rafmacalaba/data-use-mentions-extended huggingface dataset:
data constitute cross-sections rather than a panel, the information can be analyzed to see how activity patterns change with age and lifetime events such as household formation.<sup>14</sup> We distinguish between seven stages: (i) youth (15-19 years of age); (ii) men (women) of childbearing age (20-44 years) without children, living in a household where no other women (men) of childbearing age are present; (iii) men (women) of childbearing age (20-44) with children, living in a household where no other women (men) of childbearing age are present; (iv) men (women) of childbearing age (20-44) without children, living in a household where at least one woman (man) of childbearing age is present; (v) men (women) of childbearing age (20-44) with children, living in a household where at least one woman (man) of childbearing age is present; (vi) mature men (women) (45-64), living without children in the household; and (vii) mature men (women) (45-64), living with children in the household. As previously mentioned, children are defined as 14 years of age or less, except for South Africa and Pakistan where we define children as 6 years of age or less.

Youth is the stage of the life cycle where both men and women work fewer hours in the market. Young men dedicate on average 1.9 hours per day to paid work compared to 0.8 hours for women (Panel A of Figure 10). Men’s time devoted to this activity follows and inverted u-shape pattern, that is, men’s time allocated to the market peaks in cohabitation and when transitioning into

> 14 Nationally representative time use surveys rarely follow individuals over time.

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The greater reduction in variance is more clearly evident when disaggregating poverty measures at lower territorial levels. For example, Table 2 shows standard errors and the coefficient of variation of poverty measures estimated at the governorate level in Tunisia. Since sub-sample sizes are decreasing from regions to governorates, direct standard errors increase exponentially, that is, the coefficients of variation are higher than 10% in one-third of the governorates. On the other hand, standard errors of poverty rates calculated according to the poverty mapping show a very low increase. In fact, the coefficients of variation are all less than 10% except in the case of the Governorate of Ariana.

In the analysis of the poverty mapping measure at lower levels, in particular at the PSU level, direct estimates are affected by too high sampling errors and are therefore no longer significative. On the other hand, estimates from poverty mapping still show very limited standard errors. In this context, we shift our presentation from poverty rate to per capita consumption expenditure. This is done for two main reasons: i) per capita consumption expenditure estimates are usually more precise (see Betti _et al._ , 2018 in general, and INS, 2020 in this context); ii) due to their higher precision, per capita consumption expenditure is the variable used in our proposal for implementing implicit stratification in Section 4.

As previously mentioned, in Tunisia there are about 39,000 clusters used as PSUs. Before presenting results from the poverty mapping at cluster level, it is fundamental to bear in mind that although the PSU-level estimates are not suitable for dissemination and for policy making, they can still be very useful in informing sample design. In order to presents such results, we synthesized measures at the governorate level in Table 3.

# **Table 2. Poverty rates** **<u>by Governorate</u>**

|**Governorate**|**E**<br>**Poverty**<br>**rate
receipts. Its revenue stems from this direct tax on mining capital value-added (VA, which equals the operating surplus of the sector, defined as gross sales revenue net of labor and intermediate input costs). The direct tax rate on mining capital VA changes over time on the basis of data on projected mining sector government revenue and mining capital VA. Other tax rates – direct taxes on households, and indirect taxes on market sales, activity gross revenues, and imports – are all based on the Social Accounting Matrix (SAM) but scaled so that the revenue level is similar to projections for 2012. Among non-tax receipts, both domestic transfers and domestic government borrowing are fixed as shares of GDP. Grant aid and (concessional) borrowing from abroad are both exogenous in real US dollars; their projections are based on the IMF.

On the spending side, the interest payments depend on the levels of the government foreign and domestic debt stocks (the evolution of which depends on net borrowing) as well as the levels of real interest rates, which are exogenous. Transfers to households are an exogenous share of GDP (like the transfer that goes in the opposite direction).

Government final demand (consumption and investment), disaggregated by function, are the only remaining spending items. It is assumed that, for each function, the government’s capital stocks and consumption grow in tandem – consumption depends on the level of the capital stock. Given this, an expansion of consumption (a measure of service provision) must be preceded, in one or more preceding years (the details depend on the gestation lags), by investments that sufficiently raise the level of the capital stock. This ensures that O&M costs are scaled up after the completion of roads and that additional teachers are not employed without already having a new school in place.

Each year, the government budget clears via adjustments in government consumption and investment spending. The real growth rates for the eight different government functions are defined in two steps. First,
is scarce, the data available suggest migrants often lack knowledge of the components of a remittance cost, the methods available, or how to compare methods (Gibson et al, 2006; 2007). There therefore seems to be promising scope for financial literacy training to change remitting behavior.

