rafmacalaba/data-use-mentions-extended huggingface dataset:
household ownership of DVD, television, MP3 player, cellular, phone, computer, and calculator.

Table 6c: Impacts of PROSPERA on School Enrollment: All 6th Graders in Medium, High and Very Highly Marginalized Communities 6<sup>th</sup> graders in 2008 Nearest Neighbor Matching Models

|||Enrollment||
|---|---|---|---|
|Variables|7thgrade 2009-2010|9thgrade 2011-2012|12thgrade 2014-2015|
|Propensi|ty score matching with exact|matching on age and state (nu|mber of matches)|
|Girls|0.0688***|0.080***|0.0480**|
||(0.018)|(0.0187)|(0.0231)|
|Observations|6,654|6,654|6,654|
|Boys|0.0670***|0.0888*|0.0447**|
||(0.0212)|(0.022)|(0.0222)|
|Observations|4,853|4,853|4,853|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Variables which enter the propensity score include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, calculator, student teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade, average teacher education levels and marginality index components. Community level variables include the margination index, proportion illiterate, proportion of households without piped water, proportion without electricity and proportion without drainage.

Overall, these results are highly consistent with Schultz, 2004; Behrman
for both Rajasthan and Rest of India.

The share of contract labor is about 6-7 percentage points lower in larger control firms, across Rajasthan and the other states. There is, however, no statistically significant difference between the treatment and control group as far as the share of contract labor in total labor is concerned. The estimated log expenditure on material inputs (relative to labor) is also not statistically different across both treatment and control, for Rajasthan as well as other states.<sup>30</sup>

# **5 Estimation results: State-level synthetic control analysis**

In this section, we present the results of the analysis using the 1980-2018 long panel, looking at aggregate output and employment in the formal manufacturing sector. We first consider the impact on total employment. Figure 3 presents the evolution of aggregate employment in Rajasthan and its synthetic control, and the MSE ratios from the placebo estimations. Table 13 in the appendix presents the balance between Rajasthan and the synthetic control for employment, and the weights on the control states in the synthetic control.

The synthetic control tracks Rajasthan closely between 1990 and 2014, adding to our confidence in its ability to replicate what would happen to Rajasthan absent the treatment (Panel A). But there is no sizable widening of the gap in total employment levels between Rajasthan and the synthetic control in the three post-treatment years. The average post-treatment gap in employment between Rajasthan and the synthetic control is 1 percent.

Panel B presents the key results of the placebo tests. Recall that inference testing in synthetic control is based on conducting placebo estimations on control units and examining the distribution of the ratio of the post and pre-treatment MSEs for the placebos. The purpose of this exercise is to assess the likelihood of obtaining a post-treatment gap (relative to the pre-treatment gap) as large as that observed in the treatment unit through sheer chance. In this case, the placebo tests indicate that Rajasthan’s MSE ratio is not unusually large compared to the ratios obtained when treatment is assigned randomly
these variables to benchmark the claims of public officials. For each indicator, we subtract the corresponding value in the benchmark data from the relevant claims in our data set, such that a positive error is an overestimate.

Our core analysis aims to bring together the full set of claims in a unified framework. However, individual errors are not comparable across indicators. Making an error of 0.5 in a district’s pupil-class ratio is large for that indicator, but tiny for the population indicator. To jointly assess the scale of errors across multiple indicators, we undertook a number of transformations of the raw error data. First, we took absolute values of the errors so that deviations of different signs are comparable.<sup>11</sup> Second, we trimmed

> eral/regional level respondents are asked about the value of the 25th percentile district in their (chosen) region, the 50th percentile district in their (chosen) region, and the 75th percentile district in their (chosen) region.

> 10One proxy of the quality of the benchmarking data is their stability over time. By assessing the extent to which indicators vary within districts over data collection rounds, we aim to understand whether the data reflect a noisy collection process. Table A2 presents summary statistics and tests for the presence of a random walk for each of the indicators used in the analysis where panel data are available, and the Appendix describes the results. We find evidence that the benchmarking data is consistent over time.

