rafmacalaba/datause-encoder-data huggingface dataset:
Our main objective is to estimate vulnerability to child nutritional deficiency in the West African Sahel. However, this involves several steps. The first step is to estimate the welfare costs of shocks. Second, we need to obtain the probability distribution of the shock or the likelihood of the shock that a household faces. Third, we need to estimate expected loss from a shock given its average impact and probability distribution. Finally, we need to evaluate the resulting welfare relative to a standard. We now describe how we obtain values we need in each of these steps.

Our first objective is to estimate household welfare losses from shocks. To identify the impact of shocks on household outcomes, we exploit spatial and historical variation of shocks in each location using a difference-in-difference approach. Although drought affects many households at once, it tends to have strong spatial patterns. We will use spatial variation of monthly historical rainfall recorded in the Sahel since 1998 to estimate the average impact of rainfall on child nutrition. Droughts lead to large scale crop and livestock losses, which in turn lead to high food prices and reduce access to food for many households. This is the first obvious channel for the close link between drought and child nutrition. However, even when households can protect the calories of children, they may do so by forgoing dietary diversity, which denies children essential nutrients for their growth. We therefore estimate the following difference in difference model.

Where W is a welfare outcome (child malnutrition, measures of dietary diversity, consumption, food security, and so on), and h, r, c, and t indexes household, region, country and time; S is the shock, μ is a regional fixed effects, δ is a country fixed effects, θ is a time fixed effect and ε is a household level error term.

16
other forms of renewable energy, such as electricity derived from biomass, wind and solar energy. At least 23 countries where reported as having mandates to blend biofuels into fossil transportation fuels. While 2008 was relatively calmer than previous years for the enactment of new biofuels policies, many countries adjusted their tax incentives, national targets, and blending mandates (REN21 2009).<sup>24</sup> A vast majority of these policies do not differentiate between first and secondgeneration biofuels. That is, they would incentivize the supply and utilization of both generations at the same levels, regardless of costs of production and or the relative value of the benefits they may provide (e.g., net carbon reduction). However, exceptions can be found in the renewable energy legislations of some countries, including the US and the EU. The US Energy Independence and Security Act (EISA) of 2007 guarantees a market for advanced biofuels. The minimum size of that market is set at 21 billion gallons (79.5 billion liters) by 2022, of which 16 billion gallons (60.5 billion liters) are reserved for cellulosic biofuels. Additionally, it provides for funds in the form of grants and loans for R&D, and development and construction of advanced biorefineries. Regulations implementing EISA indicate that cellulosic ethanol will be counted at a rate of 2.5 to 1 towards the renewable fuel standard. In practice, this means that the Renewable Identification Numbers associated with cellulosic biofuels can potentially be worth 2.5 times that of corn ethanol, which tends to reduce the costs differential between them. On a separate piece of legislation, the Farm Bill of 2007 increased the blender’s tax credit for cellulosic ethanol to $1.01 per gallon, while reducing that of conventional (corn) ethanol to $0.45 per gallon. Financial incentives for the production of crops for bioenergy and assistance with collection, harvesting, storage and transportation of biomass to biorefineries are among the measures in the farm bill directed towards second-generation biofuels.

The EU also provides additional benefits for second-generation biofuels, compared to those given to conventional biofuels, but to a lesser extent than the US. Under the draft Directive proposal of 2008, requiring 10% of renewable energy used in

> 24 A listing of important features of biofuels policies implemented by several countries can be found in REN21 (2009) and in Timilsina and Shrestha (2009). Examples of policies directed at different points of the supply chain, from the production of biomass until the consumption of fuels can be found in OECD (2008).

38
|County FE|yes|yes|yes|yes|
|---|---|---|---|---|
|Year FE|yes|yes|yes|yes|
|Observations|4,924|4,924|4,924|4,924|
|R‐squared|0.130|0.070|0.152|0.367|

Notes: These regressions were run on the main sample, aggregated up to the county‐year‐industry (2digit) level. The reported coefficients are beta coefficients, t‐values in parentheses. Dependent variables are 3‐year changes.

