rafmacalaba/data-use-mentions-extended huggingface dataset:
with a discussion of the results and discusses policy recommendations.

# 2. Data

This analysis uses cross‐sectional household‐level data from two waves of the SHFS. Wave 1 interviewed 4,117 urban, rural, and IDP households in February and March of 2016, representative of 9 of 18 Somali pre‐war regions, excluding inaccessible areas in the south. Wave 2 expanded coverage to all but one, 2
/sup> <mark>(see Figure A4 in Annex A). The UNHCR and the Tanzania Ministry of Home Affairs decided to settle most of these refugees in temporary camps located within 40 km from the border and at a short distance from Tanzanian villages. Refugees were finally settled in 13 main camps</mark><sup>10</sup> <mark>covering the Karagwe, Ngara, Kasulu, Kigoma and Kibondo clusters in Nortwestern Tanzania (Figure A1). With a population of 2.5 million people, this massive human displacement led to an increase of the population in the two host regions (Kagera and Kigoma) by over 50%. In some clusters, refugees outnumbered Tanzanians five to one (Whitaker, 2002) and by the end of May 1994, Benaco refugee camp located in Ngara cluster (Kagera region) was the largest in the world (see tables A1, A2 and figures A5, A6 in Annex A). This forced displacement crisis has been recently ranked as the largest in modern history in terms of share of refugees over the local population (Verme and Schuettler, 2019). The magnitude of such a sudden population shock was most likely to bring a profound change in the livelihood and economic activities of the hosts.</mark>

> 6 See Runtinwa (2006) and Milner (2013).

> 7 <mark>The country was ranked first in 1998 and 1997; third in 1996, second in 1995 and 1994 and fourth in 1993 (UNHCR Statistical overview, 1998).</mark>

> 8 <mark>The second Congo civil war lasted 4 years, 11 months, 2 weeks and 2 days from August 1998 to 18 July 2003.</mark>

> 9 <mark>By the end of 1996, most refugees from Rwanda had been repatriated, whereas Burundian refugees were sent home over a longer period of time, at least until 2004, due to the continued instability in their country of origin.</
and provincial governments, as well as between neighboring local governments facing similar service shortfalls. In this light the new Provincial Planning Commissions are a positive governance step.

Finally, we must note the strongly gendered implications of the above accessibility challenges. Many women will avoid overnight stays outside their or a relative’s home, implying inaccessibility for many services over 4 hours’ travel away (RAP Nepal 2018). In the context of high and predominantly male international migration to India, the Persian Gulf and Malaysia this is a particularly acute problem; the majority of remittance receivers are women, who may be the effective heads of households, yet unable or unwilling to access distant services (Shakya _et al._ 2014, RAP 2018). In any case, they may be unable to forego one or more days of agricultural labor. Seen through this gender lens long travel times to financial, hospital and governmental services are worrisome. Access to banks is necessary to collect remittances from relatives abroad and ANC/PNC medical services important to maternal health and child mortality. Women may therefore pay extra to access remittances from local, informal financial services providers and/or skip essential maternal health services for lack of access.

# Looking forward

The importance of travel times to understanding remoteness in Nepal, and the versatility of the raster‐based data set, suggest a number of follow‐up analyses. Examples include:

## **Planning and administration**

- Establishing relationships between accessibility to services and household development indicators using existing survey data (e.g. DHS, NLSS, HRVS)

- Objectively calculating remoteness bonuses and travel stipends for government and development project staff working in remote areas
, Porto and Rijkers (2019).

We start by plotting the developing world distribution of the gains from trade to show how HIT data can be used to analyze global implications of trade with a specific focus on farm households.<sup>8</sup> The variability in the gains from trade across countries is sizable; the average standard deviation of the income gains across the income distribution is 1.01% across countries. In Ukraine, the country with the lowest variance in the gains from trade, the standard deviation is 0.19 percentage points while in Burundi, the country with the highest variability, the standard deviation is 2.68 percentage points. It is interesting to note that the variability

> 8Artuc, Porto and Rijkers (2019) investigate the trade-off between the income gains and the inequality costs of liberalization for countries separately using raw data. Here, we show how related policy problems can be tackled at the global level using conveniently organized HIT data.

9
transferred by the government might be as important as those invested by households in determining human capital outcomes, information on private spending is very limited and difficult to compare across countries.<sup>2</sup> For this reason, we primarily focus on domestic government spending on education, health and social protection. However, data limitations extend to public spending as well, since these three sectors often adopt different definitions when classifying their spending and this makes it almost impossible to achieve a common definition of social spending that holds across countries.

