rafmacalaba/datause-encoder-data huggingface dataset:
recently, empirical studies that discuss demand sensitivity have started to emerge. Claessens, Tong and Wei (2012) investigate the impact of both the demand shocks and credit crunch for 7,722 listed firms. They show that the crisis negatively affected firms with greater demand and trade sensitivity. Isyuk (2013) focuses in U.S. non-financial firms and shows that around the collapse of Lehman Brothers, liquidity shocks had a greater impact, while in the first few months of recovery, improvements in demand mattered more.

This short paper is an effort to contribute the debate. We explicitly look for the impact of demand channels on firms. Using the Financial Crisis Firm survey conducted by the World Bank in six Eastern European countries, this paper shows that the drop in demand for firms’ products and services is very severe, and is reported as the most damaging factor on firms in these countries. In addition, the firms’ change in sales is significantly correlated with the sector’s demand sensitivity, and not with various proxies used for firms’ dependence on external finance.

Our paper has two main contributions. First, it provides evidence about the _relative_ importance of demand and credit shocks on firms. It is probably not so surprising that in this severe crisis, both credit and demand factors are at play. Different studies have indeed pointed out that both credit and demand factors have negative impacts on firms during the crisis. Nevertheless, it is important to be able to provide some evidence about the relative important of the factors because they have fundamentally different policy emphases. Traditionally, with balance sheet data it is difficult to directly compare the relative impacts of the two factors. The World Bank’s Financial Crisis Survey is unique in that it asks firms to judge directly demand and credit channels, among others. While subjective, the data strongly indicate that declining demand is more important for the vast majority of firms.

Our second contribution is regarding the representativeness of the sample. The comprehensive coverage of the survey allows us to look at impacts on a more representative set of firms. In this sense our paper complements the existing literature. Tong and Wei (2011) and Claessens, Tong and Wei (2012) use data of publicly listed firms, which are generally large and well-established. Because of the data constraint, they do not include actions for small and medium firms, which constitute the majority fraction of the economy. The World Bank’s Financial Crisis Survey collected data from a more random set of firms (see section 2 for data description), which allows us to examine the impacts on small and private firms –those that can potentially be more vulnerable to demand shocks or credit shocks. Our results show that firm size does not seem to matter for sales, but it does for firms’ capacity and employment, in opposite ways. While small firms’ capacity is significantly more correlated with demand and credit sensitivities than large firms’, small firms’ employment is significantly less so.

3
Despite much higher consumption of gasoline by the rich, in relative terms the gap between different wealth groups narrows substantially. On average, Iranians spend about 2.1 percent of their household budget on gasoline, but the poorest and the richest 10 percent spend about only 1.8 percent and 1.6 percent respectively. It is the population in the middle of the distribution who spend the largest share of their budget on gasoline (Figure 5). This implies that while increases in gasoline prices will affect all households, it is those in the middle of the distribution who will be most affected, despite the highly skewed benefits from subsidies towards the richest. That is, the middle class will be more affected than both the poor and the rich, because the poor consume less gasoline and the consumption of the rich is still a relatively small amount of their total expenditure.

Figure 5. Expenditure on gasoline as a percentage of total expenditures by deciles, <u>%</u> <!-- Start of picture text --> 3.0<br>2.5 2.5 2.4 2.4<br>2.5 2.3 2.3 2.2<br>2.1<br>2.0<br>1.8<br>2.0<br>1.6<br>1.5<br>1.0<br>0.5<br>0.0<br>deciles<br>%<br><!-- End of picture text --> Source: Authors’ calculation using forecasted HEIS 2018/2019.

# 4. Impact of reform on poverty and government revenues without mitigation efforts

In this section we simulate the direct and indirect impacts of the reform on poverty and inequality without any mitigation steps by the government, as well as the associated impact on government spending.

- 4.1 Direct impact on poverty and inequality without mitigation efforts

To estimate the direct impact on poverty and inequality, we assume that, except for the change in prices, all other determinants of welfare, in effect presenting a first-order approximation of the true impact of the reform. The approach thus simulates the ‘direct effects’ of subsidy reform, which are the price and quantity changes that apply to the final consumer when subsidies on final products are changed. These include the short-term crude change in prices (also called first-round effects) and the medium-term behavioral response of the final consumer in terms of quantities consumed (also called second-round effects, behavioral changes or demand responses to price changes). The change in welfare is equal to the deflation of subsidized expenditure following the increases in prices (Araar and Verme 2015). The use of elasticities does not affect the estimation of the impact of subsidy reforms on household welfare. Households can reorganize consumption as they wish but the impact on total household welfare will not change. Higher prices on subsidized products will reduce overall consumption/expenditure, negatively 7
# Robustness check: Expanding the set of countries