# 2.3 Empirical strategy

Our specification for our baseline regression model is: where i indexes firms, t years, j industries and r regions. Δy�� is three‐year growth rate (between t and t�3� of the main firm performance variables: labor productivity, TFP, employment (in logs), sales (in logs), average wages (in logs), and ROA.<sup>19</sup> spillover��� are the variables of interest, described in the previous subsection. X�� captures three firm level characteristics, all related to productivity growth: (log) employment, labor productivity and capital intensity (or log(assets/employee)). Note that these variables are included as levels in year t , an initial value before the growth measured by the dependent variable. Together with the differentiation of the dependent variable, they are likely to capture a rich set of heterogeneous firm‐level characteristics.

We also include two types of fixed effects to capture industry‐level and regional shocks. First, we include the (1‐digit industry) sector‐year dummies to control for macro sectoral shocks ( μ�� ). Second, we include region‐year fixed effects to control for regional macro‐shocks ( ρ��� .<sup>20</sup> Both types of fixed effects are measured at a somewhat more aggregated level than the spillover variables themselves, to allow for some variation across the industries within sectors, and across counties within regions. The results are robust to including other types of fixed effects.

Given that both the dependent variable and the spillover variables are calculated for 3‐year periods, running the regression at the annual level would inflate the number of observations artificially. Therefore, we run the regressions at 3‐year periods (and for 2‐year periods at the end of the sample).<sup>21</sup> The periods included are 1998‐2001, 2001‐2004, 2004‐2007, 2007‐2010, 2009‐2012, 2011‐2014.

The different temporal nature of the variables is a feature of our identification strategy. The spillover variables measure the _stock_ of HGFs – the share of firms in their HGF phase – in year t . The dependent variable measures how subsequent medium‐term performance growth – between year t and t�3 – is correlated with this stock. We think that this specification captures medium‐term effects of HGFs. We control for firm characteristics which may affect firm‐level growth at time t . Modifying these timing assumptions does not affect the results significantly.

> 19 We winsorize these 3‐year changes at the 5th and 95th percentiles.

> 20 Note that regions include typically 3 counties – at which level most of our variables are measured.

> 21 As the full sample covers 17 years, not divisible by 3, the three last periods intersect.

10
14 One answer to this question is to use a measure of decentralization that takes into consideration the interaction between the relative size of subnational governments and their fiscal autonomy. Such a measure will be computed here as the “own revenue ratio”. It is the ratio of subnational governments’ own revenue, to aggregate government revenue. Opposed to this, we can also easily compute the “dependent revenue ratio”, which is the ratio of subnational governments revenue controlled by central government, to aggregate government revenue. These measures are illustrated as shares (black/white areas of bar graphs) of the revenue shares in Figure 2.

# _The models_

This paper replicates:

- The models of Davoodi and Zou, and Oates using their original variables. This reveals if results are influenced by a sample effect. Both studies originally used sample of the world economy on a certain period of time. It could be normal then to have different results if this study uses a different sample representing the particular economy of European transition countries at another period of time.

- The models Davoodi and Zou, and Oates using Ebel and Yilmaz variables. These authors based their study on surveys from OECD (2001), which only included data from six of the ten European transition countries. The replication of their estimations, adding data from surveys of OECD (2002) augment the size of their sample and add strength to their results

- The models of Davoodi and Zou, and Oates and of Ebel and Yilmaz using new variables. This gives the opportunity to compare all results and to test if subnational governments’ fiscal autonomy matters when analyzing decentralization effects.

All estimations are based on an unbalanced panel data model with fixed effects:
# ANNEX C Multivariate filters

The unobserved components model can be expanded to include additional indicators of domestic demand pressures to help identify the output gap (Benes et al. 2010). The most commonly used indicators are inflation and the unemployment rate. Specifically, the univariate model (1-4) is further augmented with a Phillips Curve relationship between inflation and output gaps (equation 5), an Okun’s Law relationship between unemployment rates and output gaps (equations 6-9), a relationship between capacity utilization and output gaps (equations 10-13), and a set of equations describing the Taylor rule (equations 14-17).

