rafmacalaba/data-use-mentions-extended huggingface dataset:
subgroups of the population along the welfare distribution that are most likely to be affected by economic slowdown, we plotted density and Growth Incidence Curves (GIC). Figure 8a shows the pre-crisis and post-crisis consumption distribution under Scenario 2, along with poverty line (red line). One can see that the poor will become poorer due to the economic shock induced by COVID-19. The GIC curve presents this information in a different way and plots the growth rate of consumption per capita across two time periods under Scenario 2, pre-crisis and post-crisis, for each percentile of the distribution. Not surprisingly, Figure 8b shows that for almost all subgroups of the population of Tunisia along the welfare distribution, there is expected to be a decline in consumption per capita (see that GIC is below 0). Households with per capita consumption in the poorest 20 percent of the distribution are simulated to be hit most, experiencing large declines.

> 12 We have also considered the growth of certain sectors in our simulations (such as agriculture).  In some African countries such as Uganda, the remittances have increased as per their Central Bank estimates. In the case of Tunisia, we do not know which way remittances would move. Intuition suggests that given the economic situation in Europe, remittances would probably decrease but there is no evidence of an increase or decrease in remittances. Hence, we do not consider this in our simulations.

> 13 According to World Bank (2020), the COVID-19 pandemic is estimated to push about 49 million people into extreme poverty in 2020 and will hit the poorest the hardest. <u>https://blogs.worldbank.org/voices/covid-19-will-hit-poor-hardest-heres-what-we-can-do-about-it.</u> According to the latest Poverty and Shared Prosperity Report (2020), The COVID-19 pandemic is estimated to push an additional 88 million to 115 million people into extreme poverty this year, with the total rising to as many as 150 million by 2021, depending on the severity of the economic contraction. Extreme poverty, defined as living on
not contribute to the observed height-for-age z-scores. The simulation of this increase in boys‘ mortality on the data from 1992 of NFHS demonstrates a 0.12 percent increase in the height-for-age z- score. This clearly has a negligible impact on the difference in nutritional status between boys and girls.

Finally, taking our model a step further, we simulate the health outcomes of Indian children if the mortality rates in India were as high as one of the highest rates current in Africa. In this simulation we change the surviving status of living children with lowest probabilities of survival to reach the mortality rate of 15 percent for both boys and

> **12** Table A2 in Appendix presents the simulated impact of the matched imputations of height-for-age z- scores for children ages 3–36 months who did not survive till the age of 36 months. Neonatal mortality was excluded in the hazard estimates as it could have a different set of correlates, but the results are not particularly sensitive to the exclusion of this age group.

> **13** Our empirical model fails to account for the possible selection bias due to high levels of maternal mortality in India (e.g., Ronsmans and Graham 2006). Correction of this bias would increase the gap in anthropometric outcomes between the deceased and living children. Unfortunately, the nature of IFHS sample makes it difficult to look on the relationship of maternal mortality and children health outcomes.

> **14** In principle, one could, simulate a more desirable decrease in girls‘ mortality to close this gap, but it is more direct to use the estimates from boys actually in the sample then to make projections for girls who would otherwise have been in the sample.

12
<br>|0.209|0.155|0.897|0.562|0.078|1.273|23.8|
|Other|_|_|_|||||
|Total|-|1.000|-|-||-|100.0|
|(b) Rural Jordan||||||||
|Government<br>employment|0.526|0.735|0.658|0.625|0.302|0.718|69.7|
|Private sector|0.145|0.183|0.919|0.528|0.089|0.848|20.6|
|Unskilled labor|0.079|0.073|0.965|0.477|0.034|0.804|7.8|
|Self-employment|-|-|_|_|-|_|_|
|Other|0.006|0.009|0.999|0.898|0.008|1.568|1.9|
|Total|-|1.000|-|-||-|100.0|

# Notes: All estimates are based on annual per capita household income.

- (a) Source ginis (GK) are high because they include households with zero and negative incomes from different income sources. Source ginis can exceed unity if many of y1 are negative.
# **Annex Table 5: Development Drivers of the Life Insurance Industry**

