rafmacalaba/data-use-mentions-extended huggingface dataset:
period 1990-2017). The study finds that access to and use of service inputs seems to be an important driver for GVC integration. The depth of GVC integration depends on the intensity of services inputs. Firms which are highly productive, larger in size, pay a higher wage per value added, have a higher leverage ratio and are older are more likely to be more deeply integrated into GVCs. Firms with low service input intensities are unlikely to be integrated in GVCs. If firms not participating in GVCs increase their service input intensity, the likelihood that they begin participating in GVCs increases substantially. The probability to integrate at different levels (limited, low, medium, high) increases with the service input intensity. Moreover, the composition of services matters for GVC integration. Indian firms using complex services and IT and IT-enabling services are typically more deeply integrated into GVCs.

This paper is structured as follows. Section 2 provides an overview of the extent of India’s integration in GVCs using macro data. In section 3, we describe our data and the empirical approach to analyze the relationship between GVC participation and service input use at the firm level. Section 4 presents descriptive analysis on GVC integration and service input use by firms. Section 5 presents the results of our estimation on the probability of integrating into GVCs in any mode and service input usage. Section 6 concludes.

# 2. Background

The GVC participation index as proposed by Koopman et al. (2014; 2010) and subsequently used in empirical studies is used to measure the GVC participation of India in comparison to other economies. The cross-country data used to construct the index is from the Trade in Value Added and exports database of OECD. The participation index considers two sides of GVC -backward participation or the ‘sourcing’ side and forward participation or the ‘supply’ side. GVC backward participation captures the foreign inputs a country sources into its production process for exports and is expressed as a ratio of foreign value added in gross exports to gross exports. The GVC
country level, the lag values of Chinese and province _i_ ’s exports at the product level and at the destination country level are included, as well as the

> 5This dataset, which is constructed using COMTRADE original data, provides bilateral trade flows at the six-digit product level (Gaulier and Zignago 2010). BACI is downloadable from http://www.cepii.fr/anglaisgraph/bdd/baci.htm.

> 6The world countries’ GDP per capita are taken from the World Development Indicators database (World Bank).

> 7The provincial GDP per capita are taken from the China Statistical yearbooks.

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the migration alternative. Beegle, Joachim and Stefan (2011) document that precipitation anomalies increased both the probability of people leaving the village and the distance moved in Northern Tanzania. Mueller et al. (2020) combined NASA’s high-resolution climate data with longitudinal microdata on migration, labor participation, and LSMS-ISA data (see also Section 4), to test whether climate variability affects temporary migration to rural and urban East Africa and whether climate-induced migration coincides with a lack of local job opportunities. The data included surveys conducted in Ethiopia, Malawi, Tanzania, and Uganda over six years (2009–2014). They found that climate variability significantly affects temporary migration decisions in eastern Africa, specifically that temperature and rainfall shocks cause a reduction in temporary urban out-migration. Mueller et al.’s (2020) findings are consistent with the results of Hirvonen (2016) for rural Tanzania and challenge the narrative that temporary migration acts as a safety valve in response to climatic push factors.

Grace et al. (2018) found that rainfall did not affect temporary migration rates in two Malian villages. The authors combine unique data from highly detailed stories of migration collected over 25 years in two rural communities in Mali, and document that a poor rainy season is not correlated with extreme or even above-average emigration rates. Even accounting for some known sources of variability (age, gender, etc.), a decrease in rainfall does not directly lead to a higher emigration rate. Instead, the results suggest that during low-rainfall years outmigration is lower. Henderson et al. (2017) estimate the effects of climate variability and change on African urbanization patterns over two different temporal and spatial scales: i) local, within-district urbanization for an unbalanced 50-year panel of census data from 359 districts in 29 countries; ii) urbanization patterns from 1992 to 2008 in 1,158 cities. Their estimates show that climatic conditions do affect urbanization rates, with better conditions delaying urbanization and adverse conditions leading to faster urban population growth, but that these effects are
estimation (SAE) – and show that it can provide more disaggregated poverty estimates even when using only remotely-sensed satellite data.

