rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1 Introduction**

Governments subsidize agriculture to the tune of US$635 billion per year (Gautam et al. 2022). These subsidies have many impacts; some intended, and some unintended. Agricultural subsidies are often intended to increase agricultural efficiency by incentivizing the increased use of inputs such as fertilizer, improved seeds, or water from irrigation, or encouraging increased production of certain crops to achieve domestic self-sufficiency goals (Ellis 1992). Subsidies are also often part of rural development, livelihood, or safety net programs. Indeed, with 80 percent of the world’s extreme poor and 75 percent of the moderate poor living in rural areas (Castaneda et al. 2016) and engaging largely in agriculture, such subsidies can act as crucial income support for the rural poor, reducing production costs through input subsidies and increasing the marginal revenue of production through output subsidies.

In many countries, these subsidies make up significant shares of both government budgets and agricultural production value. In the 53 countries and regions<sup>1</sup> with available data on subsidies, the average estimated total subsidy to agriculture—including input and output subsidies, and public service provision such as research and development, infrastructure, and markets—is 18 percent of total agricultural production value. In some countries, it exceeds 40 percent (figure 1). Given the magnitude of these subsidies their distributional implications are of significant consequence. An important indicator of their impact on society, then, is their progressiveness, or the share that is captured by the lower versus higher income quantiles. But global estimates on the equity impacts of agricultural subsidies are lacking, particularly in developing countries.

In richer countries, such as Canada, the United States, and European Union (EU) members, evidence suggests that support to agriculture is disproportionately distributed to larger farms (Moreddu 2011). This is a natural outcome when agricultural support is tied, as it often is, to production or related factors such as land (FAO, UNDP
6 Surveys (DHS) in 1993, 1998 and 2003. Stifel and Christiaensen (2007) estimated the relationship between assets and consumption in the 1997 national household budget survey and subsequently applied these estimates to comparable asset data in each of the DHS surveys to predict household consumption and poverty in rural and urban areas. This yielded useful insights into the dynamics of poverty in Kenya between 1993 and 2003. To mitigate potential bias from the parameter stability assumption, the authors excluded household assets whose returns were considered more prone to change over time, such as labor and education variables, and included factors that affected the returns to assets over time such as rainfall and nutritional status. Even though the predictions of poverty from this study looked plausible when compared with trends in other indicators of wellbeing, it remains that the underlying assumptions for these predictions could not be verified.<sup>6</sup> Clearly, empirical validation of the different model specifications and their underlying parameter stability assumptions is necessary before the kind of poverty prediction techniques described above can be routinely used. Such empirical verification requires, at a minimum, settings in which comparable consumption data _are not_ missing. In such settings the exercise can be performed _as though_ the data were missing, and afterwards predicted poverty can be checked against the ―truth‖.

This paper makes a first contribution to filling this void. It compares the poverty measures obtained directly from the data in a series of settings that have comparable expenditure data across time, with those obtained through application of the SAE-based approach also employed by Kijima and Lanjouw (2003), Christiaensen and Stifel (2007)

> 6 Grosse, Klasen and Spatz (2009) provide a similar application to Bolivia, this time adjusting the parameters of the different regions based on the parameter changes observed over time in the urban subsample for which consumption data was available in all periods.
district is prevalently Muslim, and the standard deviation of income within the district. In all regressions we employ county fixed effects.

> 34 The 2008 LSMS is the third household survey conducted by the Albanian National Statistics Office with the technical assistance of the World Bank. The LSMS sampling relies on a stratification scheme based on four regions: Coastal Area, Central Area, Mountain Area, and Tirana; and included 450 Primary Sampling Units (PSUs) and 8 households in each PSU, for a total of 3600 households. The sampling is designed to be representative of Albania as a whole. The survey provides information about household characteristics, including demographics, education, as well as measures of social capital. Finally, we also collected data, from official records, on district-level voter turnout in the 2009 parliamentary elections.

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attended secondary school.

