rafmacalaba/data-use-mentions-extended huggingface dataset:
translates into 701 country-pairs having time variation in the depth of RTAs in the period 2000-2020. This time variation comes mainly from the entry into force of new trade agreements.<sup>9</sup> Since in the empirical strategy we rely on the within country-pair variation in RTAs’ depth, the final estimation sample considers only such 31 exporting countries and contains 4,659,362 observations with non-missing information on export value and content of RTAs. The estimation sample shrinks to 2,924,126 observations because of missing values in the tariff data.

Among the 31 exporting countries having _within_ variation in the RTA depth values, 15 countries account for 87% of the overall variation in the RTA depth. These are: Croatia, Georgia, Colombia, Chile, South Africa, Serbia, Slovenia, Peru, Mauritius, Guatemala, Madagascar, Nicaragua, Tanzania, Ecuador, Malawi. In Figure 1 we show the empirical distribution of country-pairs exports (panel a and b) and firm-destination specific exports (panel c and d) for the sub-sample of the 15 countries that provide much of the variation in RTA depth.<sup>10</sup> In panel (a) of Figure 1 we show the empirical distribution of bilateral exports for country-pairs with and

> 8In case of breaks in the firms’ identifiers over the time period covered, the Exporter Dynamics Database contains two separate data sets per country (before _versus_ after the break in the firms’ identifier). In case of breaks in the firms’ identifiers, we keep the most recent period (i.e. after-break period).

> 9In our final sample, 72 RTAs involve more than two members, while 2 are bilateral for a total of 74 agreements contributing to the within variation of our RTA depth variable.

> 10In Figure A2 we provide the same evidence for the full set of 31 countries with time variation in RTA depth.

7
SADC (less the EAC) and Rest of the World.<sup>30</sup> In our model, we sum the shares of EAC and SADC (less EAC) to obain the shares of our Africa region.

# **Ownership Shares of Banks**

Each bank’s market share was then allocated among geographic regions according to the shares of ownership of the bank. We then summed across the banks to obtain total market shares by region. In many cases, however, the Bankscope data was inadequate to allocate ownership shares by region. In these cases, we investigated bank websites, to obtain the required ownership information. The results of our supplementary inquiries are listed below.

The results we get are that owners of the banking sector of Tanzania are as follows (in percent): Tanzania, 77; EU, 6; EAC, 2; SADC, 1; ROW, 14 (see Table B2).

## **Supplementary Information on Ownership Shares of Tanzanian Banks**

- Tanzania Postal Bank – 100% Tanzanian: “United Republic of Tanzania , 45.3%; Revolutionary Government of Zanzibar, 11.0%; Tanzania Posts Corporation, 33.2%; TP & TC SACCOS, 10.5%.” http://www.postalbank.co.tz/about.htm.

- Azania Bank Limited – “The Current shareholders of the Bank are as follows: National Social Security Fund (NSSF), 40%; Parastatal Pension Fund (PPF), 35%; Public Service Pension Fund (PSPF), 14%; East African Development Bank, 7%; Tanzanian Individual including staff, 4%.” <u>http://www.azaniabank.co.tz/azania/about.htm</u> .

- Consolidated Bank of Kenya Limited –- “The bank is fully owned by the Government of Kenya with the majority shareholding in the bank (51%) held by the Treasury
measures the amount of evaporation that would occur given sufficient water resources over the period 1950 to 2000. This variable is labeled _pet_ and plotted in Figure A2.3 (right panel).

