rafmacalaba/data-use-mentions-extended huggingface dataset:
demeaned on a country-by-country basis, which effectively controls for country fixed effects in the regressions.

We also employ another database that is an unbalanced panel with the same cross sectional and time series coverage as before but at the annual frequency. This includes the conditioning variables that are not explicitly required for the identification scheme to be valid in the VAR model but are necessary to estimate the interaction terms. These are government debt and fiscal balances as percentage of GDP which are drawn from the IMF’s WEO (October 2014) database; and government consumption-to-GDP ratios which we obtain from the World Bank’s WDI database.

# 3 Results

## 3.1 Unconditional Multipliers

To establish a benchmark, we first report estimates of the unconditional multiplier from a standard panel SVAR. For that, we suppress the law of motion for the coeffi cients in Equation (2). This renders the coeffi cient matrices Al in Equation (1) invariant across countries and time. Figure 1 presents the unconditional multipliers for the select horizons: on impact, 1 year, 2 years, and long run (5 years). Barring only a few periods in the impulse horizon, the unconditional impulse responses of output due to a positive fiscal shock are either negative or insignificant.<sup>14</sup> Indeed,

> 13 The list of countries is presented in Table A1 in the Appendix. Our developing-country coverage comprises primarily emerging and frontier market economies that have some ability to tap into international financial markets, which renders the fiscal solvency risks that underpin our nonlinear crowding-out mechanisms relevant. We exclude low-income countries not only because of data reliability issues, but also because they primarily rely on concessional finance for government expenditure, which would not reflect the crowding-out mechanisms.

> 14 When we split our sample into advanced and developing economies, our estimates of the unconditional multiplier are very similar to the ones reported in Ilzetzki, Mendoza, and Vegh (2013). See Figure A2 in the Appendix.

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pattern to the within-sector differences in firm product and different demand facing firms, more precisely, that women are making garments for women, while men produce male garments, and women garment makers operate in more ‘crowded’ markets and have more competition for a set demand.

> 3Some papers in the literature have documented a smaller gender gap in the formal sector as well. Hallward-Driemeier (2013), for example, estimates that women-owned enterprises are 6% less productive than men-owned enterprises in the formal sector, compared with a gender gap of 50% in the informal sector. Such comparisons between formal and informal sector enterprises, however, tend to draw on different data sources (comparing for example, businesses in the World Bank Enterprise Surveys to enterprises captured in multi-topic household surveys) and differences in questionnaire content, question phrasing, and survey implementation might contribute to these findings.

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dummy that identifies movements in the BRER corresponding to a real depreciation; ∗ is the iitt GDP of the trading partner; are time fixed-effects for ; are trading-partner fixed-ll tt ii effects for that should reflect cost arising from geography, trade policy, and other θθ tt= (1, ... , TT) λλ institutional frameworks; ii= (1, ... , NN) are product fixed-effects at HS6 level that capture ; and is the error jj iitt in the model. μμ εε The parameter 5 shows the impact of nominal exchange rates in export unit values. A value of zero implies that prices in current dollars are orthogonal to the movements in the nominal exchange ββ rate. The expected negative values reflect situations in which markup adjustment tends to stabilize prices in rupees, while positive values account for amplifications in the export prices. The main parameter of interest is 5, which represents the additional impact for depreciations. A value of zero implies that there are no significant differences between appreciations and depreciations. φφ When the value is of the same sign as the 5, the observed effect amplifies in depreciations. When the sign is the opposite, the effect is moderated in depreciations. ββ

# **4. Results**

# _Macro-Level Baseline_

We begin with a simple log-log specification linking Pakistan’s merchandise exports at the macro level with the RER, and world income, to estimate long-run elasticities. Results are reported in Table 1, reporting results relying on quarterly data for the period 1996-Q1 to 2019-Q3. As expected, exports are responsive to RER changes and to the world’s income. A 10 percent depreciation of the RER is associated with a 4.92 percent increase in exports in the long run. A 10 percent growth of world income is associated with a 10.46 percent expansion of exports in the long run. These results are in line with those reported by Pirzada (2019) when using an Autoregressive 21
# **Appendix B: Data Description**

