rafmacalaba/data-use-mentions-extended huggingface dataset:
living abroad between 2017 and 2021.<sup>19</sup>

# **4.4 Validity of the exclusion restriction**

The exclusion restriction requires that Foreigners Share _m,_ 1990 affects outcomes solely through its influence on forced displacement outflows, not through any independent time-varying channel correlated with the instrument. We offer several exercises to support this assumption.

**Parallel trends between municipalities with different foreign settlement shares.** We examine whether municipalities with high and low shares of foreign settlements in 1990 followed similar trajectories in the outcomes of interest prior to the oil price shock and the intensification of international sanctions, which marked the onset (after 2013) and intensification (2017), respectively, of the exodus. Figures 6 and 7 show that pre-treatment trends are statistically indistinguishable across municipalities in both pre-crisis windows (before 2013), supporting the validity of the identifying assumption. The divergence in outcomes observed from 2014 onward is therefore attributable to the differential forced displacement that municipalities with a larger historical Colombian presence experienced in response to both shocks rather than to pre-existing differences in economic trajectories.

**Effects are not driven by differential oil production trends.** A potential concern is that municipalities with higher foreign settlement shares in 1990 also had differential exposure to oil production, which could independently drive outcome divergence from 2014 onward through channels unrelated to forced displacement. We address this by controlling for the location of oil fields interacted with time trends. The results are unchanged (Table E.1), confirming that the outcome divergence observed from 2014 onward reflects differential forced displacement rather than differential oil sector exposure.<sup>20</sup>

> 19Although ENCOVI allows validation of the imputed outflows measure, the survey covers too few municipalities to support a more rigorous econometric analysis.

> 20Oil field geographic variation comes from the Global Oil and Gas Features Database, which standardizes and integrates information on global oil and gas infrastructure from over 380 sources worldwide, encompassing

25
CPI covers 244 for price information. The Laspeyres formula computes urban and rural CPIs using weights from the Household Integrated Income and Consumption Survey (HIICS 2015/16).<sup>2</sup> These regional CPI are then used to compile the national CPI. That is, the weighted geometric mean is computed to obtain the national CPI, using the proportion of urban and rural consumption received through HIICS 2015/16 as their respective weights. It is important to mention that the commodity groups in the CPI are consistent with COICOP categories. The issue, however, is that this CPI assumes the same weight of a COICOP category for all households (See Table A1 in the Appendix). For instance, the national CPI assumes that each household spends 34.58 percent of its budget on food and non-alcoholic beverages. The urban CPI assumes this to be 40.87 percent for all urban households while the rural CPI assume this to be 30.42 percent for all rural households. In reality, however, and we will see in the next section, the weights for these commodity groups (or COICOP categories) vary across income groups.

The fact that commodity groups in the CPI are consistent with COICOP categories allows us to combine the two datasets at the commodity groups level (COICOP categories) to estimate the household-level, and subsequently decile-wise, inflation rates in Pakistan. The household-specific inflation rate for each household, _i_ , is calculated as follows: ii ππ

> 2 The weights for commodity groups obtained from HIICS are different for rural and urban CPIs as shown in Table A1 in the appendix.

5
Hirabayashi, Yukiko, Roobavannan Mahendran, Sujan Koirala, Lisako Konoshima, Dai Yamazaki, Satoshi Watanabe, Hyungjun Kim, and Shinjiro Kanae. 2013. “Global flood risk under climate change.” _Nature Climate Change,_ 3 (9): 816–21. https://doi.org/10.1038/nclimate1911.

Hourcade, Jean Charles, and Franck Nadaud. 2009. “Le prix du pétrole, les prix des carburants et nous: un regard rétrospectif.” _Collection Eclairages_ R2DS N°3 (39873): nc.

