rafmacalaba/data-use-mentions-extended huggingface dataset:
- How can we predict and model potential policy reforms using this study as a base?

**The analysis comes at a time when poverty in the Palestinian territories remains elevated after the COVID-19 shock and has become increasingly concentrated in Gaza.** Based on the PECS 2016/2017, about 29.2 percent of the Palestinian population lived on household consumption levels below the national poverty line of NIS 2,470 per month for a reference household of two adults and three children (equivalent to about US$7.92 in 2017 PPP per day per capita). About 20.5 percent lived on household consumption levels below the Upper Middle Income Class Poverty Line of US$6.85 (2017 PPP) per day per capita. However, poverty rates differed substantially by region: 13.9 percent of Palestinians in the West Bank (including East Jerusalem) and 53.0 percent of Palestinians in Gaza lived below the national poverty line (Figure 1-1).<sup>8</sup> Consequently, the Palestinian poor have increasingly concentrated in Gaza. GDP per capita measured in 2017 PPP dollars had been increasing steadily since 2004, but poverty-reduction has been achieved primarily in the West Bank. Poverty rates in Gaza remain persistently higher and increased markedly from 2011 to 2016-17. Inequality as measured by the Gini coefficient was relatively low at 0.30 nationally in 2016-17, and slightly higher in Gaza with respect to the West Bank. Since 2016-17, poverty rate estimates spiked during the peak of the COVID-19 pandemic, and while they have gradually declined, they have not yet recovered to pre-pandemic levels.

# **Figure 1-1 Poverty and inequality are higher in Gaza than in the West Bank**

Poverty and Inequality in the West Bank and Gaza, 2017 Panel a: Poverty by location Panel b: Inequality by location <!-- Start of picture text --> 53.0<br>0.3<br>0.27 0.28<br>29.2
s s 1<sup>+xx</sup> s 2<sup>+ XX</sup> 3<sup>+ uu</sup> s ss<sup>) = xx</sup> ss<sup>ββββ</sup> ss<sup>ββ</sup> ss<sup>, s=1,...,S,m=1,...,Ms; a=1,...,Asm.</sup> The state-level effect (2) G(yy is omitted because of the inclusion of a full set of state dummy variables Xs as independent variables. This mechanically causes the estimated variance of to be zero, due ss to the lack of variation across states in the residual after controlling for state dummies, which υυ ss effectively drops from the model. In other words, the state-level estimates are generated from υυ what is essentially a fixed effect estimator instead of a conditional random effect estimator. This ss is possible when generating state-level estimates, but not municipal level estimates, because the υυ survey is sufficiently large to estimate state level results precisely.<sup>8</sup> To generate the small area point estimates, the procedure simulates 100 draws from a normal distribution for both the random error term s s estimates) for each AGEB j. These estimates are then back-transformed, averaged across ss<sup>and the area effect υυ(or υυ</sup> ss<sup>for the state level</sup> simulations, and finally aggregated across AGEBs to generate municipal or state-level estimates. uu When aggregating across AGEBs, we weight using estimated population from WorldPop.<sup>9</sup> Mean squared error estimates are obtained from a parametric bootstrap procedure, as proposed by Gonzalez-Manteiga et al.(2007) and implemented in the R EMDI package (Kreutzmann et al., 2018), based on the theory developed by Butar (1997) and
*|19.4|19.4|13.4|24.5|20.3|
|Infrastructure inputs|<br>|<br>|<br>|||
|lHardware*|[<br>7.7<br>|1<br>3.4|]<br>3.5<br>|<br>16.0||<br>8.1|
|Alternative teacher education strate|gies|||||
|Curso de Qualificacao|5.0|1.2|2.5|_5.0_|11.3|
|Logos|8.3|8.3|6.8|13.5|4.6|
|4 years primary school|6.7|8.5|13.2|0.0|5.3|
|<br>13 years secondary school|1.9|2.5|3.9|0.0|1.5|
|Teacher salary||||||
|Teacher salary|1.0|1.0|1.0|1.0|1.0|