The objective of this note is to describe the trends in government spending in health, education and social protection across countries and over time, starting from 2000 onwards, and the association between spending and human capital outcomes, as proxied by the HCI and by its sector-specific components. Far from providing a full answer to the question raised above, the analysis will help to identify those countries for which increasing the amount of social sector spending represents a first order concern when thinking of strategies to improve their outcomes. It is important to stress that also for those countries, increasing the amount of social sector spending represents a necessary, but not sufficient, condition to improve human capital outcomes.

The note is organized as follows. In section II, we will define the main variables of interest and present the main data sources for spending and human capital outcomes. We will devote great attention to highlighting the gaps in spending data. In section III, we discuss how government spending in the social sectors has evolved over time, by region and income group. In section IV, we will analyze how aggregate and sectorspecific spending correlate with the HCI, and its key ingredients. In section V, we provide a discussion about the main messages and a possible research agenda moving forward.

# II. Data

# A. Sources and definitions

The data on social spending come from a variety of sources and adopt different definitions that are discussed below. Information on the variables and the sources used for our analysis is summarized
inland to their own free zones/customs warehouses as exports.<sup>10</sup> We drop from our dataset the observations for sales to free zones since

> 6 Note that the data is available to researchers but can be accessed only in _Turkstat_ ’s premises. The universe of export transactions is defined as all export transactions above 100 USD.

> 7 The value of the transaction is provided as a Free on Board (FOB) figure.

> 8 An exporting firm in the dataset may be an actual producer of the products exported or an intermediary exporting products on behalf of other (possibly smaller) producers. As in most other export customs transaction datasets it is impossible to identify which firms are actual producers and which firms are intermediaries in our dataset. Thus our results can only be interpreted as reflecting direct export transactions. Note also that according to the Turkish Exporters Association's Top 1000 Exporters Survey, there were in 2011 7 trader companies among the top 50 exporters and another 9 firms were trading companies selling the products manufactured by the corporation to which they belong (see http://www.tim.org.tr/tr/ihracat-arastirma-raporlari-ilk-1000-ihracatci-arastirmasi.html#).

> 9 The only other study using a very disaggregated HS classification level that we are aware of is that of Fabling and Sanderson (2010) for New Zealand for which products defined at the HS 10-digit level.

> 10 The “special trade regime” considers transactions where the goods are sold from the domestic territory _only_ to both third countries and free zones/customs warehouses of the origin country as exports. In contrast, the “General

6
credit from a financial institution and 0 otherwise (Line of Credit); quality of physical infrastructure proxied by the number of power outages experienced by the firm in a typical month during the last year; dummy variable equal to 1 if the firm uses its own website to communicate with clients and suppliers and 0 otherwise (Website); and dummy variable equal to 1 if the firms uses technology licensed from a foreign company and 0 otherwise (Foreign Technology).

# 4.5.2 _Additional controls_

Additional country-level controls included in the robustness section are as follows (data source in brackets): annual growth rate of GDP (WDI, World Bank); the rule of law measure and the control of corruption measure (WGI, World Bank); and country-year level average of the number of visits or required meetings with the tax officials reported by the firms (WBES). Firm-level controls taken from WBES are: dummy variable equal to 1 if the firm is part of a larger establishment and 0 otherwise (Multi establishment firm); dummy variable equal to 1 if the firm had formal training programs for its full-time permanent workers and 0 otherwise; dummy variable equal to 1 if the firm was registered when it started operations and 0 otherwise; severity level on a 0-4 scale of tax rates as an obstacle for the firm’s current operations; the severity level on a 0-4 scale of labor laws as an obstacle for the firm’s current operations; and the severity level on a 0-4 scale of inadequately educated workers as an obstacle for the firm’s current operations.

# **5. Base regression results**

The base regression results are provided in Table 1. All the specifications control for industry fixed effects, year fixed effects, and country fixed effects. The remaining baseline controls are added sequentially. Panel A in the table contains the estimated log odds ratios, and the marginal effects 22
_</sup> 2<sup>=</sup><sup>_z_ ̄.Third,anincreaseinGPbudget,</sup><sup>_G,_willincreasetheequilib-</sup> rium resource amount allocated to hamlet s, _zs_<sup>_∗_, for all hamlets</sup><sup>_s_, but it will not change the relative</sup> resource inequities between hamlets _s_ 1 and _s_ 2, for all _s_ 1 _, s_ 2.

# **5 Data and empirical strategy**

## **5.1 Data sources**

The data that we use in our analysis come from multiple sources. First, baseline data was collected prior to the rollout of the second phase of the PVP project between December 2012 and January 2013. The baseline survey covered all 198 GPs in our sample and collected data on demographics and household characteristics of GP presidents through a GP president module; and hamlet-level information on new public investments in roads, streetlights, water, and sanitation. The latter also included data on the hamlet-wise provision of any new IAY grants—which are the most significant private transfers in terms of magnitude, during the current president’s tenure.