## **Annex Table 5: Development Drivers of the Life Insurance Industry (Source: Feyen, Lester, and Rocha (2010)**

The dependent variable is the logarithm of the volume of life insurance premiums as a fraction of GDP. The sample consists of annual data for the period 200008. All regressions are estimated using pooled Ordinary Least Squares. Year-fixed effects and a constant were estimated but are not reported. LSCHOOLING, LLIFE_EXP, LINCOMETOP20, and LCONTRIB_RATE are sample period averages. In all regressions, White‟s heteroskedasticity-consistent (clustered) t- values are given in parentheses (brackets). ***, ** and * represent significance at 1, 5 and 10% level respectively.

||1|2|3|4|5|6|7|
|---|---|---|---|---|---|---|---|
|LGDPPC|1.06<br>(16.9)***<br>[6.04]***|0.96<br>(14.4)***<br>[5.32]***|0.92<br>(12.8)***<br>[5.56]***|0.61<br>(12.5)***<br>[6.52]***|0.37<br>(9.03)***<br>[3.31]***|0.29<br>(4.99)***<br>[2.07]**|0.43<br>(3.63)***<br>[2.03]**|
Table D.26. Effects of Guerrilla Territorial Control on Expropriation, Invasion, and Non-Democratic Believes

||Invading|Occupying|Overturn|Taking Law in|Non-Democratic|
|---|---|---|---|---|---|
||Property|Buildings|the Government|Own Hands|Engagement (sum)|
||(1)|(2)|(3)|(4)|(5)|
|Guerrilla control|-0.110*|0.0616|-0.00269|-0.0734|0.804|
||(0.0605)|(0.0815)|(0.0721)|(0.136)|(1.922)|
|Observations|248|175|248|245|172|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|2.266|
|Dependent mean|0.0580|0.109|0.0740|0.245|10.69|

_Note:_ The table presents the results of estimating equation 1 for our outcomes related to believes regarding how acceptable it is to engage in certain actions against private property or the government. These are measured in a 1-10 scale, thus, we assume that individuals support these behaviors whenever their agreement level is above 5. Columns 1 to 4 show the share of individuals who think that invading property, occupying buildings, overthrowing the government or taking law in their own hands are acceptable. Column 5 shows the effects of guerilla control on an index comprised of the sum of the raw scores . The information was recovered from the LAPOP Surveys. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrillacontrolled
is measured using different time scales to identify different drought types. Short-time scales are mainly related to soil water content and headwater river discharge; medium-time scales involve reservoir storage and river discharge, while long-time scales are associated with variations in groundwater storage. Thus, Acosta et al. (2021) express that SPEI-1 (1 month of scale) is unsuitable for an economic analysis in which researchers are interested in determining how persistent climatic events affect productive systems and farmers’ living conditions. They decide to use SPEIs 12, 24, and 48 months. Acosta et al. (2021) compute a severe drought dummy derived from the SPEI index, which takes one if the value of the SPEI is in the lowest five percentiles of the SPEI distribution and zero otherwise. Birhan et al. (2021) use the SPEI-6 for a shortterm drought variable and SPEI-24 for a long-term drought variable. They construct a dummy to capture exposure to a short-term drought that takes the value one if the score of SPEI-6 is -0.5 or lower and zero otherwise and another dummy to capture exposure to a long-term drought that takes the value one if the score of SPEI-24 is -0.5 or lower and zero otherwise.<sup>11</sup> For our analysis, we use the SPEI-6 and SPEI-24 index and their squared.<sup>12</sup> Negative values of SPEI indicate dryness and positive values wetness. The SPEI database enables imputation at the lowest available household survey unit.

> 11 Vollenweider (2014) uses the SPEI-3 as a standardized measure of weather. Other authors such as Edossa et al. (2014), Bischiniotis et al. (2018), and Drumond et al. (2021) provide a classification of SPEI in categories of extremely, severely, moderately, or normal drought or wet period. Edossa et al. (2014) and Bischiniotis et al. (2018) classified the SPEI value for Africa as Extremely dry if SPEI ≤ -2; Severely dry if SPEI >
# **Food Security and Wheat Prices in Afghanistan: A Distribution-sensitive Analysis of Household-level Impacts**

Anna D‘Souza * Dean Jolliffe ‡ JEL Codes: D12, I3 Keywords: Afghanistan, food prices, wheat, food security, nutrition, poverty, quantile regression, influence functions.