* Hardware: water, bookcase, teacher table, pupil chair, pupil desk, two classrooms, large room, director's room, kitchen, toilet, store cupboard Software: writing material, chalk, notebook, pencil, eraser, crayons, textbook usage Source: Derived from Harbison and Hanushek (1992), Table 6-2

Table 2 provides similar estimates from a large scale study of student achievement in eight states in India (World Bank, 1996a). The cost effectiveness of increasing teacher salaries is again normalized to 1. The cost effectiveness of spending on improving physical facilities is 17
where _Registeredij_ corresponds to an indicator variable of whether an individual _i_ in municipality _j_ registered to vote. The coefficient of interest is _π_ 1. Here we assume that the eligibility indicator affects registration to vote only through enrollment in FA. This is a plausible assumption given the smoothness of the distribution of the _SISBEN_ score and other covariates at the program eligibility threshold (Figure 2 and Figure 3). Since eligibility is assigned at the household level, we cluster the RD results at this unit. We identify tables using this RD methodology with the word “RD” in the title.

# **4.2 Fixed Effects and 2SLS Regressions**

Individual level voting data is rarely (legally) recorded. To overcome the absence of information on the actual vote for each person, we use voting outcomes at the voting booth level, which is the most disaggregated level available. We start by using OLS polling station fixed effects regressions of the variable of interest (turnout, incumbent’s party vote share, margin of victory) on the proportion of FA participants (men and women or women) in the voting booth ( _b_ ) of the form: Where _p_ designates a polling station, _b_ a voting booth, and _r_ an electoral round (first round or runoff). _Y_ is the outcome of interest which is identified in each table. _Prop_ _~~F~~ A_ is the proportion of registered voters at the booth who are FA beneficiaries (men and women beneficiaries, or women beneficiaries). _SISBEN_ corresponds to the booth level average of the _SISBEN_ score to proxy for poverty. _controls_ corresponds to a vector of other booth ( _b_ ) level characteristics specified in each table and usually includes: female proportion, age, household size, children, cohabitation, education level, employment status, homemaker, student status, renter, pensioner, and length of time between the _SISBEN_ interview and the elections (distance), and all of the 13
data to create future consumption distributions. For price shocks, the number of times large increases in the CPI have been observed since 2005 is used to calculate the probability that monthly inflation exceeds 1.5 percent on average in a given year. We do not assume any covariance in drought and price shocks for the following two reasons: (i) although there is a positive relationship between drought and price inflation in Ethiopia the two episodes of very high inflation observed since 2005 have been driven by high global food prices and monetary factors, not by poor rainfall, and (ii) drought is often local in Ethiopia and the best estimates available suggest that since 2004 crop losses have had limited local price effects.

These household-specific probability distributions are used to draw 6,000 sample realizations of each shock. The impact of the shock on consumption in each scenario is the realized shock drawn at random, multiplied by the impact of the shocks, _δ_ and _γ_ as estimated from equation (5). We also incorporate a random error for each draw based on the empirical distribution of the regression residuals.

Although this method is appropriate for the type of shocks observed in the data, recall that the drought shock included in the regression analysis is a continuous measure, reflecting the proportion of normal crop yields lost due to insufficient rainfall, and that the two years of household survey data capture the moderate but not severe droughts experienced during the 17 years. Developing an accurate distribution of drought-induced consumption losses thus requires out-of-sample predictions on the magnitude of the loss.

For out-of-sample large drought shocks, we use other empirical evidence to guide the 11
climate relationships in order to derive biomass (Saenger and Snedaker 1993, Berger and Hildenbrandt 2000, Simard et al 2006, and Hutchison et al. 2013); biomass then determines the mangrove density (Berger and Hildenbrandt 2000; Mazda 1997) and density finally determines the flow velocities (Horstman et al. 2013) and the wave attenuation function (e.g. Horstman et al. 2012).

