rafmacalaba/data-use-mentions-extended huggingface dataset:
extended impacts of the pandemic have made additional fiscal stimulus necessary (see Appendix A) to support vulnerable groups and maintain economic activities.

The paper examines the redistributive impact of taxes and public spending, following the Commitment to Equity (CEQ) framework (Lustig 2018). The framework aims to provide a comprehensive picture of the redistributive effects of fiscal policies on household income and consumption. Here the CEQ framework is used to assess Grenada’s fiscal policies, focusing on pre and post fiscal incomes in order to determine the net beneficiaries and payers of these policies (Lustig 2020). For the Grenada context, a fiscal incidence analysis through the CEQ framework is relevant, because it can provide evidence concerning the extent to which the government’s fiscal policies are impacting the welfare of the country’s households and inform future policy decisions.

This paper makes multiple contributions to the existing literature. First, we assess the fiscal system of an SIDS, Grenada, which faces resource constraints, natural hazards, and a high poverty rate. These characteristics make it important to determine whether the country’s fiscal system has a redistributive impact. Furthermore, Grenada is located in a region where fiscal incidence is understudied. Previous work on Caribbean islands using the CEQ methodology has focused on bigger and more developed islands such as Jamaica (Katayama et al. 2021),

> 1 Before the COVID-19 pandemic, Grenada was making a strong commitment to economic reform and resilience building with the support of international organizations. It achieved an average annual growth of 4.5 percent between 2014 and 2019, surpassing the regional average. Fiscal reforms, including the Fiscal Responsible Act (FRA) of 2015, led to a significant reduction in public debt and the accumulation of a fiscal surplus. Nevertheless, Grenada’s economy remains less diversified and highly susceptible to climate change and natural disasters.

> 2 MFMOD Database, the World Bank’s World Development Indicators (WDI), and GEM databases, IMF. Most sources available in https://data
government to build cheap and affordable houses for rural migrants who have been living in shacks at the periphery of cities for years).<sup>35</sup>

# _3.2.4) Consequences of migration for the economy as a whole_

Some empirical studies have been more ambitious and have tried to measure the national changes in production, income and inequality resulting from internal migration. The issue at stake is whether migration allows workers to move to areas where they are more productive and if efficiency gains occur at the cost of rising inequality. From an applied point of view, only structural models of internal migration or calibrated computable general equilibrium models can pretend to shed some light on this complex issue.

Several works have integrated internal migration in their assessment of trade barriers reduction. Taylor, Yunez-Naude and Dyer (1999) investigate the impacts of NAFTA on rural employment and wages and rural out-migration in Mexico following a reduction in the government support price of corn. This is done using a village-town model which blends microeconomic household farm models with economywide modeling.<sup>36</sup> The model is estimated on survey data from households located in towns and surrounding villages in the Michoacan province. The authors find only a small impact of trade liberalization. Zhai and Wang (2002) simulate the effects of a reduction in trade barriers on rural-urban migration and urban unemployment in China. Their simulations show that there is a great need to co-ordinate trade, labor and internal migration policies. In the context of trade liberalization measures (a gradual reduction of import tariffs), if ruralurban migration controls are relaxed (increasing labor mobility) without a labor-market

> 35 It should be noted, however, that _accommodationist_ policies conceal an important ambiguity. In South Africa, there is a tension between preventing ‘land invasions’ (informal settlements appearing on vacant land within the city) and helping informal settlers ameliorate their housing conditions. Pakistan facing a problem of squatter settlements, was confronted with a similar tension between legalizing such settlements and evicting them
, moving average imputation based on past values results in biases when used for real-time monitoring. When prices are rising, a moving average imputation of _a_ 6 would be based on an average of lower prices and a real-time monitoring system using moving average imputation would regularly suggest false trend reversals. These simple interpolation approaches are also highly influenced by outliers. Interpolating linearly from _b_ 2 to _b_ 6, through outlier _b_<sup>_∗_</sup> 4<sup>,replicates</sup> information into two new outliers _b_<sup>_∗_</sup> 3<sup>and</sup><sup>_b∗_</sup> 5<sup>.</sup> More flexible spline methods are known to introduce spurious cycles in time series data and may result in explosive extrapolation due to their quadratic or higher order nature, particularly in the presence of outliers.

