rafmacalaba/data-use-mentions-extended huggingface dataset:
between when reform is implemented and when measured by the OECD in a particular year. However, Figure 3 shows that for instance in the case of France the transport services sector for which information is available on a yearly basis the reform process is rather uneven. Therefore, we prefer the constant interpolation strategy.

# **2.2 Firm‐Level Performance**

The firm‐level data for estimating our TFP measures were retrieved from the Amadeus / Orbis database from Bureau van Dijk (BvD). In fact, the Amadeus database is a sub‐set of the world‐wide Orbis database which only contains European firms. This corresponds nicely to our interest in EU countries and so we exclude non‐EU European firms from our analysis. Although our aim is to include as many European countries as possible, unfortunately Amadeus does not report all variables needed in order to calculate TFP for each country (see below). Moreover, some microstates such as Luxembourg, Malta and Cyprus show few firm observations for which we also check the robustness of our results. Data in Amadeus are given for the period 1995‐2014, but with substantially improved firm‐level observations as of 2005, and even better after 2007. As said before, both manufacturing and services firms are considered in our TFP computations so as to take stock of the wider downstream economy as most European countries nowadays are relatively more dependent on services in terms of value‐added than manufacturing. See Table A3 in the annex for a yearly overview of firm observations for services and goods.

One note of caution is warranted for our firm coverage. Although preferably one would like to have an entirely balanced panel data set with only surviving firms, in our case that seems rather difficult. Although a balanced set can be found for the years 2008‐2013, our preferred time frame is 2006‐2013 which covers a less than perfect panel format of surviving firms. The main reason for preferring the latter time period is that with purely surviving firms as of 2008
|66|83|

Source: Authors’ compilation from the GMD, ASPIRE and Findex databases.

Notes: Income refers to having less than 1.5*$2.15 per household member, education refers to no adult having primary schooling, social protection refers to not receiving any social protection, and financial inclusion refers to not having a bank or mobile money account. Water refers to access to limited standard water, and access to electricity is as written. The coverage year for education, water and electricity is based on the filters of +- 5 years from 2019, and for social protection is based on the filter of +- 5years from 2017.

# 5 RESULTS

## 5.1 EXPOSURE TO EXTREME WEATHER EVENTS AND POVERTY

Results are first presented for the number of people exposed to flood, cyclone, drought, and heatwave (Figure 6). Exposure numbers are presented for a range of return periods (from 5 to 100) and using different intensity thresholds to define extreme events: flood inundation of 15cm, 50cm and 150cm; category 1, 2 and 3 winds for cyclones, drought affecting 30 percent and 50 percent of the local area, and heatwaves of 32-36 degrees Celsius. Exposure falls with the event severity and increases with the return period. The number of people exposed to heatwaves is the highest: 3.7 billion people are exposed to the lowest intensity event we consider with a 100-year return period. For floods 1.8 billion are exposed to the lowest intensity event we consider with a 100-year return period, and 1.5 billion for cyclones. 1.4 billion people are exposed to drought with about a 40-year return period. Exposure drops considerably for cyclones when going to the next level of intensity—0.6 billion are exposed to category 2 winds for a return period of 100 years. The drop is less pronounced for other types of events. These graphs show how the choice of severity and probability determine the total exposure (and other subsequent numbers).

23
|Intial productivity||-0.000408||-0.00198*|
|||(0.00104)||(0.00109)|
|Panel controls|Yes|Yes|Yes|Yes|
|Other controls|Yes|Yes|Yes|Yes|
|Observations|14078|11086|14368|11227|

**N** otes: All continuous variables are winsorized at the 1% and 99%. Recovery is calculated as the difference in the changes in sales across rounds of data collection. Robust standard errors in parentheses * p<0.10, ** p<0.05, ** p<0.01 Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

_Initial thresholds_ : A third exercise related to our _productivity for classifying informal firms_ classification of firms according to the views of informality is related to the threshold in terms of the level of pre-pandemic productivity of formal firms (quartiles). We tested with alternative thresholds, for example, top and bottom 30% or top and bottom 20%. In all cases, our results remain robust, with the magnitude of the coefficients being within the one percentage point range of the base model.

: _Alternative definition of informality_ A final robustness check is related to using a sharper definition of informality following Ulyssea (2018) and Ulyssea (2020). To do this, we define an extensive margin as those firms that do not have a registration and do not pay taxes. We use an intensive margin definition identifying among those that are formal on the extensive margin, which ones do not provide social security for their workers or do not use a bank account. Our results using such dichotomous variables (instead of the ones from continuous measures or types of informality) show very similar magnitudes.

