rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 3: Multiplier effect of export policy**

_Source:_ Giordani, Rocha and Ruta (2016).

# 3. **Initial evidence from China**

China is the first country that was hit by Covid-19 and that enacted containment policies aiming at reducing the spread of coronavirus. The cases were first noticed in December 2019 in Wuhan, Hubei province. Contagion accelerated in January and February 2020, with a deceleration in March thanks to the government’s containment policies. In this section, we look at recently published trade data for January and February 2020 as an early indication of the impact that Covid-19 could have on food exports elsewhere in the quarter following the outbreak of the pandemic as production is affected by the health crisis and by the policies aimed to contain it. Because of its large manufacturing base, China’s food exports account for less than 1 percent of its total exports. But thanks to its size China is still an important player in the global food market, accounting for 10.8 percent of world food exports. For fish meat, a key staple in many diets, China is the largest exporter, with 16 percent of global exports. For several products, including groundnuts, rice and soya beans, China is among the top-10 world exporters.

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related to innovation, and (d) {Δ _τ_ , _ψ_ , _τmin_ , _τmax_ , _hτ_ , _bτ_ } are parameters related to rentseeking in the form of bribing.

The strategy I used to calibrate the parameters in the steady state has three parts. First, I set some of the parameters according to existing microeconomic and macroeconomic evidence in the literature. Second, I calibrated the technological parameters to an undistorted economy where _τ_ = 0 for all firms. In order to map this economy to the data, I followed the literature and used the U.S.

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participation which may also lead to errors.<sup>10</sup> All these measurement errors in the EORA GVC data may bias

> 7The number of countries in our estimating sample is at most 121 due to missing data on determinants and to the exclusion of countries with problematic (negative) values for the GVC participation measures (Liberia, Moldova, Mauritius, South Sudan, Sudan, and Zimbabwe).

> 8In robustness checks we use as alternative sources the TIVA database 2016 and 2018 editions covering 64 countries over the periods 1995-2011 and 2005-2015, respectively, the WIOD 2013 release covering 40 countries in the period 1995-2011 and the WIOD 2016 release covering 43 countries in the period 2000-2014, described in Timmer et al. (2015, 2016). Further details, summary statistics and correlations among the GVC participation measures are provided in the Appendix. One interesting fact to note from the correlations is that the cross-country correlation between backward and forward GVC participation is negative.

> 9United Nations (2019) describes the major limitations in detail and proposes solutions to help substantially improve the input-output coe¢ cients used in the current international input-output tables.

> 10The GVC literature emphasizes governance of lead ...rms and their relationship with suppliers as a major feature of GVC trade but such information is unavailable in current international input-output tables.

5
Figure 4: Expenditure Shares of Subsidized Food Products across Countries and Quintiles <!-- Start of picture text --> Flour Bread<br>7 6<br>6<br>5<br>5<br>4<br>4<br>3<br>3<br>2<br>2<br>1 1<br>0 0<br>Djibouti Morocco Libya Iran, Islamic Rep. Iran, Islamic Rep. Libya<br>Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5<br>Sugar Vegetable Oil<br>9 4<br>8 3.5<br>7 3<br>6<br>2.5<br>5<br>2<br>4<br>1.5<br>3<br>2 1<br>1 0.5<br>0 0<br>Djibouti Libya Morocco Djibouti Libya<br>Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5<br>Expenditure share (in %) Expenditure share (in %)<br>Expenditure share (in %) Expenditure share (in %)<br><!-- End of picture text --> _Source:_ World Bank estimations from Household Budget Surveys.

# A Policy Dilemma

It should be clear by now that there is a certain trade-off between the share of expenditure on subsidized products in total household expenditure and the dollar amounts of subsidies received. To illustrate this trade-off, figure 5 plots these two dimensions across population percentiles for LPG in different countries. For most countries, the curves are negatively sloped for the expenditure shares, meaning that poorer households spend a larger share of total expenditure on subsidized products than richer households (figure 5, panel a). Also in most countries, richer households receive larger amounts of subsidies in per-capita
3.b. demonstrates how we incorporate subsidies to energy inputs and estimate their relative impact on total ERPs. Section 4 discusses specific data issues and the level of disaggregation possible.