> 11The probability of a negative (as opposed to a positive) error in the data we use for our core analysis is 0.404, such that the likelihood of over- and under-estimates is relatively equal. We have also transformed the errors to be percentages

11
**Figure 4: Incidence of ninis by urban/rural status**

# **(ninis as % of youth aged 15-24)**

<!-- Start of picture text --> 30.0%<br>26.2%<br>23.7%<br>25.0%<br>21.3%<br>20.0%<br>16.8%<br>15.0%<br>10.0%<br>5.0%<br>0.0%<br>Rural Urban Rural Urban<br>1992 2010<br><!-- End of picture text --> Source: authors’ own computation with data from CEDLAS and The World Bank (2015) _Income:_ Figure 5 shows the incidence and distribution of ninis across the income distribution, as measured by deciles of household income per capita. As might be expected, youth from poor households are more likely to be ninis than their better-off peers. While the percentage of ninis in every decile has decreased over the past 18 years, there remains a huge disparity across income deciles: in 2010, 30.5 percent of ninis in the poorest deciles were neither working nor studying, compared with only 6.6 percent of those in the richest decile. Moreover, considered from the perspective of the distribution of the nini population across deciles, this disparity is growing. In 1992, an average of 36.8 percent of nini youth were in households in the three poorest deciles of the population, while 18.9 percent were in the three richest deciles. By 2010, the share of the three poorest deciles had grown to 45.1 percent of ninis, versus only 13.5 percent in the three richest deciles. Thus on average across the region, the nini problem has become increasingly concentrated among the poor over the past two decades.

9
%|15.2%|14.3%|13.7%|13.4%|13.6%|13.8%|12.7%|12.6%|14.0%|
|<br>||||**E**<br>|**mployment**<br>|||||||
|POEs|83.3%<br>|84.0%<br>|84.3%<br>|85.0%<br>|86.5%<br>|87.5%<br>|84.2%<br>|84.7%<br>|86.1%|85.7%|85.1%<br>|
|BOSs|16.7%|16.0%|15.7%|15.0%|13.5%|12.5%|15.8%|15.3%|13.9%|14.3%|15.0%|

_Source: Author’s calculations using WB BOS database and ORBIS._ Table 2 describes some summary statistics by country and shows some indications of performance gaps between BOSs and POEs. BOSs seem to be older and larger than POEs when measured by number of years in the market and number of workers across all countries in the sample. Yet, in most countries BOSs seem to be less profitable than POEs and obtain lower returns of investments -measured by assets and equity. BOSs also seem to achieve lower revenues per worker compared to POEs, except for four countries -Estonia, Spain, Italy, and Portugal. In terms of financial leverage, in about 90 percent of the countries, BOSs have a lower ability to cover short-term and long-term liabilities, measured by the current ratio and solvency ratio, respectively.

> 9 The total sample includes over 19.
participants (de Mel, McKenzie, and Woodruff 2014; Sonobe, Suzuki, and Otsuka 2011). However, concerns have been raised about the ability of low‐skill participants to implement what they have been taught (McKenzie and Woodruff 2014). Given that noncognitive skills are known to be strong determinants of entrepreneurial ability and that the program had impacts on these outcomes, an analysis of the heterogeneity of effects by this dimension is important.

In terms of skills formation, the program had a greater impact on both cognitive and noncognitive skills among those participants with lower initial skills by nearly all measures of noncognitive skills (appendix A, table A6, columns 6–9). However, these skill gains translated into labor market impacts differently depending on the initial noncognitive ability, possibly because of differences in the ability of beneficiaries to mitigate shocks. Among those participants with higher noncognitive skills (that is, impulse control) at baseline, the program had larger effects on employment, earnings, and the accumulation of capital stock (appendix A, table A6, columns 1–5). The direction of the interaction is similar to the interaction of employment and self‐employment, although the coefficient is not significantly different from zero. Meanwhile, those participants with lower initial ability experienced larger impacts on consumption.

At first glance, this result appears to confirm the second hypothesis above that more highly skilled individuals were better able to utilize the skills acquired through the program for productive ends in the short term. However, because of the Ebola crisis, the story is likely to be more complex. Although, because baseline data on wealth were not collected, the study has not been able to test whether initially poorer individuals achieved larger impacts, statistical analyses suggest that noncognitive ability is strongly associated with wealth: on average, youth in households with a lower proxy means test score have lower noncognitive ability. A 1 point reduction in the proxy means test score is associated with a 4.1 percent, 6.7 percent, and 9
147)|
|Other urban area|0.729%|
|Ref: Rural area|(0.135)|
|Additional controls?|‘Yes|
|Numberofobservations|1356|

Semi-parametric Cox proportional hazard model stratified by the polygamy status of the husband's father; exponentiated coefficients are presented (hazard ratios); robust standard errors in parentheses

* p< 0.10, ** p < 0.05, *** p< 0.01

The estimation sample includes all monogamous unions and polygamous unions made of husband and first-rank wife aged 15 to 60, in the first wave of the PSF Survey.

