rafmacalaba/datause-encoder-data huggingface dataset:
# **4.2 Support for energy subsidy reform**

## **4.2.1 Unconditional support for energy subsidy reform**

On average, in the absence of any other policy, less than half of respondents were supportive of a reduction in energy subsidies, and only around a quarter were supportive of their elimination. A similar pattern held across most countries, in which the share of respondents supportive of eliminating subsidies was around half the share supportive of reducing them (see Figure 4). Across countries, around 30 percent of respondents stated they would support a reduction in subsidies that resulted in an energy price increase. Thus, the share of respondents supporting a reduction that increased energy prices was below the share who would support a reduction without a specified price increase, but higher than the share who would support an elimination of energy subsidies. Multivariate regression analysis shows that these measures of unconditional support for subsidy reform were most positively associated with trusting the government and knowing that energy subsidies existed, and they were most negatively associated with respondents’ perceiving themselves to be either middle class or relatively poor and believing their household had a right to receive subsidies (see Figure 5).

## **[Insert Figure 4] [Insert Figure 5]**

## **4.2.2 Conditional support for energy subsidy reform**

Over 95 percent of respondents in each country supported a reduction in energy subsidies that increased energy prices if accompanying policies were implemented. This figure was determined by examining the share of respondents who selected at least one of the seven policy measures proposed to accompany energy subsidy reforms (see Figure 6) or who themselves proposed accompanying policies that would lead them to support reform (see following subsection). The most popular policy measure to be implemented with the savings from re32
estimates of the DVAR of Chinese processing exports, along with the 95-percent con...dence intervals based on 100 randomly drawn samples with replacement. Chinese processing exports’DVAR has been increasing from 0.46 in 2000 to 0.55 percent in 2007. Depending on the year, the 95-percent con...dence interval is between 5 to 11 percentage-point wide, with an average of 7 percentage points over the 8 years in our sample. Most importantly, based on the bootstrapped standard errors, the di¤erence between the DVAR of Chinese aggregate exports in 2007 and that of 2000 is statistically signi...cant, lending strong support for KWW12, who also ...nd an upward trend of similar magnitude based on IO tables and aggregate trade data. Figures A4 and A6 in the Appendix show similar trends, despite using samples with di¤erent cuto¤s from (8), and a sample that includes multi-industry ...rms.

Figure 2 plots the DVAR of processing exports across time for di¤erent industries, together with the 95-percent con...dence intervals based on 100 random samples drawn with replacement. The DVAR increased for all industries besides two (wood and articles; and base metals). For the industries that exhibit an upward DVAR trend, the tight con...dence intervals convincingly reject the null hypothesis that the DVAR estimates are the same between 2000 and 2007 (see Table A9 in the Appendix for details). For wood and articles, and base metals industries, their DVAR are not statistically di¤erent between 2000 and 2007. Overall, none of the industries exhibits a declining trend in the DVAR that is statistically signi...cant during the sample period.

The micro data also permit a decomposition of the aggregate trend into between- and within-industry changes. Speci...cally, the change in the aggregate DVAR (from year t � 1 to t) can be decomposed according to the following identity: 13
Second, we find that the regions that monetary poverty and the FCS are identifying as having the worst deprivations are corroborated by other food access surveys. Figure 1 illustrates that both measures identify the north of Nigeria as being the poorest and having the worst food access. Importantly a wide variety of other sources identify the north as having the worst food access (e.g., FRAYM 2020; IPC 2021b; etc.). By contrast, Figure 1 also illustrates that the FIES identifies the south of the country as having worse food access, which is inconsistent with monetary poverty, the FCS, and other sources.<sup>29</sup>

# **Section 6b. Potential Reasons for Poor Alignment between the FCS and Other Welfare Indicators**

Combined, the results suggest that one of the reasons that the FIES and the FCS are poorly aligned is that the FIES is likely identifying a segment of the population as having the worst food access that does not have the largest macro- and micro-nutrient deprivations. We further investigate differences in the patterns described in Table 4 by the component questions of the FIES to try to infer why this might be the case. Specifically, we re-estimate Specification (2), but use each of the component questions of the FIES as the dependent variables. The results are presented in Table 6.