In addition, we extract an aridity index and potential evapo-transpiration from models created by Trabucco and Zomer (2009). The models use data from WorldClim (Hijmans et al. 2005) as input parameters. The global mean Aridity index is Mean Annual Precipitation divided by Mean Annual Potential Evapo-Transpiration for the period from 1950 to 2000. High values of this index represent humid conditions, while low values represent arid conditions. A generalized climate classification scheme by UNEP (1997) suggests: hyper arid and arid are values less than 0.2, semi-arid has values 0.2-0.5 and dry sub-humid and humid have values above 0.5.<sup>19</sup>

# **Environment:**

The environment provides context to human settlements and surrounding economic activities. Land cover provides important information on the extent and type of human activity. We use the MODIS land cover products to select the crop and urban areas from the Annual International Geosphere-Biosphere Programme (IGBP) classification. These data area are the result of both supervised classifications of MODIS Terra and Aqua reflectance data and subsequent post-processing refinements for specific classes from prior knowledge and ancillary information. We select the crop and urban classes: _crop_ and _urb_ . The forest data are from Hansen et al. (2013) version 1.6 that provide both the stock of the forest from 2000, which is defined as “Tree canopy cover for year 2000, defined as canopy closure for all vegetation taller than 5m in height”, and forest loss, which is defined as “a stand-replacement disturbance (a change from a forest to non-forest state)”: _forloss_ and _forest_ .

To measure pollution, we use the fine particulate matter (PM 2.5) of air pollution data estimated from a model that excludes dust and sea-salt particles (van Donkelaar et al.
toxic pollution intensive exports grew at nearly the same rate as the increase in overall manufacturing exports. There was a 100 percent increase in toxic pollution intensive exports between 1997 and 2002. Water and air pollution intensive exports remained fairly constant. Thus we find that most of the predicted pollutants grew at the same rate or grew by slightly less than the exports suggesting that there was not any profound shift towards cleaner sectors.

In order to get a complete picture of the changes in composition of trade, we need to consider the changing pollution intensity of imports. The figure on pollution intensity of imports (Figure 5) shows dramatic changes in the composition of imports. Unlike exports, the greatest increase in pollution intensity of imports was in water pollution intensive imports which increased nearly three times over this period while overall imports doubled.

**Figure 4: Overall Manufacturing Exports and Pollution Intensity, 1997-2002 (in 10,000 VD, in 1997 constant dollars)**

<!-- Start of picture text --> 700000<br>600000<br>500000<br>400000<br>300000<br>200000<br>100000<br>0<br>1997 1998 1999 2000 2001 2002<br>airp waterp toxp exports<br><!-- End of picture text --> 9
population, poverty rate(proxy indicators)<sup>10</sup> , and village remoteness indicators such as distance to nearest district headquarters. Other variables relate to socio-demographic characteristics such as age, caste, dependency ratio, completed years of education, and characteristics of the household head. Data for the selection model were drawn from primary household and village surveys as well from Census 2011 and SECC 2013-14. In line with Khanna, Kochhar, and Palaniswamy (2015) we also use retrospective indicators of households such as ownership of amenities, income, land and dwelling characteristics. Village variables include population density, percentage of female working population, infrastructure facilities (such as road, primary school, primary health centers and agriculture marketing societies), percentage of household with amenities (electricity and bicycles), and village size.

The two primary assumptions of PSM: (a) Conditional Independence (CI); and (b) common support (CS) are then applied. CI implies that NRLM outcomes are independent of the treatment status of households in the absence of treatment, conditioned on observables that include household and village level covariates. The conditional independence condition is expressed as: where Y<sup>D</sup> and Y<sup>C</sup> represent outcomes for participants and non-participants, Di is the treatment status and Xi are the observables (Khandker 2010). Applying this, we calculate the propensity scores as in Table 2.

**Table 2: Household and Village Level Balance in the Surveyed Sample**

|**Variable**|**Me**|**an**|||
|---|---|---|---|---|
||**Treated**|**Control**|**Percent**<br>**Bias**|**P-Value**|
|Age of Head of Household|45.934|46.046
total value added at reference prices in each state as weights. This was done by first calculating the value added at observed market prices in the total production of the crop in each state. The value added measured at reference prices in the total production of the crop in each state was then estimated. The total value added at domestic ( observed ) prices in all the states covered was then divided by the total value added in these states measured at reference prices. This was then used as an estimate of the weighted average India -wide effective protection of the crop.