Lopez-Calva and Ortiz-Juarez (2014) and Ferreira et al. (2013) only use a particular set of high and uppermiddle-income countries in LAC to define the regional middle-class bounds. The limitation of the selection of countries depends on the availability of longitudinal data. Given that this is not a restriction in the proposed approach, expanding the number of countries, including high, upper-middle, and lowmiddle income, enhances the estimation results by increasing regional representativeness. Following Jolliffe et al. (2022) to check the robustness of results, both lower and upper middle-class bounds were calculated cumulatively by ranking countries from lowest to highest GDP per capita. Figure 2 shows that both vulnerability and middle-class lines estimates are robust to using fewer countries. Each point in the figure corresponds to the line estimated using the synthetic panels available for that country and all those to the left.

**Figure 2** : Cumulative middle-class bounds for LAC countries in 2017 PPP ordered by country GDP

<!-- Start of picture text --> 15 85<br>80<br>14<br>75<br>13<br>70<br>12 High income (H) 65<br>11 60<br>Upper middle income<br>(UM) 55<br>10<br>Lower middle 50<br>9<br>45<br>8 40<br>hnd bol slv ecu per pry col bra dom mex cri ury arg chl pan<br>Country<br>Vulnerability line Middle-class line (right axis)<br>Vulnerability line Middle-class line<br><!-- End of picture text --> Source: Own estimations based on SEDLAC (2022) Note: World Bank Analytical Classifications using data for 2017. Results were obtained using 38 synthetic panels built with available surveys within +/- 2 years circa 2017 and 100 bootstrap repetitions.

# Robustness check: Increasing the time span

When estimating the International Poverty Lines, Jolliffe et al. (2020) select for each country one survey that was conducted in 2017 or the closest year. The proposed analysis in this paper followed the same principle by choosing a two-year interval around 2017 (i.e., 2015 and 2019) for LAC countries to capture their income and poverty dynamics.<sup>14</sup> However, expanding the time span - including more rounds- might increase the statistical support by embracing changes that are not necessarily assigned to a specific period. To check the robustness of both thresholds, new two-year synthetic panel rounds were constructed for the 2010–2015 interval. Figure 3 shows that when using more than 90 synthetic panels (2010-2019), variations in bounds estimates are not significant: the median lower-middle class line

> 14 Lopez-Calva & Ortiz-Juarez (2014) exploit the longitudinal data for 3 or 4-year intervals: Chile (2001-2006), Mexico (20022005), and Peru (2002-2006).

13
According to the 1990 Census, Indians remain the most marginalized group of Mexican society. More than 40 percent of the Indian population above fifteen years of age was illiterate; roughly three times the national rate. Thirty percent of Indian children between 6-14 years of age did not attend school. Indians also had significantly higher morbidity and mortality rates associated with infectious and parasitic illnesses, higher levels of nutritional deficiencies, and less access to such basic services such as indoor plumbing, piped water, and electricity.

# **6.  Poverty-Reduction Strategy for Rural Mexico**

Over the medium to long run, what is needed to alleviate the high levels of poverty is broad-based growth. However, this is not enough to alleviate poverty, particularly in the short run. Measures are needed to protect vulnerable groups and to ensure that the rural poor are able to take advantage of the greater opportunities in the economy. In order to address these latter needs, this paper has examined the profile of the poor in rural Mexico and the rural poverty correlates.

The government of Mexico has taken important steps to reduce poverty, for example though the continuation of _Oportunidades_ . However, it needs a rural poverty alleviation strategy that sets clear and appropriate priorities and goals for poverty reduction efforts within a framework of a continuation of economic policies that would promote growth. The challenge and test of the government’s resolve will be to what extent current and future policies and programs are governed by that strategy. In order to ensure that the poor reap the benefits, poverty measurement and monitoring are called for, including tracking changes and making appropriate adjustments in existing programs to reflect these changes.

## **A THREE-PRONGED POVERTY-REDUCTION APPROACH FOR MEXICO**

The differing characteristics of poor households call for multiple paths out of poverty aimed at: (i) small farm sector intensification, (ii) improved employment opportunities in dynamic commercial agriculture, (iii) growth of the rural non-farm sector, (iv) migration of the young, and (v) provision of safety nets for those “trapped” in poverty. The recommended measures include improving human capital endowments, reforming the land, labor and financial markets, enhancing research and extension, improving the supply of public goods and services, and transfer programs.