25
identification at the individual level which allowed for a description of the native population (Instituto Nacional de Estad ́ıstica y Censos, 2015a,b,c,d,e,f).<sup>34</sup> Table 1 shows the population of each of the 32 native groups described in the 2010 census. The largest group is the Mapuche with more than 200,000 people, and the smallest one is the Tapiete with 407 people. Almost a million people self-identify as Native

> 3 In 1869, the first national census estimated the indigenous population based on reports from local chiefs and they were not considered Argentines. In the national censuses of 1895 and 1914, the population was also estimated. The 2001 census asked if one of the members of the household self-identified or belonged to a native group but did not identify this member individually. In total 281,959 households were identified with at least one indigenous person. A sample of individuals in these households was surveyed by the National Institute of Statistics in 2004 and 2005. We study the data from the 2010 census as it allows for a cleaner comparison as all individuals were surveyed on the same day.

> 4 In Appendix A, we have included a screenshot of the questionnaire form from the 2010 National Census, showing the section in which the questions to identify Native and Afro-Argentine groups were included (Figure A1).

3
consumption is 3.7 percent among the bottom 50 percent and about 3 percent among deciles 6–8, and it declines to 2 percent among the top decile.

**Figure 2. Tobacco Consumption, by Per Capita Consumption Decile**

- **a. Share of households consuming b. Tobacco consumption in total tobacco household consumption**

<!-- Start of picture text --> 6% 6% 5% 5% 5% 5%<br>5% 5%<br>5% 5% 5%<br>68% 69% 69% 71% 69% 66% 65% 64% 65% 4%<br>60% 4% 4% 4% 4% 4% 4%<br>55% 3%<br>3% 3%<br>3% 3%<br>3% 2%<br>2%<br>2%<br>1%<br>0%<br>1 2 3 4 5 6 7 8 9 10 All<br>1 2 3 4 5 6 7 8 9 10 All<br>Per capita household consumption decile<br>Deciles of household per capita consumption<br>All households Conditional on positive consumption<br><!-- End of picture text --> _Source:_ Calculations based on data of HIES 2016/17.

_Note:_ Tobacco includes cigarettes, bidis, tobacco leaf, gul, betel leaf, betel nut, zorda, lime, khoer, rolled betel leaf, and other smoked or smokeless products. Consumption deciles are defined based on household consumption per capita deflated spatially to control for differences in prices across areas.

To obtain a sense of how different the consumption patterns are relative to the patterns in other countries in the region, one may compare Bangladesh with India, a neighboring country with similar tobacco products, particularly bidis. Information from India Consumption Expenditure Survey, 2011– 12 reveals
children may suffer a more severe emotional loss (Cortes 2015); finally, because men and women have different preferences over investments on children (Thomas 1990; Duflo 2003; Qian 2008), so that the change in the structure of the household induced by migration of one of the spouses is likely to have different effects depending on who migrates and who stays.<sup>4</sup> The present paper moves in the latter direction and complements the existing literature by investigating the allocation of resources within the household in order to understand whether spousal migration generates diversions of the household budget that affect children.

Answering this question requires understanding what are the determinants of female migration. The few existing papers that investigated this aspect seem to suggest that female migration would be a means to provide the family left behind with a more stable and reliable source of income than what would be in case of male migration, this would happen because the jobs chosen by migrant women are typically less risky than those chosen by men (Lauby and Stark 1988) and because women are intrinsically more attached to the family left behind and thus tend to send more remittances (de la Briére et al. 2002). Building on this literature, I propose a sequential model in which first the two spouses cooperatively decide who should migrate so as to maximize the expected returns from migration while minimizing the associated risk, second, the migrant spouse chooses how much to send back home through remittances, and finally the spouse left behind chooses how to allocate the available household budget across different commodities. The resulting subgame perfect Nash equilibrium will be one in which diversions of the budget off of children-related expenditures are offset by a strategic change in the amount of remittances that the migrant sends back, so that the share of budget devoted to childrenrelated expenditures is the same no matter whether it is the mother or the father that migrates, even if the two have different preferences over investments on children.

The empirical analysis, based on Indonesian data, shows indeed that, when the
flexibility” and “special and differential treatment” in the WTO (and regional) trade agreements. Given the presumption that trade policy cannot do much to address the sources of market and government failure that impede supply responses, international cooperation (trade agreements) can help by creating institutional mechanisms to help identify what policies would be effective and efficient in attaining specific goals set by governments, and by increasing the transparency of policies and their effects (outcomes) through multilateral monitoring (Hoekman, 2005).

# **References**

- Alexandraki, Katerina and Hans Peter Lankes, 2004, “Estimating the Impact of Preference Erosion on Middle-Income Countries,” IMF Working Paper WP/04/169, Washington, DC: IMF.