INSEE ‐ DGFiP ‐ Cnaf ‐ Cnav ‐ CCMSA. 2015. “Fichier Localisé Social et Fiscal (FiLoSoFi).” Jean-Baptiste, Nathalie, Veronica Olivotto, Emma Porio, Wilbard Kombe, and Antonia Yulo-Loyzaga. 2018. “Housing and Informal Settlements.” In _Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network_ , edited by C Rosenzweig, W Solecki, P Romero-Lankao, S Mehrotra, S Dhakal, and S Ali Ibrahim, 399–440. New York: Cambridge University Press.

Jongman, Brenden, Philip J Ward, and Jeroen C J H Aerts. 2012. “Global exposure to river and coastal flooding: long term trends and changes.” _Global Environmental Change,_ 22 (4): 823–35. <u>https://doi.org/10.1016/j.gloenvcha.2012.07.004.</u> Michel-Kerjan, Erwann, and Howard Kunreuther. 2011. “Redesigning flood insurance.” _Science,_ 333 (6041): 408–9. https://doi.org/10.1126/science.1202616.

Mills, Edwin S. 1967. “An aggregative model of resource allocation in a metropolitan area.” _The American Economic Review,_ 57 (2): 197–210.

Muth, Richard F. 1969. _Cities and Housing; the Spatial Pattern of Urban Residential Land Use._ Chicago: University
Networks have increasingly been used to insure against covariate shocks as mobile money and higher rates of rural to urban migration have increased the geographic reach of any network (Yang and Choi, 2007; Jack, Ray and Suri, 2013; Blumenstock, Eagle, and Fafchamps, 2016; Meghir et al., 2022; Apeti, 2023). In Kenya, mobile money has strengthened risk-sharing to the point that consumption is fully insured against shocks—because of increasing the number of transfers and the diversity of senders (Jack and Suri, 2014). Similar findings were found for floods in Mozambique (Batista and Vicente, 2023) and for violence in Kenya (Morawczynski and Pickens, 2009). In Tanzania, consumption is fully protected from small village-level rainfall shocks for those who have mobile money (Riley, 2018) and households are protected from falling into poverty and reducing investments in human capital (Abiona and Foureaux Koppensteiner, 2022). As is discussed in more detail in the next section, data on the use of mobile money is not as widespread as access to a bank account. We thus use access to a bank account to indicate whether households have access to financial services to smooth consumption in the face of a shock.

There are dimensions of vulnerability that although very important are hard to measure consistently across countries so are difficult to include in a global measure. For example, race, ethnicity and political exclusions matter, all else equal, in determining the impact of a disaster on a household and how well a household is supported and can cope with the losses. However, defining these for a global measure is challenging even though these could be included in a country level analysis.

# 4.2 DATA USED AND METHOD FOR OVERLAYING

Many of the indicators come from the same data source. We start by describing this data and then discuss the other data sources used.

## The Global Monitoring Database and the Global Subnational Atlas of Poverty

Our measure of income is based on the per capita income or consumption measure
0.3592|0.4799|0<br>1|
|**Gaining all type of work experiences**|4972|0.0440|0.2052|0|1<br>1896|0.0438|0.2047|0|1|<br>3076|0.0442|0.2056|0<br>1|
|**Understanding the meaning of life**|4972|0.0603|0.2381|0|1<br>1896|0.0533|0.2246|0|1|<br>3076|0.0647|0.2460|0<br>1|
|**Gain self-esteem**|4972|0.1185|0.3232|0|1<br>1896|0.1028|0.3038|0|1|<br>3076|0.1281|0.3342|0<br>1|
|**Living in rural areas**|4972|0.4055|0.4910|0|1<br>1896|0.5527|0.4973|0|1|<br>3076|0.3147|0.4645|0<br>1|
|**Living under the poverty line**|4972|0.1961|0.3971|0|1<br>1896|0.1065|0.3086|0|1|<br>3076|0.2513|0.4338|0<br>1|

