rafmacalaba/data-use-mentions-extended huggingface dataset:
due to the large increase in online consumption of baby food. Baby food and formula are the top two package foods sold online in China.<sup>33</sup> Using the CFPS data, we find that breast feeding is negatively correlated with e-commerce development; the coefficient between food consumption and e-commerce z-score is larger for households with young children under three years old.<sup>34</sup> This is consistent with the Chinese culture and the strong willingness of parents to spend on children, while the judge is still out for the impact on nutrition.

The fast growth in food consumption may also stem from the online purchases of food varieties by higher-end consumers. In more developed areas, particularly tier one and tier two cities, people can purchase online specialty fresh produce, meat, or seafood (which are not available in traditional markets) with guaranteed fast delivery. End-to-end value chain digitalization, such as the Alibaba’s Hema store, which sells online high-end groceries and readymade food with guaranteed fast delivery, is one example that might be changing the pattern of online-offline retails in the future.<sup>35</sup> However, the contribution of such high-end online-offline grocery purchases to food consumption is likely to be limited for the period of our analysis given the limited coverage and the high requirement of logistics, although this might increase in the future, particularly in more developed areas.

As to personal services, such as education, medical, fitness, and health services for which personal interaction is more important, consumption growth is not associated with e-commerce development.<sup>36</sup> These services involve strong personal interactions. For example, high quality of service in education often requires instructor customization of teaching strategy, content, and speed according to students’ needs and feedback. As labor mobility is lower than the mobility of goods, it is costly to send service providers to distant customers.

# **VI Robustness**

> 33 See https://www.
# **V. Results**

This section presents the OLS and instrumental variable estimates on whether robots lead to increases in export quality. First, we present our baseline estimation of quality for all countries and products and distinguish these effects across developed and developing country exporters. Second, we examine differences in quality gains across product types. In particular, we explore if robot diffusion results in a catch-up or a divergence in product quality over time, by contrasting the quality growth of products based on their initial distance from the “quality frontier”. We also consider heterogeneous quality gains across more vs less complex products and across new vs existing export varieties. Thirdly, we exploit novel data from the IFR to examine whether there are heterogeneous quality gains across different robot applications – contrasting more and less advanced robots which undertake handling, cutting and welding.

# **_i. Baseline_**

In this section, we examine whether the diffusion of robotics leads to quality gains overall and decompose these effects across country income groups – developed and developing economies. Earlier descriptive statistics show that developing countries have simultaneously experienced faster growth in robot adoption over the period 2000-2015, albeit from a far lower initial level, as well as convergence in the quality of their exports towards those of developed economies (see Figure 1 and 6). These trends may be suggestive of a potential link between robot diffusion and quality, however it is a priori unclear for which group of countries quality gains are expected to be stronger. On the one hand, emerging economies typically export lower-quality products, further from the frontier, and so have larger scope for quality increases (Henn et al., 2020). On the other hand, extensive research has shown that obtaining the benefits of technology requires a host of complementary assets, such as skills, organizational capital, management and so on (see for example Brynjolfsson and Hitt, 2000; Bresnahan et al., 2002; Brynjolfsson et al. 2002; Leung, 2004; Van Ark and Inklaar, 2006;
# **Table A.1: Summary Statistics**

|||**All Chi**|**ldren**||**Co-resident**|**Children**||
|---|---|---|---|---|---|---|---|
||**Mean**|**Median**|**p/****c**|**N**|**Mean**<br>**Median**|**p/****c**|**N**|
|||||**BANGL**|**ADESH**|||
|Years of Education of|||**Both S**|**ons and Da**|**ughters Sample**|||
|Children|4.97|5.00||18587|5.52<br>5.00||5852|
|Father|3.39|2.00|0.92|14017|3.74<br>3.00|1.09|5599|
|Mother|1.46|0.00|0.55|14527|1.81<br>0.00|0.69|5523|
|Average of Parents|2.33|1.00|0.70|18505|2.78<br>2.00|0.82|5806|
|||||**Sons S**|**ample**|||
|Children|5.84|5.00||9056|5.56<br>5.00||3873|
|Father|3.38|2.00|0.85|
|Subnational (=1 if municipal)|-0.565***|-0.565***|
|---|---|---|
||-0.027|-0.068|
|Constant|4.393***|4.393***|
||-0.059|-0.239|
|**Observations**|**29,712**|**29,712**|
|R-squared|0.137|0.137|
|Sector FE|Yes|Yes|
|Cluster|No|Yes: Sector 2d|
|Robust standard errors in parentheses|||

