rafmacalaba/data-use-mentions-extended huggingface dataset:
analysis in global impact evaluations, we first developed a set of keywords to represent each of the VFM methods based on extensive review of the VFM literature. Keyword sets were developed to represent cost-effectiveness analysis, costbenefit analysis, cost-utility analysis, financial analysis, social return on investment, basic efficiency resource analysis, multi-criteria appraisal, and rank correlation of cost vs impact.

A text analytics algorithm was conducted over a full-text search of each of the 236 impact evaluation studies to generate tables of keyword frequencies. This method also was applied to a “benchmark sample” of 65 studies drawn from a database of 168 World Bank Group impact evaluations studies published between 2002 and 2011 (World Bank Group impact evaluation database).

The benchmark sample was formed by combining IEs from two groups of World Bank impact evaluations. The first group contains 35 IEs identified as including some kind of (unverified) efficiency analysis. The second set of studies was pulled randomly from the remaining (untagged) 131 studies contained in the near census of World Bank impact evaluations described above. The outcome variable for the untagged impact evaluations is “No VFM.” Since IEs in the first and second group do not have the same probability of being selected into the benchmark sample, we applied a sampling weight. The probability of selection into the benchmark sample is 1 for the 35 tagged studies. The probability of selection for the untagged studies is 30/131 = 0.229 for the 30 untagged studies. Hence the sampling weight, 10
# _6.1. Description of the variables_

The final set of explanatory variables (all community-averages) includes the average time to haul water (in minutes), the average household size, the average number of children below five years of age, the average number of children aged 5 to 15, the average number of women aged 16 to 65, the average number of men aged 16 to 65, the average proportion of male household heads, year and regional dummies, and dummies to control for the month of interview. The education of the household head and the household’s wealth index have not been included in the model because of their strong correlation with the variable measuring hauling time (the coefficient of correlation with hauling time is -0.38 and -0.53, respectively).<sup>13</sup> In 7% of the surveyed clusters, the average time to the water source is zero. In order to take this particular feature of the data into account we include a dummy variable which takes the value 1 if and only if the time to the source is 0, following Battese (1997).

For each cluster, community-averages are calculated using data on households in which at least one girl aged 5 to 15 is present (with no girl aged 5 to 15, the proportion of such girls attending school must be zero). In order to control for possible selection bias, the total number of children aged 5 to 15 is treated as endogenous and instrumented in a first-stage regression. The identifying instruments are the age of the household head (linear and squared versions of this variable) and a dummy variable indicating whether the household head is a widow(er) or is divorced. We also consider the time to haul water as potentially endogenous, and use as identifying instruments the dummy variable indicating whether the household head is a widow(er) or is divorced, whether the household lives in a rural or urban community, and the one-period lagged hauling time. The
an indicator equaling one if not all school-aged children attended school in the past month; _X_ denotes a vector of household control variables24; and _V iolence_ _~~S~~ everity_ denotes a number of measures of severity used in different empirical specifications, including an indicator equaling one if there was a conflict fatality in the month of the survey in the district, the number of conflict incidents in the district, and the number of conflict fatalities in the district.

Estimates of _β_ are reported in Table 2. In all specifications, there is little evidence of a relationship between school attendance and the location of violence. Column (1) demonstrates that there is no relationship using the indicator equaling one if there was a conflict fatality in the district as the measure of severity, column (2) demonstrates there is no relationship using

> 22Comparisons between the WFP survey to estimates of mobile phone users in the 2014 HBS data demonstrate that every single indicator of food security collected that is replicable in the 2014 HBS dramatically declined, consistent with the reports of widespread food insecurity (e.g.,  IPC 2017;  FEW NET 2018;  etc.);  and access to a wide range of other basic services also declined, which is consistent with the reporting of humanitarian needs (e.g., OCHA 2020).