_Note:_<sup>_1_</sup> _For the definition of actual work experience adopted see the data section. Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._ Table A2. Descriptive statistics by <u>gender</u>

||||**All**<br>|||||**Women*
*<br>**Close to the**<br>**target**|**Far from the**<br>**target**|
|---|---|---|
|**Average GDP per capita growth (1990-2009)**|2.4<br>1.8|1.2|
|_Source:_Authors’ calculations based on data from the World Developme<br>**Country Policy and Institutional Assessment Index (2009)**|nt Indicators database.<br>3.7<br>3.5|3.3|

_Source:_ Authors’ calculations based on data from the World Development Indicators database. _Note:_ The pairwise correlation between average GDP per capita growth and the CPIA index is 0.32 (significant at 0.01 level). GDP per capita, purchasing power parity constant 2005 international dollars. A country is ―close to the target‖ if its distance to getting on target (that is, its gap of trajectory) is smaller than the average gap of all lagging countries. Otherwise, it is ―far from the target‖ (that is, its distance is greater than the average gap).

**_Both factors--initial conditions and subsequent growth and policy--also point to why the MDGs are such big challenges for the poorest and most fragile countries._** The world’s 79 poorest countries serviced by the World Bank’s International Development Association (IDA) have a threshold per capita gross national income of $1,165 for fiscal year 2011, with average per capita growth and recent institutional performances well below average.<sup>14</sup> Half the IDA

> 14 Average GDP per capita growth in IDA countries (1990–2009) is 1.36, a point below average growth in non-IDA countries (2.38). The CPIA index in 2009 is,
text --> <!-- Start of picture text --> Log subsidy amount (among recipients & controlling for<br>productivity level)<br><!-- End of picture text --> Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019.

Note: The sample includes all firms. Employment is the log of the number of employees. Wage level is the log of average wage bill, where average wage bill is (real) labor cost divided by number of employees. Assets per worker is (real) total assets divided by the number of employees. Labor productivity is (real) revenue per worker. Employment growth is calculated as the difference between the number of employees in year t and t-1 divided by the average number of employees in year t and t-1. Wage growth is the difference between wage level in year t and t-1 divided by the average wage level in year t and t-1. Total asset growth (used as a proxy for investment) is calculated as the difference between (real) total assets in year t and t-1 divided by the average of total assets in year t and t-1. Labor productivity growth is calculated as the difference between (real) revenue per worker in year t and t-1 divided by the average revenue per worker in year t and t-1. Estimates in graph (c) are odds ratios indicating for the probability of receiving a subsidy based on a logit model with a dummy variable taking the value one if the firm reported a positive subsidy amount. The estimates in (d) are in logarithmic terms with the dependent variable being the log of the subsidy amount the firm reported as received. SOE variables are dummy variables taking the value one if national or subnational governments have ownership stake of 10-100% in the firm. The reference group is private-owned enterprises (POEs), defined as firms where national or subnational governments own 0-9.9%. All models include 2-digit NACE industry and year-fixed effects. Standard errors clustered at
corresponds to the case depicted in Figure F1.IU in section 2.3 above. An inspection of Figure F1.IU shows that the returns to education must be smaller for the curve with higher ( _φ_<sup> ̃</sup><sup>_j_</sup> _unexpected_<sup>_′′_</sup> _inmath_ ) and ( _q_<sup>_j_</sup> ). Since the returns to education is smaller for the sons (see Table T4.I), the higher curve must refer to the sons, implying that the parents have systematically higher ability estimates for the sons, and/or the girls face substantial hurdles in the schools. It is important to note that these conclusions hold irrespective of the value of _δ_ 5.

The above analysis of the mechanisms of gender bias in the observed educational investment in urban India is based on the assumption that the parents are aware of the fact that returns to education are, in fact, higher for the daughters. One might wonder what are the implications of the alternative assumption that, despite the evidence, the parents believe that the returns from education are lower for daughters. If _R_<sup>_cd_</sup> _< R_<sup>_cs_</sup> , then an

> non-market sources are even higher. Thus the expected total returns for women are likely to be higher than that suggested by the estimates of labor market returns.

> 25Most of the estimates of returns to education in India are based on NSS data, which do not provide years of schooling information, only the highest completed level (primary, secondary etc).