*** p<0.01, ** p<0.05, * p<0.1

# 7. Conclusion

This paper describes a novel methodology for the construction of a global data set of state-owned enterprises, the Global Business of the State (BOS) database. The BOS database is more comprehensive than existing efforts to date since the BOS database (i) builds on a harmonized definition and expands the threshold beyond majority owned companies allowing for a more comprehensive and yet flexible approach, (ii) identifies and unveils both direct and indirect linkages of companies including presence at the national and subnational level as well as across borders, (iii) includes all type of sectors including natural monopoly sectors, which are relevant for assessing the state footprint in the markets, (iv) adjusts for the measurement error in ORBIS through the extensive and systematic use of supplementary databases and country team validation that allows to identify firms that are considered as private in ORBIS, (v) minimizes the omission error including companies that are operating in markets with state participation with the support of complementary sources and government counterparts, but are not captured in ORBIS; (vi) includes non-corporatized companies that are aligned to our proposed definition, but for which the shareholder information in ORBIS is incomplete,
with resistance. The authoritarian governments that were in place until the late 1980s sought to reinforce traditional Confucian principles of loyalty to the ruler and filial piety in order to maintain social and political stability. Following democratization in 1987, however, women’s groups gained the momentum necessary to push for key changes to Family Law. The 1989 revisions to Family Law stated that daughters and sons had equal rights to inherit property, that child custody rights and property rights in case of a divorce would not be automatically given to the father/husband, that married couples must jointly decide on the domicile, and that a husband could enter his name in his wife’s natal family register if he so chose (Shin 2006). Urbanization contributed to easy implementation of new inheritance laws as well; distribution of urban assets to daughters was easier than distributing lineage assets such as land (Chung & DasGupta 2007).

There are no rigorous evaluations of how this change in Family Law affected parents’ investments in or valuation of daughters. Such evaluations are complicated by the fact that the timing of these legislative changes broadly coincided with (and arguably, even followed) normative changes within civil society. In the late 1980s and early 1990s, the Korean population as a whole was increasingly starting to question and challenge traditional ways of organizing daily life (ibid).

Another significant piece of legislation in Korea in the 1990s was pension reform. This reform was/is important because the expansion of social insurance obviates some of the need for sons during old-age (Ebenstein & Leung 2013; Ebenstein 2014). Up until the 1990s, social protection expenditure by the state was limited, and the emphasis was on families to serve as the primary 42
_HOTTER PLANET, HOTTER FACTORIES_ _3_ between 2006 and 2021. Using geomasked coordinates, we merge the WBES firm-level data with gridded historical monthly temperatures and relative humidity data between 1980 and 2021 obtained from the European Union’s Copernicus Climate Change Services (C3S). The gridded climate data have 0 _._ 25<sup>0</sup> _×_ 0 _._ 25<sup>0</sup> horizontal resolution, which is a grid of approximately 27 _._ 5 _×_ 27 _._ 5 kilometers (km). Climate change in this study refers to variations in climate patterns captured by temperature anomalies. We use both near-surface temperatures and relative humidity to calculate the wet-bulb temperature (WBT), which is used to calculate the heat index. The heat index is considered a good measure of heat stress conditions that can affect the human body.<sup>3</sup> Our main source of identification is the exogenous variation in area-level deviation of the WBT from the long-term average.<sup>4</sup> The area-level WBT is measured within a radius of 30 km around the establishment, and the area-level long-term average is the mean of the WBTs for each month between 1980 and 2021. We estimate a nonlinear regression model of log sales per worker—a good proxy for firm productivity—on WBT deviation, controlling for firm- and location-specific characteristics and country, subnational, region, and year fixed effects. We also perform heterogeneity analysis by firm size and industry classification, thus contributing to the rapidly growing literature that evaluates the impact of climate change on various aspects of the economy.