# **B1. Poverty data**

To implement the analysis, we assemble the most comprehensive data on poverty taken from the Global Subnational Atlas of Poverty (GSAP), produced by the Poverty and Equity Global Practice, coordinated by the Data for Goals (D4G) team, and supported by the six regional statistics teams in the Poverty and Equity Global Practice, and Global Poverty & Inequality Data Team (GPID) in the Development Economics Data Group (DECDG). All the teams are at the World Bank.

For each survey data, the geographical area choice is based on the survey representativeness based on the sampling and sample design and survey documentation when available. For most of the database, surveys are representative at the first administrative level (ADM1) or statistical regions (areas) for the purpose of survey. On average, there are 14 subnational areas for a given country and year observation. For 18 small countries (13 percent), there is no subnational data available from the surveys, thus the national level data is used.

Subnational poverty rates are calculated using official household or income surveys for the purpose of global poverty monitoring. Poverty rates are provided at the subnational level that is representative for the associated household or income survey used. Overall, cross-sectional poverty statistics are shown for about 5,500 subnational areas based on survey representativeness and availability of matched spatial geographic boundaries.

Geographic boundaries must match the subnational regions in these surveys. In many cases, there is a one-to-one association between the regions in a household survey and the areas defined at an administrative level in the country. In cases where there is not a one-to-one association, geographic boundaries are altered to fit the representativeness of the surveys. In some cases, the geographic representation is at the level of “urban”, or “rural”. In these cases, subnational areas in the household survey are aggregated to levels that can be appropriately represented by boundaries. Several sources of geospatial files were
to their own contributions, to accounts either in new migrant destinations or in their home counties (State Council, 2009). In mid-2011 a new Social Insurance Law explicitly stipulated that employees should participate in social insurance programs, and that the self-employed may participate voluntarily. Further, fines may be imposed on employers who fail to make contributions to their employees’ accounts in a timely manner (National People’s Congress, 2011).

Despite efforts of the central government, the urban social insurance participation rates of migrant workers in urban programs remain low. Evidence from the RUMiC survey suggests only a modest increase in pension and health insurance participation rates over the period from 2008 to 2016 (see Panels A and B of Figure 1). By 2016 the health insurance and pension participation rates for the full sample were both just over 34%, with higher rates for employees than the self-employed. Among employees (Panels C and D of Figure 1), it is evident that higher participation rates are strongly associated with having a written contract. Participation rates among individuals without written contracts are similar to those of the self-employed. While participation among employees increased between 2011 and 2012, when fines were imposed on employers who failed to contribute their share to 6
**Father’s Years of Education:** We use three sources to calculate father’s years of education. Below we discuss each source:

_Household roster:_ As noted above, the IFLS household roster reports education information of the household member. Moreover, the roster data also provides household member ID of father and mother of each child listed in the roster. Starting with the latest (fifth) wave, we check if the father has co-resided with children at any wave. If the father has co-resided with the children during the any wave, we obtain the education information from the latest wave in which the father has co-resided with the children.

_Non-resident parents module:_ For any household member who is 15 years and older, the IFLS asks about parental education if the parents are not residing in the same household. If the child did not co-reside with the father in any of the survey waves, we use this module to obtain father’s years of education. Using the panel ID, we identify the latest wave in which the children have reported parental education of non-resident parents and extract that education information.

_Mothers marriage module:_ Ideally, we should be able to find education information all fathers using the above two sources. But in some cases, the fathers’ education is missing because of non-response or the reported value exceed any meaningful years of schooling value. The IFLS asks all married women about their husband’s years of education if the husband is not residing in the household. If we unable to find father’s years of education using above two sources, we identify if the mother has co-resided with the child in any of the waves. If the mother co-resided with the child, we then use mother’s marriage module to obtain fathers’ schooling information.

