rafmacalaba/datause-encoder-data huggingface dataset:
# **Table of Contents**

**1.** Introduction

2. What is the Welfare Objective?

3. How are the Benefits from Public Spending Currently Distributed?

- 3.1 Measurement of Benefits

- 3.1.1 Benefit Incidence

- 3.1.2 Behavioral Approaches

- 3.1.3 Incorporating Behavioral Approaches in Incidence Studies 3.1.4 Data and Policy

- 3.2 Results

- 3.2.1 Lessons from Benefit Incidence Studies

- 3.2.2 Lessons from Behavioral Approaches

- 3.2.3 Lessons from Incorporating Behavioral Responses in Incidence Studies

4. How can the Distribution of Public Spending be Improved?

- 4.1 Targeting: Issues and Options

- 4.2 Choices and Tradeoffs

- 4.2.1 The Costs of Targeting

- 4.2.2 Evaluating a Targeted Scheme

- 4.2.3 Design and Implementation can Help Reduce Targeting Costs

5. Conclusion Notes References
48 On the income side, per capita household income has increased by over 70.7 percent between 1985-92 (Tables Al.6-A1.8), mainly due to a big increase in farm income(324 percent), while non-farm increased by 120.9 percent. Non-farm income showed the biggest increase in Middle and Southern belts. Within the non-farm income group, the biggest increase were due to rent received and other income. Wage income received by households decreased by 5.8 percent during this period. Similar patterns were observed in rural and urban areas and by geographical areas. Wage incomes showed the largest increase in the northern belt and the smallest increase in the southern belt. This may also be reflecting the fact that even at lower real wages more members of the household might have been employed.

**Table 5.1: Evolution of (Real) Per Capita Expenditure by Type and Poverty Group (Poverty Line=N395.41)**

||**PO**|**OR**|**NON-P**|**OOR**|**ALL NI**|**GERIA**|
|---|---|---|---|---|---|---|
|**Expenditure Type**|**85/86**|**92/93**|**85/86**|**92/93**|**85/86**|**92/93**|
|Own Consumption|28.85|56.24|59.00|370.80|44.74|263.55|
|Food Cash Expend.|171.73|119.888|479.14|343.51|346.89|267.27|
|All Food Expend.|197.58|176.12|538.14|714.31|391.62|530.82|
|Non Food Expend.|56.69|50.50|310.30|371.14|201.19|261.82|
|P. C. Expend.|254.27|226.62|848.43|1085.45|592.81|792.64|
||**EXTREM**|**E**<br>**POOR**|**OTH**|**ERS**|**ALL**NIG|ERIA|
||||**(Poverty**|**Line=N197.71)**|||
|Own Consumption|15.83|40.16|48.68|298.85|44.74|263.55|
|Food Cash Expend.|107.81|55.45|379.45|300.74|346.89|267.27|
|All Food Expend.|123.64|95.61|428.13|599.59|391.62|530.82|
|Non Food Expend.|24.30|28.98|225.28|298.62|201.19|261.82|
|P. C. Expend.|147.94|124.59|653.41|898.21|592.81|792.64|

Source: NCS, 1985/85 and 1992/93.

**Table 5.2: Evolution of (Real) Per Capita Expenditure by Type, Region and Gender (Poverty Line=N395.41)**

||**RUR**|**AL**|**OTHER**|**URBAN**|**LAGOS**|**URBAN**|
|---|---|---|---|---|---|---|
|**Expenditure Type**|**85/86**|**92/93**|**85/86**|**92/93**|**85/86**|**92/93**|
|Own Production|53.24|62.54|30.56|18.63|1.20|8.76|
|Food Cash Expend.|288.08|443.96|446.99|577.57|530.63|726.20|
|All Food Expend.|341.32|516.72|477.55|608.36|539.87|748.24|
|Non Food Expend.|189.96|109.91|220.77|183.38|222.80|237.70|
|P. C. Expend.|531.28|707.63|698.32|791.74|762.67|985.94|
||**MALE H**|**EADED**|**FEMALE**|**HEADED**|**ALL NI**|**GERIA**|
|Own Consumption|52.75|44.43|43.64|54.26|44.74|45.45|
|Food Cash Expend.|396.41|487.89|340.07|590.77|346.89|498.48|
|All Food Expend.|449.16|542.94|383.71|659.34|391.62|554.92|
|Non Food Expend.|213.71|185.41|199.47|221.94|201.19|189.17|
|P. C. Expend.|662.87|728.35|583.18|881.28|592.81|744.09|

Source: NCS, 1985/86 and 1992/93.
intent-to-treat (ITT) comparisons between actual beneficiaries in the treatment groups and potential beneficiaries identified after applying the same ranking procedure in the control group.

