rafmacalaba/data-use-mentions-extended huggingface dataset:
10 Note that in the consumption equation, the growth rate of consumption per _adult equivalent_ is implicitly approximated as being equal to the growth rate of GDP per _capita._<sup>_5_</sup>

# _Inequality adjustment_

The assumption of distribution-neutral growth can be relaxed by adjusting consumption for each household within each sector year-by-year. This adjustment is made after the growth projection for each year. Take the percentage change in the Gini coefficient in sector _Si_ in year _t_ as ∆ _GtS_ . The adjusted level of per capita consumption for household _i_ is then This produces a proportional shift in the sectoral Lorenz curve by adjusting consumption for each household relative to its deviation from the sector-specific mean. Note that the mean here is necessarily the weighted mean, calculated with the weights given by{<sup>_w_</sup> _i_ , _t_<sup>}. This adjustment</sup> effectively redistributes consumption from households below the mean to those above the mean (for an increase in the Gini), while leaving mean consumption by sector constant. It would be possible to undertake alternative redistribution procedures that would achieve the same outcome in terms of changes to the Gini and the mean.

# **4.2 Two Survey Approach**

The Datt and Walker approach outlined above was designed to project changes in poverty based on national growth data and a single household survey, in the absence of data from multiple household surveys. For the analysis constructed in this paper, the problem is somewhat different, as the goal is to understand changes in the distribution during the period between two household surveys. While in the Datt and Walker general case the distribution of consumption is known only at the beginning of the simulated period, for Tanzania in the 1990s the full distribution is known both at the beginning and the end of the period.<sup>6</sup> An extension of the Datt and Walker method can be used to force the distribution at the end of the simulation to closely match that of the survey data
countries. Subsidy design factors (red dots) are in general around or above 1. These indicators suggest that policy makers should mainly focus on increasing access to services, although enhancing subsidy design could also be beneficial.

**Figure 8. Factors driving the targeting performance of subsidies (Ω) across countries**

_Source:_ Original calculations using data from household surveys, IBNET, and national utilities and programs. See the data and methodology section, and the appendix, for more detail. _Note:_ Of access factors, _SA = SAP/SAH_ is the share of households located in a service area for the poor relative to the population as a whole; _C = CP|SA/CH|SA_ is the relative share of households that are connected to the service conditional on potential access (being located in a service area). Of subsidy design factors, _T = TP|C/TH|C_ is the relative proportion of households with a subsidy conditional on usage; _R = RP|T/RH|T_ is the relative rate of subsidization. Factor values below 1 indicate that the factor lowers targeting performance, whereas factor values above 1 indicate that the factor boosts targeting performance. BGD = Bangladesh; ETH = Ethiopia; JAM = Jamaica; MLI = Mali; NER = Niger; NGA = Nigeria; UGA = Uganda; PAN = Panama; SLV = El Salvador; VNM = Vietnam.

Various factors influence the degree to which targeting performance is inadequate across countries. In table 3 we explore relevant access and subsidy design factors in more detail.

Four African countries—Ethiopia, Mali, Niger, and Uganda—show an estimated value of Ω lower than 0.1. In all four the main factors that decrease the efficiency with which subsidies target poor households relative to all households include (i) poor households’ lower probability of being located in a service area and (ii) their lower probability of being connected to a water network, conditional on being located in a service area.
Farm Expansion and Mangrove Loss in Thailand.” In _Environment and Livelihoods in Tropical Coastal Zones: Managing Agriculture-Fishery-Aquaculture Conflicts_ , edited by C.T. Hoanh, T. P. Tuong, J.W. Gowing, and B. Hardy. Vol. 2. CABI Publishing. https://www.iwmi.cgiar.org/Publications/CABI_Publications/CA_CABI_Series/Coastal_Zones/Hoan h_1845931076-Chapter10.pdf.