Following the methodology originally proposed by Filmer and Pritchett (2001), we use principal component analysis to create a wealth index, using a list of 32 household assets collected in the household survey and exit interviews. For the household sample, the index uses the first principal component of the entire sample of households regardless of whether the pregnancy was completed or the woman had attended any antenatal consultation. We normalize the index to mean zero and a standard deviation of one to facilitate the interpretation of the estimated coefficients. For the exit interview sample, the wealth index was constructed with the assets reported in the exit interviews applying the factor scores computed with the household data to ensure direct comparability of the scores. The average wealth in the exit interviews is higher by 0.75 standard deviation than the average wealth in the household data.

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World Bank’s Linkage model, along with the GTAP data set to project the growth path of the global economy from 2001 to 2015. They find that trade reforms have a modest impact on capital accumulation and thereby boost the projected global gains from multilateral trade reform by about one-quarter. However, they devote most of their attention to the potential impacts of increased trade on productivity growth. (It should be noted, however, that the authors focus entirely on productivity growth associated with increased manufactures exports – not services trade or investment as with the Russia study).

There is now a rapidly growing literature on the impacts of trade and trade policy reforms on productivity and Anderson, Martin and van der Mensbrugghe draw on this in their paper. When they incorporate the additional impact of openness on labor productivity, they find a substantial boost to the global gains (40% larger gains in 2015) with a disproportionate share accruing to the South and East Asia developing economies.

The poverty impacts of these alternative scenarios are elicited by first estimating the income gains to the poorest households and then applying to this an estimated elasticity of poverty reduction with respect to income growth at the poverty line. Instead of using real per capita income for the region as a whole, they use the unskilled wage rate, deflated by an index of food and clothing prices, reflecting the dual facts that the main endowment of the poor is their own labor, and they spend the bulk of their income on non-durable goods. Another critical assumption is that the poor do not pay taxes, so that any increase in tax rates required to offset forgone tariff revenues does not affect them.

Applying these estimates of earnings at the poverty line to the poverty elasticity of income in each region – which varies depending on the regional distribution of income 33
List of Tables 3

|Figure9|Cost-beneft analysis of Samurdhi scale-up<br>. . . . . . . . . . . .|29|
|---|---|---|
|Figure10|Cost-beneft analysis of scale-up versus scale-out . . . . . . . . .|30|
|Figure11|Cost-beneft analysis of post-disaster support scale-out using||
||PMT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|31|
|Figure12|Avoided wellbeing losses for various ASP programs . . . . . . .|33|
|Figure13|Household asset vulnerability (vh)<br>. . . . . . . . . . . . . . . . .|47|
|Figure14|Household asset losses and reconstruction . . . . . . . . . . . . .|48|
|Figure15|Household consumption losses during reconstruction . . . . . .|52|
|Figure16<br>list of t|Household reconstruction time (τh) . . . . . . . . . . . . . . . . .<br>ables|56|
|Table1|Asset risk from precipitation fooding, by district . . . . . . . . .|10|
|Table2|Risk to assets, socioeconomic resilience, and risk to wellbeing .|21|
|Table3|Risk to assets, socioeconomic resilience, and risk to wellbeing<br>for the poorest quintile . . . . . . . . . . . . . . . . . . . . . . . .|23|
|Table4|Per capita risk to assets and wellbeing for the poorest quintile .|24|
|Table5|Total household expenditures, by district, as reported by the<br>2016HIES.<br>. . . . . .
developing countries increasing access to non-concessional financing as they continue to develop; (iii) or increasingly relying on more expensive domestic debt financing, as saving increases and the domestic markets develop. Under this scenario the countries with an upward shift in debt dynamics include Cambodia, and Tajikistan, among the LIDCs, with the debt to GDP of Tajikistan reaching above 50 percent, and Albania, Georgia and Malaysia among the EMs, with all countries with a debt to GDP ratio above 50 percent.

