rafmacalaba/data-use-mentions-extended huggingface dataset:
It is clear from the above results that DB participants are a highly selected sub-sample. The Appendix gives the frequency distribution of the probit’s predicted probabilities (“propensity scores”) according to whether the sampled household participated in DB. We find that the sample of non-participants is heavily skewed toward zero probability of participating in DB. There are clearly a great many households in the sample who have negligibly low probabilities of participating in the program. However, there is a region of common support, in that there are at least some non-participants with similar propensity scores to all the participants.

# **4. Behavioral responses**

One cannot assess a programs’ targeting performance and impacts on poverty without taking a position on the behavioral responses to the program that influence the net income gains. However, assessing behavioral responses to a program such as DB without longitudinal (panel) data is difficult.<sup>9</sup> With only a single cross-sectional survey it is hard to be confident in the results, given the likelihood of omitted variables correlated with both program placement and the behaviors of interest. However, it is still worth seeing whether there are indications in our data of behavioral responses to the program. The key thing we are looking for is any sign that the program had an impact on the incomes of participants net of the transfers they received.

We use two approaches that can at least throw some light on whether there are likely to be significant behavioral responses relevant to our later assessment of targeting performance and impacts on poverty. First we estimate the marginal tax rate, to see if this is high enough to warrant concerns about behavioral impacts. Then we use a non-experimental evaluation method, which estimates impacts against a matched comparison group.

> 9 And even with longitudinal data there can be severe identification problems; for further discussion see Ravallion (2005b, sections 7 and 8).

10
<!-- Start of picture text --> Figure 6: Average S&P ratings at launch across governing laws<br>BBB (dollar-denominated central government bonds)<br>Rating<br>U.K. Law U.S. Law<br>BBB‐<br>BB+<br>BB<br>BB‐<br>B+<br>B<br><!-- End of picture text --> Source: Authors’ calculations based on Dealogic data.

<!-- Start of picture text --> Figure 7: Average maturity across governing laws (dollar-<br>20 Number of denominated central government bonds )<br>U.K. Law U.S. Law<br>years<br>18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br><!-- End of picture text --> Source: Authors’ calculations based on Dealogic data.

8
on the demographic eligibility criteria. Given the rigorous verification systems tied to the ISAS framework, any attempts at getting undue assistance via false declarations of the households, or subjective decisions by the SASF staff can be thwarted. All households that fit the demographic criteria – according to their digital information in the ISAS system – are deemed eligible for the program, notified of their eligibility status via SMS messages, and assigned a debit card (Kızılay Card), through which they can access their assistance.

All applicant households are then checked against meeting the eligibility criteria on a recurring monthly basis. If ISAS identifies that the household no longer meets the eligibility criteria (for instance, because of a dependent turning the age of 18, or the expiration of a disability report), the case is removed from the ESSN program. Conversely, households that were deemed ineligible at the time of their first application to the program, can become eligible at a later date through monthly digital eligibility checks (for instance if they register a new dependent such as a newborn baby on official MERNIS records, or obtain a disability report from the designated public hospitals, which get updated on their ISAS records). SASF social workers are required to make household visits to eligible applicants within a year of application to verify application information and assess living conditions. During the household visits, if a household is found to be not eligible for transfer, e.g. due to undeclared asset ownership, they are removed from the program.

After an initial pilot of the ESSN in two districts of Ankara and Sivas provinces in October 2016, the ESSN was launched nationwide in November 2016. Following the launch, sensitization activities took place through the use of printed materials at SASFs, PDMM offices, TRC Offices, and national and international NGOs; billboards; the ESSN website and Facebook page; and national television and local radio. To act as a feedback mechanism, a call center was set up to serve as a helpline free of charge – providing information on the application process
# **1. Introduction**

The international financial integration of the East Asia and Pacific (EAP) region has captured significant attention in recent years. Part of this interest comes from the fact that EAP has been the region with the highest GDP growth in the world in the past decades, accounting for 29 percent of the world GDP in 2015, up from about 21 percent in 1990. As it captured a larger share of the world’s income, became more developed, and deregulated its financial markets, EAP has been expected to play an increasingly larger role in international financial transactions along with the _North_ (G7 members, excluding Japan, and 15 other Western European economies). But EAP is not alone. The increasing participation of EAP in the global economy is part of a broader trend that includes the _South_ (non-EAP and non-North economies). Since the early 1990s, both EAP and the South have been growing faster than the North and, by now, they account for an important fraction of the world’s economic and financial activity (Hanson, 2012; World Bank, 2011, 2013, 2017; UNCTAD, 2013, 2015; United Nations, 2014; WTO, 2014; de la Torre et al., 2015; Broner et al., 2016).

