rafmacalaba/data-use-mentions-extended huggingface dataset:
to deal with the COVID-19 pandemic, such as face masks and ventilators, and for vaccines and vaccine inputs. After merging these data with bilateral product-level trade data, we measure the impact of trade policies on trade margins. We disentangle the effect of trade measures (restricting and facilitating) on medical products using monthly data for a span of time from January 2018 (prior to the pandemic, when no such measures were used) up until December 2021. We also look into product, time and geographic heterogeneity and provide a general picture of the impact on trade-cost of trade policies motivated by scarcity nationalism during COVID-19. In doing so, we separate between the direct effects of trade policy on trade costs and the indirect effects, as these measures affect the cost of trade in intermediate goods.

Interest in the effects of trade protectionism has increased in recent years as tensions in the world trade system have emerged. Our work relates to the recent literature on the economic effects of trade policy (Goldberg and Pavcnik, 2016; Handley et al., 2020; Amiti et al., 2019; Fajgelbaum et al., 2020). Differently from these studies, the focus in this paper is not on the consequences of the re-emergence of tariffs but on the broader set of trade policy measures that have targeted medical products during the COVID-19 pandemic. Moreover, we study both the direct effects of trade policy on trade costs and its indirect effects through the intermediate trade channel.

# **2 Data**

We use data from three sources. First, we construct binary indicators reflecting whether a country utilizes import or export policy measures or not. With import and export measures that can be restricting or facilitating, there are four indicators. The source of these data is the Global Trade Alert (GTA), and the data vary by country _j_ , product _k_ , and month _t_ . We use them at monthly frequency between January 2018 and December 2021, and we employ export measures in a way so that they are on the exports
FEWS NET. 2018. “Yemen Food Security Alert.” Famine Early Warning System, Sana’a, Yemen, June.

Garg, T., C. Barrett, M. Gomez, E. Lentz, and W. Violette. 2013. “Market Prices and Food Aid Local and Regional Procurement and Distribution: A Multi-Country Analysis.” _World Development_ 49, 19-29 Harou, A., J. Upton, E. Lentz, C. Barrett, and M. Gomez. 2013. “Tradeoffs of Synergies? Assessing Local and Regional Food Aid Procurement through Case Studies in Burkina Faso and Guatemala.” _World Development_ 49, 44-57.

IPC. 2016. ”Yemen: Acute Food Insecurity Situation, June - September 2016.” Integrated Food Security Phase Classification. Report, Food Security Cluster, Humanitarian Response, Yemen. – IPC. 2017. “IPC Map of Yemen: March July, 2017.” Integrated Food Security Phase Classification. Report, Food Security Cluster, Humanitarian Response, Yemen. – IPC. 2018. “IPC Map of Yemen: December 2018 January 2019.” Integrated Food Security Phase Classification Report, Food Security Cluster, Humanitarian Response, Yemen.

IPC. 2019. ”Yemen: Acute Food Insecurity July - September 2019.” Integrated Food Security Phase Classification Report, Food Security Cluster, Humanitarian Response, Yemen.

Jensen, R., and N. Miller. 2011. ”Do Consumer Price Subsidies Really Improve Nutrition?” _The Review of Economics and Statistics_ 93 (4), 1205-1223.

Kurdi, S., Y, Ghorpadhe, and H. Ibrahim. 2019. “The Cash for Nutrition Intervention: Evidence from Yemen.” Working Paper, IFPRI, Washington, D.C.. http://www.ifpri.org/publication/cashnutrition-intervention-yemen-impact-evaluation-study.

Maxwell, D., A. Khalif, P. Hailey, and F. Checchi. 2020. “Viewpoint: Determining Famine: Multi-dimensional Analysis for the Twenty-First Century.” _Food Policy_ 92
r2_w|0.349|0.386|||||||
|r2_b|0.036|0.003|||||||
|r2_o|0.012|0.022|||||||
|rho|0.763|0.823|||||||

Notes: This table reports panel regressions with individual firm fixed effects for the El Salvador COV-ES data. Size information used as the interaction terms is taken from the total number of full-time equivalent workers in the baseline ES. Columns 3 through 8 report marginal effects from logistic regressions. Sampling weights from the ES are used. Standard errors are shown in parenthesis. All standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 6 for more details.