Figure 18. Scenario 2: Interest Rates at least at One Percent (Change in debt-to- GDP ratio, 2016-30) <!-- Start of picture text --> LIDCs EMs<br>20% 70% 20% 100%<br>90%<br>60% 15%<br>15%<br>80%<br>50% line<br>50% 10% 70%<br>10%<br>60%<br>40% 5%<br>50% line<br>5% 50%<br>30% 0% 40%<br>0% 20% -5% 30%<br>20%<br>-5% 10% -10% 10%<br>-15% 0%<br>-10% 0%<br>ALB MYS GEO<br>TJK KHM<br>Baseline BRI Debt to GDP ratio including BRI Baseline BRI Debt to GDP ratio including BRI<br>In percent of GDP In percent of GDP<br>Debt to GDP at end-2030 Debt to GDP at end-2030<br><!-- End of picture text --> Sources: WEO, WIND Database, LIC DSF DSAs and de Sources: WEO, WIND Database, LIC DSF DSAs and de Soyres (2019). Soyres (2019).

Debt dynamics in the medium term, during the construction phase of BRI infrastructure, signal that 12 countries (or
Having already demonstrated that plot size is correlated with a farmer's propensity to over/under-estimate plot size, we explore these relationships econometrically by estimating the following triple difference regression: following triple difference regression: 0 1 2 3 p 4 5 p 6 p 7 p + θ (1) pph pp whereRR = αα + αα stands for continuous (bidirectional) measurement error as well as percentage over- or TT+ αα XX<sup>∗</sup> + αα RR + αα TTXX<sup>∗</sup> + αα TTRR + αα RR XX<sup>∗</sup> + αα TTRR XX<sup>∗</sup> + εε under-estimation in the post-harvest, post-treatment survey, and pph stands for an indicator variable RR for those households/plots exposed to the informational treatment, stands for (log-transformed) TT GPS plot size measure, p is pre-treatment measurement error in self-reported plot size,XX<sup>∗17</sup> is a vector of controls, additional observable characteristics of plots and households collected before pp RR θθ the informational treatment, and is a mean zero error term. In the interest of exploring heterogenous and asymmetric responses we interact the treatment dummy with true plot size and εε pre-treatment measurement error. To facilitate interpretation, we center both the plot size and measurement error variables. The most important parameters in equation (1) are 1, 4, 5, and 7. 1 quantifies the impact of the informational treatment at the average plot size and αα αα αα measurement error in our sample, when the centered and p variables take value zero. 4 αα αα captures heterogeneous information impacts associated with plot size, holding constant pre-XX<sup>∗</sup> RR αα treatment measurement error. Similarly, 5 captures heterogenous information impacts based on a farmer's prior misperception (measurement error), holding constant plot size. Finally, 7 reflects αα the interactive effect of plot size and pre-treatment measurement error. Before we
>34</sup> Our estimates of β in column (1) will be an underestimate of the true effect if more enforcement is caused by more informality (reverse causality), and it will be an overestimate if strong enforcement and low informality are caused by a third factor, such as strong institutions and strong civic culture

> 32Gruber (1997) also fi nds small effects of social security provisions on employment using data from Chile.

> 33Even though the effect of enforcement on employment is not large, enforcement has an effect on the reported desire of the fi rm to change its labor force. Firms are asked whether they would change the size of their labor force if they could hire and fi re with no restrictions. In results available on request we fi nd that an increase in our measure of enforcement in 1 unit leads to an increase of 4% in the probability that the fi rm reports a desire to change the labor force in the absence of hiring or fi ring constraints.

> 34Using labor inspections as an enforcement measure does not produce such a strong negative correlation as using fi nes (available on request).

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domain; and (iv) **social-emotional** : The child is considered developmentally on track if two of the following are true: the child gets along well with other children; the child does not kick, bite or hit other children; and the child does not get distracted easily.

According to UNICEF a child is developmentally on track if he or she is on track in at least three of the four domains (literacy-numeracy, physical, social-emotional and approaches to learning) (Loizillon et al., 2017).<sup>3</sup> This implies that affirmative responses to (any) 7 or more of the 10 questions are sufficient to classify a child as being on track in development. In this paper, an alternative composite score for early childhood development is the standardized Early Childhood Developmental Index (or ECDI), which is generated by simply summing the number of positive responses in any of the 10 questions without taking into consideration the domain of the questions (total score ranging from 0 to 10) and then normalizing to a z-score (mean of 0 and standard deviation of 1). This is a more sensitive, though less scientifically-based, measure of child development as it simply compares the developmental status of any given child to the developmental status of the average child in the sample.

