rafmacalaba/data-use-mentions-extended huggingface dataset:
sup>st</sup> round of the National Sample Survey of India administered by the government’s National Sample Survey Organization. The fieldwork for the survey was carried out between July 2004 and June 2005.

The value of all non-purchased items was about 14 percent of total purchased expenditure, while non-purchased food accounted for about 82 percent of all non-purchased items. The former ratio tended to decline at the upper quintile levels. The latter was approximately constant across the quintile groups. The value of non-purchased non-food items may be underestimated, as the only expenditure values reported for non-purchased non-food items were fuel and lighting items and some durable goods. The expenditures on all items (both food and non-food items) were not deflated because adjustment factors were not available.

The household survey asked how much LPG the household had consumed in the previous 30 days, and also what the main cooking fuel was. Figure A2.1 shows the distribution of households by main energy source for cooking. LPG was the most common source of fuel after firewood and chips. Kerosene was used by a small proportion of the population, while electricity was scarcely used.

Figure A2.1: Main source of energy for cooking across all households in India <!-- Start of picture text --> Firewood and chips 61<br>LPG 22<br>Dung cake 7.1<br>Kerosene 3.7<br>Coal 1.4<br>Gobar gas 0.2<br>Electricity 0.1<br>Charcoal 0<br>Others 2.8<br>No cooking arrangement 2.2<br>0 10 20 30 40 50 60 70<br>Percentage of households<br><!-- End of picture text --> _Source:_ Authors’ calculations.
# **Figure 3. Tobacco Consumption, by Per Capita Consumption Decile, Bangladesh, India, and West Bengal**

<!-- Start of picture text --> a. Share of households consuming b. Share of tobacco consumption in total<br>tobacco household consumption<br>80.0% 4.0%<br>70.0%<br>3.0%<br>60.0%<br>2.0%<br>50.0%<br>40.0% 1.0%<br>30.0% 0.0%<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>India West Bengal Bangladesh India West Bengal Bangladesh<br><!-- End of picture text --> _Source:_ Calculations based on data of the Bangladesh HIES 2016/17 and the India National Sample Survey and Consumption Expenditure Survey, 2011–12.

_Note:_ Tobacco includes cigarettes, bidis, tobacco leaf, gul, betel leaf, betel nut, zorda, lime, khoer, rolled betel leaf, and other smoked or smokeless products. Consumption deciles are defined based on household consumption per capita. The consumption aggregate includes similar broad food and nonfood components; however, they may not be fully comparable due to the differences in survey design, data collection, and methodologies in computing the aggregate.

By type of product, one finds additional differences in consumption patterns across deciles (figure 4). The share of households consuming cigarettes is at 26–30 percent for almost the entire distribution, except the poorest decile, among which the share of households consuming cigarettes is significantly lower (19 percent). Meanwhile, bidi consumption clearly declines as household resources increase. While a quarter of the poorest households consume bidis, this is only true of 6 percent of the richest households. For smokeless products, consumption is similar,
of Nigerians are classified as severely food insecure. However, there is also variation between the different components of the FIES module: the most prevalent element of food insecurity is being unable to eat healthy or nutritious foods (FIES Component 2), affecting 72.4 percent of households, while the least prevalent element of food insecurity is going a whole day without eating (FIES Component 8), affecting 10.0 percent of households. Importantly, there is little the FIES can do to differentiate among the large share of the population that is categorized as severely food insecure since those individuals already affirm nearly all of the component questions.

And lastly, aside from differences between the FIES and the FCS in the share of the population with poor food access, there are significant differences in the identity of those having poor food access in the two measures. Table 2 reports that approximately 21.0 percent of Nigerians are severely food insecure according to the FIES but have an adequate FCS; and approximately 10.1 percent of individuals are either moderately food insecure or food secure according to the FIES and have either a poor or borderline FCS. Strikingly, these figures illustrate that approximately two-thirds of the population with poor food access according to the FCS, which is a critical threshold used to target emergency food assistance, would either be categorized as moderately food insecure or food secure according to the FIES.<sup>24</sup> As mentioned in the background section, these inconsistencies between the FIES and the FCS are difficult to interpret precisely. Misalignment between food access measures could be driven by differences in the severity of thresholds defining poor food access, differences in the exact dimensions captured, or poor alignment of one or more of the food access measures with the underlying food access dimensions that they are trying to capture.

Specifically, the higher share of poor food access when using the FIES could be consistent with the fact that the FIES has a lower severity threshold than the possibly overrestrictive threshold used to denote poor food access using the
correct this, program administrators launched a large-scale campaign to stress the importance of compliance.

