rafmacalaba/data-use-mentions-extended huggingface dataset:
high transport costs. As a result, a significant part of the economy was effectively an ‘autarkic village economy’ with little or no long-distance (international) trade exchanges. We develop a spatial equilibrium model that allows for the co-existence of an integrated segment characterized by sectoral (agriculture vs. manufacturing) specialization and a subsistence-based autarkic hinterland within the same country. The model includes two trade routes in a country: a route affected by the shock directly, called the “Abidjan route”, and a second route for trade through alternative ports, called the “non-Abidjan route”.

The focus of our analysis is on spatial heterogeneity and persistence in the effects of the temporary trade cost shock on different routes. We consider spatial heterogeneity at two margins of adjustment along a route: the spatial margin of market integration (henceforth SMI) and the spatial margin of specialization (henceforth SMS). The SMI separates the subsistence-based autarkic region from the integrated segment of the economy characterized by specialization according to comparative advantage driven by inter-regional and international trade. The SMS, on the other hand, separates the zone of manufacturing specialization from that of agricultural specialization within the integrated segment of the economy. To understand whether the reallocation triggered by a temporary trade shock can lead to persistent effects, we analyze irreversible investments in built-up density (housing and manufacturing structures) by taking advantage of the recently available satellite data on global human settlement. In addition, we provide evidence on the role played by reallocation of skilled labor (agglomeration externalities) and public provision of complementary inputs such as electricity (using night-lights density as an indicator).

The theory shows that a trade shock to one route affects resource allocation across routes and also along a given route at different locations differentiated by the initial (prewar) market access. An important insight is that a temporary trade shock shifts both spatial margins (SMS and SMI) along a given route leading to ambiguous overall effects on employment structure; but it also leads to well-defined heterogenous effects with respect to the initial (pre-war
_The effect of the presence of schools_ The last relationship reported here is that between educational enrollment and the presence of schools within the community. The sample is restricted here to rural settings as identifying "communities" in urban settings for this purpose is very difficult. In addition, the number of countries for which this relationship can be estimated is much lower as community questionnaires were not carried out in the majority of the DHS (the results are available for 21 surveys in 19 countries).

The estimating equation given in (1) is augmented with a dummy variable equal to one if there is a primary school in the community, a dummy variable equal to one if there is a primary and a secondary school in the community, and interaction terms between these and the dummy for male gender.<sup>2 4</sup> These school presence variables are constructed from the response by the community survey respondent to the questions "is there a primary school in this community" and "is there a secondary school in this community". Although the children under analysis (ages 6 to 14) are not likely to be attending secondary school, the access to secondary places may have an impact on primary schooling and the dummy variables for secondary school are therefore included in the multivariate analysis (see Lavy, 1997).

In addition to these school facility variables, the equation includes a set of community infrastructure variables in order to ensure that a relationship with school presence is not simply reflecting the fact that communities with more infrastructure in general, including schools, may tend to have higher enrollment. While the exact list of variables varies from survey to survey, the typical list includes: a dummy variable equal to one if the nearest urban center is less than 10 kilometers away; a set of dummy variables each equal to one if there is a post office, a local

> **24** In addition a dumrny (and interaction) equal to one if there is a secondary school, but no primary school,
global pandemic is how individuals and communities are to strike the balance between the health benefits and the economic costs of managing the spread of the virus.<sup>_11_</sup> Even in high-income countries, which tend to be data rich in terms of health and economic information, striking this balance frequently proves politically difficult. By contrast, low-income countries, which tend to be resource-constrained, are data poor in terms of reliable and timely information on the spread of SARS-CoV-2 and on the economic impacts of anticontagion policies. Our objective is to directly, at the household, individual, and child levels, the socioeconomic impacts of the pandemic and the policies implemented to slow the spread of the virus. Our hope is to learn from how individuals in these four countries cope with the socioeconomic effects of the virus. This can inform decisions by governments and international aid organizations regarding how best to mitigate the persisting effects of the COVID-19 pandemic.