- Barbier, Edward B. and Mark Cox. 2004. “An Economic Analysis of Shrimp Farm Expansion and Mangrove Conversion in Thailand.” _Land Economics_ 80 (3): 389–407. https://doi.org/10.2307/3654728.

- Barbier, Edward B., Evamaria W. Koch, Brian R. Silliman, Sally D. Hacker, Eric Wolanski, Jurgenne Primavera, Elise F. Granek, Stephen Polasky, Shankar Aswani, Lori A. Cramer, David M. Stoms, Chris J. Kennedy, David Bael, Carrie V. Kappel, Gerardo M. E. Perillo and Denise J. Reed. 2008. “Coastal Ecosystem-Based Management with Nonlinear Ecological Functions and Values.” _Science_ 319 (5861): 321–23. https://doi.org/10.1126/SCIENCE.1150349.

- Bertinellli, Luisito and Eric Strobl. 2013. “Quantifying the Local Economic Growth Impact of Hurricane Strikes on JSTOR.” _Journal of Applied Meteorology and Climatology_ 52 (8): 1688–97. https://www.jstor.org/stable/26175862?seq=1#metadata_info_tab_contents.

- Beuermann, Diether, Moises Schwartz, Juan Pedro Schmid, Christian Dippel, Klaus Schmidt-Hebbel, Jeetendra Khadan, Camilo Pecha, Jose Fajgenbaum, Claudio Loser, Allan Wright, Kimberly Waithe, Elton Bollers, Kevin Bonnett, Mark D. Wenner, Camila Mejia, Henry Mooney and Lodewijk Smets. 2018. “Nurturing Institutions for a Resilient Caribbean.” _Nurturing Institutions for a Resilient Caribbean_ , September. https://
Transport costs are measured as a function of the travel time to reach other regions from the most populated district (upazila) in each Indian state (Bangladeshi district). Travel time, calculated using GIS network techniques, is the shortest time given all possible routes in the primary and secondary road network. Travel times in Bangladesh are based on actual speeds collected from more than 200 intercity road segments across the country and random speed assignment to the remaining segments based on the distribution of speeds in nearby segments. Travel times in India come from Krishna and Leemput (2018), based on GPS data from Blackbuk, an Indian logistics company that provides an online marketplace platform for freight shipments.

The elasticity of substitution between traded varieties is assumed to be _σ_ = 5, as in Head and Mayer (2014). For robustness, we also consider 4 and 6 as alternative values of this elasticity. The aggregate TFP elasticity, _γ_ , is calibrated to 0.05, following Ciccone and Hall (1996). The Cobb-Douglas share of labor, _βL_ , is set to 0.39, and the share of private capital, _βK_ , to 0.49, using data from KLEMS for India, and to 0.46 and 0.26, respectively, for Bangladesh, using to data from the World Integrated Trade Solution database (WITS). The share of housing expenditure 1 _− αC_<sup>_W_issetto0.35forBangladeshand0.44forIndia,</sup> using the share of household consumption expenditures in total expenditures from UN data. Following Fajgelbaum et al. (2015), we set the calibrated shape parameter of the random component of utility, _εW_ , at 1.5. For robustness, we considered other values for _εW_ , to study how the results vary with workers’ mobility.

# **4 Counterfactual Scenarios**

In the baseline, Indian trucks are not allowed to use Bangladeshi routes and must bypass Bangladesh through the Siliguri corridor to reach the northeast, and Bangladeshi trucks are not allowed in India
60 months of age in the sub-sampled households were weighed and measured. After excluding children for which information on height or weight is missing or implausible, 3,596 observations were used for this analysis (for further details, see National Institute of Statistics et al. (2001)). We first derived the height-for-age and weight-for-age Z-score measures, which were then converted to the height and weight for 24 month-old females with the same Z-score measures.

The second source of data is the Cambodian National Population Census, the first population census to be conducted in Cambodia since 1962. The census covered virtually all persons staying in Cambodia at the reference time of midnight of March 3, 1998.<sup>8</sup> The census data 8Due to military operations, about 0.5 percent of the population was not covered.