> 23The p-values were obtained from a regression of the post indicator on each of the variables reported in table 1. For each shock, the sample is restricted to observations from the two months before and the months during each shock in the region that the shock affected.

> 24Control variables include the gender of the household head, the age of the respondent, and the household size.

12
accounts for 0.2% of the sample (Online Appendix Table 2). Some of the largest outliers include Iraq during the Gulf War, Rwanda during the 1994 genocide, and Lebanon during the civil war in the 1970s-1980s; different data sets disagree dramatically as to growth rates during these periods. We also drop Liberia as it has many extreme growth outliers, and Myanmar due to irregularities in its PWT9 growth data (e.g., a three-fold increase in GDP per capita in 1970).

A major caveat is that our results are only as good as our growth data. These are not only noisy but also possibly even manipulated. For example, Martinez (2018) uses the brightness of nighttime lights as a proxy for GDP and finds that autocracies systematically overstate GDP growth. We address this data quality issue in Online Appendix 3 by calculating leader effects using two other growth data sources and find that the set of significant leaders often changes.

**Estimates of variance components in the data.** Table 2 shows estimates of the variance components { } used to construct shrinkage factors and for the LS leader estimates. μμ ii εε They are calculated using PWT9 growth data and Equations 9-11 above. In the pooled sample of σσ , σσ , σσ ψψ γγ all leaders, our estimate of the underlying standard deviation of leader quality is = 1.33%, μμ which is similar to Jones and Olken’s (2005) all-leaders estimate of = 1.5%. The standard σσ� μμ deviation of the country effect is = 0.65%, and the standard deviation of the iid error is much σσ� larger, around =4.6%. σσ�ii rr σσ� **Table 2: Estimates of Variance Components in PWT9 Growth Dataponents in PWT9 Growth Dataonents in PWT9 Growth Data**

**Table 2: Estimates of Variance Components in PWT9 Growth Dataponents in PWT9 Growth Dataonents in PWT9 Growth Data**

|**SD(leader)**<
8

**Data Quality.** In Web Appx. Section A.1,<sup>9</sup> we verify that the I2D2 database generates global patterns that are broadly consistent with patterns observed when using other global databases such as the _World Development Indicators_ (WDI) database of the World Bank. More precisely, we find that our samples are globally representative in terms of per capita incomes, age structure, education, and self-employment.<sup>10</sup>

# **3. Methodology for the Estimated Returns**

We calculate _potential work experience_ – which we call “experience” in the rest of the analysis – as follows: (i) For individuals with at least 12 years of education, we assume children start school at age 6 and calculate experience as age - years of education - 6; (ii) For individuals with less than 12 years of education, we assume that experience before age 18 is inconsequential and calculate experience as age - 18. With these calculations, the mean (population-weighted) number of years of experience differs little between developing economies – 19 – and developed economies – 22. Indeed, while workers in developed economies study longer on average, they also tend to live longer.

**Estimated Returns.** For individual _i_ and sample _t_ , we use OLS to estimate the following model for _each_ country one by one:

where the dependent variable is the log of monthly wages ( _lnWit_ ). Experience is categorized into seven bins ( _expite_ ). The bins are [5-9 years] (which we call 5), [10-14] (10), [15-19] (15), [20-24] (20), [25-29] (25), [30-34] (30), and [35+] (35). The omitted bin is [0-4] (0). We control for the number of years of education ( _eduit_ ). We include sample fixed effects ( _θt_ ) to capture
variable to approximate the labor earnings structure. As Székely and Hilgert (2007) point out, the cross-country comparison of labor income inequality is meaningful and shows a

> 16 The countries in which the effect was present and in which it was feasible to control for the effect are those on which data exist on two years in the same period, one for the series before the change and the other for the series after the change.

> 17 This information has been constructed based on a knowledge of the SEDLAC Project and survey metadata and in close contact with the staff of each NSO.

