produce a distribution.

43
**Table 9: Cereal yields in Argentina, Canada, China, Colombia and ECA, tons per hectare**

||1992-1994|1997-1999|2007-2009|
|---|---|---|---|
|Argentina|2.91|3.45|3.82|
|Canada|2.57|2.82|3.22|
|China|4.48|4.91|5.44|
|Colombia|2.52|3.04|4.04|
|Albania|2.47|2.83|4.04|
|Armenia|1.66|1.65|2.42|
|Azerbaijan|1.79|1.78|2.67|
|Belarus|2.60|1.93|3.27|
|Bosnia and Herzegovina|3.56|3.59|4.06|
|Bulgaria|2.75|2.88|3.20|
|Croatia|4.12|4.77|5.77|
|Czech Republic|4.06|4.15|4.99|
|Estonia|1.69|1.62|2.85|
|Georgia|1.93|1.97|2.04|
|Hungary|3.57|4.68|4.62|
|Kazakhstan|1.06|0.91|1.20|
|Kyrgyzstan|2.35|2.45|2.64|
|Latvia|1.76|2.08|3.04|
|Lithuania|1.93|2.34|3.27|
|Republic of Moldova|2.98|2.88|2.17|
|Romania|2.44|3.10|2.57|
|Russian Federation|1.61|1.51|2.22|
|Serbia and Montenegro|3.10|4.05|4.14|
|Slovakia|4.04|4.10|4.35|
|Slovenia|4.02|5.54|5.35|
|Tajikistan|0.95|1.26|2.44|
|<br>Macedonia, FYR|2.45|2.92|3.18|
|Turkey|2.10|2.19|2.56|
|Turkmenistan|2.38|1.87|2.89|
|Uzbekistan|1.68|2.28|4.34|

Source: FAO (2011) 46
Without loss of generality, assume for now that consumption data are available for survey 1 but missing for survey 2, for which we are most interested in the poverty estimates. Let z2 be the poverty line in period 2, and ��� be the imputed consumption for survey 2, the poverty rate P2 in this period could be estimated with the following quantity ����� ���� (4) where P(.) is the probability (or poverty) function that gives the percentage of the population that are under the poverty line z2 in survey 2.

Imputation methods differ in how they estimate ���. We discuss in this section methods that have been developed to impute consumption, either within the same type of surveys or across different survey types. We start first with discussing a commonly used method, proxy means testing, before discussing how consumption can be imputed to measure poverty trends and “poverty-mapping” (or small area estimation) methods.

# **IV.1. Proxy Means Testing**

Most of the estimates based on proxy means testing are usually estimated as where the vector of coefficients ��� is often obtained from the regression using another data set (see, e.g., Grosh _et al._ (2008), Coady _et al._ (2014), Brown, Ravallion, and van de Walle (2016)). For example, ���can be obtained from a regression using the household consumption data, and then be imposed on the data from a special—and often smaller—survey that aims at targeting poor households for a social protection program. Alternatively, ���can also be obtained from a regression using data from a neighboring country. But regardless of the specific application, the error terms ��� ��� (in Equation (3)) are often omitted in Equation (5). As a result, the mean and the variance of the predicted consumption based on proxy means testing would likely provide 19
<!-- Start of picture text --> Upward mobile Downward mobile<br>Urban Peninsular Rural East Malaysia Urban Peninsular Rural East Malaysia<br>wo wo<br>= ~ 3 x.<br>2 oy 2 oo<br>ci oO a<br>2.oC . 2.oS .<br>5 Oo G Oo —-_——— ———S—<br>FESP FSShr © KC8 SFof FSaSP FSSOe © SKOtS eSSP Fesoh Pe SF2 ot FHSSP Seseog 9 a<br>Vulnerable in both periods Secure in both periods<br>Urban Peninsular Rural East Malaysia Urban Peninsular Rural East Malaysia<br>wo [oe]<br>~<br>23©<br>©o<br>oO iv<br>5 5 eee<br>a (=) G o<br>ssS? FHvd X© KKPYSSKSPEseSHSMEP MPa ssSFr FHSr FX© KK© gr SSKiSP SHSSP© MSS<br>—————_ Bounds e Point estimate PF +/- two standard deviations<br>Subgroup shares are based on synthetic panel estimates for household heads aged between 25 and 60 in the first round of each interval.<br>The poverty line used is RM 527, the vulnerability line RM 1120; note that the vertical axis scales vary across graphs.<br><!-- End of picture text -->
# **Table 2: Determinants of IPM Adoption in Rice Production**

||Linear<br>Probability|Probit|
|---|---|---|
|Age|0.008<br>(3.41)**|0.029<br>(3.34)**|
|Education|0.089<br>(5.37)**|0.296<br>(5.11)**|
|Ownership|0.129<br>(2.42)*|0.570<br>(2.55)*|
|Training|0.375<br>(4.27)**|1.057<br>(3.69)**|
|Experience|-0.003|-0.012|
||(1.27)|(1.36)|
|Farm Scale|0.001<br>(0.11)|0.003<br>(0.13)|
|Poor Health|-0.063|-0.205|
||(1.70)|(1.53)|
|Constant|-0.232<br>(2.62)**|-2.504<br>(7.19)**|
|Observations|551|551|
|R-squared|0.13||

Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1%

# **5.  Comparative Input Use, Productivity and Profits**

We use two quantitative techniques for assessing IPM and conventional techniques in rice production: Comparative estimates of input-output relationships, and production function estimation. In both cases, we control for farmers’ characteristics (age, education, farming experience, ownership status, prior training, production scale, health status) that may affect both productivity and the propensity to adopt IPM.

# **5.1  Input-Output Results**

For each farm in the sample, we calculate input-output (IO) coefficients for land, family labor, hired labor, capital, irrigation, seed, fertilizer and pesticide. We test for significant differences between mean coefficients for conventional and IPM farming by regressing the IO coefficients on a dummy variable for IPM use. Since all distributions of IO coefficients are highly skewed, we guard against outlier effects by estimating log 8
<!-- Start of picture text --> 130 Ethiopia Kenya 125 Bangladesh Cambodia<br>Tanzania Uganda Ethiopia Vietnam<br>Ghana Mozambique<br>120 120<br>Zambia<br>110 115<br>100 110<br>90 105<br>100<br>80<br>95<br>70<br>Source: Data from NBE.<br>Jan-14 Mar-14 May-14 Jul-14 Sep-14 Nov-14 Jan-15 Mar-15 May-15 Jan.-14 Feb.-14 Mar.-14 Apr.-14 May.-14 Jun.-14 Jul.-14 Aug.-14 Sep.-14 Oct.-14 Nov.-14 Dec.-14 Jan.-15 Feb.-15 Mar.-15 Apr.-15 Mar.-15<br><!-- End of picture text -->

**Figure 9. Official and Parallel Exchange Rates, and Parallel Market Premium**

<!-- Start of picture text --> 35 Panel A. Official and parallel exchange rates (2002/03-2017/18)<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Source: Data from NBE.<br>2002/03_Q1 Q3 2003/04_Q1 Q3 2004/05_Q1 Q3 2005/06_Q1 Q3 2006/07_Q1 Q3 2007/08_Q1 Q3 2008/09_Q1 Q3 2009/10_Q1 Q3 2010/11_Q1 Q3 2011/12_Q1 Q3 2012/13_Q1 Q3 2013/14_Q1 Q3 2014/15_Q1 Q3 2015/16_Q1 Q3 2016/17_Q1 Q3 2017/18_Q1 Q3<br><!-- End of picture text --> <!-- Start of picture text --> 40 Panel B. Parallel Market Premium, % (2005/06-14/15)<br>35<br>30 Official exchange rate<br>Parallel exchange rate<br>25<br>20<br>15<br>10<br>5<br>0<br>Source: Data from NBE.<br>2002/03_Q1 Q3 2003/04_Q1 Q3 2004/05_Q1 Q3 2005/06_Q1 Q3 2006/07_Q1 Q3 2007/08_Q1 Q3 2008/09_Q1 Q3 2009/10_Q1 Q3 2010/11_Q1 Q3 2011/12_Q1 Q3 2012/13_Q1 Q3 2013/14_Q1 Q3 2014/15_Q1 Q3 2015/16_Q1 Q3 2016/17_Q1 Q3 2017/18_Q1 Q3 2018/19_Q1<br><!-- End of picture text --> 8
11

# Cross‐Regional Perspective

Broadly, access expansion mirrors economic development. Does access in Africa as a function of income or depth of poverty fall in line with the rest of the world? To answer this question, access in different countries was compared as a function of the poverty gap at $3.10 and $1.90 and per capita GDP. Correlation coefficients between access and the two measures of the poverty gap and per capita GDP are statistically significant at 1 percent, with correlation coefficients ranging from ‐0.72 for the logarithm of GDP per capita at the market exchange rate to ‐0.89 for the poverty gap at $3.10.

Figure 1 plots the relationship between access and the poverty headcount at $3.10 for 2012. There is a downward sloping line on which most countries in regions other than Africa lie, whereas several countries in Africa lie markedly below, as do Cambodia and Vanuatu. When access is regressed on any one of the measures of the poverty gap or per capita GDP together with 1/0 regional dummies, Africa is the only region for which the coefficient for the regional dummy is consistently negative and statistically significant at 1 percent. The predictive power of the poverty gap at $3.10 was the highest, followed by the poverty gap at $1.90, the logarithm of per capita GDP at purchasing power parity, and finally the logarithm of per capita GDP at the market exchange rate.

