rafmacalaba/datause-encoder-data huggingface dataset:
assumed in this paper) the economic variables of interest are (log)-normally distributed. Finally, the Mexican household survey we use to implement our general approach is a rotating panel that follows individual workers for five quarters over time, which means that the panel dimension of our income data is somewhat limited. Thus, our data do not allow us to assess with certainty the persistence of income shocks beyond five quarters. However, a comparison of our estimates of the income risk parameters with existing results that use data sets with a much longer panel dimension suggests that a large fraction of the income shocks we label “persistent” in this paper last indeed for many years (see Section II.5 for more details). In short, the welfare results presented here do not necessarily show that trade liberalization is costly, but they do provide strong evidence that any comprehensive welfare analysis of trade liberalization ought to take into account the cost of increased labor market risk.

In summary, in this paper we articulate a general framework that allows us to study empirically the impact of trade reform on individual income risk and to evaluate the corresponding welfare effects. We use this framework to study the Mexican economy, which, as we have argued above, seems well-suited for such an analysis. In our empirical implementation of this methodology using longitudinal data on Mexican workers, we find economically significant effects of trade policy on income risk.

We conclude this introduction with a brief comment on some of the earlier empirical literature on the relationship between trade policy and factors related to labor market risk. The impact of trade liberalization on short-run worker displacement has been investigated in the well-known papers of Currie and Harrison (1997), Gaston and Trefler (1994), Levinsohn (1999) and Revenga (1997), among others. More recently, in an innovative paper, Trefler (2004) has analyzed the short-run adjustment costs borne by displaced workers simultaneously with the long run benefits (of higher firm productivity and resource allocation) that accrued in the context of the trade agreement between United States and Canada. While 6
# 5. Conclusion

The need to expand the assistance and protection for conflict-affected populations beyond humanitarian aid cannot be overemphasized. The debate on how to expand government supports to IDPs has been ongoing for several years. Although certain frameworks are available in the literature, there is limited evidence of practical application of frameworks in a real-world setting. This paper adds to this ongoing debate and goes beyond framework development to a demonstrated application on this topic in Iraq.

This paper investigated a possible approach to bringing the humanitarian caseload into the government SP programs by exploiting data from a field test. As part of the CLC's recertification and enrollment process in the Northern conflict-affected governorates, a field survey asked the complete set of questions required to construct a full set of variables used in both the humanitarian and government cash transfer targeting models from the same households. It also collected information on perception and willingness to be referred to the government’s program. Just because a household is likely eligible does not necessarily mean it is willing to be referred for assistance from the government. A combination of likely eligibility and willingness will ultimately determine how many potential MoLSA beneficiaries could come from the humanitarian database.

Analyzing the field-test data, this paper reconfirmed the findings from the desk review that a significant number of households that qualify for the humanitarian MPCA assistance are also eligible for the government’s cash transfer program. Among households identified as <mark>extremely vulnerable by</mark> the SocioEconomic Vulnerability Assessment Tool (SEVAT) used by the MPCA since 2019, 40 percent of the beneficiaries would be eligible for the government’s program when the targeting is limited to those below the national poverty line (a medium-small program). The referral rate would increase to <mark>70 percent when the program expansion included everyone in bottom 35 percent nationally or within 1.36 times the poverty line (a large program). Under the same two expansion scenarios, 20 and 48 percent of the extremely vulnerable group identified by the old MPCA targeting tool, Legacy-VA, would be eligible for the referral. Intriguingly, a meaningful share of households that are deemed non-vulnerable and, hence, ineligible for any humanitarian assistance would, in fact, be eligible for government support - 3 and 26 percent among SEVAT non-eligible, and 16 and 44 percent among Legacy-VA non-eligible under the medium-small and large program expansion scenarios. These differences could be due to the fact that the two programs use different base data sets, and have slightly divergent focuses (vulnerability versus monetary poverty). Moreover, the data showed that a significant, one-third, share of h</mark> umanitarian 24
adoption, and an interaction term. Additionally, we controlled for the same variables as in the previous equation, encompassing location, business sector, time dummies, and business category. The equation is presented below.

In the equation: _Ei_ represents businesses exiting the tax net by the end of April 2019; _α_ is a constant. _Wi_ is an indicator of women’s ownership; _TBi,t_ represents the tax burden, calculated as the ratio of tax paid to annual sales; _Xi_ includes control variables for sectors, sub-city, categories, and sales registry machine adoption; _ei_ accounts for the error term, _i_ is an indicator for individual businesses, _t_ is a time dummy/ _β_ measures the percentage point increase/decrease in the probability of women-owned businesses exiting the formal tax system; and _γ_ represents the differential impact of the tax burden on women exiting the tax system.