Once data on actual beneficiary status for households in the treatment group became available from the project unit, they was merged with the baseline survey data set. The actual beneficiary status was compared to a predicted beneficiary status re-estimated from the baseline survey for the treatment group. The comparisons highlighted that the prediction of beneficiary status based on the baseline survey was imperfect. Figure 1 illustrates that the PMT score from the registry census accurately predicts beneficiary status. It shows a sharp drop in the probability of being a beneficiary around the eligibility cut-off. This means that the PMT selection procedure was faithfully implemented based on the registry census data.<sup>13</sup> However, the PMT scores recalculated from the baseline survey predict beneficiary status imperfectly. As can be seen in Figure 1, the likelihood of being “predicted” to become beneficiary based on the baseline survey variables is not as strong a predictor of actual beneficiary status. While the likelihood of being a beneficiary is still strongly associated with the predicted PMT score, the slope of the curve is flatter, and no sharp drop is observed at the cut-off. This is likely due to measurement errors in household variables that enter the PMT score calculation (possibly in both the PMT and baseline surveys). It may also be due to split-off households between the two surveys.<sup>14</sup> These issues likely reflect teething pains during the establishment of the Niger safety net system, including with the quality of rapid, large-scale data collection for the registry census in a context characterized by low administrative capacity. Indeed, the PMT targeting was shown to be relatively efficient and to suffer from little measurement errors in subsequent phases of the program (Premand and Schnitzer, 2020). In the next section, we discuss the preferred identification strategy given that predicted beneficiary status from the baseline survey imperfectly matches actual beneficiary status in the treatment group.

The cash transfer program was implemented between February 2013 and April 2015. Compliance with cash transfer participation was high: 98% of households selected to receive the

> 13 In fact, the formula was programmed in SQL as part of the program monitoring and information system, and then separately in Stata. The results were compared to ensure that no programming mistake was made.

> 14 Defining household units in the context of large, frequently polygamous households in rural Niger is challenging, and some discrepancies in the application of these definitions may have taken place between surveys. Compared to this type of measurement error, manipulation in household answers is much less likely given the formula was not made public and had not previously been used in study areas.

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corruption was considerably _lower._ If these results are replicated at the international level, it suggests the potential for considerable bias in measures such as the CPI.

We further probe whether expert opinion about overall corruption accurately reflects observations reported by firms. Firms probably know better, whether and to what degree they themselves are exposed to corruption. At the same time, firms might underreport based on fear of discovery (Thompson and Shah, 2005). Bias may also lead to over-reporting in answer to questions such as „firms like yours.‟ As Svensson (2005) argues, this might not matter much for cross-country comparisons, if such misreporting is not systematically related to country characteristics. We use the enterprise surveys (ES) of the World Bank to test this hypothesis.

For our exercise we compare rankings. Even if firms were systematically to underreport corruption in highly corrupt countries, this need not change that country‟s ranking if underreporting is similar across countries. Of course, it could be that in some corrupt countries firms try to understate the extent of corruption, whereas in others they do not care to do so. We cannot control for such an effect, but highlight some intriguing cases.

Enterprise surveys, which by now cover 125 countries ask firms whether “an informal gift of payment [was] expected or requested” for eight transactions, to obtain licenses for operation, construction and imports, when paying taxes or applying for a government contract as well as connections to electricity, water and telephony. The correlation between these different indicators of bribery ranges from 0.41 to 0.82 (Table 1 reports selected correlations). Not being very strongly correlated, it thus seems that they may capture a range of information about bribery.