# **2. Data and Methods**

Our findings are based on the longitudinal data from the high-frequency phone surveys conducted in Ethiopia, Malawi, Nigeria, and Uganda with support from the World Bank. Starting in May 2020, every month, the phone surveys aim to interview a national sample of households that had been interviewed face-to-face prior to the COVID-19 pandemic as part of the national longitudinal household surveys that have been supported under the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) initiative.

2
_‘Measurement error is an ever-present, generally significant, but usually neglected, feature of survey based income and expenditure data.’_ Chesher and Schluter (2002: 377)

# **I.  Introduction**

Household consumption surveys are at the heart of attempts to measure living standards, especially in developing countries where labor and income surveys cover neither the full population nor all economic activity. All surveys likely suffer from measurement error but the complexity of consumption surveys makes them especially prone to error. Yet surprisingly, validation studies to describe the nature and consequences of measurement error are mainly for labor market settings, using either administrative data (Bound and Krueger, 1991) or firm records (Pischke, 1995) as their measure of truth. Such validation studies also are limited to rich countries, including for the one type of consumption – out of pocket health expenditure – for which validation has been attempted (Cohen and Carlson, 1994).

This note reports on a consumption survey experiment in Tanzania that is potentially informative about the nature of measurement error in developing country consumption data. We randomly assigned eight alternative survey designs to 4,000 households (three households per design in each of 168 sampling units), covering urban and rural areas.<sup>1</sup> Validation from store data is neither feasible nor useful in this setting, but one resource intensive design – individually kept diaries with daily supervision for 14 days – represents a “gold standard” for consumption measurement in developing countries. The cost per household for this design was almost ten times the cost to survey consumption by recall interview (Beegle, _et al._ 2012), indicative of the resources needed for careful tracking of all commodity in-flows (harvests, purchases, gifts, stock reductions), out-flows (sales, gifts, stock increases, food fed to animals), daily attendance at meals, and acquisitions and disposals by children and other dependents the diary-keeper reported on. Survey diagnostics, such as the time profile for diary entries and
539|

NOTES: Estimates corresponding to the specification in column (3) of Table 3 estimated separately for various birth orders. Each column is a separate regression. The outcome measures mortality as % of births that do not survive. Baseline mean refers to the average likelihood of under-5 mortality in the pre-ultrasound period. Standard errors in parentheses are clustered by state. Data: NFHS. *** 1%, ** 5%, * 10%.

56
>are paid for after delivery.|
|Inf. Competition|<br>City-industry share of firms who say that they directly compete with informal firms.|
|Outage|City-industry share of firms that experienced a power outage in the survey year.|
|Web|City-industry share of firms that answer that they use websites to conduct business.|
|Cap. Agglomeration|The share of firms whose number of employees exceeding 50, as a proxy of capacity<br>agglomeration.|
|Lngdppc<br>Education|Log of GDP per capita, in constant USD, from World Development Indicators.<br>5th pillar: Higher education and training, from World Economic Forum (WEF).|
|Urbanization<br>Barrier: time required to start a<br>business for females (in days)|Urban population (% of total), from World Development Indicators.<br>Time required to start a business, female (days), from Doing Business for “time required<br>to start a business for female entrepreneurs”.|
|Barrier: financial institution<br>account, female (% for age<br>15+)|Financial institution account, female (% age 15+), from Global Findex of World Bank.|
|Empowerment indicator:<br>Fem. labor participation rate|Labor<br>force<br>participation<br>rate,<br>female<br>(%<br>of<br>female<br>population<br>above ages 15), from World Development Indicators.|
|Empowerment indicator:<br>Fem. Primary completion rate|Primary completion rate, female (% of relevant age group), from World Development<br>Indicators.|

24
the opposite pattern holds true in Nigeria. The extent of the decline in the rural employment-to-population ratio partly reflects the extent to which male and female farmers are engaged simultaneously in non-farm household enterprises and/or wage work. In other words, many farmers who produce only or mainly for family consumption remain employed under the revised standards because they are engaged in these other, mostly non-agricultural, activities (for at least one hour during the reference week). Not surprisingly, under the revised standards, the share of rural employment in agriculture is significantly smaller than under the previous standards.