As part of the interest in EAP’s role in the world economy, one strand of the literature has concentrated on documenting the international financial integration patterns of the region. A natural way to do it is by analyzing the geographical composition of its investments, which requires crossborder bilateral data.<sup>1</sup> Several papers focus on the comparison of EAP’s global and regional financial integration using gravity models (Eichengreen and Park, 2005a; Lee, 2008; Kim et al., 2008; GarciaHerrero et al., 2009; Borensztein and Loungani, 2011; Pongsaparn and Unteroberdoerster, 2011; Lee et al., 2013; Ananchotikul et al., 2015). This literature typically finds
.6|35.9|13.6|1.8|1.9|1.6|
|**Out of labor force**|13.3|17.8|1.7|1.7|1.5|17.2|16.4|1.8|1.7|1.6|
|**TOTAL**|**14.3**|**16.5**|**1.6**|**1.7**|**1.5**|**19.1**|**14.5**|**1.8**|**1.8**|**1.6**|

Source: Authors’ estimates from Fedesarrollo (2008, 2009) FSLS.

In addition to questions about their valuation and beneficiary status, individuals are also asked to report their knowledge of public social programs. Valuation is high on average—close to four out of five possible points—a finding common across all social programs, with little difference across occupational status (Table 4). Nor are large differences found between social security (health care and pension, whether contributive, non-contributive or private) and other programs of social protection and assistance. These results are consistent across individuals reporting to know either a lot or little about the programs they are asked to value and whether or not they are beneficiaries of such programs (results not shown).

11
of intense conflict. We examine also the overall impact of the 25 years of conflict on educational outcomes. This analysis allows us to consider the full long-term impact of the conflict on educational outcomes of different generations of children in Timor Leste.

# **_4.2.1. The educational impact of the 1999 wave of violence_**

We exploit variation in the number of killings over time and across districts to identify conflict affected individuals. Our intention here is to analyze whether individuals exposed to the violence during their primary school age show different primary school completion rates eight years after the end of the war, relative to those not affected by the conflict. The outcome variable in which we are interested is whether individuals completed primary school in 2007.

Figure 7 shows average primary school attainment for all individuals in our sample. The graph shows an increasing trend in primary school completion across cohorts and a progressive reduction of the gender gap. The gap among the younger cohort (those born after 1987) is almost zero. The drop in the curve for the younger cohort confirms the presence of significant delays in school attendance.

For the purpose of this analysis, we use the TLSS 2007 dataset and the HRVD dataset contained in the CAVR data publication. As explained in section 4, the HRVD dataset contains data on the number of human rights violations occurred since the start of the conflict in 1975 until its end in 1999 for each district. The types of violations recorded are killings, deaths due to deprivation and disappearances. We use only the number of killings to identify years and districts affected by the conflict. We exclude the deaths due to deprivation because the districts in which this occurred may very likely not be those where the conflict was most intense, but were simply places were the victims were hiding as a consequence of escaping from the troops, and died for starvation. In addition, since killings are less likely to affect entire families than deaths due to deprivation, there is a lower underreporting bias attached to the former measure relative
well-structured network of services to deliver the care needed for HIV/AIDS. There are preventive, diagnostic and treatment services (including those for opportunistic diseases), as well as palliative and support services. On the one hand, the Study found no evidence indicating that new or different services need to be developed to meet the needs of patients with HIV/AIDS in Colombia. On the other hand, the current network of services could be complemented with programs and interventions for targeting the most at risk groups as identified by research at the local level.

> 50 MPS (Febrero 2011) Encuesta Nacional de Demografía y Salud 2010, Bogotá: MPS.

48
Finally, Alami estimated a dynamic error correction model for the dollarization ratio. The results showed that demand for foreign currency deposits varies with changes in the interest rate differential and not the exchange rate, which implies that the reason behind holding foreign currency denominated deposits is related to portfolio shifts and not to currency substitution, i.e., Egyptian residents substitute the currency in an attempt to preserve the value of money rather than using it as a medium of exchange. The empirical evidence in this paper suggested that foreign currency denominated deposits will have no significance when studying money demand as a monetary instrument.

