rafmacalaba/data-use-mentions-extended huggingface dataset:
|0.121|_0.074_|-1.738|-2.002|0.264|_0.191_|
|1998|-1.425|-1.860|0.436***|_0.087_|-1.399|-1.289|-0.110|_0.344_|
|**waz**|||||||||
|1992|-1.636|-1.712|0.075|_0.056_|-1.611|-1.559|-0.052|_0.147_|
|1998|-1.365|-1.300|0.065|_0.067_|-1.373|-0.995|-0.378*|_0.213_|

_Note_ : ***, **, * - significant at 1,5, and 10% level respectively; standard errors are estimated using bootstrapping based on 200 iterations (one-to-one matching). Source: NFHS I and II 33 Children aged 0-4 in the 1992 survey, and 0-3 in the 1998 survey. Data for Sikkim were not available for 1992.

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As national-level analysis has pointed out (World Bank, 2011), in MP too, women aspire to market work. When asked whether they would be willing to do paid work outside their homes, about 38 percent of working age women in urban MP sampled for the NSS (68th round) responded ‘yes’. Aspirations were higher among younger women: almost half the women under the age of 30 said they would like to work outside the home, with their aspirations for work having risen over time. Given the fact that about 20 percent of women in MP complete high school, and that half of all young women want to work outside the home, there is clearly a disjunct between women’s aspirations, their skills and their reality (World Bank, 2012).

<mark>The rest of the paper is organized as follows. The next section presents a short review of the literature on how attitudes held by employers about women may shape the latter’s options for work. Section 4 describes the data collected from employers in MP, the sampling strategy, the survey instrument used and the challenges in measuring norms and attitudes. Section 5 provides a brief profile of the enterprises in the sample. Section 6 describes the hiring process used by firms, employer attitudes towards hiring women, and whether these differ by the gender of the respondent. Section 7 presents the multivariate results and Section 8 concludes.</mark>

# **3. Attitudes and discrimination in the labor market**

There is a rich body of empirical literature on bias in hiring, which encompasses both implicit and explicit bias along lines of gender, race, ethnicity, place of residence, among others. certain - Evidence on discrimination along the lines of gender in the labor market shows that gender biases affect both the demand and supply of female labor. The case of women in science, technology, engineering and mathematics (STEM) is salient, as women are hugely underrepresented even in countries where larger proportions of them acquire STEM
Our findings are similar when we use an alternative classification (Table A.8). There is an increase in the value of non-export crops that are traded, with no difference between male- and female-headed households. We also find evidence of decrease in non-export crop production among female-headed households.

Why is it that households do not switch towards more commercial crop production in response to the availability of more options for commerce, as <mark>Shamdasani (2016)</mark> finds in India? There are a few possibilities. First, it is worth remembering that the response of a farmer depends on the types of markets that are now newly accessible. If rural roads offer connectivity to local markets rather than export markets, there may not be as strong an incentive to switch to export crops. Emerging evidence from the trade literature on the high costs of intranational trade (Atkin et al., 2015) suggests that this may be the case: domestic markets may be better thought of as a collection of small fragmented markets rather than a homogenous single market. In that case, better roads improve the links between these fragmented local markets where the demand for export crops may not be very high.

Second, switching may take more time than our quantitative data allow us to capture. Several respondents in our qualitative data collection exercise told us that they did start to cultivate certain types of trees. However, since our study spans a period of six years, we would not expect to see impacts on crop production or trade associated with increased cultivation of acacia, orange, or cinnamon in the VARHS data: the cropping cycle for these trees is seven to eight or 10 years. The decrease in production of nonexport crops among female-headed households may reflect this gap in production immediately after the switch to a more profitable crop in the long run.

“ _I started planting acacia in 2012. First, I planted on 2 ha, and planted rubber on the remaining 1 ha. In 2016, I planted acacia on all 3 ha
- _Dual Exchange Rate Dummy_ . This binary indicator specifies if a country has more than one exchange rate that may be used simultaneously for different purposes and/or by different entities. It comes from the IMF’s _Annual Report on Exchange Arrangements and Exchange Restrictions, 2003_ (ARREAR).

- _Financial Risk_ . This indicator assesses a country's ability to pay its way by financing its official, commercial and trade debt obligations. It is based on the following components: foreign debt as a percentage of GDP, foreign debt service as a percentage of exports of goods and services, current account as a percentage of XGS, net liquidity as months of import cover, and exchange rate stability. It comes from the International Country Risk Guide (ICRG).

- _Bank Concentration_ . This measure of bank concentration is calculated by taking the assets of the three largest banks in a country as a share of the assets of all commercial banks. It comes from the World Bank’s Financial Structure Database.