Table 12 presents the results obtained from a pooled ordinary least square regression using a linear probability model. The data reveals that the probability of women-owned businesses exiting the tax net is around seven percentage points higher than that of men, which stands at 29%. However, the data does not provide evidence that is caused by the the tax burden, as the interaction term between gender and tax burden is not statistically significant.

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Figure 2 presents aid received by refugees in current US dollars.<sup>5</sup> how refugees are assisted become visible. Poor refugees in Jordan receive US$ 529 per year, six to ten times more than those in Niger (US$ 86), Colombia (US$ 58) and Costa Rica (US$ 60).

**Figure 2: Aid received by poor refugees (expressed in US$)**

<!-- Start of picture text --> 600<br>529<br>500<br>400<br>311<br>290<br>300 270<br>194<br>200<br>149<br>86<br>100 58 62<br>0<br>Chad Ethiopia Niger Uganda Kenya Colombia Costa Rica Jordan West Bank<br>& Gaza<br>2.15 3.65 6.85<br>IPL<br>US$<br><!-- End of picture text --> As may be expected, the provision of aid reduces poverty among refugees considerably (see Table 2). The contribution to poverty reduction is largest in Jordan, where refugee poverty reduces by almost half, from 59 to 32 percent, thanks to the provision of aid. Nowhere else is the contribution of aid this significant. In Niger, where aid levels to refugees are low, aid leads to a decline in poverty incidence of just 7 percentage points; while in Costa Rica and Colombia, the effect is even smaller at 2-3 percentage points. Thus, there is a clear relation between the amount of aid spent and the reduction in poverty.

Figure 3 uses this data to show how much it costs, on average per refugee, to reduce refugee poverty by one percentage point. These numbers should be interpreted with caution. Even when aid does not reduce poverty it may be spent well by alleviating the suffering of extremely poor refugees while being insufficient to bring them across the poverty line. With this caveat in mind, Figure 3 demonstrates the large variation that exists in the efficiency with which aid dollars can reduce refugee poverty. In Chad and Niger, US$ 1012 “buys” a percentage point reduction in refugee poverty. In Ethiopia, Colombia, and the West-Bank and Gaza, three times more money is needed for poverty incidence to fall by a percentage point. Interpreted differently, if poverty reduction was the only objective of giving aid to refugees, then a reallocation of aid resources between these economies would likely achieve far greater results.

5 All calculations convert these 2017 PPP dollars in current US dollars using an exchange rate of 1.23 current US dollars for every PPP dollar (January 2024).

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**Table 5. Shared prosperity numbers for three simulation scenarios (negative values) based on EUSILC data**

||||shar|edprosperityind<br>|icator,%<br>||∆<br>|
|---|---|---|---|---|---|---|---|
|countrycode|base|end|negative<br>numbers<br>(1)|zeroes replace<br>negative<br>numbers(2)|excluding<br>negative<br>numbers(3)|change in %,(<br>(3)/(1)‐<br>1))*100|Absolute<br>difference in<br>pp, (3)‐(1)|
|Austria|2006|2011|0.71|0.71|0.71|‐1|‐0.01|
|Belgium|2006|2011|0.20|0.29|0.33|59|0.12|
|Bulgaria|2007|2011|1.40|1.39|1.39|0|0.00|
|Cyprus|2006|2011|‐1.14|‐1.14|‐1.14|0|0.00|
|Czech Republic|2006|2011|1.85|1.85|1.85|0|0.01|
|Denmark|2006|2011|‐0.57|‐0.25|0.13|‐123|0.70|
|Estonia|2006|2011|1.40|1.43|1.51|8|0.11|
|Finland|2006|2011|1.97|1.97|1.98|0|0.00|
|France|2006|2011|3.30|3.24|3.15|‐4|‐0.14|
|Germany|2006|2011|‐6.16|‐6.14|‐5.60|‐9|0.56|
|Greece|2006|2011|‐0.54|‐0.54|‐0.53|‐1|0.00|
|Hungary|2006|2011|‐3.09|‐3.05|‐2.95|‐5|0.15|
|Iceland|2006|2011|‐3.90|‐3.90|‐3.90|0|0.00|
|Ireland|2006|2011|‐0.78|‐0.82|‐0.98|27|‐0.21|
|Italy|2006|2011|0.35|0.37|0.51|47|0.16|
|Latvia|2006|2011|1.07|1.08|1.18|10|0.11|
|Lithuania|2006|2011|‐1.70|‐1.54|‐1.43|‐16|0.27|
|Luxemburg|2006|2011|1.18|1.11|0.92|‐22|‐0.26|
|Netherlands|2006|2011|4.80|4.60|4.37|‐9|‐0.43|
|Norway|2006|2011|5.62|5.61|5.60|0|‐0.02|
|Poland|2006|2011|0.07|0.07|0.07|0|0.00|
|Portugal|2006|2011|1.58|1.48|1.43|‐10|‐0.16|
|Romania|2006|2011|3.40|3.40|3.57|5|0.17|
|Slovakia|2006|2011|8.40|8.40|8.39|0|‐0.01|
|Slovenia|2006|2011|1.47|1.40|1.39|‐5|‐0.08|
|Spain|2006|2011|‐0.81|‐0.73|‐0.57|‐29|0.24|
|Sweden|2006|2011|2.51|2.62|2.70|8|0.20|
|Switzerland|2007|2011|2.04|2.00|1.92|‐6|‐0.11|
|United Kingdom|2006|2011|‐1.27|‐1.10|‐1.02|‐19|0.24|