Under BDH, beneficiary households (again the mothers) receive grants of US$15 per month on the condition that children of ages 6-16 years are regularly enrolled, with at least a school attendance rate of at least 80 percent per month. Poor households with children of ages 0-5 years, additionally, have to fulfill the condition that they make scheduled visits to health centers for growth and development checkups and immunizations.<sup>23</sup> According to Schady and Araújo (2006), the monthly US$15 transfers account for only 7 percent of household expenditure. BDH coverage reached approximately one million households (5 million people), covering 40 percent of the Ecuadorian population with an estimated cost of 0.6 percent of GDP in 2005.

The baseline survey, collected between June and August 2003 for the evaluation of the BDH program, was drawn from four of 22 Ecuadorian provinces around the country.<sup>24</sup> Within all four provinces, “paroquias” (parishes) were randomly assigned. For each selected paroquia, a random household sample was selected, generating a total sample size of 1,488 households. Around 90.5 percent of the household sample had at least one child aged 6 to 17 years at the time of the follow-up survey. None of the households in the sample were enrolled in BDH or _Bono Solidario_ prior to the sample selection. Thereafter, some of the sample households were selected for immediate participation in the program while others were not eligible to receive any transfer in the first two years. The follow-up survey took place between January and March 2005 and collected information on households after program implementation, generating a panel sample of 1,306 households (2,875 children between six and seventeen years of age).

According to Schady and Araújo (2008), around 27 percent of households in both treatment and control groups had stated that school attendance was a prerequisite for
from the one during the last year covered by the ENDEI 2 survey. While regulations and restrictions on trade policies were prevalent during the ENDEI 1 reference period— especially when the survey was conducted (2013)—they fell off sharply during the last year covered by the ENDEI 2 survey (2016) and dropped even more when it was conducted (2017–18).

This IV strategy rests on the idea that regulations and restrictions on trade policies imposed by the government affect the perceived obstacles to innovation in a heterogenous way for firms of different sectors, regions, and sizes, which in turn may limit the resources they dedicate to innovation activities. In particular, we hypothesize that import barriers on key goods needed to carry out innovation activities are sensitive to regulations and trade policies. We would then expect that changes in trade restrictions in 2016 decreased perceived obstacles to imports of goods for innovation activities, and consequently fostered investments in innovation between 2014 and 2016.

Hence, we proxy a firm’s restrictions on imports in 2014 through the sector-region-size _proportion of firms_ claiming to suffer from import barriers during the ENDEI 1<sup>21</sup> period; we assume that firms belonging to the same group are similarly affected by these barriers. Hence, the variability in our instrument comes from differences in the intensity of perceived obstacles to imports across sector-region-size groups of firms. We then observe how these perceived obstacles relate to the _change_ in innovation investments between 2014 and 2016, given that between these two years many import restrictions were lifted. We expect a positive correlation between the perception of obstacles in the past and the change in innovation investment, given that firms that were more restricted are the ones that increased their investment in innovation activities to a greater extent once these restrictions were removed. The conditional exogeneity assumption is plausible, given that the effect of import restrictions on goods that _are key for innovation_ affects productivity levels precisely through their effects on the firm’s decision to conduct or increase its innovation activities, once these barriers are
344<sup>_∗∗∗_</sup>|0.001|-0.337<sup>_∗∗∗_</sup>|
||(0.082)|(0.008)|(0.078)|
|Private Tutoring<br>6.21%|-0.537<sup>_∗∗∗_</sup>|-0.020<sup>_∗∗_</sup>|-0.337<sup>_∗∗∗_</sup>|
||(0.186)|(0.008)|(0.078)|
|non-Education<br>97.86%|-0.111<sup>_∗∗∗_</sup>|0.002<sup>_∗∗_</sup>|-|
||(0.022)|(0.001)||
|Observations|20,731|||

_Source_ : Iranian Households Income and Expenditures Surveys (HIES). Note: Table presents estimated coefficients of Treat _×_ Post2007 ( _γ_ in Eq (1)). Dependent variables are total family expenditure, spending on education (by item), and spending on non-education goods and services according to COICOP classification. Dependent variables are log transformed and deflated by CPI which equals 100 in year 2011 (education spending is deflated by the Education Price Index (EPI)). The sample includes households with children aged 6-24. For school, tuition the sample consists of all households with children aged 6-24 who have not graduated from high school. For university tuition, the sample consists of households with children aged 6-24 who have graduated from high school. For spending on books and private tutoring, I consider all households with children aged 6-24. The time period is 1995-2013. I control for covariates, as well as time, province, and industry fixed effects
established to reduce administrative discretion and corruption, increase transparency and predictability and enhance incentives for accountability.