Section 5 discusses briefly the methods used to estimate the ad valorem tariff equivalent of NTBs. The estimates for selected industries were estimated by Dr. Khaled Hanafy, and his approach is detailed in Annex 3. Section 6 presents the results of the ERP calculations using 2009 Egyptian applied tariffs at a fairly disaggregated industrial level making use of cost structure information for selected industries available from a previous study done a decade ago. We then compare our results to those of the previous study that used 1998/1999 applied tariff levels. We also present for some industries the ERPs for 2009 including NTB price-wedge estimates where available.

But these disaggregated cost data do not adjust for energy subsidies and so Section 7 presents the applied nominal tariffs and the levels and dispersion of ERPs for 23 industrial sectors where energy input cost shares for the five fuel sources mentioned above are available from a recent input-output matrix of the Egyptian economy. These ERPs adjust for tariffs, output subsidies and energy subsidies. In addition, the ERPs are also estimated adding the estimated tariff equivalent for NTBs. In presenting the results, non-tradables and food and agriculture are underlined, and one is led to conjecture to what degree energy subsidies could be considered partial compensation for the implicit taxes on the sectors from tariffs on tradable inputs. In addition to discussing these results, we identify some differences with those of Lederman (2007), who also used an approach based on input-output coefficients but with less information on the participation of energy inputs in sectors’ cost structures. Section 8 concludes with a discussion of the policy implications of the results.

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electricity bill. For residential customers, the average electricity consumption is derived from the IEA energy balances by taking the total residential electricity consumption and dividing by the number of connected households. Since there are limited data on the distribution of electricity consumption within a given country, median electricity consumption may lie below the lifeline tariff level in more cases than implied by the analysis. As a result, the results might overestimate the average electricity tariff faced by households in certain developing countries. For non-residential customers, a “representative” consumption level is adopted based on expert consultations and the characteristics of each sector. Commercial, agricultural and public sector consumption is assumed to be 1,000 kilowatt-hours per month, while industrial consumption is set at 5,000 kilowatt-hours per month.

Similarly, assumptions were made about the representative load level for each customer group, as well as the pattern of peak versus off-peak consumption in cases where time-of-use tariffs exist. Following internal consultations, the commercial load factor was set to 11 kilovolts, while the industrial load factor was set to 33 kilovolts. In cases where customer groups were not explicitly defined by the tariff schedule, these load factors were used for categorization.

In order to apply time-of-use to a single average nominal tariff for each customer group, a weighted average volumetric time-of-use charge was computed for each customer class based on the following time zones: peak times were defined as 6pm through 10pm for commercial customers, and as 6pm through 9pm for industrial users. Day time was defined as 8am through 6pm, and nighttime as 10pm through 8am for all users. Where a choice between linear tariffs and time-of-use tariffs was presented in the tariff schedule, the option resulting in the cheapest average nominal tariff based on the user’s assumed monthly consumption level was selected.

4
# **1 Introduction**

Latin American countries achieved important social progress in the 2000s. Poverty fell by over 16 percentage points, from 39.5 to 23.3 percent of the population between 2004-14; while extreme poverty was more than halved (declining from 24.5 to 10.8 percent) over the same period<sup>1</sup> . The middle class in the region expanded<sup>2</sup> . And income inequality, a persistent malady in Latin America, fell substantially during the 2000s (the regional Gini coefficient for per capita income declined from 0.57 to 0.51 in 2000-14) (World Bank, 2016). The role of rising labor income stands out, among other factors contributing to these achievements, as a key channel of transmission between growth and social gains. Indeed, income from labor has been a central element in the reduction of poverty. For instance, Rodriguez-Castelan et al. (2016) establish labor income as the leading driver behind the reduction in poverty between 2003 and 2013 in the region. What happens in terms of labor market dynamics is thus fundamental to understanding wellbeing in Latin America and the Caribbean, particularly if the interest is on the poorest segment of the population. As documented by Cord et al. (2014), data from household surveys across the region show that incomes from labor make up 60 to 80 percent of total household income.