In a cross country study like the one presented in this paper, specifying location‐specific bathymetry, mangrove species (their allometric characteristics: trunk width, root system and leaf area which determines the extent of bed resistance to the flow of water from storm surges), forest density, and forest width is beyond the scope of the analysis. Instead, we estimated the coastal protection services of mangroves using the algorithm described below:

1. The storm surge inundation zone protected by mangroves is derived from the inundation zone modeled for an extreme 100 year return period storm surge<sup>10</sup> with mangroves and a storm surge zone without mangroves (the counterfactual). The inundation area protected by mangroves (mangrove protection zone) is only calculated upstream of an area of mangroves greater than 3 arc seconds (90 sq. m).

# ��_�����∗����� � ��∗�����

Where _SS_PA_ refers to the storm surge inundation area that is protected, _SS_ refers to the 1 in 100 surge height in meters, _wave_ refers to the wave attenuation function, _n_ refers to without mangrove and _m_ refers to with mangrove.

2. For storm surge areas without mangroves, a linear distance decay of waves of 6.3 cm/km, where _d_ is the distance in meters, was adapted from observational data summarized in McIvor et al. (2012) for salt marsh:

> 10 <mark>It is a statistical measure of the average recurrence interval over a long period of time and is the inverse of the probability
foreign firms pay wage premiums, even controlling for a variety of firm and industry characteristics (e.g., industry, firm size, capital intensity, and inputs). A small number of studies adjusted for workers’ educational attainments for developing countries. For instance, using Indonesian manufacturing plant data which include information on the distribution of workers by level of education completed, Lipsey and Sjöholm (2004) find that foreign-owned plants hire more educated workers than domestically-owned ones and pay higher wages given the educational level of workers.

For Vietnam, using firm-level data from annual enterprise surveys for the period 2000-2004, Ramstetter and Ngoc (2007a) find results in accordance with previous studies: Multinational Corporations (MNCs) are found to pay the highest compensation followed by state-owned enterprises (SOEs) and then by private firms; but the MNCs-private compensation differential is reduced after controlling for a series of firm- and industry-level characteristics and, as a proxy for worker quality, for the proportion of science and technology workers in total employment. However, little is known about how foreign firms reward different types of workers. One advantage in using the VHLSS is that the detailed individual level data permit the researcher to analyze the differential impacts of foreign ownership on wages by running Mincerian earnings regressions separately by skill levels and by gender.

The existence of a foreign wage premium after adjusting for observable personal characteristics does not necessarily imply that workers’ wages increase by working for foreign firms. For instance if individuals with some _unobservable_ attributes (e.g., higher ability) selfselect into (or are picked up by) foreign firms, the estimated coefficient for foreign ownership in cross-sectional data would be biased. In order to address selectivity bias, several recent studies trace the change in wages of _identical_ individuals over time using matched employer-employee 3
take these values to be the true values for each AGEB. We then randomly sample AGEBs from the full set of urban AGEBs to form a pseudo survey. We select AGEBs with probability proportional to size within states, which serve as the strata. We then choose the number of AGEBs per stratum (state) using sample sizes similar to INEGI’s labor force surveys.<sup>5</sup> Like INEGI, we assume an average of 2.7 people 12 years of age and older per household when selecting AGEBs.<sup>6</sup> Out of 50,942 AGEBs with non-missing geospatial auxiliary features, 7,642 – or 15.0 percent – are included in the pseudo sample.

After selecting AGEBs, we simulate sampling individuals within each selected AGEB. The census data indicate the number of men and women the number of people 12+ living in each AGEB, as well as the number of people 12+ who are active labor force participants as well as the number employed. Since being employed is conditional on being in the labor force, we can use this information to simulate a sample of men and women within each AGEB based on these variables. We essentially construct the total population and randomly select 40 individuals within each AGEB – or all individuals if there are fewer than 40 men and women 12+ in the AGEB. These are then be used to construct gender-specific labor force participation and unemployment rates in the sample.