The rural poverty profile and determinants of poverty analysis provide guidance on a social agenda and poverty alleviation strategy for rural Mexico. The strategic principles for reducing poverty involve seeking to strengthen the key assets of the poor, taking into account geographic differences in the poverty situation and priorities.

The government of Mexico could apply a three-pronged poverty-reduction approach: _First,_ targeted programs should focus on the extreme poor and prioritize among groups in rural areas, especially in the dispersed rural areas. Given the distribution of poverty, first 34
|Table 16: Medium-|term impacts of_Mais Educação_on|Mdi<br>2007 Portuguese te|Low<br>st scores|
|---|---|---|---|
||High Portuguese|eum<br>|Portuguese|
|Dependent variable|<br>test scores in 2007|Portuguese test<br>scores in 2007|<br>test scores in<br>2007|
||(1)|(2)|(3)|
||Panel A: Impacts on fifth-grade va|riables||
|Dummy_Mais Educação_*|year 2011<br>–2.153|–3.266|–2.458|
||(3.357)|(2.047)|(2.070)|
|_R_ <sup>2</sup>|0.816|0.801|0.798|
|Observations|624|732|760|
|Dummy year 2011|YES|YES|YES|
|School fixed effects|YES|YES|YES|
||Panel B: Impacts on ninth-grade va|riables||
|Dummy_Mais Educação_*|year 2011<br>–5.676|–2.510|–1.999|
||(4.287)|(2.614)|(2.451)|
|_R_ <sup>2</sup>|0.743|0.744|0.778|
|Observations|574|666|640|
|Dummy year 2011|YES|YES|YES|
|School fixed effects|YES|YES|YES|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined _Mais Educação_ in 2008 and remained in the field until 2011, divided into groups according to their Portuguese achievement in 2007. This table reports coefficients from the ordinary least squares estimation of equation (1) in the paper, where the dependent variable in all columns is the Portuguese test scores. In column (1) we show results for treated schools with high Portuguese test scores in 2007, in column (2) schools with medium Portuguese test scores, and in column (3) schools with low Portuguese test scores. All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weightsts. Panel A reports these estimates for schools from the fifth-grade sample. Panel B reports these
# <mark>3.1.1 Survey Sample</mark>

The selection of the HFPS sample of households proceeded differently in the four countries. In Nigeria, 3,000 households were selected from the frame of 4,934 GHS-Panel households with contact details. The target number of successfully interviewed households to produce nationally representative estimates with reasonable precision was 1,800. Given the large amount of auxiliary information available in the GHS-Panel for these households, a balanced sampling approach using the cube method (Tille, 2006) was adopted for the selection of the 3,000 households for the HFPS.<sup>8</sup> For the first-round of the Nigeria HFPS, all the 3,000 sampled households were contacted and 69 percent of them were successfully reached by the interviewers (2,057 households). Of those contacted, 94 percent or 1,950 households were successfully interviewed. This yielded an overall crude response rate<sup>9</sup> for the sample of 65 percent.

Figure 3.2: Phone Ownership Share of Households Who Own a Phone <!-- Start of picture text --> 100.0<br>88.7 88.0<br>90.0<br>80.0 73.3 76.0 75.7<br>70.5<br>68.8<br>70.0 65.6<br>58.9<br>60.0<br>51.8 National<br>47.9<br>50.0<br>Urban<br>40.0 35.7<br>Rural<br>30.0<br>20.0<br>10.0<br>0.0<br>Ethiopia Malawi Nigeria Uganda<br><!-- End of picture text --> Source: Authors’ calculations from the ESS 2018/19, IHPS 2019, GHS-Panel 2018/19, & UNPS 2019/20 For the Ethiopia HFPS, to obtain representative estimates at the national, urban, and rural level, the target sample size for the HFPS was 3,300 households: 1,300 in rural and 2,000 households in urban areas. To account for non-response and attrition all of the 5,374 households with contact information in the ESS were called in round 1 of the HFPS. In rural areas 1,413 households owning a phone and 771 households with reference phone were contacted. Whereas in urban areas 3,213 households owing phone and 24 households with a reference phone numbers were contacted. A total of 3,249 households (2,271 urban and 978 rural households were fully interviewed yielding an overall crude response rate of 60 percent (67% for urban and 50% for rural households).

> 8 The variables considered in the cube sampling approach are the same variables included in the bias correction calibration below (displayed in Table 3.2).

> 9 Response and contact rates reported here are “crude” in that they do not cleanly correspond to AAPOR standard definitions. This crude calculation is retained for the sake of simplicity.