Figure 1: Relationship between poverty gap and electricity access in 2012 _Source_ : World Bank staff analysis using data from World Bank 2016 and World Bank and IEA 2015. _Note_ : Bubbles are in proportion to population.

Figure 2 compares access and the poverty gap in Africa in 2012 with corresponding values in three other regions in 1990. China stands out as a (positive) outliner, with the access rate far above that of other countries at similar levels of the poverty gap. Several countries in Africa again lie below the global trend line. When access (in 2012 in Africa and in 1990 elsewhere) is regressed on the poverty gap at $3.10 (in 2012 in Africa and in 1990 elsewhere) together with regional dummies as dependent variables, the Africa dummy is again negative and statistically significant at 1 percent. While these are simplified analyses, these findings point to the special challenges facing Africa in expanding household access.
26

# Flgure 4

# Grouth and Poverty Under Alternative Adjustment Package

# AfrLcan Archetype

# Latin American Archetype

# Real CDP Index

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Using the worldwide increase in life expectancy at birth of 30 years between 1922 and 1987, these estimates suggest that rising life expectancy increased lifetime earnings by 27%. The gains are largest for males (36%) and smallest for rural residents (21%).

# **6.3 Returns to schooling**

The Mincerian earnings function (29) generates a measure of the returns to education. This can be compared to the Wald estimator.<sup>17</sup> Estimates from the Mincerian earnings function are reported in Table 5, and these estimates are compared to the corresponding Wald estimators in Table 6.

In Table 5, additional schooling increases lifetime earnings irrespective of the specification used. For the pooled sample, the coefficient of years of schooling shows that one additional year of schooling increases lifetime earnings by 11.8% in the simple specification I; this shrinks to 9.4% once we include a broad set of controls.<sup>18</sup> The estimates on the age terms, percentage male, percentage urban-rural, percentage married, and cohort size exhibit the usual signs. The estimates of the earnings function by gender and urban-rural residence do not show any notable variation across groups. Specification II reveals that one additional year of schooling increases lifetime earnings for males, females, urban, and rural groups by 10.6%, 7.3%, 9.5%, and 8.8%, respectively.

In Table 6, the Wald estimate of the returns to schooling is obtained by dividing the life expectancy coefficients from specification IV in Table 3 by the life expectancy coefficient from specification IV in Table 1. Similarly, for the male, female, urban, and rural groups, we divide the group-specific life expectancy coefficient obtained from specification II in Table 4 by the life

> 17 The Wald estimator for r <u>g l y g y</u> r 18 In all specifications reported in Table 5, we have used survey fixed effects to facilitate comparison across ρρr dd ρρtt ddl rrρρl rr rreerrddlldd gee lrrttll 1 yrreerr geelldd lldd ddl rr rreepprrjjρρeeddjj y eeρρ bbllrrρρh RReettRRrrRR ttoo EEddRRSSaattEEooRR= ρρr dd ρρtt eejjht ddllddgg llrrttll 1 yyrreerr  ggeelldd lldd ddl rr rreepprrjjρρeeddjjyy eeρρ bbllrrρρh countries and time periods.

> ~~.~~

21
within each elected community to select households to be interviewed.<sup>6</sup> The survey team contacted 1,868 households. A total of 1,502 questionnaires were completed, with a response rate of 84 percent. Among the 1,502 observations, 24 outliers were excluded from the analyses; the remaining 1,478 observations were kept in the sample.

The questionnaire included nine sections: (1) respondent’s identification; (2) characteristics of respondent’s residential house/building; (3) household water supply and consumption; (4) alternative water sources and solution for water shortage; (5) waste water and water sanitation; (6) WTP; (7) current water costs; (8) health and environmental awareness; and (9) household information. The WTP part adopts the MBDC approach, which covers a price range for water from 0.5 yuan/ton to 15 yuan/ton.

The WTP section of the questionnaire has several components: a question about problems of current water service; a description of new water service plans; the need to raise water tariffs to make ends meet under the new plan; a list of daily water usage and potential savings from the improved water supply service; a reminder that their water tariffs would be the sum of the water fee and sewage treatment fee in questionnaire type A, while no reminder in type B; and MBDC questions. Appendix 2 provides details of the WTP section in the questionnaire.