# **Effective Subsidy**

In India, farmers receive subsidies in various forms, and in addition are exempt from income taxes. The fertilizer subsidy is one of the largest, and that is treated in the estimation of the nominal protection coefficient of fertilizers and captured in the effective protections coefficient of the individual commodities. Apart from this, there are three other major subsidies, two on non tradable inputs, canal irrigation water and electricity principally used for pumpsets, and subsidized credit. A number of the original commodity studies include extensive discussions of the methods used to estimate these subsidies, and there is also a considerable body of empirical research on this subject that built on these original estimates. The original results have been incorporated into the incentive estimates included in the background papers for the period 1981-90. In some cases estimates for the last few years of the 1980s were not available, and we projected them using the all-commodities wholesale price index.

In the background papers these subsidies are in the first instance quantified by estimating effective subsidy coefficients ( ESCs). The ESC is simply the numerator of the effective protection coefficient plus the total subsidies per unit of the commodity, divided by value-added in reference prices i.e. by the effective protection denominator. That is: ESC = (VAD + NS) / VAR where, ESC = Effective subsidy coefficient VAD = Value added at domestic prices VAR = Value added at reference price NS = Net subsidies on non traded
that the instrumental variable does not impact consumption per adult equivalent by another channel than the presence of refugees, the so-called exclusion restriction. With our distance-based shift-share instrument, one of the main threats to the exclusion restriction is the fact that other time-varying factors would be correlated with the proximity to the border. One major threat would come from conflict in neighboring countries driving both forced migration and the economic lives of people living in areas close to borders. For instance, trade channels have been shown to have non-trivial impact across borders (Bayer & Rupert, 2004; Glick & Taylor, 2010). Spillovers may also arise from the so-called peace dividends. For instance, the return of South Sudan to relative stability coincide with large increase of exports in bordering areas in North-Western Uganda (Brenton & Isik, 2012). Therefore, controlling for conflict spillovers can help to show that the distance variable in our IV is an excludable weighting measure allowing the IV to properly predict the number of refugees being hosted in particular localities, without capturing alternative channels such as changing trade.

We use the Armed Conflict Location & Event Data (ACLED) to create a conflict fatalities index which we apply as a proxy for conflict spillovers (Raleigh et al., 2010). The assumption is that conflicts resulting in fatalities capture the intensity of the violence towards potential migrants and large disruption to trade or economic activity in neighboring countries/regions. The conflict spillover index measures the number of fatalities from the conflict events in neighboring countries, in a particular year, taking place in the areas nearest to the refugee hosting country, weighted by the distance from the conflict area to the clusters in the refugee hosting country. Initially we construct this conflict spillover index by restricting the conflict source countries to the neighboring countries including refugee source countries (i.e. Burundi, DRC, Eritrea, Ethiopia, Rwanda, Somalia, South Sudan, Sudan, Tanzania, Kenya). Later we further restrict the sample to only include the closest neighbors and closest trade partners that are
# Table 2: Sources of Household Data

|Country|Survey Name|Years|Original _n_|Final _n_|
|---|---|---|---|---|
|Ethiopia|Ethiopia Socioeconomic Survey (ERSS)|2011/2012|3,969|1,689|
|||2013/2014|5,262|2,865|
|||2015/2016|4,954|2,718|
|Malawi|Integrated Household Panel Survey (IHPS)|2010/2011|3,246|1,241|
|||2013|4,000|968|
|||2016/2017|2,508|1,041|
|Niger|Enquˆete Nationale sur les Conditions de Vie des|2011|3,968|2,223|
||M ́enages et l’Agriculture (ECVMA)|2014|3,617|1,690|
|Nigeria|General Household Survey (GHS)|2010/2011|5,000|2,833|
|||2012/2013|4,802|2,768|
|||2015/2016|4,613|2,783|
|Tanzania|Tanzania National Panel Survey (TZNPS)|2008/2009|3,280|1,907|
|||2010/2011|3,924|1,914|
|||2012/2013|3,924|1,848|
|Uganda|Uganda National Panel Survey (UNPS)|2009/2010|2,975|1,704|
|||2010/2011|2,716|1,741|
|||2011/2012|2,850|1,805|
|Total|6 countries|17 waves|65,608|33,738|

_Note_ : The table summarizes the household data details for each country, per LSMS Basic Information Documents.