– The study of job quality including characteristics such as whether jobs are – stable and pay, at the very least, a living wage is useful to shed further light on 1Based on poverty lines of $4 a day and $2.50 for poverty and extreme poverty, respectively (World Bank, 2016).

> 2Growing from 23 percent of the population in 2003 to 35 percent of the population in 2014 (World Bank, 2016). Following Lopez-Calva and Ortiz-Juarez (2011), and Ferreira et al. (2013), the middle class is defined
using any single one of these variables, what is needed to obtain consistent estimates of inequality, we argue, is a combination of the information coming from the consumption and income data. Section 4 appeals to multiple-imputation methods in order to achieve this.

> 8 Calibration estimation, where the sampling weights are adjusted to make certain estimators match known population totals, is commonly used in survey sampling. Details on the subject can be found in, for example, Deville, Särndal, and Sautory (1993).

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a whol'-, and underuttilization of facilities constructed as a part of Bank projects is reported to be a problem in some countries.

The recruitment and training of different program staff is another major element in Bank projects. It receives !;omewhat less attention in the audit reports than does construction, but the poor performance of workers is often cited as a problem. For example in the Bangladesh PCR it is observed:

> 1 9 ProJect Performance Audit Report: Banaladesh, First Population Project, pages vi-vii.

46
line with previous studies on the risk of poverty such as that of Hill and Porter (2017) and Skoufias et al. (2021), we also adopt a different approach where we directly use our nationally representative sample of households and simulate food consumption responses under the 10,000 synthetic scenarios to infer households’ probability distribution of food consumption. This in turn allows to estimate the share of households exposed to the risk of food insecurity being defined as, for instance, those with a probability of being food insecure greater than 0.5. With this definition, we find that 31% of households are at risk of food insecurity in rural Mauritania.

> 43We fit a multivariate normal distribution to historical 5-month SPI values in the 34 departments of the study area over the period 1981-2022, and we generate 10,000 scenarios based on the estimated mean and covariance matrix.

> 44Comparison of this model’s estimates against historical data for the period 2011-2015 show a performance that is comparable to that obtained with the optimal model of section 4.2.

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Planted maize area harvested (%)||1.828***<br>(0.510)|||||||
|Constant|47.25**|-278.1|71.41***|-482.3|312.0***|2,710*|428.3***|1,023***|
||(19.89)|(525.7)|(24.72)|(583.6)|(81.05)|(1,418)|(35.87)|(356.9)|
|Observations|568|544|493|490|442|442|842|842|
|Adjusted R-squared|0.666|0.718|0.460|0.557|0.249|0.386|0.026|0.398|
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|

Note: Regression of self-reported realized harvest value (per hectare) (Panels A + B) or production (Panel C) on expected harvest value (per hectare) (Panels A + B) or production (Panel C), planted area loss (only IHPS19), and a number of plot, manager, and household controls as well as inputs and district fixed effects. Controls: Inputs - Plot area, total labor (p. ha), any hired labor dummy, seed quantity (p. ha), improved variety dummy (only IHPS19 and IHPS15), inorganic fertilizer quantity (p. ha), any organic fertilizer dummy; Household - dependency ratio, head employed for wage dummy, any HH member owns non-ag enterprise dummy, any HH member has primary education dummy, agricultural asset index; Manager - female dummy, age, primary education dummy; Plot - intercropped dummy, precipitation of wettest month, soil
<mark>we know with certainty whether they were living in refugee affected areas or not for at least part of the crisis. The sub-sample of households who migrated after 2001 is only used for our placebo tests. The reason is that there is no way to tell if those who migrated to their current place of residence after 2001 were previously in refugee affected areas or not prior to 2001 (the DHS does not provide information on multiple migration spells, only migration to the current place of residence).</mark> <mark>In an effort to provide some explanations to the findings of the main analysis, we also use older DHS data to study the socio-economic characteristics of grandmothers of children in 2015/2016. We use the 1991/1992 DHS to empirically assess labor market outcomes of grandmothers prior to the refugee influx in 1993. We also use the 1996 DHS data to assess the short-term impacts of refugees’ influx on under five years old children’s health outcomes and grandmothers’ labor market participation during the first phase of the influx from October 1993 to December 1996.</mark>