This work shows that as the 19<sup>th</sup> ICLS standards are being rolled out in household surveys in SubSaharan Africa and other developing regions, indicators and statistics on employment and labor force participation will change, sometimes drastically and particularly in rural areas, for reasons that are purely or primarily related to the introduction of new statistical concepts and definitions. Moreover, the new standards create an imperative to move away from employment and labor force participation as the headline indicators of labor supply and towards a full set of indicators outlined in the 19th ICLS resolution (besides 22
|21.3|
|Neutral distribution (0.86 passthrough)||24.3|20.7|16.8|20.5|16.0|13.9|
|Democratic|21.9|23.9|20.9|17.8|19.9|20.3|20.1|
|Plutocratic||24.1|21.0|17.9|19.80|20.2|20.2|
|3-Sectors||24.7|20.8|16.7|21.2|17.6|15.7|
|11-Sectors||25.0|20.5|16.2|22.8|20.8|18.8|

Source: Authors’ estimations based on HIES 2018/19 Note: Results from FY20 onwards are projections. Preferred specification follows equation (1), including information from nominal growth from eleven economic sectors to model labor income and household-specific inflation as described in the methodological section until FY23. Due to the lack of disaggregated inflation projections, past FY23 projections have been deflated using headline inflation. All other projections rely on headline inflation and nominal growth rates. All models have the total household expenditure vector except for the preferred specification. Growth and poverty changes between FY16 and FY19 are used to estimate the elasticity for the Poverty Growth Elasticity and the Growth Incidence Curve for the Quintile growth contribution. Estimate poverty growth elasticity for real GDP per capita and official poverty rate between FY16 and FY19 is -1.06. The neutral distribution model uses a passthrough of 0.86 to reflect the consumption share on total GDP; results using higher (lower) passthrough rates present marginally more (less) pronounced variations of poverty to growth. Quintile growth contribution projections include a Democratic approach, where households within a quantile receive the same per-capita income amount, and a Plutocratic Approach, where each household receives an income based on its share in
<br>_[0.01]_<sup>_***_</sup>|(0.241)<br>_[0.01]_<sup>_***_</sup>|
|Missing income group|-0.357<sup>**</sup>|-0.360<sup>**</sup>|-0.524<sup>***</sup>|-0.525<sup>***</sup>|
||(0.164)|(0.163)|(0.153)|(0.154)|
||_[0.08]_<sup>_*_</sup>|_[0.07]_<sup>_*_</sup>|_[0.01]_<sup>_***_</sup>|_[0.01]_<sup>_***_</sup>|
|Observations|46894|46894|36973|36973|
|_R_<sup>2</sup>|0.234|0.234|0.245|0.245|

Notes: OLS regressions where the dependent variable is the perceptions of equality expressed in Gini index equivalent (minimum value 20, maximum value 42). Columns 3 and 4 restricts the sample for those observation with non-missing Intergenerational Elasticity of Education (own cohort – born after 1940). Country and year dummies included in all regressions but not reported. Clustered standard errors at the country level in parentheses. Wild bootstrap country level clustered p-values in brackets. Significance: * p<0.10, ** p<0.05, *** p<0.01.

Many of the individual characteristics included in all specifications in Table 2a show precisely estimated coefficients, indicating
The pension schemes in the United Kingdom and the Province of Quebec in Canada operate with traditional EET schemes.

**Table 5a. Tax incentives in back loaded voluntary** **<u>pension schemes.</u>**

||**United Kingdom**|**USA**|**Canada/Quebec**|
|---|---|---|---|
||Workplace pensions|401k|VRSP|
|Type of Tax system|EET|EET|EET|
|Maximum<br>Employ|ee|||
|deduction allowed (USD)|7, 000 (2013‐2014)|17,500|21,300|

Source: national sources

**Table 5b. Tax incentives in front loaded voluntary** **<u>pension schemes</u>**

||**New Zealand**|**USA**|
|---|---|---|
|Name of the plan|Kiwisaver|Roth 401k|
|Type of Tax system|TTE|TEE|
|Employees can deduct upto|NA|17,500|
|Tax credit Employee|410|0|

Source: national sources Neutrality between consumption now and consumption in retirement is one of the most relevant concepts for taxing savings, and neutrality is achieved by the use of an expenditure tax. Tax neutrality is broken once the taxes are imposed on interest earned, as they impose inefficiencies in the savings decisions of contributors.