Handy (1999) estimated a standard money demand function to measure the impact of inflation on money demand in Egypt using annual data during the period 1970/71–1996/97. Nominal monetary aggregate was regressed on the Consumer Price Index (CPI), real gross domestic product index, inflation rate, average interest rate measured in local currency, and an equivalent offshore interest rate using the augmented ARDL procedure. To check the stability of the money demand function, the monetary aggregate was identified first as currency in circulation, then as domestic liquidity, excluding foreign currency deposits, and finally, as total liquidity. The results indicated that: (1) money demand in Egypt is strongly related to price level, real output, and inflation; (2) in the second half of 1980s, there was a tendency towards dollarization that took the form of foreign currency deposits; and (3) the two forms of money defined as currency in circulation and total liquidity were robust over the period. Thus, targeting money demand should not be regarded when setting monetary measures in Egypt.

El-Sheikh (2002) estimated demand function of M1 using quarterly data covering the period 1960– 1973 using the maximum likelihood technique. M1 was regressed on the expected real income, seasonal income variations, a set of relevant yields, and a set of institutional, distributional, and war variables. The following variables were found to be significant in determining M1
and only 22% immovable property, financial institutions are reluctant to accept movable property as collateral. Banks heavily prefer land and real estate as collateral (suitable collateral).<sup>11</sup> This dichotomy is clearly present in the MENA region, where the gap is the lack of reliable frameworks for financial institutions to accept more movable property as collateral.

> 4 See World Bank Group Enterprise Surveys

> 5 Based on the latest datasets available on www.enterprisesurveys.org on the date of access (February 10, 2010)

> 6 Enterprise Surveys, 2007

> 7 Enterprise Surveys, 2006

> 8 Enterprise Surveys, 2003

> 9 See ―Secured Transactions Systems and Collateral Registries‖, Chapter 1:  Economic Rationale, World Bank Group, January 2010

> 10 See ―Reforming Collateral Laws to Expand Access to Finance‖ Fleisig, Safavian, De La Pena, 2006.

> 11 ―Reforming Collateral Laws to Expand Access to Finance‖ Fleisig, Safavian, De La Pena, 2006.

4
Second, countries that rapidly accumulate a factor see their production and export structures systematically shift towards industries that use that factor intensively.

In the second strand of trade literature, Harrigan (1997) was the first to empirically test the factor proportions theory assuming that technological differences across countries are Hicks-neutral and industry specific. He uses manufacturing output shares in GDP and factor endowment data for 10 developed countries across 7 industries (Food, Apparel, Paper, Chemicals, Glass, Metals and Machinery) for the period 1970-1988. The results are roughly consistent with those presented by Leamer (1984, who uses net exports as the proxy for the dependent variable) and Harrigan (1995) and suggest that capital and mediumeducated workers are generally associated with larger manufacturing output shares, while non-residential construction and highly educated workers lead to lower output shares.

The seminal paper by Harrigan (1997) improves substantially upon previous empirical work, but his data shows little cross-country variation as high-income OECD countries have similar factor endowments and sectoral output shares. Harrigan and Zakrajsek (2000) overcome this drawback and exploit the cross-country variation by expanding the sample to include 28 OECD and non-OECD countries and 12 industries over a longer period (19701992). Their evidence is broadly consistent with the neoclassical theory in that human and physical capital abundance raise output in the heavy industrial sectors, while physical capital lowers output in food and apparel-textiles.

In a similar vein, Fitzgerald and Hallak (2004) use a cross-section of 21 OECD countries in 1988 to estimate the effects of factor endowment on the pattern of manufacturing specialization, but allow factor accumulation to respond to productivity. Their results suggest that the failure to control for productivity differences across countries produces biased estimates of the Rybczynski coefficients. Their model generates robust results that explain two-thirds of the observed differences in the pattern of specialization between the poorest and richest OECD countries. Using a similar approach, Redding (2002) concludes that in the short run, common cross-country effects such as technological progress are more important in ex4
br>commodity’s<br>share<br>of exports|0.6|-1.6|-2.2|+|
|Real effective exchange rate index|8.9|11.7|2.8|-|
|Labor tax and contributions (% of<br>profit)|9.0|15.9|6.9|-|
|Access to electricity|3.9|4.3|0.4|+|
|Gross primary enrollment rate|1.5|1.2|-0.3|-|
|Life expectancy|1.3|0.9|-0.4|-|
|Control of corruption|-7.8|2.1|9.9|+|
|Government effectiveness|16.2|3.1|-13.1|<br>-|

Source: Authors’ calculation based on data from different sources as described in the text.