- _Dollarization Dummy_ . This binary indicator equals unity for any year when a country is officially dollarized.

- _Restrictions on foreign-currency deposits held abroad_ . This indicator specifies whether resident accounts that are maintained in foreign currency and held abroad are allowed. It comes from ARREAR.

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# Caloric intake and energy expenditures in India

### Shari Eli and Nicholas Li

Nicholas Li (corresponding author) is a professor at the University of Toronto, Toronto, Canada; his email address is nick.li@utoronto.ca. Shari Eli is a professor at the University of Toronto, Toronto, Canada; her email address is shari.eli@utoronto.ca. The authors thank Eric Edmonds, two anonymous referees, Pierre-Olivier Gourinchas, Chang-Tai Hsieh, Ronald Lee, Ted Miguel, Gustavo Bobonis, Stephan Litschig, Daniel Lafave, Pranab Bardhan, Tom Vogl, Anne Case, Jeff Hammer, as well as participants of seminars at Berkeley, Princeton, Toronto, NEUDC and PAA and Lucas Parker for outstanding research assistance.

JEL Codes: D12, I31, I32, O12, O15 Keywords: India, calories, energy, expenditure, activity, height, weight, metabolism

## **1 Introduction**

Caloric intake and food expenditures are often viewed as key indicators of an individual’s ability to satisfy the most basic of needs and as good predictors of net nutritional outcomes, which has made these indicators fundamental to the measurement of household welfare since the work of (Engel 1895). Applications include the construction of India’s national poverty measures, guided by household caloric “norms” of 2400 calories per day in rural areas and 2100 in urban areas, as well as equivalence scales (Barten 1964; Deaton and Muellbauer 1986). In this context, (Deaton and Dr`eze 2009) note the puzzling finding that based on India’s National Sample Survey (NSS), per capita caloric intake in India fell substantially between 1983 and 2005 despite rising per capita expenditures. They consider explanations ranging from measurement error in consumption data to consumer-driven changes (due to tastes, prices, or changes in the set of available goods) to changes in
one in five, while in Haiti (which has a much larger population than the other three countries), the share of the population affected is lower at about three percent of the population. Hydrological events tend to affect a smaller share of the population, on average well-below 10 percent in each of the study countries. Importantly, the impacts of geophysical and climatological events in Haiti tend to be sizeable, affecting about a tenth of the population, or over a million people on average.

We can compare exposure to the major events listed in EM-DAT to reported exposure to disaster risk in the survey data. The vast majority of households in the five countries in our sample indicates being exposed to at least one of the twelve measured disaster risks (see Figure 2).<sup>11</sup> Even in the country with the lowest aggregate risk, Belize, over 75 percent of households perceived at least one natural hazard as a direct risk. In the other countries, exposure to at least one risk exceeds 80 percent and even 90 percent in Haiti. In line with the high number of households affected by meteorological events according to the EM-DAT data, meteorological risks are mentioned often. In the four countries located in the Caribbean hurricane belt (Belize, Dominica, Haiti, and Saint Lucia) over half of households indicates that they are exposed to tropical cyclones and hurricanes, and a large share of households is exposed to severe storms. Households in Haiti and Suriname indicate being exposed to extreme rainfall, the latter in accordance with several major floods hitting the country in recent years.

Moreover, the information from the household survey indicates that households perceive exposure to a wider range of risks, including those that are geophysical, climatological, and hydrological in nature. Earthquakes are the most commonly mentioned geophysical risk. In Dominica in particular, 70 percent of households indicates being exposed to earthquakes and 44 percent to volcanic eruptions. Households in Saint Lucia also commonly refer to the risk of earthquakes (62 percent). While these
epidemic, during which face-to-face data collection came to a standstill, we used 2019 as benchmark year, implying that any censuses done after 2009 and surveys done after 2014 were considered up to date.

Our findings are threefold. First price data are typically collected (often at a monthly basis), but census and survey data are often out of date. Only 14 out of 20 countries are current on their population census; 9 out of 20 are up to date on their economic census. Only 5 out of the 20 countries carried out an establishment survey recently and about half the countries are up to date with respect to their health, labor force and consumption surveys (having been completed in 9, 11, and 14 countries respectively). The implication is that in almost half the cases, no or outdated microdata are used to produce core statistics including National Accounts and SDG reporting.

