rafmacalaba/data-use-mentions-extended huggingface dataset:
(panel b), referred to as the regional intersectoral productivity gap. These gaps are all negative, which means that average productivity is lower in the informal sector than in the formal sector in every region. However, the productivity gap is statistically significant at the 5 percent level in five regions (Upper East, Northern, Eastern, Western, and Greater Accra). The intersectoral productivity gap averaged across the five richest regions (154.9 thousand cedis per worker) is three times larger than that for the five poorest regions (51.1 thousand cedis per worker).

Greater Accra has the largest productivity gap (at 177 thousand cedis per worker). It is more than four times larger than the productivity gap averaged across the other nine regions (which comes to 42 thousand cedis per worker). Greater Accra’s large productivity gap is due mainly to the substantially higher average productivity in its formal sector compared to the formal sectors in other regions.

The ratio of average productivity between the informal and formal sectors ranges between 0.08 and 0.83 across regions. The average ratio across all regions, weighted by the regional shares of total value-added, is 0.26. These numbers are in the ballpark of informalformal productivity gaps estimated by other studies using microdata. For example, Busso et al. (2012) estimate a ratio of 0.38 using data for Mexico for the period 1998–2008.

As a sensitivity check, we also estimate regional intersectoral productivity gaps controlling for characteristics other than the formality status of the enterprise that potentially influence enterprise productivity. See Table A1 for average values for these characteristics by the formality status of the enterprise (including differences in average values in these characteristics between informal and formal enterprises). And see Table A2 for results from region-specific, enterprise-level OLS regressions of log labor productivity on formality status, controlling for these other characteristics. These regressions provide estimates of regional log conditional intersectoral productivity gaps.

The conditional intersectoral productivity gap is statistically significant at the 5 percent
employment activities’ (Grosh & Glewwe, 2000, chapter 9). In practice, most LSMS‐ISA surveys do not systematically clarify the concept non‐farm business, although the question sometimes includes examples of non‐farm businesses. The question in the 2015/16 Nigeria LSMS‐ISA survey, for instance, reads as _‘During the past 7 days, has [name] worked on his/her own account or in a business enterprise belonging to [name] or someone in this household, for example, as a trader, shop‐keeper, barber, dressmaker, carpenter or taxi driver?’._ Stylized questions are also used in LSMS‐ISA surveys to measure unpaid work and collect time use data on domestic activities, such as collecting water and firewood. For instance, the LSMS‐ ISA surveys in Malawi, Tanzania and Uganda include the questions ‘ _How many hours did you spend collecting water? (hours)’_ and ‘ _How many hours did you spend collecting firewood (or other fuel materials)? (hours)_ ’. The 2015/16 Ethiopia LSMS‐ISA survey starts with a yes‐no question to screen whether the respondent participated in the activity and then records time spent by activity in hours and minutes, whereas the 2015/16 Nigeria LSMS‐ISA survey only records minutes. LSMS‐ISA surveys do not collect data on domestic activities (or service provision), such as preparing meals or taking care of children/elderly. The 2013/14 Uganda LSMS‐ISA survey is the only exception and includes a single question on domestic activities (‘ _How many hours did [name] spend on domestic activities? (hours)’)._ Stylized questions may contain keywords that are not always clearly defined. The measurement of ‘casual labor’ in LSMS‐ISA surveys is, for instance, problematic. Most labor

> 13 One variant, common in LSMS‐ISA surveys, is open questions _(‘How many hours in the
,<sup>9</sup> but the actual rate at which stocks deteriorate is unknown. For private storage costs, following (29), the maximum storage subsidy compatible with a stable steady state is equal to 1.4. Similarly, for public storage costs of US$ 87 per ton, the maximum subsidy is 1.1.