# **5.** **<mark>Empirical Strategy</mark>**

<mark>Our empirical approach follows the main strategy used by the literature on measuring the impact of forced displacement on host communities where the interaction between the treatment group (</mark> _<mark>T</mark>_ <mark>) and the refugee shock (</mark> _<mark>R</mark>_ <mark>) is the variable of interest.</mark><sup>16</sup> <mark>The general equation is described as follows:</mark> <mark>where Y ����</mark><sup><mark>is the Height for Age Z score or the probability of Stunting (HAZ <-2 SD) of the child</mark></
Table 5: Impact of tradable service on (non-tradable) consumer services

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Consumer|services|Non-consu|mer services|
||Employment|No. of Firms|Employment|No. of Firms|
|Log tradable services|0.511**|0.386*|0.340|0.103|
||(0.250)|(0.215)|(0.221)|(0.204)|
|Education|-0.003|0.003|0.011|0.005|
||(0.009)|(0.007)|(0.009)|(0.006)|
|Observations|1,173|1,173|1,173|1,173|
|District FE|yes|yes|yes|yes|
|Year FE|yes|yes|yes|yes|

*** ** * Standard errors (in parentheses) are clustered at the district level. _p <_ 0.01, _p <_ 0.05, _p <_ 0.1. Consumer services are (i) retail trade, except of motor vehicles and motorcycles, (ii) personal services, (iii) human health, (iv) residential care, and (v) accommodation and food services. Non-consumer services are all other non-tradable services.

Finally, to further assess the consumer demand channel, we examine the impact of tradable service employment on consumption expenditure by category, at the household level in Table 6. To be consistent with previous literature, following the analysis on district-level household expenditure in India in Fan et al. (2021), we use state fixed effects instead of district fixed effects. Column (1) shows that a
Africa’s MTN and Orange of France dominate the mobile market, which is one of the highest in Cameroon’s peer group, as measured by the Herfindahl-Hirschmann Index (figure 25).<sup>13</sup> There are numerous Internet service

**Figure 25. Market concentration in Cameroon and selected African peers**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2005 2008<br>Source: World Bank 2008<br>Herfindahl-Hirschmann Index<br><!-- End of picture text --> providers, but the market is led by CAMNET, a CAMTEL subsidiary offering ADLS connections, and the two mobile operators.

> 12 Cameroon Telecommunications.

> 13 The Herfindahl-Hirschmann index (HHI) is a commonly accepted measure of market concentration. It is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers. An HHI of 100 indicates the market is a monopoly, while a lower the HHI the more diluted is the market power as exerted by one company/agent.

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2. _The Parenting Stress Index-Short Form (PSI/SF) (Abidin, 1990),_ an adapted 43-item scale that asks caregivers to report on their perceptions of parenting the target child in the study. The PSI/SF consists of three 12-item subscales: _Parental Distress, Parent-Child Dysfunctional Interaction_ , and _Difficult Child_ . The PSI/SF has been used in South Africa (Allen et al., 2014) and Kenya (Oburu, 2005). Sample items include, “You expected to have closer and warmer feelings for [CHILD] than you do, and this bothers you,” “[CHILD] makes more demands on you than most children,” and “You often have the feeling you cannot handle things very well.” All items were translated and back translated, and changes were made per recommendations by the PSI/SF author. During administration, all items were read to the caregiver, who indicated the degree to which they agreed with the statement. The three subscales were summed to create a Total Stress score. Higher scores indicated more stress related to parenting this child. We added 7 items to also capture positive feelings related to caring for the child, which were interspersed into the PSI/SF, which were not used in calculating the Total Stress score.<sup>32</sup> The final index was obtained by standardizing the Total Stress score using the control group’s mean and standard deviation at each round (baseline and 18-month follow-up) and then multiplying it by -1 so that a higher score indicates less parental stress.