**Table 1:** **<u>Correlation of selected rankings for bribe payments</u>**

|**_Transaction_**|Construction<br>permit|Operations<br>license|Government<br>Contract|
|---|---|---|---|
|Water<br>connection|0.65|0.65|0.41|
|Construction<br>permit||0.82|0.62|
|Operations<br>license|||0.68|

We then simply aggregated the eight indicators into a composite one using equal weights and compared it to the CPI. The correlation between the two indicators is about 0.7. Hence it seems that the CPI may capture important information about overall corruption, but possibly with a large error margin.

We then explored whether the differences between firm level responses and those of experts generating the CPI might have a systematic reason. We speculated that the experts who cannot be fully familiar with corruption in a country might draw conclusions about corruption by 4
Figure 15. Recessions with financial disruptions: duration, amplitude and cumulative loss <!-- Start of picture text --> Duration<br>(in quarters)<br>6<br>Recessions associated with Recessions associated without<br>5 ***<br>**<br>4<br>3<br>2<br>1<br>0<br>Credit Crunches House Price Busts Equity Price Busts<br>Amplitude<br>(in percent)<br>0<br>-1<br>-2<br>-3 **<br>-4 * ***<br>Recessions associated with Recessions associated without<br>-5<br>Credit Crunches House Price Busts Equity Price Busts<br>Cumulative loss<br>(in percent)<br>0<br>-2<br>-4<br>-6<br>***<br>-8 ** ***<br>Recessions associated with Recessions associated without<br>-10<br>Credit Crunches House Price Busts Equity Price Busts<br>Source: Claessens et al (2012a).<br><!-- End of picture text --> Note: The amplitude and cumulative loss statistics correspond to sample medians. Duration corresponds to the sample mean. Disruptions are the worst 25% of downturns as represented by the amplitude. ***, ** and * imply significance at the 1%, 5% and 10% levels, respectively. Significance refers to the difference between recessions with and without financial disruptions. For other definitions, see the notes to Figures 13 and 14.

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# Green sectors

As mentioned above, this paper uses the level of GHGE per worker to classify sectors into green and nongreen categories. To calculate the level of GHGE by sector, this paper follows the methodology proposed by Alcantara (2007), Alcantara et al. (2010), and Bin Su et al. (2013). This is because to account for the true ‘greenness” of a sector, we should consider both the direct and indirect GHGE. For example, while the financial sector may have low levels of direct GHGE per worker, its total GHGE may be substantially higher once we consider its links with, for example, the utilities or transportation sectors. The methodology applies the Input-Output model proposed by Leontief, W. (1986) to a vector of emissions multiplier. The original Leontief model to calculate gross production has the following structure: Where corresponds to a column vector containing the level of gross production by economic sector, YY= [II−AA]<sup>−1</sup> ff is the identity matrix of order _n_ corresponding to the _n_ -sectors, is an _n x n_ matrix containing the YY II technology coefficients for each included sector, and is a column vector corresponding to the final AA demand for each economic sector. ff An emission multiplier matrix is incorporated into the previous equation to calculate the level of emissions by sector. This matrix is diagonal and contains the number of emissions per production unit for each sector. The new equation for calculating emissions by sector has the following structure: Where corresponds to a column vector containing the level of emissions by economic sector, and CC= cĉ[II−AA]<sup>−1</sup> ff is a diagonal matrix containing the emissions multipliers for all sectors. CC cĉ The level of emissions per worker by sector is estimated as the level of emissions divided by the number of workers aged 15 to 64 years in each sector. In this section, three primary sources of information are implemented to estimate GHGE per worker by sector: the Socioeconomic Database for Latin America and the Caribbean (SEDLAC),<sup>11</sup> the 2018 edition of the Organization for Economic Co-operation and Development (OECD) country-level Input-Output Tables (IOTs),<sup>12</sup> and the Climate Watch (CAIT) Historical

> 11 SEDLAC is a database of harmonized socio-economic statistics constructed from the Latin American and Caribbean (LAC) household surveys. SEDLAC includes information from over 300 household surveys carried out primarily in 18 LAC countries for which a comparable income aggregate (for welfare analysis) can be created: Argentina, Bolivia, Brazil, Colombia, Costa Rica, Chile, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, and Uruguay. The SEDLAC database and project were jointly developed and are jointly maintained by CEDLAS (Universidad Nacional de La Plata) and The World Bank’s LAC Team for Statistical Development (LAC TSD) in the Poverty and Equity Global Practice.