7 Private storage costs in India are competitive relative to the global benchmark used here. Estimates of storage costs from National Bulk Handling Corporation, a private entity, are equivalent to about US$16 per ton in general, but a bit higher at about $19.2 in the National Capital Region or port locations (Anil Choudhary, Managing Director and CEO, _personal communication)_ . Data from IFC from recent modern grain silos projects indicate a cost of storage at about US$ 19.75 per ton in Punjab, where the private developer of storage provides land, and $11.5 per ton in Madhya Pradesh, where land is provided by the government. The Food Corporation of India has fixed ceiling rates for storage rental from private providers under the “Private Entrepreneurs Guarantee” (PEG) scheme at Rs 4.78 per quintal per month, which translates to only US$ 9.56 per ton (see Department of Food and Public Distribution website at http://dfpd.nic.in/?q=node/34). Finally, the base storage rental rates established by the Central Warehousing Corporation for private sector bidding for wheat storage under normal access conditions (that is flat terrain with easy access such as may be expected in the national capital region) are about US$ 18 per ton (see http://www.cewacor.nic.in/Docs/commodity_tariff_2013-14.pdf).

8 The economic cost of the Food Corporation of India available at http://fciweb.nic.in/finances/view/9, accessed May 12, 2014. For the financial year 2012/13,
|
|**Panel C: Informality (ACUs)**||||||
|Area cult. 2020 (mn. ha)|33.87|||||
|F29 overlapping (mn. ha)|6.42|0.09|0.20|0.67|5.46|
|F29 non-overlap (mn. ha)|14.59|0.23|0.14|0.76|13.45|
|SAR only (mn. ha)|3.14|0.43|0.24|0.28|2.20|
|Informal (mn. ha)|9.71|||||
|**Panel D: Informality (AACs)**||||||
|Area cult. 2020 (mn. ha)|11.86|||||
|F29 overlapping (mn. ha)|1.09|0.01|0.02|0.08|1.00|
|<br>F29 non-overlap (mn. ha)|5.75|0.08|0.07|0.38|5.23|
|SAR only (mn. ha)|0.22|0.02|0.03|0.05|0.12|
|Informal (mn. ha)|4.80|||||

_Source:_ Own computation from SAR, 2020 Form 29 data from SSSU and crop maps based on satellite imagery for 2020 as discussed in the text.

_Note:_ In panels C and D, ACU is areas controlled by Ukraine and AAC are areas affected by conflict. Region is not reported in the SAR for 1,629 farms cultivating a total of 39,735 ha.<sup>a</sup> Area cultivated 2020 is based on satellite imagery.

20
term (World Bank, 2018). These results should not be understood to suggest that CSA is some kind of “magic bullet”. If carefully packaged, some CSA practices may have potential to raise resilience. In our case, CSA includes the use of MT, inorganic fertilizer, and hybrid seed, and the use of MT seems to be the biggest factor in improving resilience (Table S7) even if we do not further investigate the causal chain. Prioritizing what works where and under what circumstances is key in scaling CSA. Zambia’s CSA investment plan identify cereal-legume crop diversification, commercial horticulture, agroforestry and reducing post-harvest losses as some of the most promising CSAs in the country (World Bank, 2018).

We urge some caution in interpretating the results of this paper:

- First, while we attempted to address some key endogenous factors and their effects on vulnerability and resilience—namely, CSA adoption, crop diversity, and access to credit—the success of our approaches depends on how good the instruments used are, and how well the exclusion restrictions are met. These are difficult empirical issues and ones that every economist will question. Collapsing RALS data to cross sectional data that allows us to measure vulnerability and resilience after three-waves complicates matters. We are, however, confident that our approaches—which allow us to better measure vulnerability and resilience and apply instrumental variable probit models—add value to existing studies linking climate shocks, CSA, vulnerability, and resilience.

- Second, we acknowledge that CSA as defined in this paper (minimum tillage, inorganic fertilizer, and hybrid seed) captures only short-term effects. This is because we are not able to assess for how long farmers have used these CSA practices on the same plots. We propose that future research should control for long-term plot-level CSA effects on vulnerability and resilience.