_Acknowledgments:_ The authors are grateful to Central Statistics Organization, Government of Afghanistan (GoA), for granting access to the data for poverty research. Findings from this analysis provide background information for the World Bank‘s poverty assessment for Afghanistan, and have also informed the GoA Ministry of Economy. Jolliffe gratefully acknowledges support from the World Bank‘s Research Support Budget. The authors wish to thank for comments Amanullah Assil, Maurice Landes, Ismail Rahimi, Silvia Redaelli, Byron Ponce-Segura, Nick Wilson, and seminar participants at the World Bank, Kabul office; ERS, USDA; and the 2011 AAEA Annual Meeting. D‘Souza is a Research Economist at ERS, USDA; Jolliffe is a Senior Economist at the World Bank, and also holds affiliations with the Institute for the Study of Labor (IZA) in Bonn and with the National Poverty Center (NPC) at the Ford School of Public Policy, University of Michigan. The views expressed here are those of the authors and may not be attributed to ERS, the USDA, the World Bank, IZA, or NPC.

* Economic Research Service, USDA; Contact: adsouza@ers.usda.gov

‡ World Bank, Poverty Reduction (POV) Sector; Contact: djolliffe@worldbank.org
some cases, all three). With the exception of Akresh (2005), none of the above-referenced papers rely on nationally-representative survey data. Dearth of nationally-representative, methodologicallysound data collected in heterogeneous settings across sub-Saharan Africa has in turn inhibited the computation of externally-valid, rigorous estimates. Our study seeks to start filling this gap by providing a nationally-representative analysis of the gender gap in Malawi, using a different econometric approach than existing studies.

Our econometric approach is underlined by the use of an identification strategy that has been utilized extensively in labor economics since the seminal studies of Oaxaca (1973) and Blinder (1973), most notably in the analyses of the gender wage gap, union wage gap, and growing wage inequality. Specifically, we decompose the average difference in agricultural productivity between male-managed and female-managed plots into (i) the portion that is driven by gender differences in levels of observable attributes (i.e. _the endowment effect_ ), and (ii) the portion that is driven by gender differences in returns to the same set of observables (i.e. _the structure effect_ ). To our knowledge, this is the first time this method has been applied to understanding the gender gap.

> 9 Agricultural productivity is commonly proxied by major crop production quantity per hectare or gross value of crop output/profit per hectare.

> 10 Cultural roles that are assigned to males and females regarding domestic duties and those that may underlie the gender segregation in crop production (i.e. staple vs. cash crop cultivation, high-yielding vs. low-yielding variety cultivation, etc.) could be thought of as informal institutional constraints.

3
an old idea in **the** school **effects** literature. Coleman et al (1966), for instance, found that students who perceived that their achievement **was** due to their own efforts, end not to **their** teachers or school, achieved **at** higher levels (controlling for their class background). More recently, Brookov-r *t **al.** (1979) found that pupils achieved **at** higher levels when they felt that their effort on school work **was** recognized **and** rewarded by their teachers (again, independent of pupils' social class). Natriello (1987) also found that whera students' work **was** more frequently and carefully evaluated, achievement levels were higher. For a recent review **of** factors related to mathematics and science achievement, **s*e** Lockheed et al. (1985).

Relevant social class indicators. Most studies of family background characteristics in developing countries **have** employed two indicators of social **class:** parental educational attainment **and** occupational status. As H-yneman has noted, in Third World settings there may be little variation in terms of education or occupation; educational levels of adults **are** uniformly low, and occupations are preponderantly related to rural agrici lture (at least in the lowest income countries). Yet social class differences are quite pronounced, and have been found to strongly effect child school participation and performance. For example, in the Jamison and Lockheed study, which employed indicators of parents' social class that were relevant to the Nepalese setting -- amount of land owned by the father, his level of literacy, caste membership, and district of residence, capturing inter-regional differences in wealth -- child school participation was strongly determined by these social class elements.

- 7-
# **II. Data and Measurement**

The main data sources for this paper are the World Bank’s Enterprise Surveys (WBES) in around 80 countries. The WBES data are collected by the World Bank to benchmark the investment climate in developing countries across the world and to understand the determinants of firm performance and behavior. In each country the survey was based on the universe of eligible firms obtained from the country’s statistical office with stratified random sampling with replacement, and the result is a representative sample of the non-agricultural private economy in the country.<sup>6</sup> Stratification was based on three criteria: the sector of activity, firm size, and geographic location.<sup>7</sup> For each country in the sample, we use the most recent survey available. Consequently, this paper focuses on explaining cross-country differences in firm performance, but by controlling for industry effects we will focus on differences within specific industries. With our primary focus on the manufacturing sector, we will mainly use the manufacturing sample of around 12000 firms. The number of countries in sub-Saharan Africa is 32 (see the Appendix for the list of the SSA countries).<sup>8</sup> Typically the stratified sampling yields 100 to 1000 firms per country.