> 18 This change limited the sample in Argentina to the urban areas covered since 1992, Gran Buenos Aires, and other major urban areas that include around 50 percent of the population. It also limited the sample in Uruguay to the urban population in the metropolitan area of Montevideo (about 70 percent of the population).

> 19 Among other trimming procedures, the north rural areas of Brazil were excluded because these were not covered in the 1990s (less than 2 percent of the population); also urban areas in new departments in the Amazon and Oriental regions in Colombia, only recently covered (less than 4 percent of the population).

> 20 See Bakis and Sezgin (2015) for the case of Turkey, with a fall between 2002 and 2004 and a slight increase in inequality since 2004. See Leibbrandt Arden, and Woolard (2013) for the case of South Africa, with a steady increase since the 1990s and a slight increase during the first decade of the 2000s. See Autor, Katz, and Kearney (2008) for the case of the United States, with a slight increase from the 1990s to the end of 2010. See Calvo, López-Calva, and Posadas (2015) for the case of the Russian Federation, with a slight increase at the end of the 1990s, but a steady fall during the first decade of the 2000s.

5
4 the groups are defined by specific household attributes, such as regional location, urban-rural status, or age, gender, race or education of the head.

However, the personal distribution of income does not only reflect differences in these household characteristics, but also differences in the extent to which households have access to formal employment, vis-à-vis a reliance on self-employment, and indeed variation in their access to capital or transfer incomes. Therefore, this section also examines the income sources of each household and their relationship with inequality in total household income per capita.

The next two sections turn to the dynamics of inequality and poverty. Section 4 discusses a dynamic decomposition methodology due to Mookherjee and Shorrocks (1982), which separates _changes_ in inequality into components due to changes in the mean incomes of different groups, changes in the composition of these groups, and unexplained changes. It also presents the Datt and Ravallion (1992) decomposition of changes in poverty into a growth and a redistribution component. Section 5 briefly reports on the correlations between poverty, inequality and a couple of macroeconomic variables, focusing on the rate of inflation. Section 6 concludes and presents the candidate hypotheses that our analysis suggests might explain the recent changes in Brazil’s income distribution. Some of the questions raised for future research are briefly discussed.

# **2) The Data and What it Says.**

The data sets we use are the household-level micro-data from the _Pesquisa Nacional por Amostra de Domicílios_ (PNAD) for 1981-2004, produced by the _Instituto Brasileiro de Geografia e Estatística_ (IBGE)<sup>5</sup> . Data were collected each year from a representative national sample of households, with a sample size ranging from 291,000 to 525,000 individuals.<sup>6</sup> The

> 5 Three years are missing from the time series presented below: 1991 and 2000 were Census years, during which PNADs are not fielded. Income data from the Censuses are based on very different questionnaires, and are not
in 2020, 72 percent of the population use the internet. However, 93.8 percent use it to communicate, 91 percent to look for information, and 89 percent to access the social network.

> 5 INEGI, Telephone Survey on COVID-19 and the Labor Market (ECOVID-ML) July 2020.

> 6In the literature on labor markets, fixed costs are those that do not vary with the number of hours a person works.

> 7 A pioneering paper, Edwards & Field-Hendrey (2002) proposed that flexible work enables women to better integrate employment and family roles.

2
The prediction is that such rules should reduce employment adjustment in response to shocks, not only decreasing the number of layoffs during upturns, but also dampening hiring in upturns because of direct hiring costs and the potential cost of having to lay off newly‐hired workers in the future (Oi, 1962; Nickell, 1986; Hamermesh, 1993). The enforcement of such rules too matters here because the expected cost of labor adjustment depends on the firm’s assessment of the probability that the law will be enforced.

Our empirical strategy for testing this prediction is the following. We first identify episodes of upturns (surges) and downturns (slumps) in different manufacturing sectors (relative to their trend growth rates) from time series data on industry output. Using a large firm‐level panel data set that includes firms from 79 regions of the Russian Federation, we then test if firms’ employment adjustment during these episodes is smaller in regions with better capacity to enforce labor laws.