- Lastly, although our income-based poverty measures are presumably broadly aligned with consumption expenditure-based measures, they may be an imperfect substitute for expenditure-based metrics, which are more commonly used in the country
a relatively large fraction of non‐poor households will be considered vulnerable, while in countries with relatively low poverty rates, relatively few non‐poor households will be classified as vulnerable. While these results are to be expected given the log‐normal distribution, as with the results in Zhang & Wan (2008), the ability of predicted vulnerability to actually capture vulnerable households may be more difficult in countries with very high or low poverty rates. Finally, Jha et al. (2010) have two periods of household data for Tajikistan, though they calculate the VEP vulnerability measure using cross‐sectional data in the earlier period and assess how well it predicts actual vulnerability in the second period. In their case, about 60% of households were below the poverty line in the first period, so that the poverty line is neither very high nor very low. And, the predicted vulnerable in the first period, 65%, was close to the observed poor in the second period, 62%.

Another point of interest from the extant empirical literature is the fact that, in most applications, the explanatory power of the variance equation estimates tends to be quite low. For instance, in Jha et al. (2010), estimated equations for different variance measures have an R<sup>2</sup> of around .05. This is similar to results found in Christiaensen & Subbarao (2005), Imai et al. (2011), and Bronfman (2014). Of the papers reviewed, the greatest explanatory power for the variance equation is found in Bogale (2012), with an R<sup>2</sup> of .20. Limited capacity to explain the variance would indicate either limited heteroskedasticity or omitted relevant variables, which gives some cause for concern about the use of this method to generate predicted variance and using this as a measure of risks households face.

In terms of empirical evidence, most studies find that poverty is the most important component of vulnerability, then
Costinot, A., A. Rodriguez-Clare (2014). “Trade Theory with Numbers: Quantifying the Consequences of Globalization,” Handbook of International Economics, Edited by G. Gopinath, E. Helpman and K. Rogoff, Chapter 4, pp. 197–262.

Deaton, A. (1989). “Rice Prices and Income Distribution in Thailand: a Non-parametric Analysis,” Economic Journal, 99 (Supplement), pp. 1–37.

Deaton, A. (1997). _The Analysis of Household Surveys - A Microeconometric Approach to Development Policy_ . Baltimore: Johns Hopkins Press.

Dix-Carneiro, R. (2014). “Trade Liberalization and Labor Market Dynamics,” Econometrica, vol. 82(3), pp. 825–885.

Dix-Carneiro, R., and B. Kovak (2017). “Trade Reform and Regional Dynamics: Evidence from 25 Years of Brazilian Matched Employer–Employee Data,” forthcoming The American Economic Review.

Dixit, A. and V. Norman (1980). Theory of International Trade: A Dual, General Equilibrium Approach, Cambridge University Press.

Dixit, A. and V. Norman (1986). “Gains from Trade without Lump-Sum Compensation,” Journal of International Economics, vol. 21, pp. 111–122.

Faber, B. (2014). “Trade Liberalization, the Price of Quality, and Inequality: Evidence from Mexican Store Prices,” mimeo, Department of Economics, University of California at Berkeley.

Fajgelbaum, P. and A. Khandelwal (2016). “Measuring the Unequal Gains from Trade,” Quarterly Journal of Economics, vol. 131, pp. 1113–1180.

Galle, S., A. Rodriguez-Clare, and M. Yi (2017). “Slicing the Pie: Quantifying the Aggregate and Distributional Effects of Trade,” mimeo Berkeley University.

Grossman, G. and E. Helpman (1994). “Protection for Sale,” American Economic Review,
While r _eal_ wages may be a natural indicator of city wage premia, the main challenge is the availability of price data at a local level. Even if prices are available, it is not clear whether the estimated agglomeration economies with respect to real wages represent sorting on human capital or compensation for adverse urban amenities rather than true productivity premia of cities (Chauvin et al., 2017). Wages are usually only proportional to and not equal to labor productivity by a factor that depends on the local monopsony power of the firm. Thus, the use of total factor productivity (TFP) is preferred since urban costs do not play a role and it avoids making any assumption about the relationship between the local monopsony power and agglomeration economies (Combes and Gobillon, 2015).