A second key data source used in this analysis is project monitoring data generated by the PVP project. These data included information on the identification of target beneficiaries, delivery of program benefits, and the implementation of key project processes related to participation, 13
concentration of investment in developing countries becomes apparent after taking into account the time-invariant characteristics of the countries (Table 1).

> 2The measure is intended to capture both the risk and ambiguity components of uncertainty. While risk reflects the (known) probability of an outcome, ”ambiguity” arises when the distribution of outcomes is itself unknown. The data are publicly available at World Uncertainty Database. Uncertainty is measured by the number of occurrences of the word ”uncertainty” in Economist Intelligence Unit (EIU) reports for 143 countries since the 1950s. A higher number indicates greater uncertainty. Uncertainty in year _t_ is approximated as the logarithm of the average number of uncertainty-related words in EIU reports in the three years prior to the investment (standardized to have a mean of zero and a standard deviation of 1).

> 3In Appendix B, we provide several robustness checks that test our findings against other potential markers of FDI concentration, such as the growing importance of the services sector and increasing concentration in source countries.

> 4This evidence is robust to a battery of robustness analyses: using different thresholds for the number of top investors (Figure A1); using different thresholds for the minimum number of investors (Figure A2); or using the Herfindahl-Hirschman index (HHI) as a measure of concentration (Figure A3).

5
This study describes the use of such an approach to generate a new high‐resolution map of stunting prevalence in Tanzania.

# **Methods**

# _Data_

Data on stunting were obtained from the Tanzania DHS 2015‐16.<sup>15</sup> This consisted of a two‐stage cluster randomized survey design and a final sample of 9,846 children under the age of five for whom anthropometric and age data were obtained and stunting rates for those children computed.

Geospatial covariates (spatial gridded data for which values are known at all 1×1km pixels across Tanzania) were also included in the model, as they may partially explain the observed spatial variation in stunting rates and thus allow more accurate predictions at locations without DHS survey data. An initial set was identified based on previous performance in predicting poverty, water and sanitation indicators and other related metrics in Sub‐Saharan African countries.<sup>32,33</sup> These included satellite‐ derived measures of vegetation density (Enhanced Vegetation Index, EVI), temperature (Land Surface Temperature, LST), land wetness (the Tasselled Cap Wetness index, TCW), and land brightness 3
were instrumental in identifying such informal institutions as key binding constraints on the achievement of gender equality in MNA (World Bank, 2012, 2013).

Responsibilities towards family are also central in women’s decision-making processes when it comes to the world of work. For example, familial obligations may prevent them from working longer hours, which may be further curtailed after marriage. A recent study describes how the beliefs of both men and women regarding gender roles may hinder women’s participation in the workplace. Using data from the Arab Republic of Egypt, Morocco, Lebanon, and the West Bank and Gaza, the study reveals that two-thirds to more than three-quarters of men support the notion that a woman’s most important role is to take care of the household, and the majority of men believe that it is their role to monitor and control women’s movement. In some countries, majorities of women appear to accept male guardianship, showing evidence of the internalization of these beliefs (El Feki et al, 2017).

> 1 All statistics are from the ILOSTAT database, the latest year for which data is available was 2018. Retrieved from <u>https://data.worldbank.org/</u> in October, 2019.

> 2 Labor force surveys were conducted in 2012, 2013, 2014, and 2015, and a demographic survey was conducted by in 2017, the most recent source for the FLFP statistic. All surveys were conducted by the Kurdistan Regional Statistics Office.

> 3 ILOSTAT database, retrieved from https://data.worldbank.org/ in October, 2019.
18 Comparisons of the 2003 and 2008 DHS data show similar trends in ITN ownership across all malaria risk zones. This indicates that a substantial number of ITNs was acquired in areas of Kenya where malaria is not a major health issue and suggests that better targeting of ITN distribution primarily to malaria-endemic zones could increase the number of lives saved. In its National Malaria Strategy 2009-2017, Kenyan Ministry of Public Health and Sanitation announced a new goal of universal long-lasting insecticide-treated bednets (LLIN) coverage for populations at risk in malaria risk areas by 2013 (Ministry of Public Health and Sanitation 2009). PSI's activities on ITN distribution are still in progress. More recently, USAID promised to contribute 2.7 million ITNs in the campaign against malaria (USAID 2011).