> 12 Available in https://stats.oecd.org/Index.aspx?DataSetCode=IOTSI4_2018.

8
comprehensive estimates of bilateral immigrant stocks worldwide in 2010 (World Bank 2010a).

# 5. Discussion of results

The model has performed well during the global financial crisis. Despite initial concerns of a sharp decline, the actual decline in remittances in 2009 has been similar to our forecasts. The model predicted correctly that remittance flows to developing countries would decline only modestly, unlike foreign direct investment and portfolio debt and equity flows. The initial forecasting exercise prepared in November 2008—see Ratha, Mohapatra and Xu (2008)—predicted that remittance flows to developing countries would fall by 1 percent in 2009 in the base case scenario and no more than 6 percent in a low-case scenario (where the last year’s flow of migrants was forced to return). Remittance flows were forecast to decline in five of the six developing regions (other than the East Asia and Pacific region) in the base case scenario. As the financial crisis deepened and the World Bank and the IMF revised down their growth forecasts, the model-generated forecasts of remittances were also revised downward, to 5-8 percent decline in in March 2009, and 7-10 percent decline in July 2009 (Ratha and Mohapatra 2009; Ratha, Mohapatra and Silwal 2009a).

The actual outcome in 2009 was a 6 percent decline in remittance flows to developing countries (Ratha, Mohapatra and Silwal 2009b, 2010). In terms of regional distribution, remittance flows declined in five of the six developing regions, but the extent of declines in some regions were larger and in others smaller than initially predicted—East Asia & Pacific, Middle East and North Africa, South Asia and SubSaharan Africa did better while Europe & Central Asia and Latin America & the Caribbean performed worse. However, as initially predicted, remittances remained more resilient compared to other types of private resource flows to developing countries.

There are several limitations of the forecasting methodology outlined above. First, the model does not explicitly feature return migration, a key risk factor. We do not have data on return migration for most migrant-destination countries. In order to account for 9
17

# _V.2 Base case scenario_

The base case scenario represents a likely macroeconomic outcome for Bangladesh in the years ahead according to the most recent Country Assistance Strategy prepared by the World Bank (1988a) for the country. The national rate of GDP growth is expected to increase progressively, reaching 7.3 percent in 2008. The average GDP growth rate for the whole planning horizon is 6.6 percent, above the 4.4 percent average observed over the last six years, but below the 7.3 percent average growth projected by the Government of Bangladesh (1997) in its draft Fifth Five Year Plan for 1997-2002. The reasons for expecting gains in national GDP growth include the commitment of the Government to maintain macroeconomic stability, a modest improvement in infrastructure thanks to private sector involvement, particularly by foreign investors, and some progress in the implementation of structural reforms in the financial sector, civil administration, legal/judicial systems and privatization. Bangladesh also faces the prospect of higher foreign direct investmnents thanks to the discovery of natural gas fields. On the other hand, political stability is expected to remain fragile due to the non-cooperative strategies adopted by major political players, leading to uncertainty in economic policy. The national rate of GDP growth in the base case is expected to come from export oriented manufacturing and services rather than from agriculture. Hence, rural growth is expected to be lower (flat 4 percent GDP growth rate assumed here throughout the planning horizon) than urban growth (increasing from 9.9 to 15.1 percent over time)<sup>9</sup> .

To translate the rural and urban GDP growth rates into changes in per capita income, we assume that the share of the national population living in rural areas decreases by half a percentage point per year, 9 The discussion in this section si similar to that in the poverty assessment prepared by the author and colleagues for the World Bank (1998b). In the RMSM-X model and in the simulations given in World bank (1998b), three sectors are distinguished: agriculture, industry, and services. The base case scenario assumes a flat 2 percent growth rate in value added for agriculture each year. This corresponds to normal climatic conditions (there may be natural disasters in some years and higher growth in other years, but this cannot be predicted). For industry, growth increases progressively from 3.6 percent in 1997 to 8.5 percent after 2004. The growth rate for services increases from 6.2 to 7.5 percent. We translated these growth rates into urban and rural growth rates for this paper in order to use the elasticities estimated in the previous section. None of the qualitative results and policy implications are affected by considering here two rather than three sectors.
# _The PISA 2006 data sets_

The third round of the Program of International Student Assessment surveys was conducted in 57 countries between March and November, 2006. Two earlier rounds were collected in 2000/2002 (in 43 countries), and in 2003 (in 41 countries). A fourth round has since been collected in 2009. Most OECD countries were surveyed, as were a number of developing countries in Asia, Latin America, North Africa and the Middle East. Sample sizes range from 339 in Liechtenstein to 30,971 in Mexico. Table 1 lists all participating countries in the 2006 round, as well as their sample sizes.

