rafmacalaba/data-use-mentions-extended huggingface dataset:
in each case. Section 3 describes the way the database of knowledge products was constructed and how the key attributes of each document were defined. Descriptive statistics from this database are used in Section 4 to show that the universe of knowledge products is broader than that typically receiving the attention of World Bank management, with both sets even displaying opposite trajectories over time. In section 5, knowledge products are classified in several relatively homogeneous categories; for each of them a statistical analysis akin to the estimation of production functions is conducted. The main patterns emerging from these exercises are summarized in Section 6. Building on them, Section 7 concludes by offering several practical recommendations to strengthen analytical work at the World Bank.

# 2. Previous studies

Based on their unit of observation, previous assessments of knowledge work at the World Bank can be classified in four groups. A few of them have focused on _research products_ , the more academic end of the spectrum. Others, more operational in nature, have tried to distill patterns from _budget codes_ that are deemed to generate analytical content. Several studies have put emphasis on the _work processes_ through which analytical products are prepared. And a couple of them have elicited and analyzed _user views_ in relation to World Bank knowledge products.

Evaluations of _research products_ tend to focus on academic recognition, with citations being an obvious measure of the World Bank’s influence on development thinking. One study in this group applied the citations metric to some 8,100 journal articles, around 6,800 books and chapters in books, and close to 3,000 working papers that had been put in the public domain by the World Bank over several decades. The set was assembled by combining records from an internal database of documents and from various external bibliographical repositories (Ravallion and Wagstaff 2011).

Using citation data from the Google Scholar platform, this study revealed the heterogeneous quality of the research products selected. A large share of them – including several _World Development Reports_ – had little discernable influence. However, within specific
.39<br>0.40<br>0.17<br>0.20<br>0.06<br>0.00<br>Micro Small Medium Large<br><!-- End of picture text --> _Source: Own estimates based on administrative tax data. The definition are as follows: micro enterprises employ fewer than 10 persons, small enterprises employ 10 to 49 persons, and medium-sized enterprises employ 50 – 249 persons. Large enterprises employ 250 and more employees._

**Among employees, the highest percentage of minimum wage earners is among the oldest and**

**youngest age groups** . A prevalent misconception suggests that individuals earning extremely low wages, those who would experience an increase if the minimum wage were raised, predominantly consist of young workers. Our results show that older workers are also overrepresented in Romania. The disparity in the proportion of minimum wage earners among the youngest employees is particularly interesting. Notably, the percentage of minimum wage earners escalates from 27% among taxpayers born in 1994 (26 years old in 2020) to 43% among those born in 2001 (19 years old in 2020). Figure 7 shows the share of minimum wage earners across birth cohorts.

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Figure 9: GDP growth rate, total labor mobility and moving cost differences, 2004-2015 <!-- Start of picture text --> 300 300<br>200 200<br>100 100<br>0 0<br>-100 -100<br>-200 -200<br>-300 -300<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>(a) Total labor mobility (b) Inverse of implied moving cost differences<br><!-- End of picture text --> _Notes_ : Panel (a) depicts the ratio between the number of workers who change jobs (within the same labor market or to another labor market) and the number of stayers. Panel (b) depicts the inverse of the difference between the moving cost implied by the auxiliary model and the actual moving cost from our estimation. All series are in terms of deviations from the mean and multiplied by the inverse of their standard deviations for comparison.