7
Figure 4. Sales of rainfall insurance by BASIX, Andhra Pradesh <!-- Start of picture text --> 2003 2004 2005 2006 2007 2008 2009<br>Year<br>Total Number of Policies Total Number of Policyholders<br>15000<br>10000<br>5000<br>0<br><!-- End of picture text --> Note: Trends in sales of ICICI Lombard rainfall insurance in Andhra Pradesh. Figure plots the number of phases of policies sold, as well as the number of distinct policyholders. Source: BASIX.
- Thomas, D., Contreras, D., Frankenberg, E., 2002. Distribution of Power within the Household and Child Health, mimeo, RAND.

- World Bank, 1994. Enriching Lives: Overcoming Vitamin and Mineral Malnutrition in Developing Countries. World Bank, Washington, DC.

- World Food Programme, 2000. Food and Nutrition Handbook. Tech. rep., World Food Programme, Rome.

- World Health Organization, 1985. Energy and Protein Requirements-Report of a Joint FAO/WHO/UNU Expert Consultation. Technical Report Series 724, World Health Organization, Geneva.

32
# **3 Empirical Strategy**

We estimate the impact of conflict exposure on firms’ economic performance by combining longitudinal firm-level data with geolocalized information on conflict events. Our main specification reads as follows: where _Yit_ represents the outcome of interest for firm _i_ in fiscal year _t_ . The analysis considers several firm-level outcomes as dependent variables, including _Sales_ , _Total Costs_ , and _Profits_ . _Conflict exposure it_ denotes the firm-specific measure of conflict exposure. In our baseline specification, it is computed as the logarithm (log of 1+) of the number of conflict events that occurred within a 20 km radius of the firm’s location during the 12 months preceding the end of the last fiscal year.<sup>13</sup> The vector _Xit_ represents a set of time-varying firm characteristics, including size, age, and export status. _μi_ denotes firm fixed effects, accounting for all time-invariant observed and unobserved characteristics of the firm that could potentially influence the outcome of interest. _θt_ represents year fixed effects, controlling for overall trends in economic activity and common shocks. As a robustness check, we also include the full set of area-year interacted fixed effects to account for all time-varying location-specific determinants of firm performance, such as infrastructure availability and local labor market characteristics. Finally, _εit_ is the error term. In all specifications, we report robust standard errors clustered at the firm level, which is the unit of observation used to measure the intensity of conflict exposure. All variables are defined in Table C1.

Our identification strategy relies on the assumption that, conditional on our full set of fixed effects, conflict events occurring near the firm’s location are uncorrelated with any latent determinant of its economic performance. Under this assumption, _β_ captures the reduced-form effect of the firm-specific conflict exposure on economic performance.<sup>14</sup> In what follows, we present our core results and examine in detail potential threats to the validity of our identification strategy.

> 13We also employ alternative measures of conflict exposure constructed using: i) different buffer distances from the firm’s location or weighting by inverse distance; ii) a different time period; iii) different types of conflict events; and iv) the ACLED dataset as an alternative source for conflict events (see Section 2.2).

> 14In our sample, the onset of conflict does not necessarily occur at _t_ 0. As a result, we cannot provide dynamic difference-in-differences estimates of the effect of conflict between _t−_ 1 and _t_ 0.

9
Policy Research Working Paper 7036

# **Abstract**

This paper assesses the impact of a change in the price of cashew received by exporters in general—and by FUNPI, a fund to promote the industrialization of agricultural products, in particular—on farmgate prices and poverty in Guinea-Bissau. The analysis builds a theoretical model of supply chains in export agriculture that includes exporters, traders, and farmers competing in a bilateral oligopoly fashion. The model is adapted to data from the country’s cashew sector and a household survey. Given the market structure, a shock on export prices or the introduction of an export tax, such as the FUNPI contribution, has a strong effect on farmgate prices, as farmers absorb about 80 percent of the tax (while exporters take up 13 percent and traders absorb the remaining 7 percent). The effect is uneven across households, as poor rural households are more exposed to price volatility and most cashew farmers are poor. It is estimated that their income falls by 12 percent as a result of the FUNPI contribution. Complementary policies can overcome the effect of the FUNPI surcharge on farmgate prices by aiming for reductions in transport, infrastructure, and transaction costs for traders and exporters. Fostering cashew processing would create added value through a displacement of volume from exporters to processors. The analysis finds it implausible that, under reasonable assumtions, a subsidy would overturn the welfare costs of the FUNPI contribution.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice Group. 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 Task Team Leader may be contacted at mhanusch@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