In each country, fifteen year-olds enrolled in any educational institution, and attending grade 7 or higher, were sampled. All children surveyed took three tests: in reading, mathematics, and science.<sup>5</sup> Their performance in these tests forms the basis for the assessment of their learning or cognitive achievement. Yet, educationalists seem agreed that raw, unadjusted test scores are of little value. Test questions (or ‘items’) vary in their degree of difficulty, and simply adding up correct answers, or weighing them arbitrarily, does not correctly measure the latent variable of interest – cognitive achievement. Instead, the educational community in charge of international tests such as PISA, TIMSS, PIRLS and IALS processes raw scores through statistical techniques known as Item Response Theory (IRT). See Baker (2001) for a general introduction, and OECD (2006) for a description of how the method is applied to PISA surveys. In essence, an item response model consists of an equation of the form: Equation (1) gives the probability of scoring _s_ in a given test, conditional on individual latent cognitive ability and test item parameters (such as their difficulty). Given an additional assumption about the distribution of latent ability in the population (usually a normal law such as ) and an observed distribution of raw scores, _F(s),_ the IRT model can be used to back up a distribution of the latent variable .<sup>6</sup> This process involves a number of functional form assumptions which are not innocuous. Brown et al. (2007) have shown, for instance, that the final distribution of test scores can be sensitive to differences in the specification of the model used to estimate equation (1). Here, however, we are concerned with the standardization that happens _after_ the IRT adjustment. Once that procedure is complete, and a new distribution of ‘adjusted’ test scores (which we denote by _x_ ) has been generated, this latter variable is standardized, according to a simple formula such as: In equation (2), _xij_ denotes the (post-IRT, pre-standardized) test score for individual _i_ in country _j_ . _μ_ and _σ_ denote their original mean and standard deviation across all countries in the sample

> 5 The data for achievements in Reading for the United States were not issued after a problem occurred during the field operations in that country.

> 6 See Mislevy (1991) and Mislevy et al. (1992) for a more detailed discussion.

5
major characteristics of growth in Africa are its low long run trend and its extreme volatility at the country level. We find no persuasive evidence of growth persistence within countries and only weak evidence of persistence at the regional level since 1990.

Section 3 examines how the growth recovery has affected the distribution of income among countries in the region. We describe the country level distribution of income in Africa and test for convergence in per capita income levels between richer and poorer countries. We find no evidence that poorer countries in Africa are converging to the income levels of their richer neighbors. We find persuasive evidence of inertia in per capita incomes for economies in Africa. Where countries began in terms of relative income in 1975 is an excellent predictor of where they ended up in 2005. Because the rich economies are growing faster than their poorer neighbors we also find that the intercountry distribution of income has become less equal over time.

The fourth section of the paper presents our hunt for the leopards. One indication that Africa has indeed reached a turning point, would be evidence that a group of African economies with high and accelerating long run growth – “leopards”, the regional equivalent of Asia’s “tigers” – is beginning to emerge. We first identify four groups of countries according to their income levels and growth experiences, and we look for some common characteristics that are associated with these groups. Two distinct and stable income groups or “clubs”, rich and poor, are identifiable in the data. Our most striking finding is that transitions from low income to higher income levels have been rare in the last 30 years. Only 2 countries, both oil exporters, made the transition. We then use the approach to growth accelerations and decelerations developed by Arbache and Page (2007) to see if a subset of countries with a high frequency of rapid, growth accelerations emerges during 1995-2005. Based on our results for income transitions, growth thresholds, and growth accelerations we identify six economies that show the potential to be Africa’s growth leaders. Section 6 concludes.

# **2.  Chaos without Change:  Africa’s Long Run Growth 1975-2005**

This section describes long run trends in per capita income growth for 44 African economies. Data on GDP per capita at 2000 international PPP prices are taken from the 3