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43|25.6|0.75|542|
|Total|480.1|0.88|0.43|13.0|0.84|21168|
|_Panel B: Private_|_supply_||||||
|6,793-10,188|16.4|0.07|0.03|0.5|0.10|5211|
|10,189-13,584|32.7|0.10|0.04|0.9|0.14|3982|
|13,585-16,980|50.7|0.11|0.05|1.3|0.15|2931|
|16,981-23,772|78.4|0.15|0.06|1.9|0.18|3931|
|23,773-30,564|135.4|0.18|0.08|2.9|0.20|2312|
|30,565-37,356|192.7|0.20|0.09|3.9|0.23|1412|
|37,357-44,148|226.6|0.20|0.09|4.5|0.24|907|
|44,149-50,940|264.5|0.19|0.09|5.2|0.25|542|
|Total|75.5|0.12|0.06|1.7|0.16|21168|

_Notes:_ This table provides descriptive statistics based on the estimation sample for 2000-2006. Indicators are first computed at the municipal-year level and then averaged across municipalities and years within each population bracket. Data on municipal population aged 4 to 6 used to compute participation in public and private preschool come from the 2000 census.
> We thus use the May ORG files from 2003 to 2018 for this analysis.<sup>8</sup>

> 6Consistent with DellaVigna and Gentzkow (2019), the mean SD in prices is small, slightly more than 25 cents, but there is wide variation depending on what markets or categories we are looking at. For example, for diapers, household cleaning items, shampoo, and paper towels, differences for the same product UPC in the same chain and quarter could be as high as 3-4 dollars across markets.

> 7Hourly wage rate calculated using the March Socio-economic supplement relies on the answers to three questions provided by the respondents - (i) the total annual earning during the previous year, (ii) the total number of weeks worked, and (iii) the number of hours worked per week. The hourly wage rate is then computed by dividing the total annual income by the total number of hours worked. However, as pointed out by Acemoglu and Autor (2011), missed responses and noise in the hours data reduce the precision of the estimates obtained from the March sample.

> 8Every household that enters the CPS is interviewed each month for 4 months, then ignored for 8 months, then interviewed again for 4 more months. Usual weekly hours/earning questions are asked only at households in their 4th and 8th interviews. These outgoing interviews are the only ones included in the extracts. New households enter each month, so one-fourth of the households are in an outgoing rotation each month.

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# 4. Concluding remarks

Our results seem to suggest that the facilitation effect of private inspection is statistically traceable in trade data. Using bilateral export volumes as our variable of interest (in order to avoid picking up the effect of improved bookkeeping on the importing side, which would have no “real” counterpart) we find that private inspection seems to have been associated with an increase in trade between 5% and 10%. Thus, as conjectured at the start of this paper, the burden of inspection and paperwork associated with inspection seems to have been more than offset by improved facilitation at destination. Indeed, anecdotal evidence seems to suggest that at least some surveillance companies have implemented programs to foster the adoption of best practices at destination ports and customs, including container tracking systems, electronic payment of duties, and so on. Technology is clearly part of the story. For instance, recent destination inspection programs have spread the use of scanners in lieu of physical inspection (physical inspection being reserved for suspicious cases). The spread of control systems in international trade may thus be similarly associated with improved logistics and enhanced used of information systems which may, in the end, facilitate transit rather than hampering it.

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the period 2015–2030. Across this 2015– 2030 horizon, our estimates of the Gini coefficient at the global level are expected to decline between 0.7 and 1.9 Gini points (in the Gini scale of 0–100). The simulation outcomes suggest some degree of uncertainty such that non-resource-output oriented countries and low-output and high-output growth countries could either decrease or increase their income inequality levels across the period 2015–2030.