21
health workforce data, which are the result of concerted efforts by the WHO to gather cross‐ national data on workforce numbers since 1990. Because the demand model requires rich historical data on health worker densities, separate models for nurses/midwife and all other health professionals could not be estimated; data for these cadres are insufficient to produce demand projections. Thus, we first predicted number of physicians from the demand model, and then apply constant ratios to obtain estimates of nurses/midwives and all other health workers (AOWs) to obtain the total projected number of health workers.

In most health systems, spending on health workforce wages and benefits represents a significant share of total health expenditures (Hernandez et al 2006). Previous studies indicate that overall economic growth as measured by national income is the best predictor of health expenditures, from which the demand for health workers is derived (Cooper et al. 2003; Getzen 1990). In other words, spending on health care tends to increase as overall income increases, which in turn suggests that more workers can be employed to deliver health services (Cooper et al. 2003; Scheffler et al. 2008).

To our knowledge, few have previously projected future health workforce labor market demand. Owing to data requirements, early works largely focus on specific developed countries for which data on health workers are more readily available (e.g. Korch et al. 2012, Basu & Gupta 2004). Leveraging efforts to obtain cross‐national and longitudinal data on health workers, Scheffler et al. (2008) were the first to forecast the demand, need, and supply of physicians for 158 countries with suitable data. While notable in the scope of global coverage, their resulting model relied on only one model parameter input—gross national income—to generate projections.

Our demand model expands on previous methods developed by Scheffler et al. (2008). In addition to income (i.e. gross domestic product (GDP) per capita), we include measures
0.11|
|**Access to electricity**<br>**% of population with access**|62|NA|86|**91**|48|31|
|**Improved Water**<br>**% of population with access**|81|94|91|**88**|82|67|
|**Improved Sanitation**<br>**% of population with access**|62|90|78|**83**|55|33|

_Source:_ World Bank Development Indicators

**Table 2: Infrastructure endowments in MENA by country grouping (averages 2005-2008)**

|**Sector**|**OIC**|**OEC**|**GCC**|
|---|---|---|---|
|**Density of paved road network**<br>**Km / 1,000 km2 of arable land**|3,220|618|16,907|
|**Telephone Density**||||
|**Fixed and mobile subscribers per**|535|538|1,351|
|**1,000 people**||||
|**Electricity generating capacity**<br>**Million KWh per million people**|0.3|0.4|2.9|
|**Access to electricity**<br>**% of population with access**|98|85|98|
|**Improved Water**<br>**% of population with access**|93|79|97|
|**Improved Sanitation **<br>**% of population with access**|84|81|99|

_Source:_ World Bank Development Indicators The infrastructure gap challenge is
by socio-cultural norms that often discourage female entrepreneurship. Additionally, women’s participation in high-growth sectors remains low, further limiting their economic opportunities (The African Development Bank Group, 2021). In summary, while Cˆote d’Ivoire has made strides in increasing female labor force participation and reducing vulnerable employment among women, significant gender disparities persist, particularly in business ownership. Future policy interventions should aim to address these gaps to foster a more inclusive and equitable labor market.

## **2.2 TVET In Cˆote d’Ivoire**

According to the UNESCO Institute for Statistics (UIS) database, only 5% of secondary education students are enrolled in vocational programs. At the same time, individuals with low

> 4World Bank Gender Data Portal.

4
Appendix A provides a detailed description of all the variables. Appendix B provides key country-level statistics. Shareholder Rights are generally highest for English legal origin countries such as India, Pakistan and South Africa and lowest for French legal origins countries that include Mexico and Venezuela. Market development as measured by market capitalization/GDP is highest for South Africa, Malaysia, Taiwan and Chile. Korea and Taiwan are some of the largest and most liquid markets. There is not much variation in settlement proficiency with most countries receiving the highest possible score of three. However, there is considerable variation in transactions cost efficiency with only four countries receiving the highest score of three. Appendix C provides correlation measures of various firm-level characteristics. In the following section we describe how our principal dependent variables are constructed.