# **7.1 Data and Simulated Flows**

In Section 4.1, we estimated _m_ �<sup>_kl_</sup> _t_<sup>,and the three parameters of the moving cost function by PPML. We</sup> used the average of the estimated moving cost parameters over the sample period to characterize the initial steady state flows for the counterfactual simulation as explained in detail in Appendix A.5.1.1. The steady state simulation backs out values to match the labor allocations in the data, but does not target flows. In fact, steady state simulations only use labor allocation data, wage data, moving cost, and labor supply elasticity parameters. We show that despite the simple moving cost function, the model matches the labor flows in the data very well. Figure A6 panel (a) shows the average number of workers switching regions in the data
<!-- Start of picture text --> Madagascar's annual trade surplus with France<br>—— 85 reported by Madagascar<br>400 — as reported by France<br>350<br>300<br>-"<br>O 250<br>2<br>< 200<br>2<br>= 150<br>=<br>100<br>50<br>Qo<br>2000 2002 2004 2006 2008 2019 2012 2014 2016 2015 2020<br>Year<br>1.A) Exports from Madagascar to France 2.B) Exports from France to Madagascar<br>et] as reported by Madagascar —<br>soa as reported by France 500] —asmo portedreported byby Maceguscar Fresoe<br>asa 450]<br>baa sq &3 ae9]<br>5 #0 =<br>=* 450aay350) =“ 350)200]<br>13]<br>300)<br>250 i)<br>spe GREE RUM EGE ROB EAH BBLS GG 2b ants one 2000 2002 «200 2002008 | 2080 202 aod) 2OB | 2020<br>Year Year<br>1.B) 2.B)<br>120k<br>120k<br>00k<br>20ak|<br>ov ‘B0k] wo<br>= = sak]<br>==<br>|60k 7<br>aF==SeokF<br>40k 49k<br>20k| zak<br>oO! =i “0.5 a a5 Fy o! =2 -0.5 0 0.5 t<br>Diserepaney Index Diserepaney Index<br><!-- End of picture text -->
# **2. Data**

The data used in this study are drawn from the national household survey (the _Encuesta Nacional de Hogares_ , ENAHO) carried out by the Peruvian _Instituto Nacional de Estadística e Informática_ (INEI). The ENAHO is an invaluable source of information which in 2009 covered more than 20,000 households. The survey collects extensive data on socioeconomic information as well as labor force activities, including whether the members of the households work in the public or the private sector, both formal and informal.<sup>4</sup> Based on 2009 survey, 15,613 employees provided information on their salary and the sector where they work (public or private). In particular, wage data are available for 3,670 public sector workers and 11,943 private sector workers (see Table 1).<sup>5</sup> Only one-third of these private sector workers have a formal contract (4032 private sector employees have a formal contract).

In order to analyze the differential between public sector and private sector pay, compensation has been computed by aggregating wages, in-kind payments, and bonuses. In addition, we focus on hourly compensation rather than annual earnings. As discussed by Blinder (1976) and Blomquist (1978), the estimation of an earning function based on annual earnings may confound pay differences and issues related to the amount of labor supplied. Based on descriptive statistics in Table 1, public sector pays higher wages than the private sector (8.81 vs 4.34 Nuevos soles per hour). Also total hourly compensation is higher in the public sector (9.38 vs 5.29 Nuevos soles).<sup>6</sup> However, caution is needed to interpret these results because of the different characteristics among workers in the two sectors and the fact that the private sector includes both formal and informal workers.

Table 1 also displays data on education attainment, age, marital status and whether workers live in an urban
consumption/income is aggregated reduce data transparency and comparability. It is therefore ideal to use consumption/income aggregates constructed using the same method. Due to lack of such data in some cases, however, we aggregated various consumption components, following the methodology proposed by Hentschel and Lanjouw [23] and Deaton and Zaidi [22].2 Even in cases where consumption data exist, their comprehensiveness varies across surveys. For example, the LSMS is the most comprehensive of all, including consumption items such as home-produced food, the imputed rental value of the household's dwelling, and the annual service value of the durable goods, as well as spending on food, non-food items, health services and education. By contrast, some surveys-such as that for Guatemala-offer only a very limited range of consumption categories with only food and certain non-food items. Readers are advised to bear the above caveats in mind when interpreting the results presented in this paper.