The economic impacts of climate change, particularly rising temperature, on agriculture and related sectors are now better understood and well documented in the literature (Mendelsohn et al., 1994; Schlenker et al., 2006; Deschˆenes and Greenstone, 2007; Cline, 2007; Fisher et al., 2012; Carter et al., 2018; Arag ́on
reasonable levels of risk aversion, below the measured risk aversion levels for our sample. This exercise provides a first suggestive source of evidence that non-price factors contribute to low observed rainfall insurance take-up rates.

# _B. Summary statistics_

We study households located in the Mahbubnagar and Anantapur districts of Andhra Pradesh, and the Ahmedabad, Anand, and Patan districts of Gujarat. Below we describe representative summary statistics of these households, based on surveys conducted in 2006. _Sample selection. –_ In Andhra Pradesh, summary statistics are based on a survey of 1,047 landowner households in 37 villages. This survey sample is exactly the same set of households used for our field experiments (details of the experimental design are presented in Section II). These households were originally selected in 2004 based on a stratified random sample from a census of approximately 7,000 landowner households (see Giné et al. 2008 for details).

In Gujarat, our survey data are drawn from 100 villages selected on two criteria: SEWA operated in the village, and the village was within 30 km of a rainfall station.<sup>6</sup> Field experiments in 2007 were conducted in a randomly selected 50 of these 100 villages. Survey data presented below are based on a baseline survey of 1,500 SEWA members in these villages, conducted in May 2006. The survey sample should be viewed as being representative of SEWA members in these 100 villages.<sup>7</sup> However, this sample is only a subset of the households subject to field experiments in 2007, the year of our Gujarat interventions. (Again, see Section II for details).

_Basic demographic characteristics. –_ Table 2 presents summary statistics for both sets of surveyed households. While there are differences in design across the Gujarat and Andhra Pradesh surveys, to the extent possible, we harmonize variable definitions. Full definitions of the construction of each variable are presented in the Data Appendix.

Overall, the state of Gujarat has richer soil and is
In June 2001, CBS conducted the tenth national population census, which counted individuals and absentee members in the entire country. I use the sample census micro-data, which covers 95 percent of the villages in the country. Within each village, one-eighth of the households are interviewed.

The census of 2001 was used to develop a sampling frame for the Nepal Labor Force Survey-II (NLFS-II) of 2008. This survey selected 800 primary sampling units (PSUs), or village wards, through a stratified random sampling method. In each of these PSUs, they randomly selected and interviewed 20 households.

For the Nepal Living Standards Survey-III (NLSS-III) of 2010, the list of PSUs selected for NLFS-II was used as the sampling frame. Of the 800 PSUs, 500 were selected, again by stratified random sampling to be part of NLSS-III PSUs. This was done to save time in the cartography and listing exercise by the CBS so that they could complete the survey before they began preparing the logistics for the census of 2011. In each of these PSUs, they randomly selected and interviewed 12 households each.

Though households cannot be tracked between these different surveys, 452 village wards are observed in all three datasets. Furthermore, these datasets also have information on the absentee members of the household: individuals that were considered member of the same household but had been absent at the time of enumeration. I use this information at the individual level to construct the migration rates at the village level. My measure of migration rate is the ratio of migrant to resident population in each village.

Additionally, I use district level migration rates from published census tables for years 1981 and 1991 to extend the panel back in time in some cases.