Using all three sources, we are able to collect fathers’ education information for almost 90 percent of the sample.

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of self-report treatment and gender of the child. These results reinforce the idea that child labor statistics, which are arguably less salient for girls, are more prone to variation by respondent type than other activities performed in the household.

The measurement error literature reports that recall bias can also be exacerbated in respondents with lower cognitive and communicative skills (Borgers et al. 2000). To test whether cognitive burdens of reporting explains the self/proxy discrepancies in child labor statistics, we rely on the differential impacts of the age of the child. Columns 1-2 in the Appendix Table A.1.3 show the estimated coefficient for the interaction between treatment status and child’s age at the extensive and intensive margins, following similar specifications as in equation (1). We find negligible and not statistically significantly differential child/proxy discrepancies by child’s age. Thus, these results suggest that cognitive burdensome bias does not explain discrepancies in the measurement of child labor statistics in our setting.<sup>17</sup> Furthermore, it is expected that the extent of complementarity of farming tasks between children and proxy respondents, or more broadly, the likelihood of monitoring child activities by the proxy respondent, would reduce information discrepancies in child labor statistics. We then investigate heterogeneous survey treatment effects for two variables of interest, the gender of the proxy respondent and land size. While we acknowledge none of these variables provide direct information on monitoring of child activities, nonetheless, they provide useful information. Based on a specification presented in equation (1) after considering interaction terms for the survey treatment indicator, the gender of the child, and gender of the proxy respondent (or land size), unreported results shows that the differential survey impact for girls relative to boys increases in 6.4 percentage points when the proxy respondent is male. However, we cannot measure this sizable heterogeneous impact with statistical precision which is expected given the small number of 21
Figure 5: Size vs. firm-level capabilities <!-- Start of picture text --> (a) Size vs. management (b) Digital readiness vs. management<br>3 3<br>2.5 2.5<br>2 2<br>1.5 1.5<br>-.5 0 .5 1 1.5 -1 0 1 2 3 4<br>Normalized management score Initial Digital readiness score<br>Necessity De Soto Parasite Fully formal Necessity De Soto Parasite Fully formal<br>ln(number of employees) ln(number of employees)<br><!-- End of picture text -->

**Notes:** Both panels control for sector, age, and country. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

# **5.3 The severity of the impact of the COVID-19 shock and the recovery speed is negatively associated with the degree of informality**

Using drop in sales across the three waves of data collection as our main outcome variable, we document that sales decreased significantly more for informal firms (an average 45% drop in sales during the first wave compared to 36% for formal firms, controlling for firmlevel attributes) (Figure 6, panel a) and remain significantly below their pre-pandemic sales (17% below). These results are comparable across the various types of informal firms, with the necessity firms being hit slightly harder. Nevertheless, there is a tight negative correlation between the degree of informality and the change in sales, pooling the three waves together (Figure 6, panel b). Similar results are obtained with respect to recovery across waves (∆ _Change in salest,t−_ 1), that is, as the degree of informality increases, recovery is slower (Figure A7).

The external attributes of firms that are associated with informality are also the ones that have been negatively associated
. 2016. "Urbanization with and without Industrialization." _Journal of Economic Growth_ , **_21_** (1), 35-70.

Gutierrez, I. A. and O. Molina. 2020. "Reverting to Informality: Unregistered Property Transactions and the Erosion of the Titling Reform in Peru." _Economic Development & Cultural Change_ , **_69_** (1), 317-334.

Hassan, M. and K. Klaus. 2020. "Closing the Gap: The Politics of Property Rights in Kenya." University of Michigan, Ann Arbor. Hazan, M., D. Weiss and H. Zoabi. 2019. "Women's Liberation as a Financial Innovation." _Journal of Finance_ , **_74_** (6), 2915-2956. Henderson, J. V. and S. Kriticos. 2018. "The Development of the African System of Cities." _Annual Review of Economics_ , **_10_** (1), 287-314.