Melo et al. (2009) show that elasticities of TFP with respect to density are on average estimated to be larger than those obtained for wages, typically around 50 percent larger. In France, for example, the elasticity of TFP with respect to density of 0.035-0.040 whereas with the same data, the elasticity is 0.027 for wages (Combes et al., 2012). It is difficult to interpret the difference between the two types of estimates. In wage equations, all the effects are re-scaled by the share of labor in the production function. Moreover, agglomeration economies percolating through the cost of inputs other than labor, such as land and intermediate inputs affect wages, but not TFP. A further possible reason for the difference in estimates obtained from wage and TFP regressions is that most researchers have not managed to successfully control for worker skills in wage regressions.

# _Underlying data_

Elasticity estimates vary by the underlying data used for analysis. Spatially aggregated industry- or worker-level data might yield different elasticity estimates relative to micro firm- or worker-level data because with spatial aggregation the information on industry, firm and worker attributes is lost. Even within micro data, productivity measures derived from firm level data versus those collected through household
footnote 10). The variable _global outputmt_ additionally controls for global sector-time trends.

The availability of production data for years before 2020 also allows us to replicate the analysis for February-June 2019, to assess if our findings are driven by the Covid-19 pandemic. The results, reported in Appendix Table 7, show that sectoral differences related to the possibility of working remotely or the level of dependence on domestically or foreign sourced imported inputs for production did not significantly influence the relationship between the growth of industrial production and bilateral trade in 2019. Similarly, a higher share of durable goods in a sector in partner countries did not significantly influence the link between the growth of industrial production and bilateral trade in the previous year. A significant positive coefficient capturing the shock on demand of final goods relative to intermediates suggests that GVCs are more resilient to negative demand shocks in years when there was no pandemic. The positive and significant coefficient of the competition effect also suggests that during non-pandemic times, the pro-competitive effect of production and trade matters. These results confirm that the differential impact of the supply and demand shocks across sectors is specific feature of the Covid-19 pandemic.

# **_Focusing on the periods of lockdowns and progressive opening_**

Figure 1 shows the contrast in the behavior of mobility and trade during the period February to April, when lockdown policies were imposed, and for the period April to June when lockdown policies were progressively reversed. The next robustness test examines these two periods separately (Tables 4 and 5). Both tables report the results using the specification that is comparable to Table 2 which covers the full period. Regression results show that, while the direction and significance of the previous findings hold, several variables changed their magnitude. Between February and April 2020, the possibility of remote work mitigated the negative trade effect of a supply shock in exporting countries more strongly, as shown 21
- Yang, D. (2008). International migration, remittances and household investment: Evidence from Philippine migrants’ exchange rate shocks. _The Economic Journal_ , 118(528):591–630.

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52
<!-- Start of picture text --> Household Income Quintiles by type of Employment<br>In European Regions, 2008-2009<br>Employee with a contract Employee without a contract<br>West East South North West East South North<br>1 2 3 4 5 NA 1 2 3 4 5 NA<br>100 100<br>80 80<br>60 60<br>40 % of workers 40<br>20 20<br>0 0<br><!-- End of picture text --> Regions sorted descending by share of low-income households among workers without a contract Data not weighted by countries' population: A worker from any country is equally likely to be sampled

# **Figure 18 Household income quintiles among formal and informal employees in European regions** _Source:_ Calculation with ESS data.

The results of descriptive analysis are reinforced by ordered probit results (Table 8 and Figure 19), which suggest that after controlling for a rich set of individual characteristics (including parental background, as well as minority/migrant background), informal employees are having the largest financial difficulties among all categories of employed population (yet they fare much better than the unemployed and discouraged).

The situation of informal self-employed varies by country group: in the UK, Ireland, Austria and Netherlands, they are as well off as formal employees; in France, Belgium, Germany, and Switzerland, as well as in the Nordic countries and in Southern Europe, they are better off than otherwise similar formal employees, but not by much; and in Eastern Europe they are substantially better off<sup>23</sup> .

Note that these results are not subject to selection bias because non-response to the question on self-assessment of household‘s financial situation is quite small, and nonemployed population is included in the sample. Yet we cannot claim that the relationship is causal because the same unobserved factors can influence propensity to work informally and

> 23 In
production of pulp and paper. Molasses is an internationally traded commodity. It is used as an input into the production of alcohol and chemicals, and as an animal feed.

