rafmacalaba/data-use-mentions-extended huggingface dataset:
the summary statistics, there was little difference between much of the country based on food access measures in the survey used here. More transparency of the data used and the ways in which the evidence was used to converge to an IPC classification could be helpful in interpreting the results.

However, it is important to note that there are caveats to this analysis. In particular, the population without access to mobile phones needs to be addressed with other survey methodologies. Although face-to-face interviews that are representative of the entire population are difficult in this setting, they could at least account for some of this difficult-to-reach population. And in cases where the security situation makes it difficult to reach these households, one can complement mobile phone surveys with key informant surveys of service providers and local government officials to at least paint as complete picture as possible about the needs of the entire population in a region.

> 41Tandon and Vishwanath (2020) demonstrate that the change in access to food and basic services was very similar across the entire country. The most likely mechanism identified was a very large increase in fuel prices, which similarly was uniform across the country. In results not shown, the increase in fuel prices was similar in all IPC regions identified in the 2017 IPC announcement.

> 42As of January 2021, the next official IPC announcement following the December 2018 announcement was still being constructed. However, there have been unofficial and partial IPC updates produced for approximately one-third of the country (e.g., IPC 2019).

16
|
|Simulated income (if the household had participated only in<br>farmactivity)|0.609|0.621|

It is true that the distribution of non-farm income is unequal among rural households, because not all rural households participate in non-farm activity, while most rural households participate in farm activity. Because of the non-zero opportunity costs of rural labor, the participation in non-farm activity and farm activity is not independent. Non-farm income is a substitute for farm income. The non-farm activity participation provides the possibility for the households with low marginal labor productivity in farm activity to diversify their production in

> 31 According to the research by the Ministry of Agriculture of China, the Gini index increased from 0.3-0.4 in 1980s to over 0.4 in the mid 1990s (Rural Economic Research Center, Ministry of Agriculture of China, 2003). Based on the LSMS data, the Gini index in rural Hebei and Liaoning is relatively high, around 0.5.

> 32 Our results on the comparison of the inequality of the distribution of the observed income and the simulated income are robust using other measures of inequality. See annex 2 for details.

19
in particular among those with a high population share, which is consistent with changes in the population-weighted Gini index. As a result, the contribution of consumption differences within countries to total inequality rose from 68% to 92% (Table 2, Panel C).

This evolution of within-country inequality is stronger than the results at the global level, where within-country inequality also increased but between-country differences remain the dominant source of inequality (Lakner and Milanovic, 2016). It is perhaps not surprising that between-country differences matter more at the global level compared with a regional analysis, as one might expect countries’ average income or consumption levels to be more similar within a region.

A similar analysis of regional inequality in Sub-Saharan Africa (Jirasavetakul and Lakner, 2016) shows a higher level of inequality than in East Asia, with the African Gini index increasing from 52 in 1993 to 56 in 2008. In contrast to EAP, Africa’s within-country component gradually declined, or put differently the increase in overall Africa inequality has been driven mainly by increasing inequality between countries (i.e. differences in growth rates

> 15 Atkinson (1975) provides the following intuition in terms of a transfer between two people, one of whom has twice the other’s income. For someone whose aversion parameter ��0.1, a transfer of $1 from the rich to the poor person would only be acceptable if $0.93 reaches the poor. With ��0.25, the requirement would be $0.84 for every $1 transfer.

> 16 The measures of inequality in the GE-class are additively decomposable by subgroup. The decomposition of GE(0) uses population shares, while the decomposition of the GE(1) index is in terms of consumption shares. Therefore, the within-country component of GE(0) can be interpreted as the residual inequality after equalizing average consumption across EAP (Anand and Segal, 2008).