Our second finding is that only in few instances where microdata has been collected, they are made publicly accessible. Of the 140 potential microdata sets we looked for (7 data categories in 20 countries) 81 had been collected and as few as 25 were accessible. Remarkably, of these 25 about a third are not accessible through the website of the NSO; they can only be downloaded from international microdata repositories. Our third finding is that recent microdata is scarce in MENA. Summary statistics are generally available –as evidenced by the Open Data Inventory (ODIN). However, many of these statistics are necessarily based on outdated microdata and decision makers relying on such information would need to consider them with care.

Hence, we present a new indicator to complement the ODIN. The new indicator assesses the degree of openness of recent microdata as well as the ease with which microdata users can access this data on NSOs’ websites. Together with the ODIN, it gives a robust picture of data openness in MENA. It is cost effective, takes less time to collect and can be updated periodically as it only requires visits by the research team to
engaged in new internal migration over this 4-year period. With international migration, even fewer households are likely to change status, reducing statistical power and requiring large samples. The second question is who are these households that change migration status, and in particular, are the impacts of migrating different for them than for those who do not change migration status? In equation 4, we had written the impact of migration, _β_ , without any subscript. But in practice, the impacts may be heterogeneous, and so the causal impact identified by fixed effects may differ from the average treatment effect. This point has been part of an important recent debate in the literature about the extent to which there is mis-allocation of labor through too little rural-to-urban migration. Incomes and productivity are much higher in urban areas than rural areas in many developing countries. However, using panel data from Kenya and Indonesia, Hamory et al. (2021) find the wage gains from moving to be relatively small. One potential explanation is that the individuals who would gain most from moving are constrained by lack of information, networks, or wealth, and so, since they are never observed to migrate, do not contribute to the estimate of the migration impact. The large gains in income seen by Baseler (2021) in a randomized experiment which provides information is consistent with this viewpoint.

The third, and most critical, question for the use of fixed effects is what caused some units to suddenly change their status? That is, our concern is that For example, in the Chen et al. (2009) study, we might be concerned that households experienced a shock that both affected their desire to migrate and the educational performance of their children. For example, a child may experience a negative health shock, which might reduce their performance in school, and entail large health expenditures in the household and less money to spend on education. A household member may then migrate to earn additional income to pay these expenses. Such an effect would bias the effect
firm ownership at the country-and-sector-pair level. Accordingly, it addresses effects at the interface of ones on direct investment as well as global value chains (GVCs). Specifically, we analyze effects of entering deep PTAs in a unique data set on the frequency of shareholder-affiliate links across all pairs of 209 countries and 38 sectors over 9 years between 2007 and 2015.

Global input-output tables permit assigning to every shareholder sector and country whether it is up the stream or down the stream of an affiliate sector and country. Hence, every shareholder-affiliate link can be classified as horizontal (within the same sector) or vertical and then forward (the shareholder being up the stream of the affiliate) or backward (the shareholder being down the stream of the affiliate).

Theoretical work on the activity of multinational firms provides guidance as to the expected effect of PTA membership on foreign ownership (see Markusen, 2002; Egger et al., 2007): whereas lower preferential tariffs should reduce the propensity of horizontal ownership, they should increase the propensity of vertical ownership (in both the forward and backward directions). On average, positive effects of PTAs on foreign direct investment appear to dominate (Orefice and Rocha, 2014; Osnago et al., 2017; Kox and Rojas-Romagosa, 2020; Laget et al., 2020). This points to a relative dominance of vertical ownership links, consistent with the findings of Alfaro and Charlton (2009). However, the evidence is implicit only, because, as Kox and Rojas-Romagosa (2020) put it, ” _we cannot separate the FDI data between horizontal and vertical FDI_ ”.

In the light of the latter, the present study provides three innovations. First, it provides a new measurement by focusing on the extensive margin of investment in terms of shareholder-affiliate ownership links. Second, it differentiates those links as to be horizontal versus vertical (and then forward versus backward) in terms in the light of GVC data. Third, it identifies parameters and provides insights based on a very large panel data
POLICY RESEARCH WORKING PAPER 2299

# Summary findings

During the transition from central planning to market economies now under way in Eastern Europe, output levels first collapsed by 40 to 50 percent in most countries, then staged a modest recovery in the last two years. Longer-term revival of growth requires a resumption of investment and thus, realistically, of domestic savings.

To explore the determinants of household savings rates in transition economies, Denizer, Wolf, and Ying studied matching household surveys for three Central European economies: Bulgaria, Hungary, and Poland.

They find that savings rates strongly increase with _relative_ income, suggesting that increasing income inequality may play a role in determining savings rates.

Savings rates are significantly higher for households that do not own their homes or that own few of the standard consumer durables - possibly because, with no retail credit or mortgage markets, households must save to purchase houses and durables.

The influence of demographic factors broadly rnatches earlier findings for developing countries.