Henderson, J. V., T. Regan and A. J. Venables. 2020. "Building the City: From Slums to a Modern Metropolis." _The Review of Economic Studies_ .

Henderson, J. V. and M. A. Turner. 2020. "Urbanization in the Developing World: Too Early or Too Slow?" _Journal of Economic Perspectives_ , **_34_** (3), 150-173.

Holden, S. T. and M. Tilahun. 2020. "Farm Size and Gender Distribution of Land: Evidence from Ethiopian Land Registry Data." _World Development_ , **_130_** .

Jacoby, H. and B. Minten. 2007. "Is Land Titling in Sub-Saharan Africa Cost Effective? Evidence from Madagascar." _World Bank Economic Review_ , **_21_** (3), 461-485.

Jansen, K. and E. Roquas. 1998. "Modernizing Insecurity: The Land Titling Project in Honduras." _Development and Change_ , **_29_*
<mark>surveys means in most instances across countries.</mark><sup>23</sup> <mark>Appendix tables A.4.7 to A.4.12 present the weighted averages along with confidence intervals for non-model variables. Notably, we reject the null hypothesis that the GMD means are same as the online survey means in more instances for the non-model variables compared to the model-variables. These details are summarized in table A.4.13.</mark> <mark>Compiling data from tables A.4.1 to A.4.12, tables A.4.14 and A.4.15 present the average of the relative deviations of online survey means with GMD means for model-variables and non-model variables, respectively. In both these tables, column (1) specifies the online survey waves that were considered in obtaining the deviation. For example, the variable age is available in all waves, and hence age was dropped to estimate models between wave-1 with GMD, wave-2 with GMD, and wave-3 with GMD. The average presented for age is the relative mean from all the three waves, and this is represented as ‘1-2-3’ in column 1. Since asset variables were only available in wave-3, the models that drop the asset variables were only estimated between wave3 and GMD. This is represented as ‘3’ in column 1 for all asset variables. Columns (3)-(8) present the deviation for each country.</mark> <mark>Results indicate that the average relative deviations for model variables between the Internet surveys and GMD range from 0 precent for cell phone usage in Egypt, to 87.1% percent for the “no education” category in Egypt. For non-model variables, the average varies widely, ranging from as low as 0.4 percent for computer usage in Brazil, to as high as 10,722.2 percent for working in “other sector” in Indonesia. Figure 5.
FHHs residing with the female heads’ fathers or older sons may still be better off than FHHs who do not have support of working-age males; for example, agricultural production may become especially harder due to fewer working-age household members working on the farm (Rogan, 2013; Brown and van de Walle, 2021). These FHHs may also have less access to productive assets such as livestock or extension services. Moreover, women in Arab labor markets have far fewer job opportunities compared to men. Their labor-force participation rates are currently among the lowest in the world; their unemployment rates are also four times the world average (UNDP, 2022). When they do work, they tend to face wage and occupational discrimination (Elhamidi and Said, 2008) or are overrepresented in the informal sector with low pay and no social insurance. Residing in a majority-female household, or in one where the majority of earners are female, affects the propensity of being poor (Rogan, 2013; Munoz Boudet _et al._ , 2018).

Ideally, an objective criterion would be used to assign headship to the family member whose income or decision-making contributes most to maintaining the family. For instance, Gammage (1998) found that using the _maintenance_ criteria to define FHHs in El Salvador and Costa Rica results in a markedly higher percentage of such female-maintained households (FMHs) compared to the _de jure_ FHH group, and higher poverty incidence. Unfortunately, household surveys in the Arab region do not provide information about individual incomes or total earnings, only aggregates for the household.