> 12Optimality in a Kullback-Leibler or Root-Mean-Square sense is only under restrictive specification assumptions which require tedious design effort and diagnosing. Generally, Kalman filtering estimates time varying states and uses current state estimates and observations to estimate future states. When observations are missing, the imputation will rely fully on the transition equation. This means that the dynamic properties deteriorate rapidly during periods of large data gaps. This may in part be tackled by considering multivariate state space approaches so that realistic predictions for one sequence can be made by utilizing recent information from another sequence. Fast multivariate filtering implementations that could be deployed at scale are for example explored by Koopman and Durbin (2000). Due to outliers, and general non-Gaussian behavior, non-Gaussian outlier models may need to be considered. The univariate context is for example explored by Shephard (1994); Koopman et al. (2019). The above methods all rely on restrictive linearity assumptions. Nonlinear filtering is explored by Wan and Van Der Merwe (2000). Deciding between approaches is complicated by the variability in quality and quantity of data, the patterns of missingness,
resources but do not seem to be able to translate this over income into reductions in the violence that higher bargaining power they face.

Table 2 provides summary statistics of the nature of the work done by women who participate in the labor force. Seventy-eight percent of women who are working work in the garment industry. These workers typically work long hours, an average of 11.78 hours versus 8.34 hours for workers. The and standard per day per day nongarment average deviation of wages is similar for garment and other workers. The average wage of 3000 taka month is 36 dollars US and twice the minimum per approximately approximately at the time of the of 1662 taka month. The worker wage survey per typical garment has been working for a total of 3.9 years and the typical non-garment worker has been working for 5.9.

10
the liberalization of trade on demand elasticities.<sup>Using monthly Brazilian manufacturing</sup> survey data and broadly following Slaughter,<sup>Paes de Barros, Corseuil and Gonzaga.</sup> (1999) regress a series of sequentially generated<sup>coefficients on both the own wage and</sup> lagged employment on aggregate total trade<sup>(exports + imports) over GDP. They find no</sup> impact of trade openness on either. Using aggregate<sup>manufacturing data from Uruguay at</sup> the two digit level, Cassoni, Allen and Labadie<sup>(1999) find a negative impact of sectoral</sup> total trade/GDP on the lagged employment<sup>coefficient. While implying more rapid</sup> adjustment, it also leads to a lower total own<sup>wage elasticity, the opposite of that</sup> predicted.

This paper first asks whether, theoretically,<sup>it is obvious that increased trade</sup> should increase own wage elasticities. Though<sup>a complete model of all the predicted</sup> effects is intractable, we do point out where<sup>the link between the two may not be tight.</sup> The paper then uses establishment level data<sup>to provide consistent dynamic estimates of</sup> labor demand functions for three Latin American<sup>countries across trade policy regimes.</sup> Estimation is done following Arellano and Bond's<sup>GMM in differences approach. 2</sup>

# II. **Labor Demand and Trade Liberalization**

Trade reform might affect own wage elasticities<sup>through at least three channels:</sup> substitutability of inputs, product demand elasticities,<sup>and the degree of collusion in the</sup>

> 2 Micro- panel data offer more precise estimates<sup>of demand functions, allow
# **Social Tariffs**

4.9 A number of Argentina provinces, and most recently the Metropolitan Area, have experimented with the introduction of various forms of social tariffs. These amount to some 10 cases in all. A brief description of the mechanics of each of these schemes is provided in Table A3 of the Appendix but most of them identify beneficiaries based on a range of characteristics such as income, housing, location, and assets.