There are two ways in which the question-specific patterns in Table 6 differ from the patterns using the entire scale presented in Table 4. First, the more subjective questions in the scale, that are more likely to rely on individual-specific scales that are difficult to compare across individuals, performed particularly poorly. Specifically, the share responding affirmatively to the first FIES question, which asked whether any adult worried about food consumption, was essentially indistinguishable between the lowest expenditure decile and all higher deciles except for the top one (Column 1). And the share answering affirmatively to the fifth FIES question, which asked whether any adult ate less than they thought they should, actually increased for higher expenditure deciles. The highest prevalence for that question was in the sixth and seventh deciles (Column 5), which is an expenditure threshold that is far above the poverty line and also above the

> 29Importantly, these regional differences in the prevalence of poor food access between the FIES and other welfare indicators survive estimating the prevalence of poor food access using the full Rasch model. See Appendix 3 for details.

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# 3.1 The External Finance Need of Exports

We start with the standard Rajan and Zingales (1998) classi...cation of industries according to their dependence on external ...nance. The Rajan and Zingales measure is de...ned as capital expenditure minus cash ‡ow, divided by capital expenditure, and is constructed based on US ...rm-level data. We use the version of the variable assembled by Klingebiel, Kroszner, and Laeven (2005), in which industries are classi...ed according to the 3-digit ISIC Revision 2 classi...cation. The Rajan and Zingales external dependence measure is reproduced in Table 1.

We combine this industry-level information with data on the structure of a country’s exports to develop a measure of a country’s external ...nance need of exports (hereafter EFNX) by following the approach of Almeida and Wolfenzon (2005). In particular, we construct the following variable for each country and period of time: where c indexes countries, t time periods, i industries, !<sup>X</sup> ict<sup>is the share of exports in sector i in total</sup> manufacturing exports from country c in time period t, and EDi is the Rajan and Zingales measure of dependence on external ...nance. Summing up across sectors in each country and year implies that our index is at country level, but potentially varies over time.

Armed with this variable, we would like to estimate the following equation: The left-hand side variable, FinDevct is a measure of a country’s level of ...nancial development. We condition on the vector of controls Zct, country ...xed e¤ects �c, and time ...xed e¤ects �t. Our hypothesis is that the e¤ect of ...nancial content of exports, EFNXct, on ...nancial development is positive (�> 0).

# 3.2 Instrumentation Strategy

It is immediate that we have an important simultaneity problem: a country’s trade pattern is surely in‡uenced by its ...nancial development, as documented by Beck (2003), for instance. Thus, in order to estimate the causal relationship going from trade to ...nancial development, we must develop an instrument for our main right hand side variable, namely the external ...nancing need of exports.

In order to do this, we expand the geography-based approach of Frankel and Romer (1999). These authors constructed predicted trade as a share of GDP by ...rst estimating a gravity regression on bilateral trade volumes between countries using only exogenous geographical explanatory variables, such as bilateral distance, land areas, and populations. From the estimated gravity equation, these 9
agriculture could result in the withdrawal of married women residing in urban areas from the labor force (World Bank, 2009).

Technology adoption and use can promote FLFP outside the house. Information and communication technologies can increase access to information about job vacancies (Viollaz and Winkler, forthcoming), can provide flexible work options compatible with family responsibilities, such as telework (Billari et al., 2019; Vazquez and Winkler, 2019; Hatayama et al., 2020), and can contribute to changing social norms as users can be exposed to more information and less traditional views (Arias, 2018; Jensen and Oster, 2019). Home technology can increase female labor supply by reducing the time women allocate to household activities, e.g., use of appliances and their declining prices over time (Greenwood et al., 2005; Greenwood and Seshadri, 2005; Cavalcanti and Tavares, 2008; Dinkelman, 2011). We conjecture that there is a positive association between FLFP and the use of digital technologies, or the availability of home appliances

# _Social Norms and Attitudes towards Women_

Social norms and attitudes towards women likely mediate women’s decision and ability to participate in the labor market (see Klasen, 2019). The cultural expectation of women as main caregiver limits their possibility to participate in the labor market, more so in selected occupations. In the LCR region, the time women allocate to unpaid care work and domestic labor is twice that of men (Bustelo et al., 2019). Studies have found that decision-making arrangements that empower women more are more likely to result in higher FLFP rates (Heath and Tan, 2020), and that social attitudes towards working women or men’s preferences and beliefs about gender roles can have an impact on FLFP (Fernandez et al., 2004; Fernandez, 2013; Bursztyn et al., 2018).