The WBES includes questions on various aspects of the business environment, including infrastructure, regulation burdens, corruption, and access to finance. The availability of these indicators about the business environment allows us to simultaneously control for various aspects of the business environment. The survey has both objective and subjective measures of the

> 6 Thus wholly state-owned firms are not in the sample.

> 7 See Ayyagari, Demirguc-Kunt, and Maksimovic (2011) for more detailed description of the WBES.

> 8 In the data set, Ghana is included but we exclude it because we suspect the quality of the data is too poor—for instance, its labor productivity is almost always 400 log points higher.  We note that the data period
164,452||||28,709|
|1999|161,439|25,696|130,863|4,880|27,672|
|2000|162,350|28,583|130,538|3,229|36,395|
|2001|170,780|44,825|117,526|8,429|24,356|
|2002|181,149|34,725|142,950|3,474|28,378|
|2003|196,204|43,433|146,605|6,166|51,295|
|2004|274,750|129,841|137,681|7,228|45,085|
|2005|271,819|42,154|226,675|2,990|25,819|
|2006|301,943|55,943|243,728|2,272|28,485|
|2007|336,742|63,284|271,629|1,829||

Note: Entrants are those that first appear in the sample in the specific year. Exiting means dropping out of the sample in the next year. The ASIF dataset includes all SOEs, and all non-state firms with sales exceeding five million yuan. Thus, a firm's entry year may differ from its establishment year. Similarly, a firm’s exiting year may differ from its death year.

``` 39 ```
We follow the international finance literature (e.g., Chinn and Prasad, 2003; Gruber and Kamin, 2007; Chinn, Eichengreen, and Ito, 2014) in using a 5-year period to smooth out the effects of business cycles and transitory (and global) factors, as well as to allow for the fact that the effect of the changes in structural drivers on FDI and RCA tend to materialize over time (adjustment lag).<sup>20</sup> We merge the indicators data set with FDI and trade data sets. The raw FDI data contained 702 sectors (sector/activity pairs in the _fDi Market_ s’ nomenclature) for 206 countries with large number of zeros (no FDI activity over the entire five-year period) for individual sectors. We first excluded countries for which the country indicators are not available over the period of analysis (2010-19). We then excluded those sectors which have fewer than 5 non-zero observations for the remaining countries, arriving to the final number of 245 sectors. Using the information on FDI flows, we construct a variable which takes value 1 if sector _i_ of country _j_ was the recipient of any FDI flow over the five-year period 2015-19, and 0 otherwise. ii,jj YY Analogously, we create a variable based on the information on country/sector pair FDI "success" over the 2010-14 period. Figure 1 shows the histogram of the empirical distribution of the number ii,jj ZZ countries which received FDI flows in any of the 245 sectors over the 2015-19 period. We see that the distribution is largely skewed toward sectors in which FDI flows went to only a subset of countries (the left-hand side of Figure 1). In the median sector (by the number of recipient countries) only 20 countries are the recipients of FDI inflows.

_Figure 1. Country recipient distribution of sector level FDI_ <!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<
losses. However, despite installed capacity nowadays (around 3,000 MW) widely exceeds demand, high generation costs and inefficiency results in “financial blackouts”, where the operators count with generation capacity but often face liquidity problems.

The quality of the service is poor, affected from technical and financial problems at the sector level. According to World Bank Enterprise Surveys (2010), surveyed companies suffer an average of 25.6 electrical outages a month (more than one blackout per working day), compared to just 3.9 percent on the average Latin American country. Moreover, 63% of the Dominican firms see electricity as a major constraint affecting daily operations (compared to 36.2% in Latin America). For this reason, some of the larger users (companies) have decided to leave the system and install their own generation equipment (10.9% compared to 4.2% in the region), resulting in a reduction in the number of customers and leading to a vicious cycle of worsening financial results at the distributor ́s level.