7 Additional controls include dummies for Koranic education of both spouses’ parents, and for formal education and sectors of activity of both spouses’ fathers. Data source: PSF Survey, wave 1 (2006-2007)
)|(0.015)|(0.007)|(0.009)|(0.000)|(0.007)|
|_ENTRE 1 j_|-0.001|0.000|-0.005|0.003|0.000|-0.003|
||(0.003)|(0.008)|(0.007)|(0.009)|(0.000)|(0.008)|
|_ENTRE 2 j_|-0.005|0.007|0.007|-0.011|-0.000|-0.007|
||(0.004)|(0.007)|(0.007)|(0.009)|(0.000)|(0.008)|
|_ENTRE 3 j_|-0.000|-0.005|-0.007|-0.013|-0.000|-0.004|
||(0.003)|(0.007)|(0.007)|(0.009)|(0.000)|(0.008)|
|Observations|147,977|145,628|147,977|145,628|147,977|145,628|
|R-squared|0.351|0.333|0.351|0.332|0.351|0.331|
|Controls at student level|YES|YES|YES|YES|YES|YES|
|School fixed effects|YES|YES|YES|YES|YES|YES|

Notes: 1 <mark>)</mark> Placebo: estimates usin <mark>g</mark> data from 2016 and 2017 <mark>,</mark> when the ENTRE <mark>p</mark> ro <
.8<br>5 2.1 2.3 2.7<br>0<br>15-24 25-34 35-44 45 and older<br>2002 2013 2016<br><!-- End of picture text --> _Source_ : High Commission for Planning (HCP).

A key reason for high and persistent unemployment in Morocco is the fact that job creation has been weak. While the working age population grew in net terms, by 270,000

> 10 MENA Economic Monitor. A New Economy for the Middle‐East and North Africa. World Bank. October 2018.

12
by Rodella et al. (2022) to the official World Bank administrative boundaries (with a special focus on SSA). The groundwater data contain the geographic centroids of each aquifer. We assign each aquifer to the country containing its centroid. To ascertain the exact shape, we use Rodella et al. (2022) to obtain the shapefiles of the major aquifers. For aquifers whose shapefiles we were unable to find, we construct circular buffers around these centroids such that the area of the circle matches the surface area of the aquifer.

We estimate aquifer statistics including the area of land covered by the aquifers, the volume of water in absolute terms, per capita, and as a proportion of the entire land area of the country to get a sense of which countries have the richest resources of shallow ground water.

We complement the Rodella et al. (2022) dataset with data from the Africa Groundwater Atlas, a dataset curated by the British Geological Survey. The Africa Groundwater

> 2In SSA, 70% of aquifers lie at a depth of 50 meters or less, 20% lie at a depth less than 25 meters, and 30% lie at a depth of less than 7 meters. This finding suggests groundwater extraction using dug wells is possible in the region, allowing for access to groundwater at lower costs.

5
and inequality, and iv) economic transformation. In section 3 we consider the legacy of the boom and prospects for the future. Section 4 concludes.

# **Section 1: The commodity price boom**

The boom period 2004-2014 marked a critical decade in Africa’s post-independence economic history.<sup>4</sup> This decade spawned much optimism, centered on the ‘Africa Rising’ narrative<sup>5</sup> ; associated with increased rates of economic growth, higher levels of inward investment, and large, sustained revenues from natural resource exports and taxation. This period also constituted the longest sustained period of high commodity prices on record.

Overall, this gave governments large increases in their fiscal space, derived in part from the receipts from resource taxation alongside debt relief for some countries post-2000. For example, at least 8 Sub-Saharan African countries obtained revenues from natural resources representing on average more than 10% of their GDP during the boom – a significant fraction by both regional and global standards (see Figure [fig:resrev_countries]). Many chose to use this time to undertake significant expansions of public service provision, such as educational enrollment, as well as increased infrastructure investment. For example, according to IMF’s Investment and Capital Stock Dataset, public investment in physical assets, including economic infrastructure – like roads, and airports – and social infrastructure – such as hospitals and schools – rose from roughly an average of US$200 per capita during the pre-boom period (1998-2003), to an average of over US$435 per capita during the boom, and US$270 after the boom period in resource-rich countries in Africa. Similarly annual spending on health services rose from 4.6% of GDP to 4.9% on average, and expenditure on education rose from an average of 2.8% of GDP before the boom, to 3.5% of GDP after the boom.

Policymakers sought to capitalize on the boom to translate investment and revenues into
3 discusses the implications of the use of multiple indexes or a single index to adjust household expenditures; section 4 constructs a benchmark to determine which approach is better; section 5 tackles the question of the sequence between temporal and price adjustments; section 6 provides an empirical illustration of the consequences of these different strategies on the estimated distribution of welfare, using the Islamic Republic of Iran’s 2019 Household Expenditures and Income Survey (HEIS); section 7 concludes, and offers a summary of the recommended deflation strategy.