4.10 The OPSM survey included a special question designed to identify the beneficiaries of these schemes, and found that barely 2% of households surveyed claimed to be beneficiaries. This result is consistent with what is known about the relatively small scope of these schemes, which are estimated to reach no more than 100,000 households nationwide. The small number of observations that appear in the survey do not make it possible to evaluate the incidence of these schemes directly. However, what can be done is to simulate the application of the eligibility criteria for each scheme to the sample of the population from the corresponding jurisdiction, in order to assess the extent to which these are successful in targeting resources towards the poor. This simulation proves to be rather artificial, given that most of these programs use household income as an eligibility criteria, and that this is accurately observed in the survey, but may not be so accurately measured by these schemes in practice. Consequently, the results obtained should be regarded as an optimistic upper bound on the targeting performance of these social tariff programs.

4.11 The results indicate that the eligibility criteria used in the social tariff schemes appear to be quite effective at targeting resources towards the poor. In particular, the criteria used in the northeastern and northwestern provinces of Argentina (namely, Chaco, Formosa, Salta y Tucumán) perform particularly well. Assuming that they are able to measure household income as precisely as is done in the survey, the simulation suggests that 70% to 90% of the beneficiaries belong to the bottom income quintile (Figure
the share of the item consumed from home production; and (5) the storability or perishability of the item.

Because most modules record consumption information on up to 58 individual food items, we construct the item-specific incidence and value error for 54 items relative to the incidence and value recorded in the personal diary benchmark.<sup>16</sup> This analysis is not possible for the 7-day collapsed list module (module 4) because that module lacks sufficient disaggregated information. In addition, the 7-day subset list (module 3) only contains item-specific information for the 17 individual items listed and consequently is also removed from subsequent analysis.

For the remaining five modules, figure 1 depicts the consumption incidence error (with respect to the benchmark) as a function of the commonality of the item (as measured by the item incidence recorded in the benchmark) for each of the 54 food items. A fitted linear regression line is also included in the modulespecific plots. Differential patterns in incidence error across modules are readily apparent. The incidence error of modules 1 and 2, while greater in magnitude for less commonly consumed items (especially for those items with a benchmark frequency at less than 0.2), shows no directed relation with the commonality of the consumed item. The fitted regression line with a slope close to zero confirms this lack of a linear relation. In contrast, the usual month module 5 reveals a decidedly negative relationship between consumption incidence and incidence error; the least commonly consumed items in the benchmark report a high positive incidence error. Clearly, the usual month exercise leads to a gross overstatement of

> 16 The long list contains 58 food times. Because of possible confusion in the diary entries among (a) paddy rice and husked rice, (b) maize grain, maize cob, and maize flour, and (c) millet grain and millet flour, these categories are combined for this item-specific analysis, resulting in a total of 54 commodities available for analysis.

13
industry, country, and year fixed effects. Standard errors are clustered at the country level. *** significant at 1%, ** at 5%, * at 10%.

# **3.3 Robustness Checks**

Our estimations illustrate gender gaps in access to credit. This section offers some robustness checks for our key results.

# **3.3.1 Considering endogeneity**

Estimating the causal effects of the gender of the top manager on a firm’s access to finance and performance is challenging in a cross-country setting. We try to address endogeneity concerns, albeit partially, by relying on information on the gender of firms’ owners available in WBES. The identifying assumption is that the gender composition of firm ownership does not directly impact decisions on access to credit in formal firms with five plus employees, and that its effect on firm

> 24To ease interpretation, we estimate separate models for more and less traditional countries instead of triple interactions.

16
is crucial. Collecting data on time spent in childcare in standard surveys can be complex, as it is often a secondary activity conducted with other domestic or paid work, and as a result underreported. The new Women’s Empowerment in Agriculture Index (WEAI) survey module includes more detail in the interviewer-guided introduction about time spent with children to lower the risk of being masked by other activities (Seymour, 2018); other approaches such as activity tracking devices or phone-based apps (Kilic, 2018; Daum, 2018) could help in recording which activities are conducted together/simultaneously, with less recall error.