> 3 The data are available online <u>here.</u>

> 4 The labor data are collected for those 12 years of age and older, hence why we use 12+ as our population. 5 More information is available here. One difference between our methodology and theirs – apart from the fact that we do not have access to actual enumeration areas – is that INEGI also includes some larger municipalities as strata. For simplicity, we only use states.

> 6 This number comes from INEGI’s labor force survey methodology
Policy Research Working Paper 7616

# **Abstract**

Industry and financial profiles of MENA firms may underpin the observation that MENA country exports are below potential and skewed toward low value-added goods that are unable to spur rapid job creation and inclusive growth. To assess this link, the paper combines analysis highlighting external financing as a determinant of export performance, and analysis highlighting sector asset tangibility and governance. Why? Because high value-added sectors tend to have higher shares of intangible assets and to create innovative products requiring substantial research and development or investments, thereby making these sectors more dependent on external financing. Using sector- and firm-level export data with country-level indicators, the results indicate that countries with more developed financial sectors and stronger governance tend to have higher exports from sectors that are more reliant on finance external to the firm, and lower exports from sectors with higher shares of tangible assets. Interestingly, financial sector development boosts exports less in MENA than in non-MENA countries. To foster expansion of higher value exports, the results suggest a critical need for: (i) deeper financial sector development that strengthens market-based systems, such as asset registries and credit reporting agencies, and (ii) strengthening of legal and governance frameworks.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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 authors may be contacted at cwood@worldbank.org and jyang4@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
happen in countries where households have very dissimilar consumption baskets. A diversity index measures how different consumption patterns are in a given country. <u>Chai et al. (2015)</u> propose an index based on whether the probability that two currency units drawn randomly from the expenditure of a household are spent on different expenditure categories is different from the same probability calculated at the country level.<sup>9</sup> According to this index, the country in the EMDE ECA sample with the least diversity in consumption expenditure across households is Moldova, and the country with the greatest diversity is North Macedonia (Figure 6).<sup>10</sup> In Moldova, households up to the eighth decile differ mainly in the consumption share of food and beverages; the remaining share of their consumption is split proportionally among other consumption groups. In contrast, in North Macedonia, consumption shares across groups change rapidly by decile.

# **6 Heterogeneity of inflation by household characteristics**

The evidence presented in the previous section shows that consumption shares differ across the populations of ECA countries and that prices of the main components of household consumption grew at different rates in 2022. These two factors point to the possibility of significant variability in inflation across households. This section explores the drivers of such variability by examining its correlation with household characteristics.

Assuming that the shares of consumption components do not change between the two time periods, we can construct an inflation index for individual households by applying (3) to the household consumption basket and the prices for consumption components.<sup>11</sup> COL inflation rates will differ across households because each household consumes a unique basket of goods and services.

Household-specific COL inflation indexes can be used as dependent variables in the descriptive regression analysis to explore the heterogeneity of the impact of price changes by household 9 This diversity index is calculated by the formula: where, sjk referes to the share in the total expenditures of the whole population of the expenditures
# **Chapter 4: Marketing**