Given the large variation in available data across economies, switches are employed to add selected equations to each country model based on the country’s specific dataset. If house prices or the unemployment rate data is not available for a specific country, the relevant equations would not be included. At minimum, all countries have output, inflation, and commodity price data.<sup>24</sup>

## Model components

The Phillips Curve relates inflation to the output gap, controlling for the impact of supply side shocks such as import prices on domestic inflation.

where _π_ t is annualized quarter-on-quarter inflation at time t, _π_ mt is import price inflation at time t, and YGAPt is the output gap at time t. Expectations are assumed to be an average of adaptive and rational expectations, weighted by _ρ_ . Inflation expectations are linked to fixed horizon forecasts of inflation from Consensus Economics where available.<sup>25</sup> Okun’s Law relates the unemployment gap UGAPt (defined as the difference between the actual unemployment rate Ut and the equilibrium, or natural, unemployment rate _Ū_ t in equation 6) to the output gap (in equation 7) as: Following Blagrave et al. (2015), the equilibrium unemployment rate process is specified in deviation from steady state. Equation (8) specifies the process for Ut . It implies that following a shock, the non-accelerating inflation rate of unemployment (NAIRU) _Ū_ t converges back to its steady state value Uss according to the parameter _τ_ 1 and has a trend component GU which has an autoregressive process (9):

> 24 Three economies—Lesotho, Namibia, and Tanzania—have only output, inflation, and commodity price data.

> 25 Fixed-horizon forecasts transform the fixed-event forecasts (for example, for 2022 and 2023) provided by Consensus Economics to be one year-ahead forecasts (in other words, at a fixed horizon in the future). See Bordo and Siklos (2017) and Siklos (2013) for details.

39
**Table 1: Fiscal accounting: Funding from government transfers**

||Conventional provision|PPP|
|---|---|---|
|Now:|Issue 100 in debt|“Save” 100 in debt|
|Now:|Spend 100 on|Spend 100 on|
||infrastructure|infrastructure|
|Future:|Collect 100 in taxes|Collect 100 in taxes|
|Future:|Pay 100 to bondholders|Pay 100 to concessionaire|

**Table 2: Fiscal accounting: Funding from user fees**

||Conventional provision|PPP|
|---|---|---|
|Now:|Issue 100 in debt|“Save” 100 in debt|
|Now:|Spend 100 on infrastructure|<br>Spend 100 on infrastructure|
|Future:|Collect 100 in user fees|Give up 100 in user fees|
|Future:|Pay 100 to bondholders|Concessionaire collects 100 in user<br>fees|

The gold standard in rules to prevent this type of fiscal trickery are the so-called Ryrie Rules, introduced in the 1980s in the U.K.<sup>12</sup> These allowed private finance of public infrastructure only if public expenditure was reduced by the same amount---i.e., pound for pound. Thus, the Ryrie Rules ensured that infrastructure spending remained constrained by the total infrastructure spending allowed by the budget. Furthermore, the Rules provided incentives to choose between public provision and PPP based on efficiency grounds alone. The Ryrie Rules were abandoned under the PFI program of the mid-1990s.

More generally, the approach we propose more or less replicates the prescriptions of the International Public Sector Accounting Standards (IPSAS) in standard IPSAS 32, Service-Concession Arrangements.<sup>13</sup> Governments are not required to follow IPSAS, however, and when they do use those standards, the resulting accounts are not often used to set fiscal rules and budget targets.<sup>14,15</sup>

# <u>2.3 PPP risks without renegotiations</u>

As a rule, demand for a socially desirable infrastructure is uncertain and, in the case of projects such as highways, this uncertainty is largely exogenous and beyond the control of the concessionaire. This implies that both the benefits of a project, in terms of consumer surplus, and the user fee revenue that the project can generate, are random variables. In addition, the contract

> 12 Engel et al. (2014), p 110.

> 13 See for example, IPSASB (2018). We thank Tim Irwin for pointing this out to us.