# **II.** STRUCTURAL DOMINANCE AMONG INCUMBENT **FIRMS**

At the national level, the degree of concentration of industrial output in Russia suggests an absence of a structural competitive problem. The average 4-firm concentration ratio (the sum of the market shares of the top four producers) is about 60%. For many industries, Russia and the United States have similar 4-firm concentration ratios, and the largest Russian manufacturing enterprises (measured by number of employees) are not unusually large compared to US firms. Indeed what is noteworthy is the lack of _small firms_ in Russia.' However, this aggregate-level analysis masks three underlying attributes of Russia's industrial landscape.

## 1. **Horizontal** Dominance

First, large Russian enterprises tend to be configured as single integrated multi-plant establishments, often located in or near a single city. In contrast, in industrialized economies a given enterprise usually has multiple establishments and they are located across domestic regions and often abroad. In Russia, products as diverse as trolley buses, potato-harvesters, motor scooters, and coal-cutting and tunneling machines--to mention only a few of hundreds--are manufactured only in a single enterprise in the whole of the country. On an establishment basis, the largest Russian enterprises are significantly _larger_ than their counterparts in other countries, including the United States. Reliance on conventional measures of national market share and concentration thus likely understate the true extent of horizontal dominance in many Russian markets. Data on 328 firms in a 1997 World Bank-Russian Academy of Science (WB-RAS) survey<sup>6</sup> reveal that the average market share at the oblast level is 43%. Recent data on concentration indicate that at the oblast level, the average 4-firm concentration ratio is above 95%.

In large part, the existing level of horizontal dominance in Russian manufacturing markets is a legacy of Soviet centralized planning. Horizontal integration is also increasing
Policy Research Working Paper 11050

# **Abstract**

Nitrogen fertilizer is essential for boosting agricultural yields and food production. However, agricultural subsidies often drive the inefficient application of fertilizer, leading to significant costs for farms, the environment, and economies. Scientific evidence indicates that nitrogen pollution has exceeded safe planetary boundaries, making it one of the world’s largest externalities. Yet, the global economic costs and consequences of subsidized nitrogen fertilizer use remain poorly understood. This paper combines data on subsidies, satellite-derived measures of crop productivity, nitrogen usage, water quality, and spatial and administrative data sets to provide globally comprehensive empirical estimates of the long-term costs of fertilizer use and the role of subsidies. The results show that in regions with large input subsidies, nitrogen overapplication diminishes crop productivity returns and increases nitrogen runoff into waterways, with lasting implications for human health and labor productivity. More than half of global agricultural production occurs in areas with high subsidized nitrogen use, where the marginal benefit of additional fertilizer is negative. This indicates significant potential to reduce fertilizer use without adversely affecting crop yields. Globally, up to 17 percent of nitrogen pollution in water is linked to inefficient input subsidies, contributing to hypoxic zones and harmful algal blooms. Conversely, decoupled subsidies not tied to production reduce these harmful spillovers. These findings underscore the enduring consequences of nitrogen fertilizer, how well intentioned but poorly designed subsidies can aggravate nitrogen waste, and the potential of policies to pave the path to reform.

This paper is a product of the Office of the Chief Economist, Planet Vertical. 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://www.worldbank.org/prwp. The author may be contacted at ezaveri@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues.
production capacity apparently caused a sharp increase in real GDP. The CEM data (which I suspect are superior) indicate an increase in real CDP, rising from 2.9 in 1975-78 to 14.9 percent during 1979-80 and then falling to 7.3 percent in 1981-83 as world oil prices declined from their peak.

A comparison of Cameroon's two boom episodes supports the conjecture above that commodity orice booms, compared to booms caused by exogenous volume increases, often have only modest impacts on real output. In the face of price or "terms of trade" shocks, therefore, stabilization Rolicies should focus on the inflationary and external balance consequences of booms rather than their effects on output and emplovment. In the case of booms caused by resource discoveries, on the other hand, more emphasis on t..e latter effects is warranted.