> 3 The 10 items were determined through multi-country field tests, validity and reliability studies, and consultations with experts. For more details on the UNICEF method for constructing the ECDI see Loizillon et al. (2017).

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system is a collection of markets designed to organize and coordinate social interaction.

7. This is a record of the foreign exchange inflows and outflows associated with trade, with factor and interest payments, and with institutional transfers, including migrant remittances.

8. It is assumed here that indirect taxes are paid at the factory gate. That is why, in the SAM, they appear as a payment by the activity account to the government account. However, such taxes may also be paid out of the commodity account.

9. The parameters can be estimated from household survey data. Alternatively, group density functions can be estimated from relevant parameterized Lorenz curves, as is done in the discussion of the PAMS framework.

10. Agénor, Chen, and Grimm (2004) consider the following categories: (1) rural workers, (2) urban workers in the informal sector, (3) urban unskilled workers in the formal sector, (4) urban skilled workers in the formal sector, and (5) capitalists-rentiers. This does not mean that any policy model should follow the same disaggregation. As mentioned earlier, disaggregation in terms of sectors, factor markets, institutions, and socioeconomic groups is dictated by the issues at hand, the availability and reliability of relevant data, and the adequacy of the resources available for information processing.

11. This presentation of the accounting framework follows Khan, Montiel, and Haque (1990).

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Tenreyro 2006). To make sure the results are not subject to aggregation bias, we also run the same specifications as in equation (3) but at the product level and using exporter-product and importer-product fixed effects, which also vary for online and offline flows.

To uncover what drives the difference in distance coefficients online and offline we use interaction terms between distance and country or product characteristics that capture information asymmetries and trust problems. The idea is that if the results are partly driven by technologies that reduce information asymmetries and trust problems, the differences in the distance coefficients online and offline should be larger for countries and products affected by information asymmetries.

# **4 Results**

Table 1 provides the results of the estimation of (3) using distance as the only trade costs in columns (1) and (5). The elasticity of distance is 61 percent smaller online than offline. In columns (2) and (6) of Table 1 we provide the estimates of (3) including the other usual trade costs variables. When we introduce these additional trade costs, the coefficient on distance declines both online and offline. Still it remains around 65 percent smaller online, suggesting a flatter world on the eBay platform.

Some interesting patterns emerge regarding the other trade-cost variables. Common legal systems, trade agreements, colonial links and borders seem to matter much more offline. On the other hand the absence of a common language seems to matter more online than offline. We test for the statistical significance of these differences by appending the online and offline datasets and estimating the gravity equation including interactions of each trade cost with an eBay dummy which takes a value of one if the flow on the left-hand side is the eBay flow and zero if it is the offline flow. As argued above we also include importer-eBay and exporter-eBay fixed effects that control for any country-level differences between importers and exporters online and offline. As seen in Table 2, we find that the difference in the effect of distance is statistically significant. What is
to reflect administrative targets rather than actual enrollment levels. For this reason, we have created two additional instruments from the ENCV data. The first attempts to replicate the level of program rollout at the municipal level. It consists of a weighted average at the municipal level of all age- and Sisben-eligible individuals who actually receive the benefit in each of the four years. This provides us with an estimate of the effective year-specific level of rollout in municipalities. A second instrument can be constructed in similar fashion at a lower level of aggregation. In addition to the location in specific municipalities, we also observe whether a household resides in a municipality seat ( _cabecera_ ), some other urban setting (defined as anything that resembles a town), or in a dispersed rural environment. We then construct the same weighted instrument for each of these subareas across all four years. More formally, our instruments are as follows: where the _g_ subscript represents the specific geographical area, and _D_ is a specific dummy variable indicating program participation. The two populations denoted by _N_ include all age- and Sisbeneligible individuals within the respective subgroups.