Figure 6. Uganda National Panel Survey, 2013-14: hours spent across paid and unpaid non-market activities in the last week <!-- Start of picture text --> Rural Urban<br><!-- End of picture text -->

# Summary and ways forward

By narrowing the definition of employment as well as highlighting the need to collect data on individuals’ total work burdens, the 19<sup>th</sup> ICLS motivates the need for additional data on labor underutilization, as well as ways that data on unpaid work could be better incorporated in socioeconomic surveys. This has important implications for rural women. Box 4 summarizes areas for future work.

## Box 4.  Summary and ways forward: Labor underutilization and time use

- Labor underutilization: Including questions on desire to work, along with reasons for wanting/not wanting to work, is important, to accompany questions on working hours, availability, and search.

- Unpaid work burdens: in developing a refined set of unpaid work categories for socioeconomic surveys, key areas to cover for rural women include market activity in agriculture (significant even among own-use producers), as well as care work. Exploring newer, more cost-effective and direct approaches (including using mobile and other technology) to collect these data could possibly shed greater insight on their working day and secondary activities as well.

- Proxy reporting also has a significant
tactless payment technology to the first and second containment shocks in response to the COVID19 epidemic in France using a proprietary data set on card-sales transactions from a representative sample of 94,199 merchants of all banks in France.<sup>2</sup> Surprisingly, I find that the lockdown due to the COVID-19 outbreak promote card sales growth of merchants with the contactless payment technology, especially small businesses, and new entrepreneurs. This result suggests that merchants with contactless payment technology are more resilient to epidemic shocks where barrier gestures is required than those still not using contactless payment technology.

By 2020, France was affected by the COVID-19 epidemic. In order to stop the spread of the virus in France, the government imposed the first containment from mid-March to mid-May 2020. In addition, in mid-May 2020, French banks increased the limit for contactless payment from 30 euros to 50 euros in order to reduce physical contact and promote the use of contactless cards instead of contact cards and cash. Following a significant increase in coronavirus contamination and deaths in October 2020, the French authorities decided again to impose a second containment. I use these three staggered shocks to study the resilience of merchants using contactless payment technology and their effects on merchant sales as well as substitution between payment methods.

I estimate a distributed lag model using the shocks as the exogenous event and obtain the impulse response of card sales. My empirical identification strategy relies on the differential benefits of the contactless payment convenience across merchants with similar characteristics. Utilizing all card and mobile payments for 17,376 matched offline merchants from a set of essential sectors during a two-year period of 2019:01 to 2020:12, I document a significant spillover effect in merchant’s sales through card payment. My findings are summarized as follows. First, compared to merchants without the latest digital payment technology, merchants with contactless payment technology experienced an average of 8.3 percent (10.2 percent) more monthly card-sales amount (count) in the 10-month period following the first containment in France
We tease out the impact of the BLAs on migration after imposing a series of fixed effects to control for the various confounds. Our most stringent, and preferred, specification controls for corridor (origin country _×_ destination country) fixed effects, origin _×_ decade, and destination _×_ decade fixed effects. That is, if country _A_ signs a BLA with country _B_ , the identifying variation comes from comparing migration within the _AB_ corridor before and after the signage (corridor fixed effects); from comparing migration from country _B_ to country _A_ relative to all potential destinations for migrants in country _B_ (origin-time fixed effects); and from comparing migration to country _A_ from country _B_ relative to all potential origin countries for migrants to country _A_ (destination-time fixed effects).