Similarly, violence against women can affect FLFP, although the direction is unclear. Higher rates of violence against women may lead to a reduction in FLFP. By non-participating in the workforce, women can avoid work-home commute and thus be less likely to become victims of violence (Chakraborty, 2017). A reduced FLFP could also be a consequence of the psychological and health costs of violence which can impact labor outcomes directly and indirectly through diminished productivity in the workplace (Currie and Madrian, 1999; Rees and Sabia, 2012). A positive correlation between intrafamily violence episodes and FLFP is also possible. Anecdotal evidence shows that partners react with violence to the increased female bargaining power (Eswaran and Malhotra, 2011; Heath, 2013). We conjecture that FLFP rates are higher when gender social norms are less strict. This happens when women participate more in main household decision making, when they are visibly and well-represented in public service, and when there is a broader view of gender parity in the society.

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_Ever in business,_ captures business experience within the household. Equals 1 when this is the case, 0 otherwise.

_Household member has held hereditary, political office_ .

_Land_ is the total owned land inside and outside the village.

_Distance_ , to CO meeting place.

_Credit constraints,_ dummy taking a value of 1 if the member faced any type of credit constraint, formal or informal.

_Household expenditures_ , expressed as logs of average monthly expenditures at the time of baseline.

_Decision Making,_ is the number of household decisions out of a total of eight that the member usually takes on his or her own. The decisions are: children’s schooling, consumption expenditures, major investments in business or land, the respondent’s participation in community or political activities, the respondent’s spouse participation in community or political activities, whether or not the respondent should work for an income, whether or not the spouse should work for an income and how much the household saves. In the analysis, a dummy is used that takes value 1 if the variable is above the median for each gender subsample.

_Bank deposit_ , dummy taking the value of 1 if the member has a bank account, 0 otherwise.

_Education_ is years of completed schooling of the respondent, if any. Top coded at 16.

# _<u>Business characteristics</u>_

_Type of business,_ dummy variables for businesses shown on brochure _Fixed location_ , dummy equal to 1 when the business is not mobile, 0 otherwise. _Operates all months_ , dummy equal to 1 when business operates year round, 0 otherwise. _Purchase on credit,_ equal to 1 if sales can be made on credit to customers.

_Records of sales and of money taken from business,_ 1 if the member does keep records, 0 otherwise.

_Number of workers,_ includes both paid and unpaid workers.

_Paid workers,_ dummy equal to 1 if the business owner employs people for wages, 0 otherwise.

_Log of SalesGood, Average, Bad month,_ considers average sales the year the baseline was taken, considering goods, average and bad month.

_Sales in ‘000 rupees,_ sales of business in an average month at the time of baseline.

# <u>Aggregate Outcomes</u>

_Business Knowledge,_ simple average of standardized z-scores of the following variables: knowledge of competition, bookkeeping and business concepts, all calculated as the first component of a PCA of several survey questions. In particular, _bookkeeping_ includes the following true / false and multiple choice questions: “Receipts are always recorded on the right side of the cash book” (1=Yes), “Sales records are useful for understanding the profitability of a business” (1=Yes) and “How often should entries in a cash book be made?” (Daily = 1). _Business concepts_ include “One can ignore product quality if it reduces production cost” (1=No), 37
One central aspect of the study depends on the classification<sup>5</sup> of the more than 8,000 food items present in the POF database using the NOVA classification, described in section 2. Table 2 presents, for each consumption decile, the food consumption pattern of households following the classification describing the four food types: in natura; culinary ingredients; processed; and ultra-processed foods. The statistics show that in natura foods represent the most important food group for every decile and, with the exception of the top consumption decile, in natura represents over 50% of the total food consumption. More importantly, ultra-processed consumption is smaller for lower deciles and grows with household consumption decile, with the top decile having a 65% larger consumption than the bottom decile. This pattern is different from countries such as the United States, where consumption of ultra-processed foods is generally associated with poorer households (Baraldi et al, 2018). Consumption of processed foods follows an opposite pattern to ultra-processed, with consumption being higher at the bottom of the distribution. This group includes foods such as bread, cheese, canned food and in general foods that contain no more than three or four ingredients.