Figure 3 shows the spatial distribution of these PSUs all over the country. There is at least one panel village in 70 of the 75 districts. In all of the estimations, I use the NLSS-III sampling weights of these PSUs to make my estimates nationally representative. The weighted migration rates in the panel are: 9
Figure 1. Individual average profitability with fibre boat over individual average profitability with katumeran for 25 households for which sales data is available for both katumeran and fibre boat fishing.

<!-- Start of picture text --> MU1<br>12<br>11<br>10<br>9<br>8<br>7 8 9 10 11 12<br>MU0<br><!-- End of picture text --> Figure 2. Adoption date over realized absolute income change for 25 households for which sales data is available for both katumeran and fibre boat fishing.

<!-- Start of picture text --> corr_dat e_f b1_num<br>60<br>50<br>40<br>30<br>20<br>-10000 0 10000 20000 30000 40000 50000<br>ydi f<br><!-- End of picture text -->
growth in Sub-Saharan Africa. However, a careful analysis of subnational data, suggests that netting out irrigated area which is important in all but Brazil, the gap between yields in Africa and rainfed areas elsewhere is smaller although still sizeable. For example, rainfed maize yields in Mexico are just over 2 t/ha and have been rising at about 1.9 percent per annum since the 1970s. Much of this yield gap would be due to higher and more widely adopted fertilizer use on Mexican maize.

Maize yield variability is extremely high in sub-Saharan Africa. Even among developing countries that have approximately the same mean yields, the variability of yields is nearly always higher in countries of Sub-Saharan Africa (Byerlee and Heisey 1997). Countries in Southern Africa have the highest coefficients of variation (Table 2). Zimbabwe‘s coefficient of variation in maize production from 1991-2007 was 41 percent, as compared to 33 percent in Malawi, 31 percent in Zambia, and only 11 percent in Kenya. Climatic factors are responsible for much of yield variability, which as discussed later also aggravates price variability. By contrast, in countries where rice is major food staple in Asia, coefficients of variation in production are in the single digits.

# _Trends in Maize Consumption and Trade_

In high-income countries, an estimated 70 percent of maize is destined for feed, only 3 percent is consumed directly by humans, and the remainder is used for biofuels, industrial products and seed. In Sub-Saharan Africa outside of South Africa, 77 percent of maize is used as food and only 12 percent serves as feed.

Maize, predominantly white, is consumed in Sub-Saharan Africa boiled or cooked. The two types of white maize (dent and flint) are largely associated with different food products (FAO, 1997). Dent maize is soft and floury and used for porridges, while flint maize has a hard, vitreous endosperm and is used primarily for gruel or couscous. In parts of Sub-Saharan Africa such as Malawi, flint maize has been preferred to dent because of
- 34 -

enterprise credit projects found that all have B/C ratios in excess of one (Kinly and d'Zmura, 1985). Overall it appears, as Kilby (1979) originally suggested and subsequent appraisals have confirmed, that programs which aim to provide a complete package of financial, technical and management assistance--nursery industrial estates, for example--are generally less effective than programs that identify and provide a single missing ingredient necessary for enterprise success.

* Women dominate many of the nonfarm activities that will grow most rapidly during structural transformation--activities such as food processing and preparation, tailoring, trading and many services. They likewise hold major interest in many of the declining rural nonfarm occupations--basket making, mat making, ceramics and weaving. Consequently, women will be key actors in the economic transition of Africa's rural economy. 15/ To facilitate their contribution to an accelerated rural transformation will require assistance agencies and governments explicitly recognizing the key role to be played by women. B. Implications for Research

This review, in sorting through the delicate and spotty available evidence, has identified several key priorities for future research.