As expected, the regression results show that compared to normal years, the annual growth in firm revenue and employment is significantly higher during surges, and lower during slumps. But unlike revenue, the degree to which employment adjusts upwards during upswings and downwards during downswings is significantly smaller in regions with stronger enforcement capacity. This suggests that the extent to which employment protection laws dampen labor adjustment by firms depends on enforcement capacity.

> 5 World Justice Project data can be found at http://data.worldjusticeproject.org/#groups/AUT. The indices are based on surveys of households and experts across countries.

2
-0.10–0.00 -0.10–0.00<br>-0.20–-0.10 -0.20–-0.10<br>-0.20–-0.20 -0.38–-0.20<br>no data no data<br>IBRD 44309 | APRIL 2019<br><!-- End of picture text -->

Note: Surveys are the same in the left and right maps in each row, but not necessarily the same in the top and bottom maps in each column.
good floor material, having good roofing material, or owning a bicycle, computer, stove, television, washing machine, fan, refrigerator, car, and land. (For a complete list of assets used in each country, see Table 3.) PCA relies on the information from the covariance structure of these assets and the hypothesized latent variable that maximizes the explained variance in the set of assets. Table 3 shows the loadings for the first principal component which are treated as the relative weights assigned to each asset to construct the proxy for wealth, or the asset index. For example, in Nigeria, owning television is a strong positive contributor to household wealth (first factor loading of 0.82), while owning a bicycle is a negative contributor to household wealth (first factor loading of -0.02). For Mali, Pakistan, and Colombia, we have data on the ownership of only three assets, namely computer, cell phone, and landline. In these cases, _asset ownership_ variable is instead created as a binary/indicator variable of owning at least a computer or landline phone. The ownership of a cell phone has quite limited variation across households, hence it has a low weight in the asset index, while owning a computer or landline has greater variation across households and is more likely to indicate household wealth.

# **3. Results**

# _3a. Regional profiles and Reclassification of Poverty Status_

By using the same data from 162 countries on household resources as used for the per-capita poverty estimates, we examine how poverty profiles would change under the assumption of household economies of scale, as reflected by allocating household resources based on the square root of household size. Our focus is on how changing the allocation rule changes the composition of who is poor and not on the level of poverty. The IPL of $1.90 (in 2011 PPP US dollars) reflects typical values of national poverty lines expressed in per capita terms from some of the poorest countries in the world (
variables are generally those reflecting the state of the economy, so any projections depend on the reliability of the economic forecasts.

* **Aggregation of the tax expenditure estimates.** Estimates for individual tax expenditures, some argue, cannot be added together to determine the cost of several tax expenditure programs. There are two reasons for this: (1) the simultaneous elimination of more than one income tax expenditure would generate different estimates because of progressive income tax rates; and (2) given the interaction of certain tax measures, the revenue impact of eliminating two or more measures simultaneously would differ from taking the independently estimated numbers and simply aggregating them.

Without the establishment of benchmark tax system for the purpose of estimating tax expenditures, the Polish Ministry of Finance calculated the revenue foregone for 18 individual personal income tax expenditure programs from 1993 to 1998 using information from tax returns. This was only a fraction of the overall revenue foregone during that period, albeit a large fraction.

Keeping in mind this simple aggregate estimation rule, and temporarily leaving the simultaneous effects aside,<sup>6</sup> the estimation of the 18 personal income tax expenditure programs (Annex Table 2) provides interesting results. The total revenue foregone for the 18 programs was over 5 billion

> **6** As discussed above, these results under-estimate the revenues foregone under tax expenditure programs because they do not account either for the progressivity of income tax rates, or simultaneity.