# **Major Statistics of the Survey**

Table 1 provides statistics of major indicators.<sup>7</sup> As shown, about 91 percent of the households surveyed have a piped water supply connection, and only 23.1 percent of the households are satisfied with the current water supply service. Average household water consumption is about 6.2 tons per month. The average water price the households pay is 2.18 yuan/ton. The average household income is about 1,211.1 yuan. The average water expenditure is about 1.1 percent of income. Of the households surveyed, 50.3 percent were located in urban towns. Seventy percent of the survey respondents were female, and the average education level of the respondents was between middle and high school. More analysis of the survey is provided in the next section of this paper.

> **6** For practical and statistical reasons, the sample size in each community was no smaller than 30.

> 7 For more statistics of this survey, see World Bank (2007).
7

# GDP-djflator elasticity of Government-Expenditure Deflator

The estimated values of the GDP-deflator elasticity of governmentexpenditure deflator, a, are given in column (ii) of Annex Table 1; column (iii) of that table contains a comparable estimate of d, a*, that is calculated as the ratio of growth rates of government and GDP deflators given in Beck (1976) and Heller (1981)"--henceforth called the comparator set. Table 1 contains summary statistics for these two sets of estimates.

TABLE 1: COMPARATIVE DISTRIBUTION OF ESTIMATED GDP-DEFLATOR ELASTICITIES OF GOVERNMENT-EXPENDITURE DEFLATOR, a AND THOSE CALCULATED FROM OTHER STUDIES a*'

|Countries2|<br>Mea|n|Std. Dev.|Test|for H.: d|- d* -|0|
|---|---|---|---|---|---|---|---|
||(a)|(a*)|<br>(a) (a*)|<br>(a-a|*)<br>s<sup>(a.a*</sup>|<sup>)3</sup><br>t|Prob'|
|ALL|1.01|1.32|0.60 0.54|0.31|0.39|0.79|0.44|
|IND<sup>5</sup>|1.49|1.80|0.37 0.35|0.31|r.28|1.11|0.30|
|MIE|0.73|1.01|0.53 0.41|0.28|0.48|0.58|0.57|
|LIE|0.41|0.94|0.18 0.17|0.53|0.04|1i.3|0.05|

- 1) Because of the unavailability of the significance levels for individual estimates of a* the comparison disregards the estimated variances of the individual a as well.

- 2) Summary statistics are for countries for whicn both a and a* are available. In total there are 30 such countries, 12 in the IND group, 16 in MIE And 2 in LIE.

- 3)

- This column contains the standard deviation of (a-d*).

- 4) (1-Prob) gives the value to which the confidence level would have to be lowered in order to r.ject the null hypothesis.

- 5) IND-Industrial Mar'-. Economies, MIE-Middle Income Economies, LlE-Low Income Economies;

The comparisons assume coefficient stability during the time periods covered by this study and the comparator studies as well as more or less constant shares of the different components within the government sector. Without these assumptions no comparisons would be possible since there are few studies that cover the same time periods or that use the same variable to measure government size.
# **6.5 Loan Repayment Rates**

A typical metric of loan non-performance used by MFIs is the “Portfolio at Risk 90 Days (PAR-90)” rate, i.e. the percentage of the portfolio that is at least 90 days overdue on scheduled repayment. Prior to the onset of COVID-19, the psychometric-appraised, uncollateralized WEDP-X loans showed a PAR-90 of around 2%. This figure is healthily low, and was similar to that achieved by regular, collateralized WEDP loans offered by the MFI. However, the psychometric loans disbursed in early 2020 performed worse than the psychometric loans disbursed prior to 2020, since loans disbursed in early 2020 were almost immediately hit by the consequences of the COVID-19 pandemic. The psychometric loans disbursed in early 2020 also performed worse than regular, collateralized loans disbursed early in 2020, although very few regular collateralized loans were being disbursed at that time. Most of the psychometric loans that fell into arrears during the COVID-19 pandemic had already been partially repaid. By September 2022, all WEDP-X loans had matured and Wasasa was no longer collecting on the initial WEDP-X portfolio. Total defaults amounted to just under 9% of total funds disbursed under WEDP-X. Out of the 62 borrowers, 54 fully repaid the WEDP-X loan, and 8 partially defaulted.

Appendix Table A8 shows that the psychometric score positively predicts repayment of WEDP-X loans within the treatment group as observed from Wasasa administrative data. — — Thus psychometric scoring has predictive power for its intended outcome repayment even among a sample that already passed a cut-off score as well as due diligence checks. We note that by design we cannot observe whether a score below versus above the cut-off predicts repayment of uncollateralized lending, since those scoring below the cut-off were not eligible for an uncollateralized loan. However, in Columns 6 and 8 of Appendix Table A1 we show that women who scored below 370 went on to have lower annual profits and monthly sales by endline compared to the impact evaluation control group who scored above 370 (and passed due diligence) but were randomly assigned to control.

23