* To improve our confidence in estimates of growth multipliers, the key parameters to be measured are the marginal budget shares and expenditure elasticities for rurally-produced nontradables. Those who analyze, and especially those who collect, rural consumption data can perform a vital service by tagging and highlighting the locational features of rural consumption decisions.

a On the supply side of the rural nonfarm economy, it will be particularly important to start filling in descriptive and analytical profiles of the service and commercial sectors of the rural economy as a complement to the important, detailed work already undertaken on rural manufacturing
>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C2. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Access to water, sanitation, electricity & internet indicator removed (weights within the Social Inclusion dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru C2.1. FOSD: Counting Vector C2.2. Dominance Analysis 47
|Taxes<br>Levied<br>(Billions)|Charges-<br>Warning<br>Ratio<br>(B/A)|
|---|---|---|---|---|---|---|
|Total|6,822|1,922|1,643|1,159|1,186.8|17.0%|
|First Half,<br>2014|1,021|283|244|198|232.8|19.4%|
|2011- 2013|5,801|1,639|1,399|961|954.0|16.6%|

Source: The National Tax Service, Korea; One area of particular interest is demonstrating to what extent ETI and EWS contributed to the reductions in tax evasions and frauds. According to data provided by the NTS, the number of early warnings since 2012 has risen from 639 in the first half of 2012 to 722 in the second half of 2013, but it fell back to 521 in the first half of 2014. There was an upward trend in early warnings during the initial stage of the ETI and EWS implementation, but it shifted downward thereafter. The most likely explanation for the upward and then downward trend is that it increased following an initial learning curb, but it began to decline as the ETI and EWS began to bite tax evasions and frauds. The number of charges for misconduct also demonstrates a 34
25 for female vs. male and 0.48 for joint vs. female). While the elimination of barriers to first-time registration 2) was estimated to have been more important than reduction of the transaction costs for registering mortgages 1) in the total sample, the opposite is (θθ true for the restricted sample (table 6); estimated coefficients on plots (sporadically) registered pre-2010 (θθ are larger than for those registered post-2010, in line with the notion that plots in outlying wards are less suitable as collateral. For registered land transfers (table 7), 2 1 is not significantly different from zero except for plots transferred by sole female owners. Point estimate for systematically registered plots (0.0014 θθ −θθ for mortgages and 0.0039 for land transfers) are of comparable magnitude to those for the entire sample.

We test if measurement error may bias results from the DID approach towards zero by comparing (accurate) registry data to responses obtained in the survey for the same households.<sup>16</sup> Spatially matching sampled plots for the household survey with the cadastral map (table 8) suggests that in 26% of cases information is reported incorrectly. Type I errors (survey respondents claiming to have lease document although nothing is present in the registry) and type II errors (respondents indicating lack of a legal land document in their household while a lease is registered in the registry) occur with approximately equal frequency. Both types

> 16 The proper way to eliminate discrepancies -and understand reasons for them- is to pre-populate the household survey with relevant information from administrative data. As digitization of registry data was carried out in parallel to household survey implementation, this was not possible here..

17
# Poor, or Just Feeling Poor?

## On Using Subjective Data in Measuring Poverty

### Martin Ravallion<sup>1</sup>

_Development Research Group, World Bank 1818 H Street NW, Washington DC, 20433, USA_

**Key words:** Subjective welfare, well-being, multidimensional poverty, scales, vignettes, latent heterogeneity

**JEL classifications:** C81, D60, I32

**World Bank Sector:** POV

> 1 For comments on the paper the author is grateful to Kathleen Beegle and Nobuo Yoshida. These are the views of the author alone and should not be attributed to the World Bank or any affiliated organization.
), "Changes in Comparative Anglo-American Productivity in Manufacturing Industries", NIESR Discussion Papers, London.

- A.D. Smith, P.M.W.N. Hitchens, and S.W. Davies (1982), International Industrial Productivity, NIESR, Cambridge.

- SPP (1979a), X Censo Industrial 1976. Datos de 1975, Resumen General, Tomo 1, Mexico.

- SPP (1979b), X Censo Industrial 1976, Datos de 1975, Desglose de Productos Obtenidos por Clase de Actividad, Mexico.

- SPP (1981), Sistema de Cuentas Nacionales de Mexico, Tomos I-VII, Mexico.

- R. Stone (1956), Quantity and Price Indices in National Accounts, OEEC, Paris.