|**Misconception**|**HB Response**|
|---|---|
|"I have sold the same products for a long time now, there is no need to<br>change my assortment."|In our survey we found that shops that introduced at least one new product in the<br>last three months were associated with significantly higher sales and profits than<br>those that did not.|
|"If a customer decides to quit buying, I cannot do anything."|From our conversations with shop owners, it became apparent that many do not<br>simply give up on an absent customer, and try to follow up to understand the<br>reasoning.|
|"I already make the best possible prices, giving discounts is not<br>necessary."|You may want to give discounts for products that are on the verge of spoiling. If your<br>supplier offers you good terms, you can pass on a bit of discount to customers. Also,<br>considergivingdiscounts forgoods bought in bulk.|
|**Reasons**|**to Adopt the Practice**|
|From our survey of small and mid-size retail shops, we gathered that succ<br>customers and win over new ones.|essful shops were the ones that adopted some simple practices to help retain|
|Shop owners who decide to get back to former customers to see the reas<br>the sales of businesses whose owners just let it go. What is more, their m|ons why they quit buying at their shop associated with monthly sales 24% higher than<br>onthly profits are 23% higher.|
|From the survey we know that shops that use discounts to attract new an<br>the sales of shops that do notgive discounts. Also, their monthly profits a|d retain loyal customers
3 recent studies have attempted to study spillovers from EMs but focusing on individual EMs mainly China (Hsieh and Ossa, 2011).<sup>4</sup> The present paper attempts to fill the gap. The main contribution of our paper is to document empirically that global spillovers are asymmetrical. Our empirical results based on a two-block set-up and covering the period 1991 to 2015 are twofold. First, we show that the size of the spillovers running from EMs to AEs is about a fifth of these running from AEs to EMs. Second, results point to spillovers from EMs to AEs having increased over the second half of the sample period. We present suggestive evidence that the (evolving) structure of interdependencies play an important role in explaining the existence of asymmetrical spillovers between these similar sized blocks.

A casual look at data on trade and capital flows between AEs and EMs suggest that global spillover may indeed be asymmetrical and changing. Figure 7 shows that the EM block depends relatively more on external demand suggesting that spillovers from AEs to EMs are expected to be large. AE’s dependence on external demand is increasing suggesting that spillovers from EMs maybe increasing (see Table 1). Figure 8 also shows that the EM block depends more on capital inflow suggesting that spillovers from AEs maybe large. AE’s dependence on capital flows is increasing suggesting spillovers from EMs are expected to increase (see Table 2). In this paper we use state of the art Vector Autogressive (VAR) techniques to systematically document the asymmetrical nature of global spillovers.

In explaining the origins of spillovers, there are three strands of theoretical literature namely the “fundamental”, “financial”, and “coordination failure” explanations (see survey by Rigobon, 2018; Arezki and Yang, 2019 and references therein). The fundamental explanation of spillovers between countries rely on real channels. The related set of papers focus on bilateral trade, trade

> 4 Also see Ashvin, and Nabar (2012) for a study of the global spillovers
those countries that are available in all benchmark years.

Each national distribution is approximated by the average consumption of the 100 percentile groups. For the microdata, percentile groups are computed directly from the household survey. PovcalNet also reports percentile groups whenever it has access to micro data. However, in some cases it only has access to grouped data, such as 10 or 20 coordinates of the Lorenz curve. The reliance on grouped data has diminished over time, but for some countries, such as China, 4 That is, our analysis includes Cambodia, China, Fiji, Indonesia, Kiribati, Lao PDR, Malaysia, Marshall Islands, Myanmar, Micronesia, Mongolia, Palau, Papua New Guinea, the Philippines, American Samoa, Solomon Island, Thailand, Timor-Leste, Tonga, Tuvalu, Vanuatu, and Vietnam. High-income countries in the region, such as Japan; the Republic of Korea; or Taiwan, China, are not included in the analysis (see p. 49 of World Bank (2016) for the full list of high-income countries).

5 Harmonized household survey data: EAPPOV created by the World Bank’s EAP Team for Statistical Development, accessed on 18 January 2016. PovcalNet: online tool developed by the World Bank’s research department, <u>http://iresearch.worldbank.org/PovcalNet/, accessed on 25 November 2016.</u> 6 Similar to Lakner and Milanovic (2016), surveys need to be within two years of a benchmark year, and surveys in consecutive benchmark years must be at least three and no more than seven years apart from each other. Because of poor availability of surveys in earlier years, we cannot start before 1988.

7 Throughout the analysis, income surveys are used for Malaysia, while all other countries use consumption. Similar to Lakner and Milanovic (2016), we mix income and consumption surveys across countries and make no adjustment. While we are fully aware of the important differences between the two concepts, income and consumption