53
matching are fairly robust relative to changes in the variables included in the propensity score model, to differences in the source of data used and to variation in the number of neighbors used.<sup>10</sup> The estimated impact for adult women and young males on earnings is around US $20$25 / month. Rate of return estimates, obtained under alternative assumptions on the discount rate, show that the program would have a positive return only if the benefits are fairly longlasting (9 years or more).

_ProJoven program in Peru_ : Ñopo, Robles and Saavedra (2007) analyze the impact of a Peruvian youth labor training program, called _ProJoven_ , on female and male youth living in urban areas. The program provided classroom training and internships lasting about three months for youths from poor families. Trainees received stipends during their training period, with mothers of young children receiving a double stipend. More than 20,000 youth participated in the program. An interesting feature of the program was that one of its explicit goals was to train female youth for traditionally male occupations so as to reduce gender segregation in the labor force.

Ñopo et al.‟s evaluation of the program is based on a sample of beneficiaries and a sample of matched controls selected on the basis of gender, age, geographic proximity, poverty status, income, schooling, number of children and employment status. These individuals were administered a baseline survey and three follow-up surveys at 3, 6 and 18 months. The impact estimates are derived from a somewhat unusual two-stage matching procedure that first selects for each treated individual three matched controls on the basis of similarity in hourly wages. The motivation for this two-stage procedure is to closely align the treatment and comparison groups in terms of pre-program earnings so as to account for the so-called “Ashenfelter Dip” problem, namely, that program participants often exhibit a pre-program dip in their earnings that is not seen in comparison group data. The two-stage matching procedure generates a similar preprogram earnings dip pattern in both
page/education_management_information_system_emis; http://hrmis.fde.gov.pk/;</u> 12 The rates used for each province are available online at https://www.pbs.gov.pk/content/final-results-census-2017- <u>0, Table 1. These are 2.4 for Pakistan overall, 2.13 percent for Punjab, 2.89 percent for KP, 2.41 for Sindh, and 3.37</u> for Balochistan. While newer population growth rates are available from the 2023 census, age-wise population estimates are not yet published. Hence these newer growth rates were not used.

4
<!-- Start of picture text --> °<br>Virtuous Data Cycle Gana<br>Investment Impact<br>Decisions/<br>Accountability<br>- 3%<br>Q<br>eS _ 43‘<br>Effectiveness = _ Value Added<br>&<br>~ Sh?<br>Innovation Adapted from OECD, based on PARIS21 and Open Data Watch<br><!-- End of picture text -->
individual pay records. In the case of Uruguay, the BPS data determine individual access to pension, health, and other benefits. Hence, it is in the worker’s interest that all information is accurately reported by the firm. Moreover, the data are thoroughly checked by government officials. In the case of Brazil, wages are also directly extracted from company records and wage information is requested to the level of centavos (cents). On average, 75 percent of our wage observations include the cents paid.

Goette et al. (2007) note that their estimator may encounter identification problems when inflation is very low, since in that case, DNWR and DRWR are very close in the wage change distribution and are therefore hard to distinguish. We are rarely constrained by this issue and are therefore generally able to obtain accurate estimates because, even in the periods with lowest inflation in Brazil and Uruguay, the inflation rate was relatively high with respect to OECD countries previously studied. Also, and in contrast with previous studies, the rate of inflation observed during the period of study in both Uruguay and Brazil is fairly volatile. It is for this reason that we separately estimate our model year by year. This allows for a flexible specification, wherein both, the parameters of the notional and the different rigidity regimes may vary across years. A potential limitation of our analysis is that it does not consider the possibility of symmetric rigidities; for instance, those associated with the menu costs of changing wages. Our assumption is justified by the analysis of Dickens et al. (2008), who examines individual wage change distributions among job stayers in 17 OECD countries and finds no evidence of menu costs in wage setting. Given the inflation history of the two countries we are studying and the high volatility of inflation during the sample period we believe that menu costs are even less likely to be relevant in our case.