58
Belhaj Hassine (2015).<sup>10</sup> Compared to income, consumption expenditure typically produces lower estimates of inequality, especially at the top. This can be explained by a declining marginal propensity to consume and by the fact that consumption surveys tend to understate the spending on durables at the top (e.g. Aguiar and Bils (2015) for the United States). For their study of top incomes in Egypt, Hlasny and Verme (2013) used income as their welfare measure. An argument for using consumption instead of income is that data on the former are often of a higher quality in developing and emerging economies and are less vulnerable to idiosyncratic noise as households tend to smooth their consumption over time. In what follows we will be abusing terminology by often referring to income inequality and the income distribution even though our data measure expenditures, not income.

As discussed in detail in Verme et al. (2014), inequality in Egypt as assessed from household surveys is low and has even declined in the decade before the 2011 revolution. The Gini coefficient of consumption expenditure declined by around 2pp from 32.8 in 2000 to 30.8 in 2009.<sup>11</sup> Our paper tests whether the low estimate in 2009 is robust to replacing the top tail of the income distribution with an estimate that is obtained using a combination of household expenditureand house price data.

# **3.2 Real Estate Data**

In late 2014/early 2015 we obtained data on houses and apartments for sale from two Egyptian real estate firms: Betak-online and Bezaat.<sup>12</sup> The two rank among the larger real-estate firms whose listing database can be accessed online; analogous to Redfin and Zillow in the United States. The data differ in detail but a listing typically consists of the asking price, the location (the city or a further subdivision), and the date when it was listed. Interviews with the Ministry of Housing in Cairo
retirement income. Mixed income includes all income from self-employment, including employment in the agricultural sector.<sup>9</sup> Capital income is defined as the sum of rental and interest income, when these are available - not all years in the survey data include those components. Finally, imputed rent is estimated at the household level for the entire period using different methodologies, and we divide it equally among all adults in the household. Notably, our measure of pre-tax income excludes several income components that are particularly important for low-income households – direct government transfers, transfers from other household and remittances are all excluded from our income definition, despite being usually included in broader income measures used to calculate official poverty and inequality estimates.

# **4.2.2 Income in tax records**

We use micro-data on individual, non-incorporated taxpayers ( _Personas Naturales_ ) to complement the income distribution from household surveys. We provide a thorough explanation of the process of generating pre-tax income from tax records in Appendix D; here we summarize the key decisions taken in building the income aggregate.

Total individual income in tax records is comprised of four components: wages, mixed income; capital income and undistributed corporate profits. Whenever we observe both self-declared and third-party information for a given income source, we take the maximum value available. Wages are computed as the maximum value observed for each taxpayer between the third-party withholding information and the self-declared PIT (when individuals file the yearly declaration). It is adjusted to include additional components (13th and 14th salaries) that are non-taxable up to certain amounts. Mixed income is similarly obtained by comparing self-declared values in PIT forms and third-party withholding, and taking the maximum value.<sup>10</sup> Income from capital is defined as the sum of dividends, interests and rental income, all available through third-party withholding forms.<sup>11</sup> Finally, we assign to each individual-

> 9As we document in Table A.1, in the period 2004-2013
Green Revolution and the<br>Productivity Paradox: Evidence from<br>the Indian Punjab|Rinku Murgai<br>|November 1999|M. Fernandez<br>33766|
|WPS2235 Beyond Capital Ideals: Restoring<br>Banking Stability|Gerard Caprio Jr.<br>Patrick Honohan|November 1999|A Yaptenco<br>38526|
|WPS2236 Valuing Water for Chinese Industries<br>A Marginal Productivity Assessment|: Hua Wang<br>Somik Lall|November 1999|R. Yazigi<br>37176|
|WPS2237 Reforming Tax Systems: The World<br>Bank Record in the 1990s|Luca Barbone<br>Arindam Das-Gupta<br>Luc De Wulf<br>Anna Hansson|November 1999|L. Barbone<br>32556|
|WPS2238 A Review of the Role and Impact of<br>Export Processing Zones|Dorsati Madani|November 1999|L. Tabada<br>36896|
|WPS2239 The EU Factor in the Trade Policies<br>of Central European Countries|Bartlomiej Kaminski|November 1999|L. Tabada<br>36896|
By education level||||
|Elementary school or less|420|0.20<br>(0.40)|0.13<br>(0.33)|
|||0.17|0.07|
|Middle school|763|(0.36)|(0.26)|
|High school or higher|232|0.14<br>(0.35)|0.07<br>(0.27)|