Neither of the two instruments is perfect on its own, and there are differences between the two. The first is municipality and year specific. Given that we control for municipality and year fixed effects, we are concerned with potential endogeneity because of unobserved time variant factors in the municipalities. In terms of the empirical model presented above, the instrument may be correlated with an additional error term, say,  _tm_ . The second instrument is constant across all four years (if it were also year specific it would be highly collinear with the first instrument), but specific to an area at the submunicipal level. Any endogeneity would thus derive from unobserved time invariant characteristics that are idiosyncratic to the specific geographical group within a municipality once we have controlled for time invariant municipal characteristics. We can think of this as an additional error term,  _mg_ . The advantage of having two instruments available for a single potentially endogenous
<!-- Start of picture text --> °°<br>&& :<br>8:8:<br>Nod<br>8 : rn 8 : oer<br>8 ~ i, —— : 8 . a ee nee<br>. — * . . a . . E id 7% . :<br>gf. + "ey<a . * a+ #2safesORS.OH foe“y 3 : —Se * : eng. eRe.ZAF@ ° @Rus . *s ”<br>ose” 8 bere @Col . wt a eco. CARG*<br>. ° ein © BRRARG . . @BRA .<br>°°<br>67 8 9 10 11 6 7 8 9 10 11<br>Log of GDP per capita (PPP, av. 1998-2000) Log of GDP per capita (PPP, av. 2008-2010)<br><!-- End of picture text -->
Citing this, Subramanian argues that the official GDP growth rates have been overestimated by 1.1 percentage points when India is compared with other middle-income countries and by 2.5 percentage points when all countries are included in the comparison. This finding has been refuted by Goyal and Kumar (2019) who defend the official statistics citing methodological and data issues in A. Subramanian (2019).

The estimate of poverty we calculate using the pass-through rate is sensitive to the growth in consumption in national accounts. Any downward adjustment of growth, as the one suggested by A. Subramanian (2019), would lead to an increase in poverty. Hence, we also present poverty estimates adjusting growth downward by 1.1 and 2.5 percentage points. It is important to note that A. Subramanian’s (2019) findings are for the period 2011-2016 and for the GDP growth rate, while our application of the downward adjustment is for the period 2016-2018 and for the growth in per capita HFCE.<sup>30</sup>

# **4.4. Poverty rate estimates**

Table 5 reports poverty estimates for 2017 using our preferred pass-through rate, as well as a passthrough rate of 1, applied to various national accounts growth rates. These poverty estimates assume that growth is distribution-neutral, i.e. all observations in the survey are scaled up by the same growth rate, similar to the standard extrapolation methods that underpin the World Bank’s global poverty numbers (see Prydz et al. (2019)). In all cases, we line up the 2011/12 CES microdata to 2015 using urban/rural-specific growth rates. These growth rates are derived using the implied pass-through rates calibrated from the poverty estimates reported in Newhouse and Vyas (2019).<sup>31</sup> The national poverty estimates are calculated as the weighted sum of the rural and urban poverty rates using the population weights in WDI in the relevant year.<sup>32</sup>
Figure 5. Country Cases: Intensifying Severity (Rows Ranked by Medium, Lower and Low Peak Severity) <!-- Start of picture text --> Brazil Chile Mexico Peru<br>50<br>40<br>30<br>20<br>1<br>1 1<br>10 1<br>0<br>Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1<br>Bolivia Colombia Russian Federation Saudi Arabia<br>10.0<br>2<br>7.5<br>3<br>5<br>5.0<br>4<br>2.5<br>0.0<br>Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1<br>Bangladesh Egypt, Arab Rep. India Iraq<br>2.5<br>2.0<br>13<br>1.5 20 16<br>1.0 20<br>0.5<br>0.0<br>Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1<br>Nigeria Pakistan Sudan South Africa<br>2.5<br>2.0 13 11 12<br>1.5<br>20<br>1.0<br>0.5<br>0.0<br>Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1<br>Daily severity ratio (percent)<br>Daily severity ratio (percent)<br>Daily severity ratio (percent)<br>Daily severity ratio (percent)<br><!-- End of picture text