> 14 New Zealand follows IPSAS. It has treated PPPs in its budgets and accounts in the way recommended in the paper—though it does not have many PPPs and as far as we know it has no user-funded PPPs.

> 15 The IMF’s manual on fiscal statistics (IMF, 2014) takes a similar, though perhaps less explicit, approach for PPPs deemed to meet certain criteria. Again, no government is required to follow these rules, but they are influential, especially with countries with IMF programs.

8
_Note:_ BP = share of households in poverty; BNP = share of households not in poverty; P = poor; NP = not poor.

# **3 Findings**

## **3.1 Distributional performance of existing subsidies**

In this section we show the overall distributional incidence of water consumption subsidies across households by expenditure decile (or income decile for El Salvador and Panama).

As explained in section 2, each figure shows the percentage of subsidy captured by each decile (i.e., percentage of money spent on subsidies accruing to all households in the given decile). In addition, we present the share of total expenditure or income in the economy accruing to each decile, to show if the subsidy reduces inequality (i.e., is less concentrated in top deciles than is the overall expenditure or income distribution) or not.

As presented in figure 2, the results for the five African countries demonstrate a common trend: richer households enjoy a greater share of subsidies since the poor have less access to piped water and, even when connected, consume less water generally. In all countries, richer households receive the lion’s share of subsidies. In Niger and Uganda, households in the top decile receive over 60 percent of all water consumption subsidies. In Mali, Niger, and Uganda, the poorest do not benefit from the subsidy at all. Nigeria is the only country where subsidies reduce rather than increase inequality.

Figure 3 shows the distribution of beneficiaries (households that receive the subsidy) across expenditure deciles for the same five countries. It complements the picture painted by the distributional incidence of the amount of subsidies. Wealthier deciles included the largest share of total subsidy beneficiaries (blue bars) and the largest share of all households within each decile that are beneficiaries (red bars). The differences between the top three deciles and the remaining ones are remarkable in all countries, although less pronounced in Nigeria. As noted above, in Niger, the poor (i.e., the first four deciles) do not benefit from the subsidy at all; Uganda shows a similar pattern, and in Mali only the fourth decile shows a very small amount of beneficiaries. We conclude that in the five African countries analyzed, and using the household data, tariff data, and the methodology described, consumption subsidies are regressive (albeit relatively less so in Nigeria).

12
positive, statistically significant, and larger than for OECD countries. In the case of India, the coefficient is positive but not statistically significant. Results for innovation-induced sales are mixed. While in the case of India, the coefficient is positive and statistically significant, the results are not significant for Nepal, and show a negative sign for Bangladesh.

Finally, one additional important finding is that the degree of novelty does not introduce any additional effect on productivity, and the returns are the same as for imitation. Thus, the evidence suggests that there are positive returns to imitation in South Asia, mostly coming from very incremental innovations in Bangladesh and India. But radical innovations do not increase firm performance above what imitation can make a firm gain. The results discussed so far are robust to alternative methodologies.<sup>11</sup> <u>Table 24 Innovation and productivity estimates in Nepal</u>

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Log(L)|0.3769***|0.3586***|0.5314***|0.5333***|
||(0.065)|(0.065)|(0.054)|(0.054)|
|Log(K/L)|0.2369***|0.2421***|0.1816***|0.1816***|
||(0.045)|(0.046)|(0.045)|(0.045)|
|Prod &/or process|1.3959***|1.5707***|||
||(0.339)|(0.297)|||
|Prod&proc*national||0.2742|||
|||(0.260)|||
|Prod&proc*inter national||0.4718|||
|||(0.721)|||
|innovation_sales|||2.4410|2.4023|
||||(2.617)|(2.645)|
|Sales*national||||-0.4030<br>(1.148)|
|Constant|8.8550***|8.8240***|9.2470***|9.2436***|
||(1.257)|(1.336)|(1.213)|(1.210)|
|Observations|470|470|470|470|

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

> 11 Given the fact that the GSEM methodology is more robust with large samples and well specified models, we also estimate the same models using three-stage least squares (3sls). One disadvantage of this methodology is that it uses the sample of the stage with lower number of estimates and also does not allow for a mixed process, since all the stages have to be estimated linearly. On the other hand, one advantage is that it is computationally less demanding than FIML and still addresses the issue of endogeneity instrumenting at each stage. The results for the returns to innovation, although larger in size to GSEM, are identical in terms of statistical significance.