There is also growing interest from policymakers in providing financial literacy training in this area. Much of the focus on financial literacy training for migrants and their families has traditionally been on either integrating immigrants into the financial system in the destination

> 1 This website has since expanded and changed name to www.fxcompared.com. The World Bank has also launched a remittance prices database (http://remittanceprices.worldbank.org) covering costs of remittances in 165 corridors.

2
indirectly by the intervention and, as a result, even the estimates of columns 2-4 in Table 3 could be downward biased.

We use two strategies to assess spillover effects beyond the geographical cluster. First, we use the GPS location of every household in the study to compute the number of other treated and control households within a given distance radius and run the following OLS regression equation: Where is an indicator for whether household _h_ is not treated but has at least one treated household within a distance radius of _d_ meters, and is the total number of surveyed households within distance radius _d_ of household _h_ .<sup>26</sup> According to Table 1, the average distance between any two households in a cluster is 194.2 meters (0.12 miles) and the median distance is roughly 100 meters. In contrast, the median distance between any two surveyed households in a village is roughly one kilometer (0.67 miles). We consequently use a radius of 75, 100 and 200 meters, which corresponds roughly to the 25<sup>th</sup> , 50<sup>th</sup> and 75<sup>th</sup> percentile of the distance between any two households within a cluster. The rest of the terms are as defined in Equation (1), and as before, standard errors are clustered at the geographical cluster level. The coefficients of interest are and . This specification allows for a simple test of the extent to which female turnout among control households near treated households resembles female turnout among directly treated households.

Panel A of Table 4 reports the results from Equation (3). The treatment effects are robustly significant and range between 12 to 15 percent. The estimate of is also significant in columns 1 and 2, and is comparable to the direct treatment effect, suggesting that geographical spillovers are large. Indeed, the _t_ -test for is never rejected. The coefficient on the number of households within a given radius,
results are not comparable to official socioeconomic indicators released by the NSOs or government agencies.<sup>17</sup> _3.3 The most up-to-date Purchasing Power Parity from the 2011 ICP round._ In order to be able to compare the standard of living of households across countries in LAC, household welfare needs to be expressed in common units. Pursuing that purpose, the ICP launched in May 2014 an update of its PPP data based on information of goods and services for almost every country in the world.<sup>18</sup> The updated PPP data allow for the comparison of household welfare across countries by providing a real exchange rate from local currency to US dollar in a particular year (2011 in this case).<sup>19</sup> The 2011 update of PPPs increased its coverage, collecting prices from 199 countries in the world, up from 146 in the previous update, and included several methodological and operational improvements with respect to 2005. See Ferreira et al. (2015) for a detailed description of the 2011 PPP data.

Why not use exchange rates to express welfare measures in common units across countries? The main difference between the PPP data and the nominal exchange rate is that the former is created as an index of prices of the same basket of goods in the same period of time, whereas the latter is the price of a local currency in terms of a foreign one (i.e., the rate by which both currencies are exchanged). The nominal exchange rate reflects prices of only tradable goods and, hence, a significant proportion of goods and services consumed by the population are not taken into account (Ferreira et al. 2015). Thus, the exchange rate is not appropriate to compare levels or changes over time of any economic indicator, as it does not express the current cost of living of an economy based on the prices of a fixed basket of commonly purchased goods and services.

> 17  NSO’s statistics and SEDLAC
Policy Research Working Paper 6298

# **Abstract**

A common behavioral assumption of micro-economic theory is that income is fungible. Using household panel data from rural China and Tanzania, this study finds however that people are more likely to spend unearned income on less basic consumption goods such as alcohol and tobacco, non-staple food, transportation and communication, and clothing, while they are somewhat more likely to spend earned income on basic consumption goods such as staple food, and invest it in education. This resonates with the widespread cultural notion that money that is easily earned is also more easily spent. Cognitively, the results could be understood within the context of emotional accounting, whereby people classify income based on the emotions it evokes, prompting them to spend hard earned money more wisely to mitigate the negative connotations associated with its acquisition. The policy implications are real, bearing for example on the choice between employment guarantee schemes and cash transfers in designing social security programs.