This vicious cycle is affecting not only the final consumer (industries, citizens...), but also public finances and government ́s ability to conduct a wide range of economic policies. The state is covering the gap between international oil price fluctuations and national oil price changes (artificially mitigated) and also losses in distribution and generation companies derived from several inefficiencies (technical losses, widespread invoice non-payment, etc.). International organizations are pushing to reduce subsidies and clear arrears to the private generators. However, reforms continue to advance slowly and subsidies accounted for approximately 1.2% of GDP in 2010, against an initial target of 0.8%.

[5]
# **Figure 3: Ethiopian inflation and world inflation**

<!-- Start of picture text --> Panel a: All-items inflation and World non-energy inflation<br>80<br>60<br>40<br>20<br>0<br>-20<br>-40<br>World non-energy inflation All items CPI inflation<br>Panel c: Cereal price inflation: World vs. Ethiopia<br>200<br>150<br>100<br>50<br>0<br>-50<br>World grains inflation Cereal CPI inflation<br>1998M07 1999M05 2000M03 2001M01 2001M11 2002M09 2003M07 2004M05 2005M03 2006M01 2006M11 2007M09 2008M07 2009M05 2010M03 2011M01 2011M11 2012M09 2013M07 2014M05 2015M03 2016M01 2016M11 2017M09 2018M07 2019M05 2020M03<br>1998M07 1999M05 2000M03 2001M01 2001M11 2002M09 2003M07 2004M05 2005M03 2006M01 2006M11 2007M09 2008M07 2009M05 2010M03 2011M01 2011M11 2012M09 2013M07 2014M05 2015M03 2016M01 2016M11 2017M09 2018M07 2019M05 2020M03<br><!-- End of picture text --> <!-- Start of picture text --> Panel b: Food price inflation: World vs. Ethiopia<br>100<br>80<br>60<br>40<br>20<br>0<br>-20<br>-40<br>-60<br>World Food inflation Food CPI inflation<br>1998M07 1999M05 2000M03 2001M01 2001M11 2002M09 2003M07 2004M05 2005M03 2006M01 2006M11 2007M09 2008M07 2009M05 2010M03 2011M01 2011M11 2012M09 2013M07 2014M05 2015M03 2016M01 2016M11 2017M09 2018M07 2019M05 2020M03<br><!-- End of picture text --> _Sources_ : Authors’ construction using data from NBE database and the World Bank’s Commodity Markets database.<sup>7</sup>

> 7 <u>https://www.worldbank.org/en/research/commodity-markets</u>

9
# **Individual MENA governments have introduced reforms and new initiatives to promote**

**financial inclusion.** „Financially inclusive‟ bank accounts have been introduced by five MENA countries (Iran, Kuwait, Egypt, Morocco, Oman), two of those in 2009.  Over a third of MENA regulators are trying to shift more government-to-person transfers through bank accounts, with five countries having made reforms in 2009 to accelerate this. Only three countries have introduced reforms to enable branchless banking through agent networks. Four out the eleven MENA countries reporting to the CGAP and World Bank Financial Access Survey (2010) reported official household or enterprise surveys that include a focus on access to finance.  Tax incentive schemes to stimulate savings are not widely used.

# **The Moroccan government has prioritized access alongside stability in its ongoing program**

**of financial reforms** , and has introduced measures to extend access through microcredit associations, banks, and a new postal bank.  In 2008 banks were invited to develop financial inclusion strategies, and the central bank more recently has signaled to banks that it is open to allowing innovations in products and delivery mechanisms. The Central Bank of Egypt has launched a multi-pronged financial inclusion agenda, including a Pensions and Payments Initiative to extend access to basic bank accounts by providing pensions, salaries and procurement payments through the financial system, and a new Licensing and Regulation Law to stimulate the creation of microfinance, leasing, and factoring companies. The Jordanian, Syrian, Tunisian, Palestinian, Lebanese and Iranian governments, among others, have also implemented notable financial inclusion policies and programs outlined elsewhere in this paper.

# **State banks and post offices can have extensive branch networks for extending access to**

**financial services.** One of the largest branch networks in the region is that of the Principal Bank for Development and Agricultural Credit (PDBAC), the state agricultural bank in Egypt.  An initial phase of reforms is showing promising results in terms of improved institutional performance and