- Anson, J., O. Cadot, A. Estevadeordal, J. de Melo, A. Suwa-Eisenmann and B. Tumurchudur (2003) “Assessing the Costs of Rules of Origin in North-South PTAs with an Application to NAFTA”, CEPR Discussion Paper 2476.

- Baldwin R. and T. Murray, 1977, “MFN Tariff Reductions and LDC Benefits under the GSP,” _Economic Journal_ , 87, 30-46.

- Brenton, P. and T. Ikezuki 2004 ‘The Impact of Agricultural Trade Preferences, with Particular Attention to the Least Developed Countries’, in Ataman Aksoy and John Beghin (eds.), _Global Agricultural Trade and the Developing Countries._ Washington DC: World Bank.

- Brenton, Paul and Miriam Manchin. 2003. “Making EU Trade Agreements Work: The Role of Rules of Origin,” _The World Economy_ , 26, 755-769.

- Bouet, A., Y. Decreux, L. Fontagné, S. Jean and D. Laborde, 2004, “A consistent, ad-valorem equivalent measure of applied protection across the world: The MAcMap-HS6 database,” CEPII discussion paper 2004–22, December.

- Brown, Drusilla K. 1987, “General Equilibrium Effects of the US Generalized System of Preferences _,” Southern Economic Journal_
projects that facilitate transit within a city improve allocative efficiency by reallocating workers from the informal to the formal economy. As a result, aggregate gains from these projects can be larger relative to those demonstrated by standard urban models that assume perfectly efficient economies. The core intuition is that in cities in developing countries, workers in remote locations prefer to work in low-paid informal jobs near their home rather than incurring the high cost of commuting to formal employment. Transit improvements may thus provide better access to formal jobs, leading to an expansion of the formal sector and a more labor allocation.

The paper makes two main contributions. First, I combine rich administrative microdata with a transit shock to provide new empirical evidence on the effect of urban transit improvements on worker reallocation across the formal and informal sectors. Second, I rationalize these results through the lens of a quantitative spatial model. To this end, I extend recent work (Ahlfeldt et al., 2015;

> 1See e.g., Atkin and Khandelwal (2019) for a recent review of market distortions in the context of the gains from market integration, and Busso et al. (2012); Levy (2018) for the effect of distortions on total factor productivity in the Mexican context.

> 2See Gollin (2002, 2008) and La Porta and Shleifer (2008, 2014) for the relationship between the prevalence of the informal sector and economic development.

> 3See, e.g., McCaig and Pavcnik (2018) and Dix Carneiro et al. (2018) for the case of trade policies and their effect on the informal economy and the aggregate gains from trade. The former paper studies the effect of the Free Trade Agreement between the US and Vietnam, and the latter the impact of the Brazilian trade liberalization episode.

1
# 3 Data

The 2007 Logistics Performance Index (LPI) is sourced from the World Bank<sup>5</sup> and is constructed on a scale from 1-5. The LPI is calculated by the World Bank using a Principal Components Analysis of six di¤erent sub-indicators. The indicators are listed below together with their weights in the LPI:<sup>6</sup>

1. E¢ ciency of the clearance process by customs and other border agencies (0.18);

2. Ease and a¤ordability of international shipments (0.20); 3. The facility to track and trace shipments (0.16); 4. The timeliness with which shipments reach their destination (0.15).

5. Transport and information technology infrastructure (0.15); 6. Local logistics industry competence (0.16).

Further details of the construction of each indicator are available in Arvis et al (2007). In summary, the index is based on more than 5,000 country evaluations by logistics professionals. The perceptionsbased measure is corroborated with a variety of qualitative and quantitative indicators. For example, Arvis et al (2007) calculate that, on average, a one-point rise in the LPI corresponds to exports taking three more days to travel from the warehouse to port. Unlike other studies, for example Wilson et al (2005), the components of the index are drawn from the same source.