9
More importantly, instrumental variable estimates are also provided. Potential and available candidates for such a role are (i) the size of the bilateral diaspora between countries i and j in 1960 (data from Özden et al. 2011) and its square, and (ii) the size of the unskilled diaspora (migrants with only primary education) originally from country i residing in country j in 1990 (data from Docquier et al. 2009) and its square. First, the stocks of migrants by country of origin in the 1960 censuses (therefore immigrants arrived between the end of the Second World War and 1960) are likely to affect the current stocks of highly-skilled migrants through network effects favoring further migration flows over the long run. Note that these figures include foreign-born people counts in dates closer to the age of mass migration than to the technological revolution of the 1990s and the 2000s. Quite probably, they are uncorrelated with current levels of cross-country collaborations, apart from influence through current skilled diasporas. Similarly, the current stocks of migrants with primary or lower levels of education correlate with current stocks of highly-skilled diasporas. The relation between existing diasporas and existing migration flows not only operates at a labor market level, but also among ethnic communities operating across different skills groups. Large stocks of unskilled immigrants in a given country will mean the existence of attractive factors— for example, amenities—which are also attractive to highly-skilled immigrants (Hunt and Gauthier-Loiselle 2008). On the other hand, uneducated migrants should play a non-existent role in boosting co-inventorship or R&D offshoring with their homelands—justifying their exclusion from the main equations, apart from their effects through inventor diasporas. Moreover, unskilled diaspora data come from the 1990 census—which accounts for the unskilled migrant flows of the 1980s—so as to be more confident that they are unaffected by unobserved factors influencing co-patenting patterns between 1990 and 2010.

28
Second, to the extent that women value household public goods such as children’s health and education relatively more than men do, then we would expect women’s empowerment to lead to faster economic growth in the future, as children are more educated, well-fed, and in better health.

Yet, the fact is that most households are comprised of members of more than one gender, particularly in rural areas. Cooperation within the household can lead to better household welfare outcomes for all members. Our theoretical model provides the conditions under which we expect to see greater gains from intra-household cooperation versus women’s empowerment, while highlighting that the greatest gains are achieved when both cooperation and empowerment are achieved. Improving women’s empowerment would yield the largest gains where social norms favor husbands’ influence in the allocation of resources, and where husbands place limited value on household public goods. On the other hand, promoting cooperation would be relatively more effective when social norms support wives’ control over income, and both place a relatively high value on household public goods. To test the model’s predictions, we use a unique panel data set on married couples in rural Malawi, and find that cooperation between spouses, proxied by the share of jointly controlled income in total income, is a better predictor of total income and consumption expenditures per capita than wives’ empowerment, proxied by the share of female income in total disjoint (male + female) income.

Supported also by cross-country qualitative research, our results suggest that household public goods are relatively important to both women and men in rural Malawi; that husbands’ capacity to control wives’ incomes is relatively limited; and that development programs that promote intrahousehold cooperation could lead to greater gains in income and household public goods provision compared to interventions focusing exclusively on women’s empowerment. The evidence, therefore, offers support for the integration of intra-household cooperation in government and donor agendas, policies and projects. Theoretically, we expect that focusing on both empowerment and cooperation together would lead to
- Li, Y., G. Guo, A. Shi, Y. Li, T. Anme, and H. Ushijima (1999) ‘Prevalence and correlates of malnutrition among children in rural minority areas of China.’ _Pediatrics International_ 41, 549–556

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- Pradhan, M., D.E. Sahn,
Herdt (1978) on the effects of the adoption of the new semi-dwarf rices and associated technology on income and employment, based on surveys conducted in 36 villages in India, Indonesia, Malaysia, Pakistan, Philippines and Thailand. The authors found that while smallholders faced considerable hurdles in acquiring inputs and credit, rates of adoption for the new varieties were similar, even in villages where there were large inequalities in the distribution of land. Bell, Hazell and Slade (1982) studied the introduction of irrigation and high-yielding varieties of rice in Malaysia’s Mudha River region from 1967 to1974 and found both an increase in mechanization and a large increase in the incomes of landless paddy workers. Blyn (1983), using data from Punjab and Haryana reported that, in practice, yields declined with farm size and that mechanization did not hamper rural wage income. Around the same time, a number of studies suggested that the indirect local effects of the Green Revolution on incomes and employment opportunities were large (Bell and Hazell, 1980; Estudilo and Otsuka, 1999) and an important element of overall economic growth (Johnston and Kilby, 1975; Mellor, 1976; Timmer, 2000).

> 6 This is not to minimize the importance of rice for food security for some regions and urban centers. See for example

> discussion in Wodon et al. (2008).

> 7 Chapter 1 in Hazell and Ramasamy (1991) provides a useful perspective. See in particular, Frankel (1971), Cleaver (1972) and Griffin (1974).