By taxing the portion of income that is consumed, but exempting the portion that is saved for retirement, there are no distortions on the savings/consumption decision. This scheme is similar to imposing a consumption tax.<sup>33</sup> A consumption-type tax treatment can, in principle, be achieved by front-loading and back-loading taxation, while being neutral for the intertemporal budget constraint of government. Examples of this tax neutrality
data is not re-apportioned based on changes. I use the unapportioned data and manually re-apportion data to the district boundaries as they were recorded in the 2001 census to align with my other data sources. I use data on district level cropping patterns and land holdings from ICRISAT.

I supplement agricultural data from ICRISAT with data from the Indian Ministry of Agriculture. I scrape agricultural census data from 2001, 2005, and 2011 at the district and subdistrict levels. This provides additional data on characteristics of agricultural holdings and planting patterns at the district level to supplement ICRISAT data and provides resolution at the subdistrict level not available in the ICRISAT data. I also scrape the agricultural input survey data from 2001, 2006, and 2011 to collect data on machine inputs to production.<sup>20</sup> I also scrape data from the cost of

> 20The agricultural census can be found here: http://agcensus.dacnet.nic.in/districtsummarytype.aspx. The agricultural input survey can be found here: http://inputsurvey.dacnet.nic.in/districttables.aspx. Cost of cultivation data is here: https://eands.dacnet.nic.in/Cost_of_Cultivation.htm.

12
**Table S19. Concerns about health and finance by gender of household head**

|Concerned about<br>household health<br>Concerned about<br>household finances|
|---|
|Female Head<br>-0.059***<br>-0.003|
|(0.022)<br>(0.016)|
|Ethiopia<br>-0.228***<br>-0.144***|
|(0.021)<br>(0.017)|
|Nigeria<br>-0.119***<br>-0.020|
|(0.017)<br>(0.014)|
|Uganda<br>-0.213***<br>-0.124***|
|(0.018)<br>(0.015)|
|Observations<br>8,814<br>8,815|
|R-squared<br>0.028<br>0.035<br>_Note_: Each column reports results from a single regression of a binary|

_Note_ : Each column reports results from a single regression of a binary variable equal to 1 if the respondent answered “yes” to the question of whether they were concerned that a household member may fall ill with COVID-19 or that the household would experience financial distress due to COVID-19. The binary variable is regressed on indicators for each country, with Malawi as the base case, along with an indicator equal to 1 if the household head is female, as opposed to male. Data come from the most recent round of phone survey data available. This is round 3 in Ethiopia, round 2 in Malawi and Uganda, and round 1 in Nigeria. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p
# **6.3 Using 2018 Data**

In this paper, we rely on data from the ELMPS 2012. Indeed, the 2012 survey is advantageous as it oversampled areas with high migration rates. As we discussed in section 3.1, the most recent ELMPS data was conducted in 2018 and instead oversampled rural communities in Egypt. Nonetheless, in this section, we replicate our analysis using the most recent publicly available survey as a robustness check.

We examine the effect of migrants’ legal status on hourly wages in 2018, unconditional on return migration, by estimating equation (1b). In Table A.14 in the Online Appendix, we report our results unconditional on return migration. We account for the various selection processes in columns (1) to (3) following the same identification strategy presented in section 4.1. In line with our benchmark results presented in section 5.1.1, we find that only documented migrants witness a wage premium relative to non-migrants, while undocumented migrants witness a wage penalty.

We also check the robustness of our results to using pooled cross-sectional data from the 2012 and 2018 ELMPS. We report the results in Table A.15 in the Online Appendix, unconditional on return migration. Our model additionally includes a year fixed effect for the 2018 survey. Our results consistently show that undocumented migration is associated with a wage penalty, while migrants’ legal status leads to a positive wage premium upon return.