These extensive sets of fixed effects ensure that some corridor-specific factors (e.g., historic bilateral relationships between two countries), or country-time-specific factors that can affect migrant labor demand or supply (such as economic cycles or the labor market situation in origin or destination countries) do not drive the relationship and that the impact can be attributed to the BLA itself. One of the key assumptions for the identification of the impact of the BLAs is that of parallel trends. We employ an event-study design to check for any pre-trends on migration prior to the signing of the BLAs (in addition to using this to explore persistence of effects).<sup>2</sup> However, as migration data is measured every ten years, the pre-trend test cannot test for anticipatory BLA signing in response to, say, temporary shocks within the decade of signing.

We find that signing a BLA increases migration from the origin country to the destination country by 76 percent (0.57 log points) in the decade of signing the BLA. The migration effect lasts up to three decades after signing a BLA. The impact of BLAs is muted at 25 percent (0.24 log points) for ‘regular’ corridors - corridors with at
# **A.3 Table A3: Data discrepencies.**

|**Indicator**|**Coverage/Source**|**Number of countries**|
|---|---|---|
||Whole country|27|
|**Employment**|Onlyurban|6|
||Unclear/Largelyurban|4|
||Onlyformal|3|
|**Elt**|Allpersons|28|
|**mpoymen**|Unknown<br>Total|7<br>37|
||LFS/HHS|29|
|**Employment**|Establishment survey|8|
||Total|37|
||Industry/Non agriculture|9|
|**Earnings**|All sectors|19|
||Unknown|3|
||Total|31|
||LFS/HHS|7|
||Establishment survey|22|
|**Earnings**|Legislated<br>Total|2<br>31|

Note: Excludes Bosnia and Herzogovina, Montenegro, and Tajikistan due to lack of GDP data.

# **Table A4: Regression results from elasticity regressions**

||**F**|**E**|**PO**|**LS**|**R**|**E**|
|---|---|---|---|---|---|---|
||Pre Crisis|Crisis|Pre Crisis|Crisis|Pre Crisis|Crisis|
|**VARIABLE**<br>**S**|Employmen<br>t Growth|Employmen<br>t Growth|Employmen<br>t Growth|Employmen<br>t Growth|Employmen<br>t Growth|Employmen<br>t Growth|
|**GDPgrowth**|
204|0.107|
|The mean value of the dependent<br>variables before the program|0.201|0.183|9.629|3.693|9.326|1.705|

Note: This table reports district fixed-effect RDD-DD regressions of loans of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

45
_Credit Growth =_ _T_ in our case is 17 since 1991 is the first year for which a growth rate can be computed. _Credit Growth_ is used as a dependent variable that captures banking development. The data on private credit is obtained from the World Bank’s Financial Structure Database, while GDP is from the IMF’s _International Financial Statistics._

# <u>B. Related Lending</u>

Because regulatory authorities in many countries do not require banks to report the percentage of loans made to related parties, and because even those that do have such requirements do not employ uniform definitions of a related party, a key challenge is measuring the extent of related lending across countries. It seems highly likely, however, that related lending is more prevalent in countries where regulators are more tolerant of cross-ownership between banks and nonfinancial firms and where restrictions on the ownership of bank capital by related parties or a single owner are less binding. Indeed, in nineteenth century New England, which is perhaps the quintessential case of the rapid development of a banking system on the basis of related lending, state regulators were quite tolerant of the fact that the owners of the major industrial firms were also the owners of the banks, and that the ownership of bank stock tended to be highly concentrated among a small group of individuals. Indeed, the banks were essentially the treasury arms of family-run manufacturing companies (Lamoreaux 1994). We therefore construct a proxy for the prevalence of related lending drawing on the database of bank regulation and supervision created by Barth, Caprio, and Levine (hereafter BCL, 2000, 2003).<sup>3</sup> Box 1 reports the questions that underlie our proxy. The first two questions refer to the ownership structure of banks (the maximum share that a single entity or individual may own, and the maximum that a related party may own). The second two refer to the degree to which banks are restricted from owning non-financial firms, and vice versa. Survey responses come