# 4 Main Results

## 4.1 Empirical Implementation

In this section, we discuss some relevant details regarding the
the distribution of monthly inspection rates in 2010 for firms that did and did not adopt the IHC program by the end of 2013. The mean inspection rate for IHC firms is lower than for non-IHC firms, and IHC inspection rates are much lower in the upper half of the distribution. For inspections it is not the case that the distribution for IHC firms is strictly below that of non-IHC firms. Many non-IHC firms are substantially smaller than IHC firms, and so many would import few shipments in a given month. Thus it is possible that even with relatively higher frequency of inspections for non-IHC firms there could be months in which those firms’ relatively few shipments were not stopped for inspection. This explains the relatively large share of firmmonth combinations with an inspection rate of zero among non-IHC firms.

**<u>Table 4.  Monthly Inspection Rates in 2010 for IHC and Non-IHC firms</u>**

|||Distribution<br>|of firm-mo|nth inspection<br>|rates in 2010<br>|||
|---|---|---|---|---|---|---|---|
||Mean|25<sup>th</sup><br>percentile|Median|75<sup>th</sup><br>percentile|90<sup>th</sup><br>percentile|Standard<br>deviation|Number of<br>firm-month<br>combinations|
|IHC firm by end of 2013|0.08|0.04|0.07|0.11|0.17|0.08|381|
|Not IHC firm by end of||||||||
|2013|0.20|0|0|0.27|1|0.34|110220|
|All firms|0.20|0|0|0.26
31|8.72|8.37|8.17|8.23|9.04|8.99|5.82|5.8|6.03|4.93|5.46|
|Share of female in T&G (percent)|53.4|59.7|54.8|56.1|57.5|61.0|58.3|56.9|59.5|55.4|72.3|69.3|73.4|72.3|71.1|

_Source:_ Calculations based on the Sri Lankan 2006 Household Income and Expenditure Survey (HIES) and the 1992–2002, 2008, 2011, and 2012 Labor Force Surveys. Hours worked were not reported in the Sri Lanka HIES 2006. _Note:_ T&G = textiles and garments.
zero days, respectively), these regions are still far behind the OECD average.

Progress in granting additional leave days to fathers has been particularly slow in countries in the Middle East and North Africa and in Sub-Saharan Africa, where the average number of leave days granted to fathers in 2021 were two days and 2.7 days, respectively (median of zero days and one day, respectively). Of the 19 countries in the Middle East and North Africa region, parental leave is, according to 2021 data, most generous in Iran at 14 days, followed by the United Arab Emirates at seven days. Ten countries in the region do not have legislation in place granting any leave to fathers upon the birth of a child. The stark difference in leave allocation to mothers and fathers in the Middle East and North Africa region is highlighted in Figure 3 below. This graph shows, sorted by largest to smallest, the leave allocation to mothers in 2021. It also illustrates fathers’ leave day allocation, as a fraction of what mothers receive.
# **Figure 1.3 Real Wages (1989=100)**

<!-- Start of picture text --> 120.0 120.00<br>100.0 100.00<br>80.0 80.00 Armenia<br>Azerbaijan<br>CEE Georgia<br>60.0 CIS non CIS-7 60.00 Kyrgyz Republic<br>CIS-7 Moldova<br>40.0 Tajikistan<br>40.00 Uzbekistan<br>20.0<br>20.00<br>0.0<br>-<br>1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001<br><!-- End of picture text --> Source: Unicef TransMONEE database (2002).

O _pen ILO unemployment_ figures are more scarce and very distant from registered unemployment figures. Proper labor force surveys, which allow for measuring ILO unemployment rates, have been very few during the early 1990s and reliable figures started to emerge only after the mid1990s. These showed that ILO unemployment rates are several folds higher than registered unemployment, in fact close to figures observed for CEE countries during the same period. Such a large gap between registered and open unemployment reinforces the hypothesis that registered unemployment figures reflected only the relative value of being registered which was - and still is - generally very low.