Source: EUSILC, microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’ calculations.

# **3.3.2 Data with zeroes**

Shares of the population with zero incomes in selected countries from LAC do not exceed 1.4 percent. In order to test the impact of having a population with zero income on shared prosperity we have run two scenarios in this sub‐section: (1) keeping people with zero income; (2) excluding people with zero income per capita. Composite figure 9 shows the annualized growth rates for the bottom 40 percent. The differences in estimates for selected scenarios are reported in percentage points in two sub‐figures in the bottom part of figure 9. We also plot sum of shares of the population with zero income in two periods for all countries.

In absolute terms (though not strictly comparable), the impact of excluding zero income is stronger than the choice of grouped versus microdata, but weaker than the impact of excluding negative income. Thus, the largest gap for excluding zero income is 0.38 percentage points in absolute terms (Brazil) compared to the highest 0.7 percentage points obtained from excluding negative income (table 5) and highest 0.14 percentage points obtained from grouped versus microdata test (figure 5).

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|**Conakry**|196|1,608,238|1,714,000|
|---|---|---|---|
|**Lusaka**|837|1,489,053|2,187,000|
|**Bamako**|478|2,967,235|2,219,000|
|**Kigali**|454|929,723|951,000|
|**Kampala**|834|3,101,666|2,577,000|
|**Cape Town**|2467|2,699,422|4,100,000|
|**Dar es Salaam**|1000|3,673,173|6,048,000|
|**Dakar**|2325|3,065,622|2,978,000|
|**Douala**|288|2,802,368|3,259,000|
|**Harare**|1454|2,273,485|1,500,000|

TABLE 1: FRAMES OF ANALYSIS The data sources used are summarized in the table below.

|**City**|**Population**<br>**Data**|**Opportunity Data**|**Transport Data**|
|---|---|---|---|
|Nairobi|WorldPop|EOA Analysis|Digital Matatus|
|Lusaka|WorldPop|EOA Analysis|WheresMyTransport|
|Kampala|WorldPop|EOA Analysis|WheresMyTransport|
|Cape Town|WorldPop|EOA Analysis|WheresMyTransport|
|Kigali|WorldPop|Rwanda Census|IGC|
|Bamako|WorldPop|Bamako Business<br>Registry|Bamako Mayor’s Office|
|Conakry|WorldPop|EOA Analysis|Plan de Déplacement<br>Urbain (EU, Min.<br>Transports)|
|Dakar|WorldPop|EOA Analysis|SAFEGE|
|Dar es Salaam|WorldPop|Dar Es Salaam Firm<br>Census|Collected by World Bank|
|Harare|WorldPop|EOA Analysis|WheresMyTransport|
|Doula|WorldPop|EOA Analysis|WheresMyTransport|

TABLE 2: DATA SOURCES

# **5 RESULTS**

The table below summarizes the results for each city. Average accessibility is the percentage of estimated employment opportunities throughout the city accessible by the average individual within 60 minutes. The best access level is the highest percentage of jobs accessible from the single best-positioned location.

||**Average Accessibility**<br>**Best access**<br>**level**|**Distribution of**<br>**Accessibility**<br>**‘Gini’**|
|---|---|---|
|**Nairobi**|28.5%<br>67.4%|0.36|
|**Conakry**|29.4%<br>65.1%|0.41|
|**Lusaka**|34.1%<br>86.0%|0.42|
|**Bamako**|34.3%<br>97.5%|0.37|
|**Kigali**|15.2%<br>23.0%|0.26|