Perhaps surprisingly, variables associated with the - household's position in the transition process including either sector of employment (public or private) - or form of employment do not play a significant role in determining savings rates.

- This paper a product of the Poverty Reduction and Economic Management Sector Unit, Europe and Centrai Asia Region - is part of a larger effort in the region to understand determinants of savings, at both the household and the aggregate level. Copies of the paper are available free from the World Bank, 1818 H Street, NW, Washington, DC 20433. Please contactAla Cubukcu, room H4-347, telephone 202-473-8449, fax 202-522-2754, email address acubukcu@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. Cevdet Denizer may be contacted at cdenizer@worldbank.org. March 2000. (14 pages)

_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
debt overhang induced by unhedged FX borrowing. By contrast, Ukraine which experienced the sharpest depreciation in the crisis (minus 55%) is unlikely to be affected, due to low levels of unhedged FX loans (8%). All in all, figure 4.1.c suggests that increases in the debt burden of FX borrowers due to exchange rate depreciations can hardly be viewed as a key driver of debt overhang in the region.

# **Finding 4.1: Debt Overhang in the Enterprise Sector**

- <mark>Based on a comparison of the share of highly leveraged firms and GDP contraction during the crisis, debt overhang is likely to pose a general threat to firm activity in only a limited</mark> number of the more advanced countries: Latvia, Lithuania, Estonia, and Slovenia.

- <mark>Leverage levels among export-orientated firms and the widespread contraction of export income in the region suggest that debt overhang in the tradable sector may be an issue for a broader set of countries: Latvia, Lithuania Estonia, Slovenia, Macedonia, Bosnia, Turkey, the</mark> Slovak Republic, Serbia, the Czech Republic and Croatia.

- <mark>Despite sharp depreciations in many countries during the crisis, unhedged foreign currency borrowing by firms does not seem to be a source of debt overhang. The countries with a high incidence of unhedged FX loans experienced only minor depreciations during the crisis.</mark>

# **_4.2 Debt Overhang in the Household Sector_**

In this section we assess potential for debt overhang in the household sector, i.e. the extent to which built up consumer and mortgage debt may curb future household consumption and investment. We use recent survey data to examine the incidence and distribution of household debt across the region as well as how household debt is correlated to the impact of the crisis at the household level.

# <u>Household Debt towards Banks</u>

Our analysis is based on the 2010 wave
lim migrants may be smaller than differences between Hindus and Muslims in the general population.<sup>19</sup> This analysis uses the NFHS-3 data that report migrant status and latrine use along with other household characteristics.<sup>20</sup> These migrants are moving from rural to urban areas, which could be within the same state or across state lines.

Table 4 reports that latrine use is similar among migrant Hindu and Muslim households, both unconditionally (column 1) and conditional on household characteristics (column 2). As a comparison, column 3 and column 4 report estimated differences in the NFHS-3 data using all households, which are similar to the estimated differences in Table 2 and Table 3. The sample size is substantially smaller when analyzing migrants only, and so there is less precision in estimating the difference in likelihood of latrine use between Hindu migrants and – Muslim migrants in the same locations (95% confidence interval: 12.2pp less likely 7.0pp more likely). This difference is not statistically different than the within-location difference for all households (column 4), but is statistically smaller than the unconditional difference between Hindus and Muslims (column 3).

# **V Conclusion**

Improvements in sanitation have the potential to dramatically improve child health and adult health (UNDP, 2006).<sup>21</sup> Substantial efforts have been made to improve sanitation infrastructure in India and reduce open defecation, where there are the largest number of children and adults exposed to uncontained fecal material, but there remain concerns that deeply ingrained religious beliefs and associated traditional practices will limit the improvements in sanitation practices following investments in sanitation infrastructure.

The estimates in this study suggest that the unconditional differences in sanitation prac-

> 19From a bias perspective, differences between migrants and non-migrants would bias the estimated differences between Hindu migrants and Muslim migrants if selection of migrants is differential by religion.

> 20The NFHS-3 survey asked about the location of migrants’ prior residence and current residence. The
# **Figure 8: Russia’s Trade Intensity, 2011**

<!-- Start of picture text --> 30<br>20<br>10<br>0<br>CHN DEU IDN JPN KOR TUR UKR USA<br>Trade Intensity Index<br><!-- End of picture text --> Source: World Bank Trade in Services Database.

Note: Trade Intensity Index (TII) is an index indicating the extent to which a country is present in the export market of a trading partner relative to other trade partner countries. CHN = China, DEU = Germany, IDN = Indonesia, KOR = Korea, ROM = Romania, TUR = Turkey, UKR = Ukraine, USA = United States of America.

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