Table 2 - Consumption statistics of food

|||Share of household food co<br>|nsumption by type<br>||
|---|---|---|---|---|
|Decile|In natura|Culinary Ingredients|Processed|Ultra-processed|
|1|54.0%|3.9%|24.4%|17.7%|
|2|58.5%|3.9%|19.3%|18.3%|
|3|58.6%|4.0%|17.0%|20.5%|
|4|58.2%|4.0%|17.3%|20.5%|
|5|56.6%|4.4%|16.6%|22.5%|
|6|56.0%|4.3%|16.2%|23.5%|
|7|54.8%|4.4%|15.8%|24.9%|
|8|53.0%|4.2%|16.6%|26.3%|
|9|52.4%|4.3%|16.3%|26.9%|
|10|49.6%|4.5%|16.8%|29.2%|
|Total|55.2%|4.2%|17.6%|23.0%|

Source: POF 2017/2018 The analysis of food consumption types by federative unit/state shows a similar pattern. Richer states in the South and Southeast regions, as well as the Federal District (DF) show a higher percentage of consumption of ultra-processed foods, while poorer states have a much higher consumption of in natura foods. In Maranhão, the poorest Brazilian state in terms of GDP per capita, on average 11% of food consumption is of ultra-processed items, while in natura represents over three-quarters of food consumption. On the other hand, Santa Catarina households consume almost three times as much ultraprocessed items (30% of the food consumption).

> 5 The full classification is too lengthy for inclusion in this paper but can be shared by the authors upon request.

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# **Table 2:  TA-Level Aid Distribution – Cumulative Households Reached per Capita Random Effects Model**

||**Aid Distribu**|**tion**||
|---|---|---|---|
|_Aid Docs_|_._|_Production S_|_hocks_|
|Low Rain |% Diff from Mean|, Rainy Season<br>0.033<br>(.009)|***|||
|High Rain |% Diff from Mean|, Rainy Season<br>0.069|*|||
|(.041)||||
|Drought Shock||-0.078||
|||(.299)||
|Flood Shock||0.141||
|||(.286)||
|SPI, Dec-Jan<br>-0.369|***|-0.491|***|
|(.141)||(.143)||
|NDVI, Nov-Mar<br>1.530||||
|(1.051)||||
|EPA % Diff Maize Yields<br>-1.398|***|-1.796|***|
|(.435)||(.441)||
|EPA Mean Maize Yields<br>-0.026||0.023||
|(.217)||(.215)||
|District Poverty Rate (2010)<br>0.004||0.003||
|(.005)||(.005)||
|Proportion Independent MPs<br>0.236||0.224||
|(.217)||(.221)||
|Proportion Opposition MPs<br>-0.025||-0.030||
|(.187)||(.180)||
|Constant<br>1.531|*|2.169||
|(.897)||(2.640)||
|Livelihood Zone Dummies<br>Yes||Yes||
|Number of Observations<br>600||600||
|R-squared (within)<br>0.342||0.334||
|R-squared (between)<br>0.581||0.567||
|R-squared (overall)<br>0.456<br>**Notes:** ***/**/* denote statistical significance at 1/5/10 percent<br>parentheses.|respectively.|0.448<br>Standard E|rrors in|

To capture potential targeting errors, we generate different categories of targeting outcomes. To do so, we must address the fact that aid is distributed throughout the country even in relatively normal rainfall years due to high rates of poverty and food insecurity even in good years. Although 2010 district 22
be supplemented by other data, such as on infant and child mortality, to obtain a more complete picture of how living standards are evolving.

We can write the poverty measure for county _i_ at date _t_ in the following generic form: Here _M it_ is the mean for date _t_ and _L it_ is a vector of parameters fully characterizing the Lorenz curve (roughly interpretable as ―inequality.‖) The mean is bounded below by _M_ * with _M it_  _M_ * with strict inequality for some _i ,t_ . The aggregate poverty measure for any date and region is then the population-weighted mean of _Pit_ .<sup>21</sup> The absolute measure is _P_ [ _Z_ ( _M_ * ) / _M it_ ,<sup>_L_</sup> _it_ ] . The absolutely poor are a proper sub-set of the poor.