This is an important finding, since traditional SAE methods require census data that is often not available in many developing countries or is available only sporadically across time. “Big data” from satellites, on the other hand, are widely available for most countries and is often available in near real-time. Recent advances in the availability of satellite and mobile phone data, as well as increases in computing power, have sparked great interest in combining these data with survey data to generate more precise estimates of socio-economic characteristics (World Bank, 2021; Burke, 2021). Traditionally, small area estimation has been carried out by utilizing survey data to estimate a model and simulating that model in household-level census data (e.g., Elbers, Lanjouw, and Lanjouw, 2003). In the last five years, however, a growing body of innovative research has used geospatial or other big data to predict poverty and generate purely synthetic predictions of poverty or welfare, often generated by convolutional neural networks (Jean et al., 2017; Yeh et al., 2020; Engstrom et al., 2022; McBride et al. 2021). These estimates are typically validated against survey data at the village level, using sample data withheld from the prediction model. The results demonstrate the ability of methods utilizing novel sources of data to extrapolate poverty estimates to countries with no sample data with a reasonable degree of accuracy.

The existing literature is, however, less informative about the performance of methods that seek to combine survey and geospatial data to generate more granular estimates within a country, for two reasons. First, much of the existing literature stops short of combining synthetic predictions with prior estimates based on survey data, which is important to generate more accurate and precise estimates for well-defined administrative levels (Masaki et al., 2022). Second, it is challenging when utilizing neural networks or other machine learning models to
higher than the level in comparative emerging markets: only 8.4% of the sample has less than a secondary education, and 22.7% have initiated or completed a higher education degree program.

> 3 It is interesting to note that most comparable financial literacy surveys, such as those conducted in the U.S. and other developed countries, have been conducted by telephone. We speculate that this might affect responses, in particular, the rate of  ̳Do not know‘ answers.

> 4 Summary statistics by gender, age, and education (% with secondary degrees) are very similar to those found in the Russia Longitudinal Monitoring Survey (LSMS), 2002, as well as the Russian National Census, 2002.  Relative to the census data, however, our survey appears to under-represent individuals in the highest income bracket.

4
of the ecosystems that have impacted agricultural activity (P. D. Fall, Carretero, and M. Y. Sarr 2010, Goldsmith, Gunjal, and Ndarishikanye 2004). In turn, this longer term migration can lead to commuting patterns as people return home to visit family and friends or receive visitors from home (Cho, Myers, and Leskovec 2011). Focusing on migrants in Dakar, A. S. Fall (1998) nds that 87% of male and 81% of female migrants visited their home areas, with the majority of visits occurring for holidays, family ceremonies and religious festivals.

Detailed studies of the Jola ethnic group in several villages nds that circular migration plays an important role, with over 80% of unmarried Jola youth traveling to the cities in October and then coming back before the rice harvest in June-July (Linares 2003). Broader research on youth in Senegal has shown that more than half of the internal migration they engage in is temporary and rural to rural or urban to urban (Herrera and Sahn 2013). Additionally there are still pastoral groups that travel within a set territory(Adriansen 2008). Understanding the movement patterns within Senegal is important for thinking through potential confounding factors between movement and malaria. The majority of the literature points to movement triggered by agricultural seasons as well as holidays. These factors and their relationship to malaria incidence will be discussed in the model section.

In Senegal, 2% of the population are international migrants while only 1.2% of the population emigrated from Senegal. Focusing on immigration into Senegal in 2013, only 0.23% of the population entered the country. While the paper focuses on the role of internal migration, the potential impact of international migration will be discussed.

# 2.4 Malaria Data

Low-incidence areas close to elimination can experience the largest externality from population mobility for three key reasons: (1) without these travelers the disease could be reduced to zero and require lower government expenditures; (2) in high malaria areas
common between the 2009 HIES and the 2016-18 DHS. Then we estimate the relationship between expenditure and each of those common variables in the 2009 HIES through many permutations based on the statistical significance of each of the common variables. Finally, we choose the empirical specification that has the highest predictive power of monetary poverty, as measured by the smallest absolute difference between actual and imputed poverty rates.<sup>9</sup><sup>_,_10</sup> The model with the best predictive power included information on region, education, dwelling, and household size. Table 1 reports summary statistics using both the 2016-18 DHS and the 2009 HIES for all variables used to impute monetary poverty in the 2016-18 DHS.<sup>11</sup> The figures illustrate patterns consistent with a struggling population. But most importantly, key indicators that are common between the surveys are roughly similar, suggesting that the questions were interpreted similarly across surveys and that there were not large changes that occurred between the surveys that might also change the relationship between monetary poverty and other household variables.<sup>12</sup> The poverty imputation results in a poverty rate of 39.3 percent at the World Bank’s International Poverty Rate of US $ 2.15 (2017 PPP).<sup>13</sup> Figure 2 further illustrates that the imputed poverty rate is higher for groups expected to be poorer and thus the imputed household expenditure is likely capturing the share of the population that is poor. Specifically, Figure 2 illustrates that the severely food-insecure population according to the Food Insecurity Experience Scale (FIES) has a significantly higher imputed poverty rate than those with better food security outcomes; the share of the population that is imputed to be poor is decreasing for higher asset deciles in the DHS; and the share of the

> 9Details on the imputation procedure and for the results from the full regression models are available from the authors upon request.