The third significant result indicates that the B40 of the income distribution is, on average, predicted to grow faster than the mean and higher percentiles over the 2015–2030 horizon and across the studied country aggregations. There are a few simulations, however, with median values Page **29** of **45**
The proposal to adapt Alkire-Foster to measure and aggregate the quality of jobs was first developed by Brummund, Mann, and Rodríguez-Castelán (2018) to benchmark job quality across the Latin America and Caribbean region. The Alkire-Foster method has much to offer, particularly a set of properties derived from an axiomatic framework that are relevant for identifying the poor and measuring poverty, including the ability to combine into a coherent framework cardinal data, such as labor income, and ordinal data, for example, benefits such as the availability of paid annual leave. These properties, although restrictive, have significant value as a desired measure of good jobs. These are decomposability, replication invariance, and symmetry. Decomposability shows that the aggregate JQM is the weighted sum of JQMs of various subpopulations, such as workers in different industries or rural and urban workers, whereby the weights are the relative sizes of these subgroups in the total population. Similarly, important to measuring job quality among different groups of interest are replication invariance and symmetry, which imply that job quality measured for groups of varying sizes are comparable and that individuals in these groups are equally contributing to the measure of job quality.

The proposed approach applies the Alkire-Foster framework to dimensions of achievement rather than dimensions of deprivation. This means that, if any of the indicators for a dimension are satisfied, the dimension overall is considered a success and is set equal to 1. The working conditions dimension is unique in that all indicators need to be satisfied to define success. A higher JQM therefore indicates greater job quality, whereas a higher Alkire-Foster poverty measure indicates greater levels of poverty. The JQM can be formally written as follows: where JQMi is the JQM for individual i ; Id,i is an indicator function set equal to 1 if individual i ’s job is a success in dimension d , and wd are the weights assigned to each dimension.

The proposed JQM sums the successes across dimensions and assumes that dimensions are weighted equally with unity weights to highlight the difference with deprivation indicators. Measuring job quality
2237|
|Gambela|37.0|-1.3|39.0|-1.6|23|
|Harari|37.3|-1.5|27.1|-1.4|21|
|Addis Ababa|26.8|-1.1|14.1|-0.9|165|
|DireDawa<br>Notes: Reported means|30.5<br>are adjusted fo|-1.1<br>r sample probab|30.8<br>ilities|-1.3|36|

The two most important risk factors contributing to malnutrition are insufficient food intake and repeated infectious illnesses. Table 3 shows the proportion of children malnourished and with diarrhea and fever by age groups and by the mother’s level of education. The proportion of children malnourished increases significantly with age— particularly between the first and second year of life. The World Health Organization (WHO) recommends exclusive breastfeeding for children under 6 months and the introduction of solid and semi-solid food around the age of 6 months. The Ethiopia data shows that most children under the age of 6 months are predominantly breastfed, although only about a third are exclusively breastfed, as it is recommended by the WHO.

The mother’s education level is likely to be a significant factor influencing child feeding practices. The data show the introduction of other liquids takes place earlier than at the suggested 6 months of age—with children as young as 2 months of age consuming other liquids. Almost thirty percent of children under 6 months are also given plain water or other water based liquids or juices. The children of mothers with a primary education are breastfed 8
differences in the same direction. Shares of other consumption items such as transportation, furnishing and household equipment or expenditures on hotels, cafes and restaurants display the opposite trend, increasing for families along the income distribution. Overall, more that 70% of all expenditures of households from the first decile go to cover basic necessities, namely food, housing, education and health. This share drops to 41% for the typical household in the top income decile and is 54% for the median household.

Figure 2: Consumption patterns vary greatly across income groups (2019) <!-- 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>1 2 3 4 5 6 7 8 9 10<br>Food and non-alcoholic beverages Clothing and footwear<br>Housing, water, electricity, etc. Furnishings, household equipment, etc.<br>Transport Hotels, cafes and restaurants<br>Miscellaneous goods and services Other Expenditure Groups<br>Source : World Bank calculations using data from TUIK<br><!-- End of picture text --> The large differences in the structure of expenditures make some households more exposed to high inflation episodes depending on the productivity shocks underlying the spike in prices. Assuming everything else constant, high food or housing inflation, for instance, is felt relatively more by poor and vulnerable households than the rest of the population, leading to regressive effects. In fact, prices of the CPI basket’s subcomponents tracked by TUIK often follow different trajectories due to a host of external and domestic factors (for instance, international price shocks, exchange rate volatility, disruptions to international and domestic supply chains and taxes, among others) that affect different goods and services unevenly.