We equivalize household consumption to take into account inter-household differences in household size. The two extreme positions on equivalization are (a) to assume that there are no economies of scale in household consumption (it costs two people twice as much to live as one) and (b) to assume that there are maximum economies of scale (two can live as cheaply as one). These two extremes, and the various possible intermediate positions, can be represented by the following relationship between equivalent consumption and actual consumption: 2 This was the case for the following countries: Guatemala, Philippines, Russia and Zambia.
interfering with the consumer price index and other official statistics. This led to inaccurate and non-credible reported inflation figures (Todesca 2017). Similarly, Greece’s debt statistics appear to have deliberately misrepresented the country’s financial situation in the lead-up to the 2009-euro crisis (Katsimi and Moutos 2010). In both cases, the deliberate manipulation of statistics was the precursor to larger crises, revelations and fiscal austerity measures that triggered social unrest and had significant social welfare implications.

Assuring that data cannot be manipulated by producers can have positive consequences on government budgets. India saved 19 percent on a rural employment guarantee scheme—the world’s largest workfare program—after introducing e-governance to the payments system. Most of the savings were incurred because local officials were no longer tasked with distributing the funds and thus could not mismanage them and misreport the data. The change decreased officials’ personal wealth by 10 percent (Banerjee et al. forthcoming).

In some cases when data are not impartial, third-party sources may notice inconsistencies. In China, for instance, media and watchdog organizations in Beijing noted inconsistencies between official government data on air quality and data from independent air quality monitoring systems. Heightened concerns about air quality fueled a dramatic expansion in publicly available, real-time data from thousands of air quality monitoring locations (Yin et al.

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preference for sons. Their empirical strategy was to use PSM to compare outcomes for women who were similar across every dimension except their TV viewership.

Similar patterns are observed in non-causal or correlation-based studies. Lin and Adserà (2013) also use NFHS-3 to find that mothers’ exposure to TV was associated with girls spending fewer hours doing household chores, and a smaller gap between girls and boys in terms of time spent doing chores. Barber and Axinn (2004) use couples-data from the Chitwan Valley Family Study in rural Nepal to identify the negative association between exposure to different media formats and son preference. Barber and Axinn (2004) try to address the limitations of the nonrandomized research design by looking at the relationship between exposure to different media formats (newspapers, radio, TV, and movies have their own determinants for access and self-selection) and son preference. Pande and Astone (2007) use NFHS-2 data to find comparable results for the association between different types of media exposure (radio, TV, movies) and son preference.

These mediums are effective because they introduce individuals to lifestyles, values and behaviors that are different from their own. In doing so, they develop in viewers a sense of awareness of how their own social interactions compare with those of the characters they see on television or film (Jensen & Oster 2009). The fictional drama format is particularly advantageous as it can be entertaining and instructional without being condescending to the viewer. As such, they are more likely to be popular among the key target audience: young women (Naqvi 2006).

However, content produced for the multi-media format need not necessarily produce the desired social response. Content producers have to walk a thin line to make sure they do not alienate their audiences. For example, _Atmajaa_ was a fictional drama centered on the issue of sex selection that aired in India in 2004. An audience impact study of the show suggested that while young women were receptive to the message of the show, older women felt negatively stereotyped, married women felt
are more intricate to treat and need further elaboration and assumptions. All details of the country’s taxes and transfers system, and how they are identified and assigned to households using the data sources described above, are presented in the methodological Annex A.

One methodological contribution we make to the approach is to treat VAT non-compliance in a distributionally-sensitive manner. Incidence studies typically treat VAT as if all households pay the statutory rates in their purchases, or as if they contribute an effective VAT rate calculated from national accounts. In the former case, households are assumed to fully comply with VAT in all their purchases. In the latter, the full compliance assumption is lifted, but all households are assumed to pay the same rate, regardless of their socio-economic background. The majority of CEQ studies fit the latter case, treating VAT evasion as uniformly distributed.

In developing countries, however, where the informal economy is more prevalent, VAT compliance is a function of consumer behavior and purchase channel, and this varies across the household income distribution. Lower income households tend to make a larger share of their purchases in informal outlets without invoice. Using this observation, we depart from previous studies and lift the assumption of uniformly distributed non-compliance.