3
database (FGGD). The FGGD 2005 data includes a suitability index for rain-fed crops measured at a spatial resolution of 5 arc-minutes (10 km) (Van Velthuizen et al. 2007).<sup>16</sup> We also include the production of plant biomass using the measurement of Net Primary Productivity (NPP), which is the result of all the carbon used in photosynthesis minus the loss of carbon from the maintenance respiration. Google Earth Engine provides the cloud-corrected MODIS product (MOD17A3) data at 1km spatial resolution summarized at the annual level from the 45 8-day Net Photosynthesis (PSN) products. Evaluating the mean level at the administrative level yields the variable _npp_ .

Naidoo and Iwamura (2007) derive a global map of gross economic rents from agricultural lands (in 2000 US dollars per hectare per year) by integrating spatial information on crop productivity, livestock density, and producer prices from FAOSTAT (http://faostat.fao.org; accessed June 2005). We extract these economic rents by pixel and summarize the mean over the administrative unit ( _agr_ _~~o~~ pp_ _~~c~~ st_ ).

# **Climate:**

Climate provides the initial conditions to agricultural production and human settlement. Increasing change in climate will arguably affect conditions to support agricultural production. In a low production scenario, Hertel et al. (2010) find that the prices for major staples rise 10–60% by 2030, which would have heterogenous impacts on the poor depending on local changes in agricultural production and the sources of income for households in the area. In the case of drought and urban floods, Winsemius et al. (2015) find evidence that the poor already are overexposed to natural hazards of floods and droughts and are vulnerable to changes in climate.

BioClim is a global gridded database at 30 arc-second (1km2) spatial resolution of bioclimatic variables that are derived from monthly temperature and rainfall values from cleaned weather station data and elevation. The bio-physical model
11 Figure A2.1 and Figure A2.2 in annex 2 reproduce Figure 2 for urban and rural areas, respectively. Unsurprisingly, far more urban residents use clean energy than their rural counterparts. Urban households on average are richer, have better access to infrastructure (electricity grid, paved roads for distribution of fuels), and enjoy lower prices for certain commercial fuels—for example, in countries with deregulated LPG pricing, bottled LPG would be cheaper in urban than rural areas because of greater economies of scale and close proximity to bottling plants and large depots. There are 11 countries in which more than half the urban population used clean cooking energy against 10 countries in which more than half used wood fuel or coal as the primary cooking fuel. By contrast, wood dominated cooking in rural areas irrespective of average per capita expenditures, with more than half the population using wood as their primary cooking fuel in 22 of 23 countries for which rural data were available. The only exception was South Africa, in which two-thirds of the rural population used clean cooking energy.

Comparison of cooking energy use between the rich and the poor in each country gives an indication of how rising income will likely shift the type of primary energy households use in that country. When the patterns of primary cooking energy use are compared between the top and bottom quintiles, predictably the top quintile cited LPG and electricity far more than the bottom quintile. In every country, a smaller proportion of the rich used wood as the primary cooking fuel than the poor. In several Southern African countries—Botswana, Eswatini, Namibia, Zambia, and, although not shown in the figure for a lack of data to compute quintiles, South Africa—rising income shifted households to cooking with electricity rather than LPG. In several low- and lower-middle-income countries, rising income shifted many households from wood to charcoal rather than to LPG or electricity. In Kenya and Nigeria, a greater proportion of the rich cooked with kerosene than the poor (Figure 3). The corresponding
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Cotula, L. 2014. "Testing Claims About Large Land Deals
cross-country variation in the degree of financial openness has widened quite significantly. Lastly, commodity intensity (shown in the bottom panel of Figure 5) also shows increasing cross-country variation over time. Its median value remained roughly constant since the 1960s until the early 2000s, and fell afterwards to almost zero in 2010. In contrast, the 75<sup>th</sup> percentile more than doubled over the sample period, while the interquartile range rose threefold.

It is worth noting also that the three conditioning variables show fairly modest pairwise correlation. While trade and financial openness are moderately correlated (.28), commodity intensity appears virtually uncorrelated with both of them (the pairwise correlations are .16 and - .07, respectively). This suggests that the three variables capture different features of the economies under consideration.