There is some evidence that ITN ownership may have fallen recently. The report from the 2010 Kenya Malaria Indicator Survey (KMIS) finds 47.9 of households owning at least one ITN, compared to 55.7 percent of households in the 2008 DHS, and the KMIS report suggests that this is because a planned distribution to replace aging ITNs did not take place in 2009. The KMIS report also shows an increase in reported malaria rates between 2007 and 2010, although changes in the geographical coverage and age window of the KMIS between the two years makes it difficult to interpret this change, and the data from the survey is not publicly available. In the light of the evidence on the benefits of ITNs‘ increased availability for infant health in high malaria risk zones of Kenya and given the cost effectiveness of ITN delivery campaigns, continuous interventions and further ITN scale-up and keep-up campaigns are likely be desirable because they are both beneficial to infant health and available at an acceptable cost.

# **6 Conclusions**

Infant mortality has fallen substantially in many countries in Sub-Saharan Africa in recent years. The drop shown for Kenya in the DHS data from 2003 and 2008 is the most rapid in the region for countries for which recent data is available. In this paper,
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# Synthesis of the Impacts of COVID-19 on India’s Labor Market: Looking at People, Places and Policies

Yiming He, Ambar Narayan, Pedro Olinto, Sutirtha Sinha Roy, Nayantara Sarma

## **1 Introduction**

The coronavirus pandemic has had an unprecedented impact on the Indian labor market. To curb the virus, India implemented nationwide stringent lockdowns at the onset of the pandemic and subsequently, localized restrictions during the second and third waves. During the first set of lockdowns in April 2020, 20-25 percent of the labor force became unemployed as most economic activity was halted.<sup>1</sup> . Headline labor market indicators recovered once the lockdowns were relaxed but workers faced many transitions, poorer job quality and reduced earnings.

This paper examines the impact of Covid-19 on a host of labor market outcomes in India using a continuous panel survey of around 170,000 households conducted three times a year. The survey is called the Consumer Pyramids Household Survey (CPHS) and is conducted by the Centre for Monitoring the Indian Economy (CMIE). We first look at cross-sectional trends of headline indicators, disaggregate them based on demographic characteristics, and then analyze the nature and determinants of labor market transitions using the panel structure of the data. As a last step, we look at the policy

> 1Based on CPHS data using the rural and urban samples from April 2020 and the Periodic Labour Force Survey using the urban quarterly sample from April-June 2020.
for: (i) the first confirmed case/death, (ii) the first 10 confirmed cases/deaths, and (iii) the first 100 confirmed cases/deaths. We re-estimate equation (1) by replacing the number of confirmed cases and deaths by these "milestone" dummy variables, which also assume time-varying values.

A country's contribution to changes in global demand for services, as captured by the Google search index, depends on its population size and internet penetration. To adjust for such factors, we weight our regressions using countries’ population with internet access. To uncover potential heterogeneities in the impacts due to policy harmonization, geographic proximity, or similarity in level of development across regions, we estimate our model separately across continents. To probe the robustness of our results, we also estimate our models only for the 15
1.373***|1.533***|1.450*|
||||||(0.0914)|(0.0965)|(0.159)|(0.219)|
|IH index|||||1.242***|1.146*|1.094|1.230|
||||||(0.0804)|(0.0767)|(0.0988)|(0.160)|
|Controls|No|Yes|Yes|Yes|No|Yes|Yes|Yes|
|Observations|2,007|2,004|745|746|2,005|2,002|745|746|

Note: *** denotes significance at 1%, ** at 5%, and * at 10% level. Standard errors are in parentheses. All columns report results from Logistic regressions. Control variables include age, years of education, marital status, number of children, dummy variables for Amman and Zarqa and if male counterpart works. The standardized indices includes items after factor analysis, as specified in Annex 2, Table 2.8.

> 5 All items as shown in Annex 2 Table 2.8. A standardized index is created with items across all the themes that load onto the first 4 or 5 factors for each of social empirical, social normative, personal beliefs, and interpersonal expectations, but ensuring that the item is represented in each of these components to be consistent with theory.

> 6 Excluding personal belief items. See Annex 2 Tale 2.10 for regressions of an index including all 4 components.

19
and tools. For drugs and ambulatory care, the expenditure data collection covered the three months prior to the survey. We therefore multiplied these expenditures by four to obtain annual estimates. For hospitalizations, only the last health problem in the 12 months prior to the survey and the related cost for each individual were reported, although individuals could report more than one hospitalization. Nevertheless, 99% of individuals who were hospitalized reported three or fewer hospitalizations; 86% reported one hospitalization, 10% reported two hospitalizations, and 3% reported three hospitalizations. We therefore multiplied hospitalization expenditures by the number of hospitalizations to obtain annual estimates. For other health services and tools, expenditure data covered the 12 months prior to the survey. Therefore, for each individual, we used the following formula to calculate annual THE: (drug expenditures x 4) + 5