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gross outmigration and gross in‐migration are both 100 percent). Gross estimates of migration and residential mobility can also be made for larger cities using microdata from the American Community Survey with a lag of about two years. Better sources are local, but these will require cooperation among government institutions. For example, school registration links students to addresses and can show how families with children are flowing into and out of a city. Use of these data with personal identifiers eliminated is complex but more feasible of school districts participate in the process of planning for economically inclusive cities. Community colleges may also be important partners for developing tracking measures. Relationship‐building with state Departments of Motor Vehicles may also support the development of systems to track resident relocations, again usually using linked but de‐identified personal data. For tracking business startups and relocations, cities can use local business licenses or commercial products such as the National Employment Time Series (NETS) database. Ultimately, understanding and tracking mobility can alert stakeholders to emerging trends that could either support or destabilize efforts to build a more inclusive city economy.

Another key challenge is unavailable, weak, or stale data. This challenge may present another opportunity in disguise. First, it may drive participants in the economic development process to connect with other efforts that have already started to make data‐ driven improvements in other systems. Impressed by the model of Cincinnati’s STRIVE initiative to improve outcomes for children and youth, for example, actors in other cities have begun their own efforts to convene stakeholders from multiple sectors to achieve specific goals for kids according to age‐based milestones. This model—known as collective impact—has since spread to other subject areas including economic and community development. Commitment to these models can lead stakeholders with access to data to share their data with one another or with trusted data intermediary organizations like those participating in the National Neighborhood Indicators Partnership (NNIP). The collection and analysis of data, in these examples, becomes valued because it is embedded and made relevant in a local context. As they grapple with questions of measurement and validity, stakeholders in the process ultimately come to define issues and outcomes more clearly.

37
# Average Prices, Cabbage (December 1991 -- August 1992)

<!-- Start of picture text --> 30-<br>2S- ........................................................ .........-.-.-.--.--.-.---.-.-<br>* IV<br>205-o -e*-Z@--.................................- -----;X^, *--....................... ............ ... .....<br>.. . .. . . .. . l ............... * ..<br>10-, ---------- ......-..-...... Vt ......................... ................... t.........<br>Oi I.I<br>DOIec90 1 /28 02/11 02125 03/31 0414 04i28 05/12 05/02 06/16 06i30 07/14 07/28 08/11 08M25<br>Date<br><!-- End of picture text --> Market Price 0'0' State Price 63
||**Stamp Duty on Leases and Tenancy Agreements**<br>First, calculate the total rent for one year. Then, take<br>away RM 2,400 from that total rent, because the first<br>RM 2,400 is free from stamp duty.<br>For every RM 250:<br>Not exceeding1 year = RM 1<br>1 – 3 years = RM 2<br>**Exceeding 3 years = RM 4.**<sup>**2)**</sup>||||
|---|---|---|---|---|
|**Mexico**|Real property transfer tax is imposed locally in range<br>from 2% to 3% of the purchase price.|||Fonseca,<br>Marcela,<br>et<br>al.,<br>Tax<br>Aspects of Structuring and Disposing<br>of Investments in Mexico, IBFD,<br>November, 2001|
|**New**<br>**Zealand**||**Stamp Duty Abolition (21 May**<br>**21 1999):**<br>Repealed two types of stamp<br>duty:<br>Conveyance duty and lease<br>duty, but still retain cheque<br>duty.<sup>1)</sup>So abolish the old<br>stamp duty as follows:<br>Payable on commercial land<br>and buildings at the rate of:<br>• 1 % on the first $50,000<br>• 1.5% on the second $50,000<br>• 2% on the balance 2)|**Comment on Stamp Duty**<br>**Abolition:**<br>The business community will<br>welcome the abolition. With the<br>removal in the last decade of<br>stamp duty on share transfer,<br>many businesses have retained<br>cumbersome corporate<br>structures in order to retain<br>commercial buildings within<br>special purpose companies.<br>Corporate reconstructions have<br>often forced large corporate<br>groups into complex tax planning<br>initiative in order to avoid fiscal<br>cost simply moving a property<br>between two wholly owned<br>companies. Removal of stamp<br>duty will facilitate a further round<br>of corporate simplification and<br>amalgamations, and reduce<br>compliance costs.<sup>1)</sup>|1)Sawyer, Adrian J., A “Minority<br>Government Budget Mixing Election<br>Year Sweetners With Restraint?”,<br>IBFD, August/September 1999<br>2)http://www.bkrlewbarr.com/taxfacts<br>/nz.htm|
|**Pakistan**|**25 September 2001**<br>Sindh government collects stamp duty on a variety<br>of business activities, property transactions and<br>financial documents. There is a stamp duty schedule<br>and rates are announced in the yearly provincial<br>budgets.||<br>**25 September 2001**<br>The individual businessmen and<br>the business concerns either do<br>not recognize Sindh<br>government's fiscal jurisdiction,<br>or if they do so, they circumvent<br>the payment in collusion with the<br>banks in case of financial<br>documents. Mostly, the officers<br>of the revenue department help<br>the businessmen and companies<br>to evade stamp duties.|http://dawn.com/2001/09/26/ebr8.ht<br>m|
|**Papua New**|A rate of stamp duty of 5% is imposed on|||http://www.bkrlewbarr.com/taxfacts/p|