To do that, we use data from the IPSOS consumption survey to estimate the share of purchases made in places that normally do not invoice (like open bazaars) by each socio-economic segment of the population. We then apply these shares of no-VAT purchases to adjust the calculation of VAT in the purchases declared by each socio-economic segment in the HBS. As expected, the share of purchases in places like open bazaars is higher among lower-income households than higher-income households. This small tweak in the methodology matters from a distributional viewpoint.<sup>10</sup> After calculating taxes and transfers for all households, we aggregate them and compare the aggregates to the public administrative records. Table 3 below present the results of the comparison. We find that HBS does a relatively good job in capturing the magnitudes of fiscal
recently compiled data set of country level health expenditures. The data are the latest available update of those which appear in World Bank (1993) and were prepared for World Bank (1 997a).<sup>8</sup> Table 4, colurmn I shows the results of adding the (natural log of) public sector health expenditures as a share of GDP into the regression. Public expenditures on health do reduce under-5 mortality, although the effect is empirically small and imprecisely estimated, and is statistically significant only at the 10 percent level."<sup>9</sup> Public spending on health explains _very_ little of the variation in under-S mortality over and above that which can be explained by non health-sector variables, the incremental R-squared is equal to 0.0015.20 This means that only _0.15_ percent of all mortality variations are explained by differences in spending. Introducing public sector health expenditures does not affect the other coefficients in the regression by much even though it is positively correlated with some, particularly GDP per capita.<sup>2</sup> ' 1 The sources for the updates are primarily country sources, e.g. ministries of health.

'9 We have included the log of "public spending on health as a share of GDP" as derived from the aggregate "health production function" discussed below. Using the log of "public spending on health per capita" yields _the same point estimate and signifcance_ for the effect of public spending on health (as a mathematical re-shuffling would show). The only difference in the latter specification is the coefficient on income.

20 The incremental R-squared is generally not appropriate as a _statistical_ procedure for resolving questions about the relative importance of variables, but if one has prior non-statistical knowledge about the causal ordering among the independent variables it is appropriate.

21 In the data set the bivariate correlation coefficient between GDP per capita and public sector health expenditures as a share of GDP is 0.63.

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3.2 to 4.4 percent under Scenario 1, and 5 percent under Scenario 2. Income inequality is also expected to increase as a result of the sustained crisis, with the Gini coefficient rising from 37.2 to 39.4 under Scenario 1 and to 41.4 under Scenario 2. Overall, we observe the economy to be hit hard due to the pandemic and the associated shutdown.<sup>12</sup>

**Table 2:  Projected impacts on Poverty and Inequality**

||Pre-COVID19|Post-COVID19<br>Scenario 1|Difference<br>Scenario 1|Post-COVID19<br>Scenario 2|Difference<br>Scenario 2|
|---|---|---|---|---|---|
|Extremepovertyrate|2.9%|7.4%|4.5|11.8%|8.9|
|Extremepoverty gap|0.5%|0.8%|0.2|0.9%|0.3|
|Povertyrate|13.7%|20.9%|7.3|25.6%|11.9|
|Poverty gap|3.2%|4.4%|1.2|5.0%|1.8|
|Gini coefficient|37.2|39.4|2.2|41.4|4.2|

Source: Author’s calculations based on EBCNV 2015 Not surprisingly and in line with global estimates,<sup>13</sup> the poor in Tunisia are more likely to be hit the hardest due to the COVID-19 crisis. To analyze the subgroups of the population along the welfare distribution that are most likely to be affected by economic slowdown, we plotted density and Growth Incidence Curves (GIC). Figure 8a shows the pre-crisis and post-crisis consumption distribution under Scenario 2, along with poverty line (red line
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components. The real estate market in Korea had becomw a substitute for the equity market and the holding of financial assets.19. It was pointed out that macro-economic policies may ave served to raise land values above what they would have been in the absence of such distortions. However, because of the almost total separation of policy formulation into "macro-economic policies" and "physical planning policies" there was little receptivity to such analyses. Policy makers continued to take a "partial equilibrium" approach to what is a "general equilibrium" prublem. Economic planners became even more determined not to fuel what they saw as "socially wasteful speculation" by not allowing the expansion of the housing finance system. Physical planners stiffened their resolve to prevent the "destruction" of prime agricultural land or open-space and tightened the land-use laws.