Notice that the gap between the relative and absolute measures can be expected to change over time with changes in the mean and in distribution. Past research has indicated that the absolute poverty rate for developing countries tends to fall with economic growth; for recent evidence see Ravallion (2012d). It is an empirical issue whether our weakly relative measure will fall more slowly than the absolute measure during any process of (positive) growth in average consumption. Ravallion and Chen (2011) discuss this issue further and identify theoretical conditions under which relative poverty will change more slowly with economic growth than absolute poverty. As we will see, that prediction is confirmed by our empirical results.

We start the series in 1981 and make estimates at three yearly intervals up to 2008. Of the 125 countries, 20 have only one survey; 15 have two; 12 have three; while 78 have four or more, of which 31 have 10 or more surveys. If there is only one survey for a country then we estimate measures for each reference year by applying the growth rate in real private consumption per person from the NAS to the survey mean,<sup>22</sup> assuming that the Lorenz curve for that country does not change. We also use the annual NAS data for interpolation purposes, given the irregular spacing of the surveys, following the method outlined in Chen and Ravallion (2010).

In the aggregate, 90% of the population of the developing world is represented by surveys within two years of 2008.<sup>23</sup> Survey coverage varies by region and over time. Table 1

> 21 The population weights (for urban and rural poverty measures, as well as across countries) are also from the World Bank’s Development Data Platform.

> 22 For a few countries for which private consumption per capita is missing from the DDP we use GDP.

> 23 Some countries have graduated from the set of developing countries; we apply the same definition over time to avoid selection bias. In this paper our definition is anchored to 2005.

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39 extremely-poor household heads are principally engaged in agriculture . Three, while the urban poor constitute the minority of the extremely-poor<sup>(31X</sup> of the three lowest household groups), their main occupation<sup>is either as nont-</sup> agricultural workers or self-employed (28%).

# IV.3 Estimates of Moderate and Extreme-Poverty.

Poverty indices Pj(a,z) for a - 0,1,2, z - z, z and j - rural, urban are presented in tables 7 and 840. Consider first panel (a) of table 7. When a - 0 I find, first, that 19.5X of the sample population<sup>could be classified</sup> as extremely-poor. Second, that 37% of the rural population<sup>is below the line</sup> of extreme-poverty, while only 9.9X of the urban population falls in this category. Given the respective share of each population in the<sup>sample total,</sup> t:is implies that the rural areas account for almost _671_ of national<sup>extreme-</sup> poverty. Based on the head count ratio, extreme-poverty<sup>is mostly a rural</sup> problem. When correction is made for the depth of poverty,<sup>a -1, the</sup> proportion of extreme-poverty accounted for by the rural population increases to 72.8%. Finally, when account is made of the distribution<sup>of poverty,</sup> a - 2, the proportion of extreme-poverty accounted for by rural groups increases to 76.6X. The fact that the share of rural extremely-poor<sup>increases</sup> with higher order indices illustrates that not only<sup>is extreme-poverty</sup> mostly a rural phenomenon, but the poorest of the extremely-poor<sup>are almost all in</sup> the rural areas<sup>41</sup> . (The significance of this is highlighted recalling that in Mexico more than two thirds of the population can be<sup>classified as urban.) It</sup> also illustrates the importance of appropriate weighting<sup>and the misleading</sup> 39Table 6 refers to the main occupation of the household head during the month in which the household was surveyed. To the<sup>extent that household heads</sup> may migrate between urban and rural locations with<sup>the agricultural cycle</sup> these results may be misleading. In addition,<sup>micro studies for poor land</sup> owning households show a diversification of income sources, with some earnings coming from off-farm labor (cf. Roberts (1982)),<sup>section V.1 below). Also</sup> note that household heads that report to be unemployed<sup>were so with reference</sup> to that month only, so that these figures cannot<sup>be used to properly assess</sup> unemployment statuis.

40The distinction between P[a,a(C)1 and P[a,&(M)] found in table 7 is explained below; at this point the discussion focuses<sup>on P[a,(C)].</sup> 41In fact, one could argue that table 7 underestimates rural poverty to the extent that some publicly provided services, whose<sup>effect on poverty is</sup> not captured by the values of P (.), are relatively more available in urban areas; see footnote 44 below.