a This includes long-term laid-offs (those that were working off-farm in September 2008 and not in April 2009) and re-hirees (those that were working off-farm in September 2008, was laid-off, but, were reemployed off-farm by April 2009). b This includes only long-term laid-offs. c Standard deviation in the parentheses.

Data source: Authors’ own data (CNRS dataset).

29
In order to answer these two questions might be useful to use georeferenced data, in particular looking at night-time light data as well as data on spatial and temporal incidence of droughts.

As shown in Figure 16, night-time light emission – a metric often used to proxy for economic activity - collapsed after the start of the conflict in 2011 and kept declining till 2016. In the following years, the rebound that followed the phase of conflict de-escalation was short lived, with emission declining again starting in 2019, the year of the financial crisis in Lebanon.<sup>35</sup> Interestingly, a drop in night-time light emissions is also visible in 2008-2009, corresponding to the severe drought that affected the country prior to the conflict start. Other drought periods affected agriculture production in 2014, 2016 and 2018 possibly driving additional decline in economic activity, as captured by night-time light emissions. More recently, drought conditions affected northeastern regions of Syria in 2021 and 2022.

Overall, evidence emerging from satellite data seems to suggest that – should a distribution neutral approach be used to nowcast poverty in Syria – using projections based on GDP in current prices deflated by the CPI is likely to provide a more accurate picture of poverty dynamics compared to GDP in constant prices which would provide monotonic poverty trends over the entire conflict period.

<!-- Start of picture text --> Figure 16: Trends in night-time light emissions<br>80000 conflict<br>70000<br>60000<br>50000<br>40000<br>30000<br>20000<br>10000<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>NTL NLT - Adj<br>Note: Night-lights data series shows in orange adjusted for intensity variations emanating from gas and oil flares.<br>Source: World Bank estimates based on Chen et al. (2021)<br>Night time light intensity<br
poverty rates do not directly impact aid distribution conditional on all other characteristics including the livelihood zone dummies, their inclusion in the MVAC model and their positive unconditional correlation with aid distribution rates suggest an important role. Thus, in addition to the weather shocks, we base our categorization on whether 2010 district poverty rates were lower than 50 percent vs. greater than or equal to 50 percent. Specifically, we create four mutually exclusive categories when shocks occur and four mutually exclusive categories when shocks do not occur:

# <u>Concerning areas that received a weather shock:</u>

1. Correct inclusion, poor district is defined as a dichotomous variable equal to 1 when aid distribution rates are greater than median distribution rates (.93), and the 2010 district poverty rate is greater than 50 percent.

2. Incorrect exclusion, non-poor district is defined as a dichotomous variable equal to 1 when aid distribution rates are less than median distribution rates, and the 2010 district poverty rate is less than or equal to 50 percent.

3. Correct inclusion, non-poor district is defined as a dichotomous equal to 1 when aid distribution rates are greater than median distribution rates, and the district poverty rate is less than or equal to 50 percent.

4. Incorrect exclusion, poor district is defined as a dichotomous variable equal to 1 when aid distribution rates are less than overall median distribution rates, and the 2010 district poverty rate is greater than 50 percent.

We expect that poor households located in areas that experienced a shock, where aid distribution rates are relatively high, and in districts with high poverty rates would be most likely to access food aid. We expect households in the other three categories would be less likely to receive aid, especially in non-poor districts.

23