# **2 Price indexes: General notation**

This section introduces the notation for dealing with price indexes, in a way convenient for the analysis carried out in the rest of the paper. We denote a generic price index by _I_ – one can think of it as a spatial or a temporal index, indifferently – and will assume that _I_ can be described by a set of 3
particularly appealing as it is more readily comparable across different group definitions as a result of the normalization by maximum BGI.<sup>23</sup>

# **3.  Evidence**

Expressing observed between-group inequality as a fraction of the maximum possible BGI can provide additional insight in the analysis of inequality. Figures 4-6 decompose inequality on the basis of three different sub-group definitions for a number of developed and developing countries. Our data come from nationally representative household surveys from each of the countries and all refer to a year during the 1990s. We consider three ways of breaking down the population in each country: by social group membership, rural-urban location of residence, and education of household head (not all countries’ data permit a breakdown along all three dimensions, however). Of these groups, only social group membership (which loosely refers to racial, ethnic, or caste breakdown relevant to each country) can be truly considered as a ‘circumstance’ or a predetermined characteristic, and as such consistent with measuring inequality of opportunity. In the rest of our empirical work, we would have ideally used _place of birth_ instead of _rural-urban residence_ , and _parents’ education_ in place of _education of household head_ . However, many of the household surveys in our database do not permit us to break the population down by these circumstance variables, and hence the variables we use instead can be viewed at best as crude proxies for an individual’s circumstances.<sup>24</sup> For each country and for each sub-group definition, the between-group share is calculated

> 23 We are grateful to Sam Bowles for suggesting to us this interpretation of _Rb ́._

> 24 Such data on individual circumstances, of course, exists for an increasing number of countries.  For example, drawing on the distinction between ‘circumstance’ and ‘effort’ variables in Roemer’s work on equality of opportunity, Bourguignon, Ferreira, and Menéndez (2003) uses a different method to decompose earnings inequality in Brazil into a
and higher value of agricultural production. Table 2 also displays the estimates of road returns for input and output prices as well as agricultural productivity. Fertilizer prices are 5 percent lower in RDP and RRMIMP project villages, and real agricultural wages for men in RDP project villages are 27 percent higher than in control villages. The aggregate price index of crops is 5 percent higher and the aggregate crop output index is 39 percent higher in RDP project villages because of rural road development; similar returns accrue in RRMIMP project villages where the aggregate crop price and crop output indices are 4 and 30 percent higher, respectively, due to road investment.<sup>15</sup> Interestingly, when computing the elasticity of the agricultural output index with respect to the road project (Table 3), we find that the elasticity is 0.27 for RDP households and 0.23 for RRMIMP households.<sup>16</sup> These figures are very close to the road-aggregate output elasticity (0.21) obtained by Binswanger, Khandker and Rosenzweig (1993), where they use district-level data in India to examine the linkages between infrastructure, financial institutions, and aggregate output in India.<sup>17</sup> Road development thus not only helps farmers due to higher crop production and prices, and lower prices of inputs such as fertilizer, but also benefits households via higher demand for labor, thereby raising the real agricultural wage rate of male labor (at least in RDP villages) up to 27 percent.<sup>18</sup> Higher wages and higher demand for labor can raise family labor supply. However, the net demand for family labor also depends on the negative effect of higher income on labor supply (thus, causing higher demand for leisure), which may reduce family labor supply. Nevertheless, as Table 2 shows, male labor supply increases by 49 percent and female labor by 51 percent in RDP villages because of a road development project. There are no similar statistically significant
shown in Table 9. In 1961-62 there were an average of 6.4 fragments (or parcels) per operational holding across all size classes. By 1981-82, this had fallen to 5 parcels per holding. The average area per parcel has also declined during this period, though marginally, from 0.31 to 0.29 ha. It is evident that the rate of fragmentation is higher in medium and large holdings.

In the absence of more disaggregated data, it is difficult to form an accurate view of regional variation in fragmentation. Knowledgeable sources claim that fragmentation is a more serious problem in irrigated and coastal areas than in western Orissa. However, and we discuss below, there is considerable resistance to land consolidation in western Orissa.

32 Fragmentation is defined as the number of non-contiguous plots per operational or ownership holding within a village.

42
Policy Research Working Paper 5918

# **Abstract**

Using a data-driven empirical case study method, the paper evaluates the impact of one identified reform program on development outcomes. The paper uses the World Bank Country Policy and Institutional Assessment Ratings to identify large scale structural and macro-level policy interventions in the last decade that were seen as being sustained and successful for IDA countries. Robustness checks are performed to show the efficacy of the method in particular cases. It was found that the method attains robustness in the case of Nigeria.

This paper is a product of the Poverty Reduction and Economic Management Network, Africa Region 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://econ.worldbank.org. The author may be contacted at sandeshd@gmail.com.

_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