34
The World Bank (hereafter “the Bank”) has been publishing on economic development since the 1950s, though an organized research program only took shape in the 1970s. Since the mid-1990s, the Bank as a whole has been publishing an average of around 100 books per year. The best known of these books—which surely everyone working on development knows—is the Bank’s annual _World Development Report_ (WDR). There have been 32 WDRs at the time of writing, going back to 1978. Bank authors have also published some 2,000 other books. But this figure is dwarfed by the Bank’s output in scholarly journals—9,000 articles to date.

This paper aims to provide an objective bibliometric overview of the Bank’s publication record and to compare it to that of other institutions, including the world’s top universities. We try to say something about the intellectual influence of all those publications, based on the uses that others have made of them, as revealed by citations. Our assumption is that any publication that has influenced development thinking will get referred to in other publications, and the more influence it has had the more it will get cited.

We focus on citations in scholarly publications: scholarly journals, books, conference proceedings, dissertations, research reports. We do not consider citations in newspaper articles or editorials, magazines, book reviews, web sites or blogs. While these can be important indicators of the short-term visibility of a publication, they do not by themselves constitute good evidence of lasting influence on thinking in a field. In short, we are interested in the scholarly influence of the Bank’s publication record, not its visibility per se.

To track Bank publications, we created our own database, taking records from several online databases, notably the Bank’s e-Library, the US Library of Congress catalog, EconLit, Elsevier’s SCOPUS database, and Thomson-Reuters _Social Science Citation Index_ (SSCI). In comparing publication counts with other institutions, we focus on SCOPUS and SSCI. We use (in separate exercises) citation data from different sources, including SCOPUS and SSCI, but also _<u>Research Papers in Economics</u>_ (RePEc) and _<u>Google Scholar</u>_ <u>. In the case of Google Scholar,</u> the interface provided by Harzing’s _<u>Publish or Perish</u>_ (POP) software is invaluable in constructing bibliometric measures for authors, journals and book series, though it is not suited to the task of assigning citation counts to items on long lists of specific publications, which is where the bulk of our analysis focuses. We had new software developed for this purpose.

The data sources (SCOPUS, SSCI, RePEc and Google Scholar) vary greatly in their coverage, which affects which documents have citation data, as well as the citation scores, since 2
and are distributed almost evenly over geographical space. Finally, the regression results suggest no signi fi cant in fl uence of access to rural towns on any of these three types of activities. When occupational classi fi cation of nonfarm activities are examined, only nontradeable services work is found to show some concentration around smaller towns.The empirical results thus highlight the need for improved connectivity of regions with higher agricultural potential to urban centers for stimulating growth in high return wage employment and self-employment in non-farm activities.

The rest of the paper is organized as follows. Section 2 discusses the conceptual framework underpinning the empirical analysis. Section 3 describes the basic features of our data. Section 4 presents the empirical results. The main conclusions are drawn in Section 5.

# 2 Conceptual Framework

We start our empirical speci fi cation with the basic insights from studies on farm-nonfarm linkages. According to this literature, the fates of the agricultural sector and nonfarm activities in rural areas are tied together because of a multitude of production, consumption, labor and capital market linkages. On the production side, a growing agriculture creates backward linkages by stimulating demand for raw materials, equipment and related services, and forward linkages by supplying farm products that need to be processed and distributed. A growing agriculture also boosts demand for rural goods and services produced by nonfarm activities. With growing labor productivity, agriculture releases labor and provides surpluses to nonfarm activities that can be reinvested. On its part, nonfarm activities reduce input costs and help induce technical change in agriculture. This idea of farm-nonfarm linkages is captured in a simple reduced form speci fi cation: 6