To assess the role of these variables in shaping the response to global shocks, we use the framework of equation (1’). Specifically, we estimate the parameters of the equation letting the set of conditioning variables **z** consist of our indices of trade openness, financial openness, and commodity-intensity (plus a constant).<sup>16</sup> Armed with the resulting estimates of � and �, we can calculate the impulse responses generated by the model for alternative values of the three conditioning variables, and we can also construct the associated decompositions of the variance of the GDP forecast error.

We implement this approach one variable at a time: we let each one of them vary while holding the other two constant – a _ceteris paribus_ exercise. For each value of the conditioning variable of interest, we follow the procedure described earlier to obtain 1,000 identified models

> 16 To minimize potential simultaneity problems, in the estimation we use the lagged values of the conditioning variables **z** . Unavailability of the Chinn‐Ito capital account openness index results in the loss of a few countries for this exercise. They are Cambodia, Angola, Botswana, Ethiopia,
driven by the period after the global financial crisis with over 26 million jobs lost between 2007 and 2018, of which 22.5 million can be attributed to China and 4 million to India, while some countries added jobs linked to apparel exports, most notably Indonesia, Türkiye, South Africa, and Mexico.

# 3.3 Are more jobs created in exporting or supplying sectors?

The sources of employment in exports can be further decomposed into the direct and indirect portions as well as domestic demand. The decomposition of employment in exports allows to identify jobs (1) directly in the exporting sector and (2) indirectly in sectors supplying to the export sector, while (3) the contribution of reimported inputs is negligible. The difference between a country’s total employment and employment in exports to a large extent reflects jobs created by domestic demand.<sup>18</sup> Figure 14 plots the full decomposition, ranked by the total employment share in exports. While the contribution of other sources of job creation varies across countries (yellow bar), the indirect contribution of supplying sectors on average tends to be smaller than the direct contribution across all countries, masking strong sectoral differences.

**Figure 14: Decomposition of employment in exports and domestic demand, 2018**

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>direct exports indirect exports reimported exports other demand<br>LUX MLT IRL BGR SVN SVK CZE EST HUN NLD LTU LVA BEL POL HRV CHE AUT CYP ISL PRT CRI ROU DNK DEU SWE ESP GRC FIN ITA NZL KOR MEX ZAF FRA CAN GBR CHL ISR TUR RUS NOR IDN AUS CHN JPN BRA COL IND ARG SAU USA<br><!-- End of picture text --> Source: Own computations. Data: OECD TiE. The country sample covers 38 OECD countries plus 13 non-OECD countries.
19 this can be done on the farm through the promotion of irrigation and horticulture enabling multiple crops a year, or through diversification into livestock products such as poultry, eggs or dairy, which are less seasonal.

Where demand for (seasonal) off‐farm labor exists, the focus may be on reducing barriers to mobility, as even small initial travel costs (compared with the potential gains) may compound the effects of inexperience, uncertainty and credit constraints to prevent subsistence farmers from accessing it, as illustrated by Bryan, Chowdhury and Mobarak (2014) in Bangladesh.<sup>16</sup> Or, in the absence of such labor demand, the role for off‐farm employment generation nearby and thus secondary town development, becomes important in accelerating poverty reduction, as discussed by Ingelaere et al. (2017) in Tanzania.

_African households are not unduly tied to agriculture ._ The common view is that families in rural Africa rely more on agriculture compared with other parts of the developing world. Davis, Di Giuseppe, and Zezza (2017) revisit this using comparable employment and income aggregates from 41 national household surveys (14 from SSA of which 6 are LSMS‐ISA) from 22 countries (9 from SSA). They confirm that agriculture remains the mainstay of rural livelihoods in SSA. Virtually all rural households have an on‐farm activity (92 percent on average across countries). But this also holds in other regions (85 percent), with little change observed across countries over time or by GDP.

Rural households also derive about two‐thirds of their income from on‐farm agriculture, compared with one‐third (on average) in other developing countries. But when differences in the level of development are taken into account (as reflected in GDP per capita), Africa is not on a different structural trajectory from the other developing regions. There are nonetheless some important differences.

Rural households in Africa are less engaged in wage employment, both on and off the farm.