Even ILO figures cannot be said to be a proper measure of unemployment in the CIS-7. The ILO itself in its recommendations recognizes that in particular situations when the labor market is very informal and depressed the formal requirements to define the unemployment status may be relaxed. When people are really poor they are obviously forced to do something with their time even if remuneration is equal or below subsistence. The World Bank poverty assessment for Tajikistan, based on a 1999 household survey, found an unemployment rate of 3% and a poverty rate of 98%. It is
|Philippines|R1: Aug 2020<br>R2: Dec 2020<br>R3: May 2021|Phone survey based on<br>survey firm sample<br>frame; Self-<br>administered web<br>surveys|Survey of Well-<br>being via Instant<br>and Frequent<br>Tracking|9,448|109.6|2,980|
|---|---|---|---|---|---|---|
|PNG|R1: Jun-Jul 2020<br>R2: Dec 2020|Random digit dialing|Wealth index|3,114|8.9|2,347|
|Vietnam|R1: Jun 2020<br>R2: Jul 2020<br>R3: Sep2020|Subsample from<br>representative 2018<br>survey|Consumption<br>aggregate from<br>previous survey|6,213|97.3|2,123|

Two primary data sets from the HFPS are used in the analysis. First, an individual-level data set that includes information on pre-pandemic employment<sup>6</sup> allows examination of how workers were initially impacted, how they have coped and recovered, and who is still left behind. Due to differing sampling methods, characteristics among the sample of respondents vary across countries (Table 2).<sup>7</sup> For example, the Indonesia HFPS only asked about the employment status of the household head or breadwinner. Thus, males are overrepresented in the sample, and the average age is on the higher end among the sample of countries. In Vietnam, information from a previous household survey was used to target the household head as the respondent, while in Lao PDR and Mongolia, the targeted respondent in the first survey round was an adult member of the household who answered the phone or was available to be surveyed. In Myanmar,
c</u>_</sup> The welfare vector for each cluster within an area is created from the model with these covariates, as follows: The dependent variable, _yach_ , is in the log scale. The poverty line in this scenario is fixed at _z_ = 12. From the created “census,” 20% of the observations are sampled in each of the clusters using simple random sampling without replacement;<sup>12</sup> this yields our “survey” data. The generation and sampling processes are repeated _L_ = 10 _,_ 000 times. In each simulation replicate, the following quantities are computed for the poverty rates and gaps in each area:

> 11 Covariates are simulated following the approach from Molina and Rao (2010) and Marhuenda et al. (2017), with slight modifications.

> 12The same units are sampled in every simulation and the values of _x_ 1 to _x_ 7 for all the census units are also kept fixed; this implies that the values of these covariates for the sample units are always the same across simulations.

8
<mark>transfers alleviated negative nutritional impacts (Ecker</mark> _<mark>et al.</mark>_ <mark>2023), and both in cash and in-kind transfers helped households switch to activities with higher return and liquidity needs or risk (</mark> Schwab 2019). In <mark>Mali, exposure to</mark> conflict also reduced use of conventional inputs by farmers, an effect that was partly reversed via cash transfers (Sessou and Henning 2024). Cash transfers fostered investment in profitable endeavors beyond subsistence for men but also affected marital migration of women (Hidrobo _et al._ 2022a).

Earlier studies used satellite data on night lights (Chiovelli et al. 2018), complemented by vegetation indices (Ben Yishay et al. 2024), to assess the impact of landmine clearance on economic activity at a granular level. Ours is to the best of our knowledge the first study that relies on administrative and remotely sensed data jointly to analyze short-term farm-level effects of an intervention in near real time in a setting where an ongoing conflict makes other forms of data collection very challenging to implement. Our ability to identify average effects that were not be detected in a survey as well as heterogeneity of such effects across locations and fam size groups suggests that use of administrative data can usefully complement traditional approaches to data collection in the agriculture sector, as noted also by Yacoubou Djima and Kilic (2024).

In Ukraine’s context, some simple administrative measures such as storing historical registry information and ensuring <mark>applications for and final award of other types of support are recorded in SAR</mark> can help to improve the quality of estimates, and regulation to govern and implement such measures is already under preparation. Improved collection of training data <mark>beyond the country’s five main crops c</mark> ould help to expand the range of outcome variables considered while donors could help ensure that future programs are designed in a way that
borders (Herat and Nimrouz) toward Afghanistan and compares it with our measure of economic shock. We observe that the number of exiting Afghan refugees from Iran increases when the exchange rate sharply increases. This finding sense checks our conclusion from Table 4 that a higher replacement rate of Afghan refugees translates to their higher exit rate from Iran and not only higher mobility within Iran’s borders.