International statistics on the sugar industry distinguish raw sugar, centrifugal sugar, white refined sugar, and non centrifugal sugar. Raw sugar is brown sugar which includes molasses and various impurities at the stage before it is crystallized. By centrifugal sugar is meant raw sugar which has been crystallized and most of the molasses spun off by the use of a centrifuge. In developed countries, most white sugar is sugar that has undergone one further refining process. There are various degrees of refining and the consequent purity and consistency of sugar crystals. As in other developing countries (e.g. Indonesia) most of the mill sugar produced and consumed in India is _plantation white,_ meaning sugar that has undergone a first stage of refining at the mill, but which is less refined than the refined white sugar consumed in developed countries. It also sells at a discount from the world price of fully refined white sugar when it is traded internationally. Non centrifugal sugar is a sugar-like material ( in India known as gur or jaggery) which includes the molasses. It is produced by primitive artisanal processes and mostly consumed in rural areas. In north-western India, mainly in UP, there is also very substantial production of _khandsari,_ which is a type of sugar produced by small scale country mills with the use of a centrifuge ( see below). However the Indian statistics do not distinguish khandsari from gur production, and in the international production statistics khandsari is lumped together with gur as a non-centrifugal sugar. With khandsari included, India is by far the largest world producer of non-centrifugal sugar, accounting for about two-thirds of world production. However its share in the total usage ("drawal rate") of sugar cane has been declining, from around 70 percent in the mid 1960s to around 40 percent in the early 1990s ( Data S4). Correspondingly, the share of
exports account for at least 5 percent of their total exports based on data obtained from the United Nations Comtrade database; other countries are considered as coal importers.

Current account surplus countries are identified as those having a positive median current account balance over the sample period; other countries are considered as current account deficit countries. Countries with larger service sectors are identified as those in which the service sector as percentages of GDP (obtained from the World Bank) over the sample period (measured by the median over time) is larger than the median country; other countries are considered as those with smaller service sectors. Countries with more capital account openness are identified as those having capital account openness (obtained from the Chinn-Ito index) over the sample period (measured by the median over time) higher than that of the median country; other countries are considered as those with less capital account openness.

Based on this database, current account balances (as percentages of GDP) are shown for different country groups of EMDEs in Figure 2. Three stylized facts stand out. First, oil exporters and gas exporters have historically higher current account surpluses compared to oil importers and gas importers, respectively, whereas there is no significant difference between current account balances of coal exporters and coal importers. Second, larger service-sector sizes are associated with current account deficits, whereas countries with smaller service-sector sizes can have positive or negative current account balances. Finally, current account deficits are more prevalent in countries with more open capital accounts compared to countries with less open capital accounts. Although these descriptive statistics provide evidence on the pattern of current account balances of certain country groups, they are silent about the effects of energy price shocks on current account balances, which we focus on next.

> 8 Note that this taxonomy results in the classification of some well-diversified economies as importers, even if they are exporters of oil.

10
>Exports (log)<br>14387<br>5.94<br>Summary statistics based on country 3-year lags in period 1<br>High-skilled labor share (%)<br>14387<br>22.02<br>Capital / labor (log)<br>14387<br>11.24<br>Rents from resources / GDP<br>14387<br>23.51<br>Rule of law index<br>14387<br>0.09<br>Internet use<br>8871<br>32.88<br>Summary statistics based on sector<br>Skill intensity<br>13319<br>0.37<br>Capital intensity<br>13319<br>60.50<br>Natural resource intensity<br>14387<br>0.37<br>Contract intensity<br>13319<br>0.58<br>Technology intensity<br>8243<br>0.20<br>Summary statistics based on country-sector 3-year lags in peri<br>Average output tari¤<br>14387<br>0.07<br>Average input tari¤<br>14387<br>0.06<br>Average market access tari¤<br>14387<br>0.04<br>Number of FDI projects<br>3743<br>13.80<br>Note: Summary statistics are based on the esti|

8