This paper is a product of the Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at lchristiaensen@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
implied volatility, and the one-year lagged GDP growth rate. Year dummies capture global macroeconomic conditions not reflected in these variables.<sup>9</sup> In addition, a set of regional dummies is included to capture the effect of long established geographic connections with a particular financial center and other region specific unobservables. Then, for a new bond indexed _i_ from country _j_ issued in year _t_ , the main specification becomes: Estimates are obtained by OLS with robust standard errors. The sample used in the estimation exercise covers the period 1990 through 2015. Because the dependent variable refers to a sample of sovereign bonds, the spread to benchmark variable is here identical to spread to treasuries.

In Table 5 we present the estimation results from the benchmark specification (equation 1). Most bond-specific and macroeconomic variables have expected signs. Post-crisis, S&P rating, VIX and governing law are significant. After 2008 the investment grade dummy becomes insignificant. Since we know that some regions have a stronger connection with Britain, we have included regional dummies. Even after controlling for the effect of regional linkages, the governing law dummy coefficient remains large and significant in the post-crisis sample.

> 8 Each observation is a tranche rather than a bond. This creates difficulty since tranches issued after the initial launch do not properly reflect issuance characteristics. Conversely, whenever several tranches are issued at the same time, their characteristics are usually similar, so that multiple records are created for the same bond. We addressed this by including in our sample only the first tranche, and, whenever more than one tranche was issued on the first day, by including the largest one only.

> 9 We use standard explanatory variables such as the maturity and size controls (Eichengreen and Mody 2004) and bond ratings (Jaramillo and Tejada 2011).

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physical output. It is possible to invert a constant elasticity of substitution (CES) demand function and recover from revenues, up to a constant, a counterfactual measure of TFPQ _j_ equal to the value of industry productivity if marginal products for all factors were equalized across establishments. This approach requires only an assumption about demand and profit maximization and does not assume anything about the structure of factor markets.<sup>18</sup> Columns (5) and (6) show this measure of TFPQ _j_ for alternative values of the elasticity of substitution parameter. The effects of financial sanctions are positive and statistically significant in both columns, indicating that in addition to raising average TFPR, sanctions increase industry-wide TFPQ, with _τ_ 1<sup>_TFPR_</sup> = 0 _._ 076(0 _._ 030) in Column (5) which assumes a relatively low substitution parameter _σ_ = 3, and _τ_ 1<sup>_TFPR_</sup> = 0 _._ 108(0 _._ 061) in Column (6) which assumes _σ_ = 5. These results confirm that the mechanism behind the effects of antitrust on aggregate productivity is largely improvements in TFPQ at all firms rather than a reduction in the dispersion of TFPR. Further they suggest that in the absence of misallocation, effects of antitrust on productivity could be higher.

**Aggregation of Effects on Productivity** How much could the Commission’s contribution to aggregate productivity growth in the status quo be, without additional reforms to reduce misallocation? GDP is simply the sum of value added in the Economic Census, plus some adjustments, and GDP grows at the growth rate of total factor productivity in the steady state. Roughly, as shown in Figure 1 the Commission has investigated 40 percent of output over 25 years, implying it can investigate about 1.6 percent of output per year. In our sample, 40 percent of investigations lead to trial. Taking the estimate from Column (1) of Table 10
## **Using machine learning to assess yield impacts of crop rotation: Combining satellite and statistical data for Ukraine**

# **1. Introduction**

While agronomic benefits from crop rotation are well documented, economic factors together with the ability to manage nutrients as well as pest, weed, and insect populations by applying synthetic fertilizers or pesticides, often prompt farmers to implement short rotations or plant the same crop in continuation. This raises issues for policy if there are externalities, either across space -e.g., from harmful pests migrating to neighboring fields or run-off from excessive fertilizer application contaminating rivers- or if, possibly due to insecure tenure, land users do not fully account for longterm effects of their actions on soil fertility, thus contributing to natural resource degradation.

Studies suggests that, in cases where such external effects are present, relatively modest incentives could prompt producers to adjust their behavior. Yet, studies to explore this issue are limited, mainly because of limitations in routine statistical data that fail to report on the crops grown in the same field in different years. Most studies are thus based on information from agronomic trials that is infrequent in nature, often removed from field realities, and not available for the production systems practices in many parts of the globe.