- _4.1 Dependent Variables - Measurement of Allocation Choice_

One of the primary goals of this research is to examine factors that determine how the total investment in a firm is divided between that firm’s ADR and its underlying security. Thus we need to construct meaningful measures of what constitutes this relative _“over or under weight”_ in the ADR relative to the underlying. Our primary measure for this allocation choice is “ _ADR Differential”._ This is constructed using the Morningstar database described above. For each firm _i_ , we first identify all funds that hold an investment in that firm. This investment can be either in ADRs, in the underlying domestic security or in both. For illustration let us assume that there are _k_ funds that hold an investment in firm _i._ To estimate the relative weighting of investments in ADR versus the domestic security for this firm we follow a two step process. In the first step, for each fund _k_ we calculate the total dollar amount of investment (sum of investment in ADR and underlying) in firm _i,_ that a fund holds. We 15
- e.g., the United States frequently applies two different TTB policies, such as an antidumping duty and a countervailing duty, to the same product and trading partner at the same time. The last columns indicate that, on average over 1995-2013, the US had 4.9 percent of imported products covered by an imposed TTB in any year, and the maximum coverage was at 6.8 percent in 2012. Finally, the mean share of imported products in a year that were subject to a new US TTB investigation - and that could potentially lead to new import restrictions - was 0.9 percent. The maximum share of imports subject to new TTB investigations was 3.9 percent of products in 2001, when the US initiated a wide-ranging safeguard investigation over steel.

Is there a pattern to which countries tend to utilize TTBs as import protection? To summarize Bown (2011b), which also provides more detail on the underlying methodology, using more recent data from the World Bank’s Temporary Trade Barriers Database (Bown, 2014a). Additional detail on the database is provided in the Data Appendix. Our analysis below is not comprehensive, however, and there are at least three other important aspects of TTB use that we mention briefly here. First, and as we describe elsewhere in this chapter, these policies are applied not only as ad valorem duties, but also frequently as specific duties, price undertakings, quotas, and tariff rate quotas. Second, our import coverage ratio measures do not address potentially significant variation in the restrictiveness of the policies; here we only note that even when these policies are imposed as ad valorem import duties, they are sometimes set at prohibitive levels of greater than 100, 500, or 800 percent. Third, while the ‘stock’ measures introduced below are affected by the duration of the imposed policy - recall that WTO rules for each of them are that they are supposed to be ‘temporary’ (i.e., duration of less than 3-5 years
includes the educational variable. We draw from the subnational INFORM Risk Index to measure a municipality’s exposure to natural hazards. We rely on administrative data on homicides to proxy for human hazards. The estimates are very similar to the ones relying on self-reported risk exposure.

**Lastly, column 5 reports results for a model specification which pools several survey waves.** More specifically, we pool data for the years 2016-2019 and estimate our model relying on the pooled data set. We control for year fixed effects and exclude the educational variable. This specification demonstrates that vulnerability is much higher when pooling several survey waves. The higher vulnerability rate is not surprising, given our findings on decreasing vulnerability over time. Importantly, it is not possible to compare vulnerability estimates from pooled data to the one relying on one single year.

_Table 4: Diffrent model specifications and estimates of the total vulnerability rate_

|**Model Specification**|**Baseline**|**Robustness**<br>**Test 1**|**Robustness**<br>**Test 2**|**Robustness**<br>**Test 3**|**Robustness**<br>**Test 4**|
|---|---|---|---|---|---|
|All|0.237|0.295|0.294|0.234|0.410|
|Rural|0.412|0.492|0.498|0.413|0.627|
|Urban|0.135|0.164|0.172|0.139|0.265|
|_Educational variable_|_Yes_|_No_|_No_|_Yes_|_No_|
|_Self-reported hazard exposure_|_Yes_|_Yes_|_No_|_No_|_Yes_|
|_Survey rounds_|_2019_|_2019_|_2019_|_2019_|_2016-2019_|

_Note: The