Figure 3 shows the level
# **El Niino or El Peso?**

## **Crisis, Poverty and Income Distribution in the Philippines**

Gaurav Datt and Hans Hoogeveen' _World Bank Washington D. C._ We are grateful to the National Statistics Office, Manila, for their help with the provision of survey data. For useful comments and other forms of help we would like to thank Arsenio Balisacan, Benu Bidani, Jyotsna Jalan, Tamar Manuelyan-Attinc, Martin Ravallion, Hugh Waters, and in particular to Norbert Schady for many rounds of useful discussions. We would also like to thank participants at the poverty seminar at the Tinbergen Institute, Amsterdam for their comments.
<mark>A few studies have attempted to understand the reasons for differential levels of infrastructure across different cities in developing countries. Arimah (2005), for example, uses the</mark> _<mark>U</mark> nited Nations Human Settlements Programme_ ’s global urban indicators <mark>database to investigate the determinants of infrastructure</mark> spending across cities in developing countries. It finds that investments in infrastructure are low for cities in Africa and Asia, moderate in the Middle East and economies in transition, and comparatively high in the LAC region. The intercity variations depend upon the macroeconomic environment, urban growth rate; quality of governance; and the financial capacity of city governments.

# **2.2. Previous studies on determinants of infrastructure development in India at the state level**

Existing studies examining the factors influencing infrastructure investments in India include Mohanty et al. (2017), Pradhan (2017), Rasul and Sharma (2014), Ghate (2008), Pillai (2008), and Umdor & Panda (2007). Ghate (2008) develops theoretical models and proves that the fixed cost of accessing the modern sector, the initial level of infrastructure, the wealth of median voters, and corruption explain variation in infrastructure investment across states. Therefore, reducing the fixed cost of accessing the modern sector and reducing corruption and appropriate credit policy will attract investment in infrastructure. Mohanty et al. (2017) analyze factors affecting per capita infrastructure expenditure in 14 major Indian states for the 1991-2010 period. They find different levels of resource mobilization, per capita income, and population density explain the differences in infrastructure levels in these states. However, other factors such as public investment, political stability, and interdependence in infrastructure spending among states are also important factors. The present study differs from Mohanty et al. (2017) in multiple ways. First, our study tries to empirically find out the drivers of infrastructure development in stock form for three categories of infrastructure –
respondents from our baseline sample (a 92.6% tracking rate). We have some differential attrition by treatment status with a slightly higher attrition rate in the treatment group (8.6%) compared to the control group (6.4%). We address this in our analysis. Figure 3 indicates the timeline of implementation and data collection activities. All 18 sites that were randomized to receive a childcare center ended up opening a childcare center, so we have perfect compliance with assignment to open a childcare center.

> 8 We measure depressive symptoms using the Center for Epidemiological Studies Depression Scale Revised Short Form (CESD-R-10) (Miller, Anton and Townson, 2008; Radloff, 1977). The CESD, a screening test for depression and depressive disorder, is one of the most used instrument to measure depression in ECD studies in low and middle income countries (Evans et al., 2022).

> 9We selected children up to age 5, even though children up to age 6 could attend the childcare centers, so that we could ensure that these children would still be eligible to attend the childcare centers at the time of the follow up survey. At baseline, two children were selected regardless of the age categories; i.e. two children aged 3 to 5 could be selected if there were no children aged 0 to 2. At endline, we collected data on children sampled at baseline, for only one randomly selected child in each age category.

11