In this paper we explore whether use of crop cover information derived from freely available satellite imagery can fill this gap. We generate a crop map for all of Ukraine in three consecutive years and use the resulting data to estimate impacts of crop rotations on yields for the country’s four main crops (maize, soybeans, cereals, and sunflower) at the village level. Results are statistically significant, economically meaningful, and very different from what is reported in the US-based literature. Contrary to the literature that is mostly focused on corn(maize)-soybean rotations, we find that continued soybean cultivation incurs only a limited yield penalty and that the magnitude of yield reductions from monocropping is often below that of unsuitable predecessor crops, mainly
prefectures adopted the stronger land property rights policies in the late TP areas (Late Taiping hereafter).<sup>26</sup> _Economic Development_ **.** Our first measure of development is population density,<sup>27</sup> and we choose it for three reasons. First, the population data are the only well-recorded data for measuring long-term prosperity coving the sample period for China. Other proxies, such as historical urbanization are either unavailable or have significant measurement issues, which render them poor candidates for our analysis.<sup>28</sup> Second, studies of the consequences of

> 25 Since the literature suggests that shared state antiquity strongly affects long-term development (Bockstette et al. 2002), we exclude prefectures with significantly lower shares of state antiquity, which include those in minority areas of China that were underdeveloped and less populated before 1953: Xinjiang, Tibet, Qinghai, and the north of the Great Wall (Inner Mongolia, Outer Mongolia); we similarly exclude three provinces in Northeast China (Fengtian, Jilin, and Heilongjiang).

> 26 Where it is obvious, we sometimes shorten them to be Early and Late.

> 27 In the regression, we use total population in the prefecture, but we control for the prefecture fixed effects. So, in essence, we are using the population density as the dependent variable.

> 28 Urbanization measurements have only been recorded since the late nineteenth century. Skinner (1977) estimates the population in the core area of cities or towns of China in 1893; similar estimates in the 1920s can be found in

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885. 1, 1, 40

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- Dupuy, A., and A. Galichon (2014): “Personality Traits and the Marriage Market,” _Journal of Political Economy_ , 122(6), 1271–1319, Publisher: The University of Chicago Press. 7

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- Fajgelbaum, P. D. (2020): “Labour Market Frictions, Firm Growth, and International Trade,” _The Review of Economic Studies_ , 87(3), 1213–1260, Publisher: Oxford Academic. 6

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- Fr ́ıas, J. A., D. S. Kaplan, E. Verhoogen, and D. Alfaro-Serrano (2018): “Exports and Wage Premia: Evidence from Mexican Employer-Employee Data,” _Unpublished Manuscript_ . 8

- Galle, S., A. Rodriguez-Clare, and M. Yi (2017): “Slicing the Pie, Quantifying the Aggregate and Distributional Effects of Trade,” NBER Working Papaer 23737. 9, 41

- Goldberg, P. K., and N. Pavcnik (2005): “Trade, Wages, and the Political Economy of Trade Protection: Evidence from the Colombian Trade Reforms,” _Journal of International Economics_ , 66(1), 75–105. 8 (2007): “Distributional Effects of Globalization in
only data from one or two rural ground monitors for the entire country. There is also often substantial temporal gaps in ground monitor data, on average ground monitors only report measurements in 45% of the months over which we have satellite measures of pollution. This can also lead to substantial bias when using ground monitor data if the ground monitor data is missing observations during particular seasons when pollution spikes (or is only available in those seasons).

In our city level results the choice of city boundaries is an important determinant of the sign and magnitude of the within-city correlation. The overall pattern of heterogeneity remains regardless of which precise city boundary is chosen but estimates for individual cities do change. In contrast, the threshold for the minimum level of RWI points within a city does not change our results in the range from 10 to 50.

In Figure SI-14 we show how the point estimates change for three cities in our data for which we have both the URCA designated boundaries as well as administrative boundaries. In some cases (e.g. Delhi) the URCA and administrative boundaries align relatively closely but in 24
tariff became<br>effective|Jan 1,<br>2000||Mar 1,<br>2004||Apr 10,<br>2006|Jan 1,<br>2007||Apr 1,<br>2009|Apr 1,<br>2010|

_Source:_ Publications of and contacts with Public Services Regulatory Commission, Republic of Armenia Using data from a nationally representative household survey of 2008 and national energy statistics, the paper provides an overview of household energy consumption patterns, highlights Armenia’s energy vulnerability, and analyzes the poverty and distributional impact of the latest gas price hike.<sup>2</sup> The higher gas price is anticipated to significantly increase the share of energy expenditures of most households, particularly during the cold winter months. Heating during the cold winter months accounts for the bulk of household energy use in Armenia (Sargsyan, Balabanyan and Hankinson, 2006). Households living in urban and rural areas are likely to face different choices of energy sources, as would those households living in different housing conditions. The results of the analysis show that the April 2010 gas price increase would result in a significant increase in households' energy expenditures and would have an adverse impact on poverty. The paper draws policy implications for designing of mitigation measures to

> 2 Baclajanschi et al (2006) undertook similar analysis of gas price hike in Moldova.

3