20. Analyses of national accounts for the late 1960s and early 1960s showed that the total domestic marginal savings rate was dominated by the margir.al public savings ratt. which was about 24 percent, while the marginal private savings rate was only 14 percent (Williamson, 1976). But it is well known that conventional national accounts ignore capital gains and only measure the accumulation of assets. In other words, national accounts measured only the cash-flow effects of policies and not their impact on the balance-sheet of various sectors, in this case households. From the household viewpoint, the reality is that the saving objective is to increase the stock of private assets to the desired relationship to current income. When income rises very rapidly as was the case in Korea, the desired level of wealth can be raised through the accumulation of new assets, the increase in the price<sup>of</sup> existing assets, or both.

21. The stock of assets has two components: land or real estate R of price Pr, and conventional capital K of current price Pk. The stock of land R being fixed (dR = 0), current saving which is simply an increase in
large as the true values, and the female parameters are one-third too small, so that RM’s method finds that male and female credit effects are about the same while the true female effect is three times as large as the true male credit effect. The Kleibergen-Papp rk rank test F statistic for weak instruments in the 2SLS has a mean value of 0.736, and the null hypothesis of “weak instruments” is never rejected in the 200 simulations. It is no surprise that the instruments are weak in RM’s approach,

> 16 RM actually estimate their model with linear LIML _not standard_ two-stage least squares.  This turns out to be another crucial error on their part that is discussed at length below.  We use standard 2SLS here so as to highlight the effect of the weak instruments bias that arises as a consequence of RM’s padded matrix of instrumental variables.  The further degree of bias arising from the use of linear LIML is estimated with these same simulated datasets below and discussed at length.

> 17 For the simulation, 40 variables Xi were generated from independent normal (0,1) random variables.  The treatment chosen by females who are offered treatment is cf = 0.5∑x + 12μ + 8εf, and the treatment chosen by males who are offered treatment is cm = 0.5∑x - 12μ + 8εm, where μ , εf, and εm are independent normal (0,1) random variables.  The outcome is y =   0.75*cf + 0.25*cm + 0.5∑x  - 12μ + 8ν, where ν is an independent normal (0,1) random variable.  Female treatment was made available to 40 percent of the sample, male treatment to 40 percent, and no treatment to 40 percent.  Half of the observations with female treatment also had male treatment, so when taking into account the overlap the percentages sum to 100 percent.  Those treated were randomly assigned to one of three groups.  Two hundred data
obtain this result. The answer is, None. The result holds for all types of technology and market behavior, as long as consumers take prices as given and are not constrained in the amount they can buy or sell at those prices. Thus, for example, the same result holds whether the TFP growth is generated by exogenous technological change, as in the Ramsey-Cass-Koopmans model; by changes in the size of the economy combined with increasing returns to scale, as in the "semi-endogenous growth" models of Arrow (1962) and Jones (1995); or by externalities or public policy in fully-endogenous growth models, such as Romer (1986) or Barro (1990). As we discuss below, aggregate TFP can also change without any change in production technology in multi-sector models with heterogeneous distortions (for example, markups that di¤er across sectors): our results show that an increase in aggregate TFP due to reallocation would be as much of a welfare gain for the representative consumer as a change in exogenous technology with the same magnitude and persistence.

Our ...ndings suggest a very di¤erent interpretation of TFP from the usual one. Usually one argues that TFP growth is interesting because it provides information on the change or di¤usion of technology, or measures improvement in institutional quality, the returns to scale in the production function, or the markup of price over marginal cost. We show that whether all or none of these things is true, TFP is interesting for a very di¤erent reason. Using only the ...rst-order conditions for optimization of the representative household, we can show that TFP is key to measuring welfare changes within a country and welfare di¤erences across countries. We interpret TFP purely from 2