11
present discounted cash flow from its optimal use. Since land values are not influenced by realized weather or prices at the time of the survey, they should more accurately reflect long-term climate and other agronomic characteristics than measures of current land productivity such as yields or net revenues. While plot values are self-assessed rather than based on records of actual market transactions, farmers are generally knowledgeable about the value of this their primary asset. Indeed, evidence from elsewhere in south Asia (Jacoby, 2000) indicates that self-assessed plot values accurately capture rental income from land. Idiosyncratic biases, moreover, would tend to wash out when averaging over a vast number of observations on individual plot values collected from tens of thousands of farmers.

Data on the value of more than a hundred thousand individual parcels of cropland nationwide are available in the NSS 59th round, along with area and irrigation status (see Table 1 for descriptive statistics at the district level).<sup>16</sup> Figure 2 shows how median land values per hectare (adjusted for regional cost-of-living differences) vary over the Indian subcontinent. The highest valued cropland lies across the Indo-Gangetic plain, the most intensively farmed zone in the country, whereas land values are lowest in the far-western desert region.<sup>17</sup> Comparison of the two maps in Figure 2 shows a distinct positive relationship between land values and irrigation, one strongly confirmed in the micro-data to which we now turn.

In Table 2, we report alternative specifications of equation (6) using different sets of explanatory variables. Since our data do not identify the main source of irrigation for a plot (i.e., canal or groundwater), we can only estimate an average return to irrigation. Model I, at any rate, assumes that this return is constant across districts. The resulting estimates (first column) indicates that, on average, an irrigated plot is 37% more productive than a rainfed plot within the same district. For the second stage, we regress the
(2005). Eight Questions About Corruption. _Journal of Economic Perspectives_ , 19 (3): 19 – 42.

Syverson, C. (2011). What determines productivity? _Journal of Economic literature, 49_ (2), 326365.

Tanzi, V., & Davoodi, H. (1997). Corruption, Public Investment, and Growth. International Monetary Fund Working Paper No. 97/139.

Transparency International. (2007). Corruption in the Education Sector. Transparency International Working Paper 4.

UNODC, UNDP, and the UNODC-INEGI. 2018. Manual on Corruption Surveys. Vienna: United Nations, Center of Excellence in Statistical Information on Government, Crime, Victimization and Justice. Available at: <u>https://www.unodc.org/documents/data-and-analysis/Crime-statistics/CorruptionManual_2018_web.pdf</u> 29
dynamics of their vegetative development, and their effects on the soil’s net nutrient balance, sequencing them in ways that capitalize on positive synergies while avoiding negative interactions can increase output or help save input costs (Hennessy 2006). Historically, addition of new crops has been a key driver of productivity growth and the ability of the agricultural sector to release labor for non-agricultural development (Brunt 1999). More recently, synthetic fertilizers, pesticides, and herbicides allow producers to maximize profits by shortening rotations and reducing their diversity (Plourde _et al._ 2013).

Crop rotation affects outcomes via three channels. The first one is the reduction of pest and disease-pressure <mark>(Hilton</mark> _<mark>et al.</mark>_ <mark>2013) by preventing development and a</mark> ccumulation of crop-specific fungi, bacteria and insects that often survive in crop residues (Gentry _et al._ 2013). Proper rotational management can also reduce greenhouse gas emissions (Behnke _et al._ 2018) and capture soil carbon. A second mechanism is to enhance plants’ ability to access nutrients or water through differences in root structure to improve capillarity and aeration of the soil, thus also reducing susceptibility to disease <mark>(Koch</mark> _<mark>et al.</mark>_ <mark>2018).</mark> The underlying dynamics of bacterial, fungal or insect populations and their interaction with climatic conditions can be captured in biological models (Regis Mauri 2019). <mark>A third element is l</mark> egumes’ ability to capture nitrogen <mark>(Archer</mark> _<mark>et al.</mark>_ <mark>2020)</mark> as well as crops’ differential nutrient requirements (Bowles _et al._ 2020). Together with data on prices for inputs and outputs, technical coefficients based on the varietal choice and crop management options