Relying on the 2018 data, we also analyze the dynamics of the effects. We ask whether the effect of legal status remains constant over time after return or varies. Accounting for the triple selection in columns (1) to (3), column (4) reports the results from the structural equation. To capture the dynamics of the effects, instead of focusing on the full 2018 sample as in Table A.14, we restrict our analysis, in Table A.16, to individuals who were interviewed in both ELMPS 2012 and 2018.
meet their expectations.

# **3 Results**

## **3.1 Data collection and attrition**

Our baseline data come from the application form and diagnostic sheets submitted by all firms as they entered the program. Administrative data from the program provide the data on attendance used in our take-up analysis above. We then have three sources of follow-up data to measure outcomes of participating in the program. The first are the scores from judges, based on the pitch decks and traction sheets submitted by firms at the end of May 2021. The purpose of these judge scores were to identify the firms that were most ready to represent the region in front of investors. They are only available for 136 out of the 225 firms (60.4%), and capture a measure of investment readiness of the firm seven months after the program started. The judges were a mix of investors and experts in small and medium enterprise growth in the region, and were blinded to treatment status. The judges gave firms a score out of 5 for seven different categories, with pre-assigned weights then used to arrive at a total score: unique value proposition (20%), technology (10%), business model (10%), team (10%), traction (30%), market expansion (10%) and finally pitch deck effectiveness 11
the current work is in estimating the gender breakdown of all migrants in destination countries in the global migration matrices. Similar to the allocation from aggregated categories in the Global Migration Database to specific origins in the master list, two measures are used for calculating gender splits; they are described in appendix 5.

# _Combining Migrant Definitions_

Only a single definition of a migrant (foreign born or foreign citizen) can be applied to each destination country in the final matrices. Switching definitions over time 17 The subregions used for the disaggregations are the 21 UN regions (see <u>http://unstats.un.org/unsd/methods/m49/m49regin.htm, with the countries of Oceania</u> aggregated into a single subregion. They do not match the large World Bank regions used in the analysis in section IV.

18 While this propensity measure is clearly inappropriate, less than 1 percent of all migrants and observations are assigned on this basis. This method is included so that every migrant in the underlying data is accounted for.

12
46 even if they had travelled considerable distances, and were provided with no form of interim protection such as condoms or spermicides.

- O Yet in practice at health centers (the next superior level of health facility), health assistants accounted for almost one third of the time spent on family planning, and it was health assistants who were observed checking the blood pressure and weight of family planning clients during consultations, while nurses appeared to do little in the way of routine checks before issuing oral contraceptives. This finding suggested that in fact health assistants could play a more active role in family planning at the health station level.

# Case 6: INDONESIA NUTRITION DEVELOPMENT PROJECT: AN APPLICATION OF SOCIAL MARKETING

In 1977, the Indonesian Ministry of Health's Health Education Directorate established a Nutrition Communication and Behavior Change Component, with Bank assistance, in five subdistricts of three provinces. The aim was to test communications techniques that would lead to improved nutritional status without involving food supplements or other costly schemes. The techniques used were those of social marketing--the adaptation of commercial marketing techniques to social sector programs. For some reason, the concept of social marketing among PHN specialists in the Bank seems to have become synonymous with the subsidized sale of contraceptives through commercial channels. The Indonesia Nutrition Project and its USAID successor demonstrate that social marketing is a broader approach, whose processes can be applied in any subsector. The assessment techniques and project interventions summarized below have since been further developed with USAID assistance, as described in a report, "The Weaning Project--New Strategies to Improve Infant Feeding Practices," prepared by the Directorate of Nutrition, MOH, Indonesia and Manoff International, Inc., Washington, D.C. (December 1986).

# Oualitative learning

Community involvement. The communities in 10 villages to be assisted under the project were helped to conduct a "community survey" which consisted of weighing all children and charting their weights on a large community graph. A community meeting was then held to discuss the survey and