E¢ ciency of the clearance process by customs and other border agencies, the ease and a¤ordability of arranging international shipments, the ability to track and trace those shipments as well as the speed with which they reach their destinations are directly relevant to international trade. Transport and IT infrastructure are relevant to all trade, whether international or domestic, as is the competence of the local logistics industry. Originally, the World Bank also collected data on a seventh component: domestic logistics costs. This component was found to be uncorrelated with the others and
children. I add one to the values of these variables and then log-transformed).<sup>52</sup> As this table shows, households cut spending on education by 58%. In particular, households spent less on primary/secondary school tuition by 40% (its share decreased by 3%.). Knowing that the sanctions did not affect enrollment in primary and high schools, this finding indicates that households respond to the sanctions by substituting away from higher-quality private schools towards lower-quality public schools for their children. Although, I do not observe the type of schools (private vs public) in the data, I use family spending on school tuition as a proxy for choosing private school for at least one child in the household and find a 5 percentage points reduction in choosing private schools among treated households compared to control households (Table S6).<sup>53</sup> Moreover, households spent 71% less on university tuition, which is consistent with a reduction in college enrollment found in the previous section. Although the number of seats at public universities is limited, a shift from private universities to public universities may explain some of this reduction in spending on university tuition. For example, before the sanctions, some students in large cities would choose to remain in their cities and enroll in private universities instead of enrolling in public universities in small cities. The reduction in family income could force these students to move to fee-free public universities. The immigration data does not support this hypothesis. Although the type of university is not observable in the data, the cross-country migration for education decreased from 9% before the sanction to only 4% in 2012. As is explained above, the sanctions had no significant effect on total immigration and distribution of different migrant groups.

Also, spending on books and private tutoring decreased by 34% and 54%, respectively. A large share of spending on private tutoring is for pre-university students to increase their probability of success at the public universities entrance examination.
<mark>IHS-4 and approximately 3 acres in the Ghana and Malawi Agricultural Labor Surveys.</mark><sup>17</sup> <mark>Maize is an important crop in all samples, grown by between 50 percent (Nigeria GHS Panel Wave 3) and above 90 percent (Malawi Agricultural Labor Survey) of agricultural households. Between approximately 12 percent (Malawi Agricultural Labor Survey) and 29 percent (Nigeria GHS Panel Wave 3) of agricultural households grow beans, and about 50 percent (Ghana) to approximately 80 percent (Malawi Agricultural Labor Survey) own some livestock.</mark>

# **4 Employment of male and female farmers**

The new ICLS standards require combining data on a person’s activity with the intended use of the output produced in the activity, if the activity is farming or fishing, to determine employment status.<sup>18</sup> This section shows how employment levels of farmers change under the 19th ICLS standards – comparing activity-level and crop-level approaches to measure the intended destination of output.<sup>19</sup>

# **_Activity-level operationalization_**

Three of the four surveys described in section 3 – the Ghana and Malawi Agricultural Labor Surveys and the Nigeria GHS Panel Wave 3 – asked household members engaged in agriculture (farming, livestock rearing and/or fishing) whether the products obtained from their agricultural activity are mainly intended for sale or for family use. This question was asked at the beginning and at the end of the growing season for the main local crop, which in the three countries is maize.<sup>20</sup> Table 1 shows a large variation in the intended use of farmers’ output, both across countries and over time.<sup>21</sup> In Ghana, 75 percent of farmers report producing only or mainly for sale at the start of the season,

> 17 There is a surprisingly large difference in the average size of landholdings between the Malawi Agricultural
level.  T-statistics in parentheses. Source: Authors‘ calculations based upon World Bank Enterprise Surveys

Note: Since dependent variables are dummy variables, probit regressions are used. Percent private-owned is omitted due to co-linearity. Country dummies are used rather than country controls because of the small number of countries in the regressions. All regressions include dummy variables indicating sector, country, and month of first interview (i.e., in 2008 or early 2009). Dependent variable is dummy variable indicating firm in existence in late 2008/early 2009 was no longer operating at time of second survey 41
Wives in households with other co-wives are slightly more likely to be involved in decisions directly than wives who live with their in-laws. But when they are not directly involved in the decision, these wives are less likely to have effective power, particularly in terms of water decisions. In these households, when one of the wives is not directly involved in these decisions, the male head of household is making the decision. In either case, wives who are not directly involved in decision-making have little influence in the decision made.

Young unmarried adults living with their parents are the least likely to be involved in decisions directly (Table 5). Few individuals in the younger age bracket make decisions in all seven activities. On average, young men and women make between two and three decisions directly, with young women making fewer decisions directly than young men across the decision-making activities. When not directly involved in the decisions, young men and women have similar levels of effective power with the exception of expenditures. Young men have more effective power in household expenditure decisions than young women. Correspondingly, the qualitative data suggests younger men often have some involvement in expenditure decisions if they are earning income.

In terms of water activities, our qualitative data shows that while both young men and women are involved in collecting water, they have little direct involvement in the water decisions. The quantitative data confirms that young men and women are directly engaged in less than one decision on average of the three water activities, and that they have similar levels of effective power when not involved in the decision (Table 5).

**Table 5.** Average share of activities young women and men who are not yet married and living with parents are directly engaged in the decision and share of those not directly involved but with effective

||Average sh<br>activities in<br>particip|p<br>are of deci<br>which wo<br>ate in the d|ower<br>sion making<br