In their comparative study of boom management in Cameroon, Cote d'Ivoire, and Senegal, Devarajan and DeMelo (1987) conclude that Cameroon's macro management during the 1970s was first-rate. The increase in spending associated with the boom was modest; the government budgetary situation which was conservative prior to the booms remained so throughout the 1970s; the real exchange rate did not appreciate sharply, in contrast to the typical pattern in booming economies (including Cote d'lvoire and Senegal). They point out that the initial windfall from the coffee boom in Cameroon and the windfall from the coffee and cocoa boom in Cote d'Ivoire accrued to their commodity stabilization funds, as prices paid to domestic producers were kept below prevailing world prices. Whether this results in a moderation of the spending effect of the boom (relative to a situation where the bulk of the windfall accrues directly to the private sector, as happened for example in the case of the 1973 phosphate boom in Senegal) depends critically on how the 34
Policy Research Working Paper 9532

# **Abstract**

Low levels of investment into modern technologies, and limited use of measures that have low monetary cost but the potential for high yields, are often regarded as obstacles to further agricultural development. This paper investigates farmers’ demand for one such measure, namely agricultural advisory services. These have modest (most frequently zero) monetary user cost but, according to some recent research, have the potential to result in large increases of yields. Yet, demand for these extension services is often low. This study proposes that costly attention may be part of the explanation for this. In the model, advisory services are available free of charge, but positive effects on production are only realized if farmers devote attention to listening to and implementing the provided advice. Modeling farmers as rational decision makers facing scarce attention, the study identifies the circumstances under which farmers may optimally abstain from demanding advisory services. The model complements the insights of other theories commonly used to explain suboptimal farm decisions and outcomes, and generates testable predictions, which are consistent with empirical evidence based on a large farm-level panel dataset from Sub-Saharan Africa.

This paper is a product of the Development Policy Team, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at dominik.naeher@ucd.ie.

_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
heterogeneity including indirect transport costs, wholesale and retail distribution costs, belonging to the WTO, the quality of institutions, domestic regulations, for example a nation‟s customs procedures or the ease of obtaining the required documentation to trade, infrastructure and geography, levels of corruption and domestic security protocol. Additionally, other channels through which migrants could potentially influence trade will also be controlled for, for example through accounting for any rise in the stock of human capital.

Equation 8 is estimated using the least squares dummy estimator (LSDV henceforth). Although algebraically analogous to the standard panel „within‟ estimator, the LSDV estimator yields an R<sup>2</sup> which may serve as the basis for comparison, for the cross-section and pooled results in this paper and the rest of the estimates in trade gravity literature more broadly. In the presence of many zero trade flows however; Santos Silva and Tenreryo (2006) demonstrate how heteroskedastic residuals may lead to inconsistent results, in which case they argue the Pseudo-Poisson Maximum Likelihood estimator is appropriate. The LSDV estimator is nevertheless chosen for analysis since there are no zero observations whatsoever in the underlying trade data. There exist missing values however. The key question therefore is whether these represent true zero values, in which case they need to be handled with care; or whether they are actual missing values. Since aggregate trade data are used, there is every reason to believe that the majority of these „missing values‟ are indeed missing. This is especially so in the later period to which the trade data refer, since small values are not reported due to the financial constraints faced by the authors (see below). Indeed, the conclusion of private correspondence with the authors of the dataset was that these values should be treated as missing, since it was argued, it was a far bigger assumption to assume these missing values are all zero. For the sake of robustness, several samples, which have varying degrees of missing values, are estimated to ensure that their
better investor protection and greater information availability facilitates better monitoring. Second, information asymmetry provides a potential channel in which shocks can be propagated through the banking system (a number of papers have used constrained information asymmetry framework to explain risk contagion and crises; see for instanceGenotte and Leland 1990, Kodres and Pritsker 2002, Hong and Stein 2003, and Barlevy and Veronesi 2003, Yuan 2005). If greater information availability provides a substitute to competition in reducing systemic fragility, then we would expect the impact of greater information to be stronger in less competitive markets. However, it is also possible that information asymmetry affects stability through different channels than competition. For instance, competition may incentivize banks to take on more diversified risks, while greater information availability may help reduce contagion of a macro shock to the banking system.

The baseline results are provided in columns (6), (7) and (8) in Table 4. Consistent with our expectations, both depth of credit information and the existence of private information collection agency significantly reduce systemic risk after controls. In columns (6), (7) and (8) of Table 5, we present results from regressions including the interaction between private monitoring variables and the Lerner index. The results indicate that the effect of investor protection and depth of credit information in reducing systemic risk is lower in less competitive markets.

# **3.3. Robustness Checks**

In this section we show that our results are robust to alternative regression specifications as well as alternative definitions of competition and systemic stability. A number of previous studies have used concentration, measured as the percentage of total assets held by top three or five bank in a given country, as a proxy for competition. While concentration is correlated with bank competition, the recent literature also emphasizes the differences between the two, suggesting concentration is a less reliable indicator of competition (Beck, Demirguc-Kunt, and Levine, 2006; Demirguc-Kunt and Martinez Peria, 2010). Nevertheless, as an alternative measure of competition,