We present estimations of equation (1) for the rest of the transition variables in Appendix Table 3, but we do not find much significant coefficient for the interaction term. This outcome is not surprising for two reasons: First, based on the above discussion, the replacement rate of Afghan refugees in the LFS sample is significantly higher than Iranians, creating a bias because the replaced sample is systematically different from remainders. Second, because of the refreshing rotating panel sample frame by SCI in 2013 and 2018 and the unavailability of the survey for 2020, the transition variables are unknown for three crucial years in the period of study in which economic fluctuation was high.

# **5.2. The unequal effect of economic shocks on expenditure and income**

In Table 5, we estimate the unequal effect of shocks on expenditure, income, and wage rate using HEIS data. The coefficient of the Afghan dummy in panel A of Table 5 shows that, on average, Afghan households spend less and have lower total income than Iranians. However, their earned income is higher than Iranians because of higher labor force participation. In the HEIS sample, while 88% of Afghan households’ heads are in the labor force (employed or unemployed), this number is 70% for Iranian households’ heads. In column (10), we find that the wage rate is also higher for Afghan households, consistent with the anecdotal evidence that they have more labor productivity in unskilled jobs than Iranians.

The effect of economic shocks is negative on expenditure but positive on income and wage rate. Although the positive impact of economic shocks on income and
and Liquidity in Risk Spreads”. _Working paper_ , Philadelphia, PA: The Wharton School

- Stiglitz, Joseph E. & Andrew Weiss (1981). “Credit Rationing in Markets with Imperfect Information”. _American Economic Review_ 71(3) (June): 393–410

- Taylor, John B. & John C. Williams (2009). “A Black Swan in the Money Market”. _American Economic Journal: Macroeconomics_ 1(1) (January): 58–83

- Wu, Ji, Alina C. Luca & Bang Nam Jeon (2011). “Foreign Bank Penetration and the Lending Channel in Emerging Economies: Evidence from Bank-Level Panel Data”. _Journal of International Money and Finance_ 30(6) (October): 1128–1156

23
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||With|individual co|ntrols|With individ|ual + househo|ld controls|With individ|ual + househo<br>controls|ld + regional|
||Earner|LFP in last<br>30 days|Worked in<br>last 30 days|Earner|LFP in last<br>30 days|Worked in<br>last 30 days|Earner|LFP in last<br>30 days|Worked in<br>last 30 days|
|Log of HH annual expenditure per capita||||-0.013|0.025|-0.037|-0.008|0.030|-0.034|
|||||(0.055)|(0.049)|(0.055)|(0.057)|(0.049)|(0.056)|
|HH living standard index||||0.031*|0.055***|0.043**|0.031*|0.057***|0.047**|
|||||(0.018)|(0.018)|(0.018)|(0.018)|(0.018)|(0.018)|
|Number of children aged 6 to 14 years||||0.049*|0.028|0.037|0.059**|0.039|0.048|
||
temporary abode of general purchasing power and a stable function of few variables (Friedman 1969). In this sense the monetary base may be as good a definition as MI or M3.

These problems led James Lothian (1976) to focus on the demand for monetary base: _"The solution that Ipropose... is to return to a narrower definition of money, highpowered money alone. The rationale is that since high-powered money is of relatively constant quality over time and space.. specification errors are likely to be less important for high-powered money than for deposit-inclusive totals.... The demandfor highpowered money should be more stable than the demandfor other monetary aggregates.... I test this proposition by analyzing the demandfunctions for high-powered money andfor various other monetary assets estimated across an international time-section sample spanning forty countries in the postwar period... Ifind that high-powered money is unambiguously the most stable total across the countries in my sample. "_ According to Lothian (1976), the obvious implication of his findings in international studies in which the stability of the demand for money is a key assumption -for example in the monetary approach to the balance of payments or to exchange rate determinationis that a simpler definition of money might be very fruitful Moreover, we have found in this paper that real high-powered money is a key factor in the debt sustainability exercise. According to Lothian's analysis, the real base money would be a stable function of a few variables. The issue has not been settled and recently 15