44
8 television) and the characteristics of the household’s home (e.g. type of floor and roof) as proposed by Filmer and Pritchett (1999, 2001). The 2012 database also expanded the range of the health data: it included indicators of adult health, including noncommunicable disease (NCD) indicators, covering: health status, e.g. arthritis; risky behavior, e.g. smoking; preventive care, e.g. cervical cancer screening; and receipt of curative care, e.g. inpatient admissions. The 2012 database also expanded the range of countries, going from 95 countries in the developing world to 109 countries at all levels of development. Finally, the 2012 database expanded the scope of the exercise from just health equity to health equity and financial protection: the new indicators included covered both catastrophic health expenditures and impoverishing health expenditures (cf. Wagstaff and van Doorslaer 2003).

<u>Figure 1: Evolution of the World Bank’s Data on Health Equity and Financial Protection</u>

|**2000**|**2007**|**2012**|**2018**|
|---|---|---|---|
|42 countries – all<br>developing<br>countries|56 countries – all<br>developing<br>countries|109 countries –<br>incl. some high‐<br>income|193 countries –<br>goal is global<br>coverage|
|42 surveys – all|95 surveys – all|285 surveys –|1,654 surveys –|
|DHS|DHS|DHS, MICS &<br>WHS|DHS, MICS, WHS,<br>LSMS, HBS, etc.|
|34 indicators – all<br>services or<br>outcomes|115 indicators –<br>all services or<br>outcomes|73 indicators –<br>incl. 4 FP<br>indicators|51 indicators –<br>incl. more NCD<br>and FP indicators|
|Focus on equity<br>in MDG<br>indicators|Focus on equity<br>in MDG<br>indicators|Not just MDG<br>indicators – some<br>NCD and FP<br>indicators|Levels of and<br>equity in MDG<br>and SDG<br>indicators, incl.<br>FP|

The 2018 edition of the HEFPI database continues this broadening-out. In addition to the ‘traditional’ MCH and communicable disease indicators from the DHS and MICS, it includes more data on adult NCD indicators, drawing on data from the WHS, the DHS, the Stepwise Approach to Surveillance (STEPS) surveys, and many other regional and national surveys. In addition, the database expands dramatically the number of financial protection datapoints – from 54 to 563. This expansion builds on World Bank research on monitoring progress towards UHC – initially in Latin
conflict indicator, 1 in 3 girls were exposed to the conflict. For ease of interpretation of the analysis results, the continuous conflict exposure indices are converted into binary values.<sup>4</sup> The cut‐off point to identify conflict exposure is the average of the continuous conflict exposure index. Clusters above the mean index (1.93) are categorized as having been exposed to conflict, while clusters below the mean are categorized as not having been exposed to conflict. Using this cut‐off, 33 percent of all clusters were exposed to conflict, most of which are from Rumbek (Figure 7 and Figure 8).

**Figure 7: Density plot of the internal conflict Figure 8: Density plot of the internal conflict**

<!-- Start of picture text --> indicator. indicator per area.<br>0.8 2<br>0.7<br>1.6<br>0.6<br>0.5<br>1.2<br>0.4<br>0.8<br>0.3<br>0.2<br>0.4<br>0.1<br>Conflict affected<br>0 0<br>0 1 2 3 4 5 0 1 2 3 4 5<br>Internal conflict index Cut-off = 1.93 Juba Rumbek Torit Yei<br>Density Density<br><!-- End of picture text -->

# **3.2. External conflict indicator**

We construct an external conflict indicator using data from Armed Conflict Location & Event Data (ACLED) project in addition to a self‐reported conflict index (see 4. Construction of external conflict indicator for more information on this data set). There are two reasons why the self‐reported conflict exposure index might be biased. First, there could have been cases of extreme under‐ or over‐ reporting induced by fear or differences in perception. This pattern of extreme reporting is evident considering the spread of the conflict index. Secondly, only 75 percent gave consent to answering the

> 4 The binary variable is more intuitive for a difference-in-difference approach, so results using a continuous variable are reported in the Appendix.

13