> 13 <u>https://unctadstat.unctad.org/FR/Index.html</u>

> 14 <u>https://www.doingbusiness.org/</u>

19
in equation (18) it is possible to proxy for the impacts of flood mitigation investments without developing and calibrating a fully-fledged urban economics model and with only a limited set of data. The data needed to inform equation (18) are the construction function parameter , the real interest rate , the depreciation rate of structures (commonly assumed to be 0.01), the flood depreciation rates, bb i.e. the percentage of damages that floods inflict on structures with and without resilience investments ii ρρ which can be retrieved as in this paper through the use of before and after flood maps combined with flood depth - damage curve functions, and finally the distribution of population in urban areas at the grid 24
in total expenditure. Data on the value of bilateral imports by industry are sourced from the ITPD-E database, which includes information on domestic sales. By considering the relationship between procurement laws and total trade (whose cross-border procurement component represents a small part), we aim to include the effects of laws on imports by domestic firms that win procurement contracts. In the appendix, we also report the results of the empirical analysis performed with data from the TiVA inter-country input-output (ICIO) tables, which permit to separate out estimates of cross-border government procurement flows from the rest of trade flows. Because of data limitations (such as the lack of time variation in the law data), as well as the lack of an empirical strategy to identify causal effects, we interpret our empirical results as suggestive of the association (or lack thereof) between protectionism in the law and trade flows.

In the main cross-country regression specification, the outcome variable equals the value of imports from all countries, relative to total spending. We estimate the association between the import share in aggregate expenditure and indicators of protectionism in procurement law across countries, controlling for the influence of participation in the WTO GPA and EU, as well as other country-level variables, such as economic development and institutional quality. The results suggest that differences in the presence of protectionist laws correlate negatively with variation in trade openness across countries. The magnitudes of the implied relationship are substantial. We find that the import share in total expenditure is 9 percentage points lower in countries that have protectionist laws in government procurement (as classified by our three indicators) relative to a country with average trade openness. The results apply to most industries, even if they are stronger for services. When we estimate the association for 2
we use the following probit estimation to estimate attritioncorrected sampling weights: Equation (5) includes the enumeration area herder-related violent events, a state-time fixed effect and the full list of time-interacted co-variates as described above. From equation (5) we calculate a predicted probability of survival (attrit=0) for each individual and time period. We then average these probabilities and invert them to produce inverse probability weights (IPWs). We use these weights throughout our analysis of panel-level data, which provide a correction for potential attrition bias by up-weighting those individuals with a higher probability of attriting but who remain in the sample. The mean value for our IPW is 3.2, with a standard deviation of 1.8, a minimum value of 1.3, and a maximum value 12.8. This adjustment relies on the assumption that attrition is random, conditional on the factors included in equation (5). We include individual-level fixed effects in all specifications to adjust for any unobserved time-invariant factors that could affect selection into exposure to herder-related violence.

> 34Note then, as well, that the interaction terms give a cleaner cohort for comparison to determine the marginal effect of recent violence (and as such in the spirit of recent papers, such as Wooldridge (2021), to isolate relevant treatment cohorts to address the problem of contaminated effects in two-way fixed effects regressions (Baker _et al._ , 2022; Goodman-Bacon, 2021; De Chaisemartin and D’Haultfœuille, 2020b)).

> 35As noted by the International Crisis Group in a 2018 report.

15
the “usual” status. And in the Nepal Living Standards Survey (LSS), household activities—such as fetching water, collecting firewood and dung, and making mats—are counted as work; but to remain consistent with other data sources, we did not include such types of “extended work “as employment. We categorized each respondent as employed, unemployed, or out of the labor force based on standard definitions, matched as closely as possible to the questionnaire of each census or survey.

Another key definitional difference between statistical instruments is the reference or recall period to which the questions pertain. For example, the Pakistan LFS uses a recall period of “one week” as well as “one year” when asking respondents whether they are employed, but the HIES in the same country has a recall period of “one month” in some years and “one week” in others. In Sri Lanka, the HIES in earlier years did not specify a concrete recall period when inquiring about employment, instead asking whether the respondent is “usually” engaged in an economic activity. The LFS in Sri Lanka, on the other hand, focuses on a “one week” reference period. The Indian NSS considers a weekly and a yearly status: the first one is constructed based on questions about time use over-half days; the second one is identified based on a specific question to the respondent.

Because of these differences, comparing employment figures across sources without standardizing them can be misleading, and relying on published data alone is potentially confusing. Therefore, we aimed to standardize labor indicators to allow for valid comparisons. Given the breadth of data available, we use 8