> 10This is only one of many different ways
the LSMS-ISA surveys. Ethiopia also presented a rare case where information on a key productive tree species—eucalyptus—was not collected.

Overall, the results suggest that trees on farms should be given more attention in agriculture, food security and poverty-related policy debates in Sub-Saharan Africa, particularly as the need to tackle climate change becomes more urgent. The data and analysis presented here provide a baseline for future benchmarking as well as building blocks for improving the information base relating to privately owned trees in Africa, including through improved data collection in future surveys. The open access LSMSISA surveys and other national survey data provide an important opportunity to do so. They deserve full support and could be strengthened in at least two ways: 1) by capturing the full range of relevant trees on farms, including those that may not have an immediate productive function, and 2) by including cost information in a way that facilitates comparison of income across LSMS-ISA countries. We were unable to include Niger in our analysis, despite its status as an LSMS-ISA country and success in farmer managed natural regeneration and agroforestry (Garrity et al., 2010; Sendzimir et al., 2011) because income from trees on farms did not cover in-kind income from these trees, only income from sales.

The findings also point to several new avenues for exploring the interaction of agriculture, trees, and forests to better understand the dynamics of rural livelihoods in Africa and beyond. One area ripe for further exploration is the relationship between trees on farms and forest areas. Are trees on farms associated with more or less forest clearing? Why are households with trees on farms more likely to be located near forests? Panel data from LSMS-ISA surveys combined with newly available, highresolution forest cover data make it possible to shed new light on these dynamics. LSMS-ISA panel data also enable study of the economic contribution of trees on farms over time so as to understand the extent to which such trees can provide a means for farmers to escape poverty or achieve more enduring prosperity. Finally, we
residents in informal units have characteristics very similar to those of households living in formal neighborhoods.<sup>4</sup> In such cases, the main difference between a formal and informal dwelling is that the informal dwelling has been built outside administrative procedures, be it at the titling level (no title for the land parcel requested or obtained from the titling authority) or at the construction level (no building permit requested or obtained from the urban planning authority), and thus faces a risk of destruction. This in turn may have implications for supply of basic infrastructure (roads) and utilities (electricity, water).

These elements show that in many countries construction of informal dwellings, rather than being an exception, is considered by households and firms as a viable alternative to constructing in the formal sector. Therefore, in equilibrium, both forms of investment coexist, and policy actions on either sector have an influence on the other sector. Hence, in order to devise efficient housing policies in those countries, one has to understand how the two alternatives compare and what drives their relative attractiveness.<sup>5</sup> Our model formalizes this observation. Formal and informal housing investment are distinguished in terms of costs and risks. Theoretically, we show that the premium for the formal sector should be higher for prices than for rents, because the difference between the two premiums is a direct measure of the risk associated with investment in the informal sector. Using household data from Pune, India, we estimate that the perceived risk on housing investment in the informal sector is equivalent to an annual destruction rate ranging between 1 and 2 percent. We use our approach to estimate investors’ risk in the informal sector for other countries from results in previous papers.

Studies on informal housing have primarily focused on two issues. The first is the estimation of price or rent differentials between squatting and non-squatting areas (Jimenez, 1984, Jimenez, Friedman and Mayo, 1988). In these two papers, the focus is
and strategies for improving financial services to the unbanked. Most importantly, there is a substantial difference in typical household incomes across the two countries. On a purchasing power parity basis, national income per capita in Mexico in 2002 was U.S. $8,540 p.a., while it was $35,060 p.a. in the United States (World Bank, 2003). This difference means that what we consider to be a lower-income household in the U.S. would often qualify as a middle-income household in Mexico. The difference in typical household incomes between Mexico and the U.S. also creates a substantial difference in the percentage of households who are unbanked. Based on 1992 data, the research division of the US Federal Reserve concluded in 1997 that 151 percent of families in the U.S. do not have any type of transaction accounts. In Mexico City, a recent survey found that 76.4 percent of households in the same situation.

Numerous other distinguishing features of the two countries contribute to differences in the percentage of unbanked households and ways that they obtain financial services. Here we mention three. First, banking in Mexico is much more concentrated than in the U.S. In 2004, the year of the World Bank study, Mexico had fewer than 20 commercial banks and a similar number of registered cooperatives and credit unions in Mexico City<sup>2</sup> . The five largest banks hold 82 percent of all deposits. The United States, in contrast, has over 10,000 banks, without counting

> 2 The number of registered cooperatives and credit unions has since grown significantly, but mostly outside of Mexico City.