In the absence of such information, based on our review of the literature (Table A.1 in Appendix A), we propose several alternative definitions of _de facto_ FHHs using demographic and socioeconomic criteria to provide a more multifaceted understanding of FHHs. Regarding household composition, we consider households where the _proportion of females among (workingage) adults exceeds 0.5_ (second type, majority-female-adult FHHs). We define _potential FHHs_ as 10
In order to adequately measure and interpret the cost data, it was necessary to gather detailed information about primary markets in each country. Global and country specific conditions can have an effect on whether a firm can access capital markets. Excessive liquidity conditions can result in an increase in investors’ desire for riskier products, and perhaps allow a host of other issuers to access the market. A wider selection of firms, an increased level of risk, and high investor demand can each in turn affect the pricing of investment bank services to these companies. The nature of the domestic investor base can also play an important role in determining the ability and process by which companies can place debt, and therefore can influence the cost structure of issuing securities. In addition, recent or upcoming changes in regulations have the potential of creating incentives (or disincentives) for firms to incur the costs of public registration or issuance. Furthermore, the actual statistical data about quantities and characteristics of securities can help direct subsequent interview questions; this data also helps to quantify appropriate issuance sizes to benchmark the costs.

The contacts for the interviews were found primarily through word of mouth. Along with personal contacts, initial emails and phone calls to the stock exchanges and regulatory bodies typically yielded additional names of investment bankers responsible for new issues.

Rating agencies are typically quite guarded about the fees they charge, but were able to give pricing parameters under the guarantee of privacy. Both the investment banks and companies were able to provide the range of rating fees as well, to corroborate the results.

# **C.  Difficulties and Assumptions**

As much of the information was gleaned from interviews, two or more sources for the qualitative data were needed to verify and refine the cost data. In particular, the investment bankers and lawyers all represented their fees as a rather wide range (for example, 1-2.5% of the face amount issued). In each country’s case, several discussions occurred to clarify the nature of the pricing mechanism and the
welfare as measured by household consumption.<sup>3</sup> We emphasize at the outset that our measures of consumption are net of within-family transfers, so coefficients on financial variables are not picking up the

> 2 See Keefer and Knack (2005) for an excellent summary of this literature.

> 3 Our work is therefore related to that of Besley and Levenson (1996), who find evidence that participation in informal lending networks led to greater consumption of durable goods in Taiwan, China, from 1977 to 1991. Using historical loan contract data from low-income rural villages in China in the 1930s, Brandt and Hosios (2010) also show that informal loans were predominantly used for consumption purposes. They document that a large share of these loans carried no interest rate and argue that these arrangements should be interpreted as “long-term reciprocal insurance or patronage relationships between households.”

2
a globally harmonized database on freshwater ln(WWQQ ) = αα + ββ ln (NN ) + XX λλ+ ffcc(tt) + θθii + γγii + εε quality developed by UNEP-GEMS, maintained by the International Centre for Water Resources and Global Change (ICWRGC) and hosted by the Federal Institute of Hydrology in Koblenz, Germany (www.gemstat.org). Data from river monitoring stations in the GEMStat database are used to measure concentrations of nitrogen in rivers. Total nitrogen in water, i , is measured using a combination of nitrates and nitrites, and aggregated to the 0.5-degree grid to match the resolution of nitrogen fertilizer WWQQ data, i . Various other factors can also affect the relationship between nitrogen fertilizer and water quality. Control variables, including various fixed effects and time trends, are included to isolate the NN impact of nitrogen fertilizer as much as possible from other factors: are year fixed effects, are grid cell fixed effects, are country-specific time trends. Finally, ′i is a vector of other control variables, ii ii θθ γγ including annual rainfall, temperature, runoff, and log of population. In some specifications, the cc ff (tt) XX extensive or intensive margin of land use such as cropped area and yields are also included. Together, these controls account for baseline differences in water quality patterns and other factors that vary by year. These are meant to control for changes in agricultural policies, development levels, input availability, technological levels, and time-invariant factors such as terrain slope, soil type, and distance to coast or water bodies.

6