3. _Support for Learning and Positive Parenting_ (UNICEF, 2010) module was adapted from the UNICEF Multi-Indicator Cluster Surveys (MICS) and other sources (Hamadani et al., 2010; Kariger et al., 2012). Support for learning was determined by both the availability of materials (books, toys etc.) that promote development, as well as activities adults do with children to encourage learning. We expanded the activities to
Annual inflation<br><!-- End of picture text --> <!-- Start of picture text --> rate<br><!-- End of picture text --> <!-- Start of picture text --> 5 4 3 2 +1 #0 14 #2 3 4 =«65«65«65«65<br>Timeline<br><!-- End of picture text --> <!-- Start of picture text --> 5 4 3 2 +1 #0 14 #2 3 4 =«65«65«65«65<br>Timeline<br>Source: Authors' calculations based on the IMF International Financial Statistics<br>** Statistically significant, 90% confidence<br>* Source: Penn World Tables 9.0<br><!-- End of picture text --> <!-- Start of picture text --> 5 4 3 2 +1 #0 14 #2 3 4 =«65«65«65«65<br>Timeline<br>Source: Authors' calculations based on the IMF International Financial Statistics<br>** Statistically significant, 90% confidence<br>* Source: Penn World Tables 9.0<br><!-- End of picture text --> <!-- Start of picture text --> 5 4 3 2 +1 #0 14 #2 3 4 =«65«65«65«65<br>Timeline<br>Authors' calculations based on the IMF International Financial Statistics Database<br>Statistically significant, 90% confidence<br><!-- End of picture text --> <!-- Start of picture text --> Def. 1: All conflicts<br><!-- End of picture text --> <!-- Start of picture text
# 4.5 Digital Health Interventions and the Role of Evidence

Digital Health Interventions are unique among other health interventions in the way that evidence is generated and synthesized. An economic evaluation typically views the range of evidence for a health intervention at a single point in time in a particular context and many methodological approaches are used to reflect how health intervention parameters may change over time, context and scale. However, there are unique attributes to the evidence base for DHIs, including the way that the implementation of a digital intervention will in itself commonly generate further evidence and that the specific attributes of a digital intervention can evolve over time to adapt to context.

The distinction between efficacy (the effect observed in a controlled and ideal environment) and effectiveness (the effect in routine conditions in a specific context) is a key component of any type of economic evaluation intended to inform decision making. Evaluation of noncomplex health interventions, such as pharmaceuticals, will typically adopt a primary effect estimate from clinical trial data and then attempt to reflect any variation from the observed effect that is expected in the context of the economic evaluation. This information is also supplemented by observed data and real-world evidence (RWE) but a common objective is to utilize efficacy information to estimate effectiveness.

The regulatory environment for a particular type of health intervention is a major driver of the approach to evidence generation. The strong traditional focus of health intervention regulation on patient safety and efficacy means that evidence generation is commonly focused on whether potential benefits outweigh potential harms rather than cost effectiveness or value for money. When conducting economic evaluation for an intervention that already has regulatory approval (such as pharmaceutical), evidence required at the point of regulation will have demonstrated efficacy and safety for a particular clinical indication. In contrast, economic evaluations of non-regulated products (or where the regulatory environment does not routinely require demonstrated efficacy through clinical trial) may not have baseline evidence of impact to parameterize an analysis.

DHIs, in common with many other complex interventions, are intricately integrated into the context within