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by Peru’s National Institute of Statistics (INEI) for the 2008-15 period. The survey is representative of formal establishments in agriculture, manufacture, commerce, construction, transport & communication and services sectors in all regions. The sample frame comes from SUNAT, the tax administration database. Each year, some establishments are selected with certainty to form the “forzosa” stratum. The forzosa stratum is defined based on net sales, which

> 16 INDECOPI had a limited number of staff available in the Commission of Elimination of Bureaucratic Barriers that was responsible to oversee the business regulations defined in the detailed TUPA’s of over 1,800 subnational jurisdictions. It thus focused its efforts on illegal practices for specific economic activities determined by the initial firm complaints in a given year.

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<mark>difficulty of capturing it with single indicators, several scholars propose to create multidimensional indicators. A recent review by S</mark> iksnelyte-Butkiene <mark>(2021) for 1994 to 2020 identified 34 indicators. For example, Day et al. (2016) embark on energy poverty from a capability framework. Similarly, Sokolowski et al. (2020) constructed a multidimensional indicator based on five dimensions of energy deprivation in Poland. Bollino and Botti (2017) generated a multidimensional energy poverty indicator based on five indicators from EU-SILC data, which is in line with this methodology. Similarly, Bouzarovski and Herrero (2016) constructed an energy poverty index for EU countries using three indicators from the EU-SILC (European Union, 2016). Pelz et al. (2018) evaluate several additional indicators that try to capture the multidimensional nature of energy poverty and evaluate their strengths and weaknesses. Further work exploring multidimensional energy poverty indicators in the EU is by Maxim et al. (2016) and Herrero and Bouzarovski (2014), among others. Other practical examples are from the Philippines (Mendoza et al., 2019), several countries in Asia (Lan et al., 2022), Africa (Nussbaum et al., 2012), and Uganda (Ssennono et al., 2021), among others.</mark>

**<mark>While multidimensional measures of energy poverty have gained increasing attention in the literature, these measures are also subject to downfalls.</mark>** <mark>Nussbaum et al. (2012) evaluate composite energy poverty metrics and conclude that these measures are useful but might be subject to important methodological shortfalls. While they help incorporate multidimensionality into one metric, there needs to be a consensus around which indicators to include, how to weigh them, and which thresholds to use for each subdimension to define energy poverty.</mark>

# 2.7. Big Data

**<mark>More recently, big</mark>
of global recessions and the ensuing global recoveries and expansions.

What happens during global recessions and recoveries? Both statistical and judgmental methods identify four global recessions since 1950: in 1975, 1982, 1991, and 2009. During these four years, there was a contraction in annual real per capita global GDP and broadbased weakness in the main indicators of global activity. Quarterly data yield similar recession dates and confirm that the duration of a typical global recession is about one year—which is also the average duration of national recessions. Global recessions are highly synchronized events, with severe economic and financial disruptions occurring simultaneously in many countries around the world. Although the four global recessions

> 30 Hansen (1939) argues that the Great Depression could lead to a prolonged period of stagnation and high unemployment because of the decline in the birth rate and excessive savings that constrain aggregate demand. For a theoretical formulation of secular stagnation, see Eggertsson, Mehrotra, and Robbins (2019). Hamilton et al. (2016) argue that the secular stagnation hypothesis confuses a delayed recovery with chronically weak aggregate demand. By examining global real interest rates over eight centuries, Schmelzing (2020) concludes that the current decline in sovereign real rates is a path to converge to the historical trend, rather than a sign of secular stagnation. Others consider the case for secular stagnation to be weak (Rogoff 2013; Taylor 2014).

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1,356**|
|The agricultural group|878|486|1,549|1,139|641|2,682|911|440|3,616|
|The low-skilledgroup|835|656|2,018|817|641|1,979|589|440|641|

Source: Own calculations based on ENEU survey Tables 9 and 10 present the teachers’ hourly labor earnings (mean monthly labor earnings divided by the total number of hours-worked per month, non adjusted and adjusted respectively). In both tables, teachers’ hourly earnings are higher in primary public schools than they are in primary private schools. At the lower secondary school level the difference is small. Interestingly, teachers hourly labor earnings and adjusted hourly labor earnings (taking into account two months vacation) are substantially above other worker’s hourly labor earnings but slightly below other _Professionals with B.A in Education not teaching_ . In 1999, median hourly earnings for primary public school teachers was above median earnings of all comparable groups.

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