rafmacalaba/data-use-mentions-extended huggingface dataset:
transparency and external debts. For example, a one-point increase in the data transparency index conditional on lower external debt would reduce sovereign spreads by 0.00224 and conditional on an improvement in ICRG would decrease sovereign spreads by 0.713. As a consequence, a combination of higher data transparency and better institutional quality would reduce bond spreads, and hence lower the costs of borrowing.

The econometric results from Tables 4 and 5 are consistent with the results from fixed effects and are more robust. For example, the coefficients of GDP per capita and GDP growth are consistently positive and significant, therefore, higher GDP per capita and higher economic activity would reduce sovereign bond spreads (lower costs of borrowing). Robust results of positive and significant coefficients of CPI inflation indicate that higher domestic inflation increases the costs of borrowing. The coefficients of VIX are robustly positive and significant and the coefficients of 10-year US Treasury bond yield are robustly negative and significant. Accordingly, higher global market volatility and lower US bond yield increase the costs of borrowing. As a result, both push (global market volatility and US Treasury bond yields) and pull (i.e. GDP, CPI inflation, institutional quality, data transparency) factors matter for sovereign bonds spreads.

Table 6 includes trade and financial openness variables as well as its interactions with data transparency indicators. Overall the results from IV estimations are consistent with the results from the fixed effect estimations. Trade openness and financial openness have a negative and significant impact on sovereign bond spreads. Therefore opening-up markets will bring more benefits in terms of lower borrowing costs. For instance, a one-point increase in trade openness and in financial openness decreases borrowing costs by 0.53 points ([1] in Table 6) and by 0.899 points ([3] in Table 6), respectively. When both variables are jointly included in the specification, a one-point increase in both trade and financial openness reduces borrowing costs by 0.51 and 0.0635, respectively in the same regression (
Equation 2.<sup>16</sup> A distributed lag (DL) specication is standard in the MPC literature for one-o transfers, such as in JPS and Parker et al. (2013). Although the direct projections approach is more popular in the multiplier literature, regressions on simulated data reveal that it has diculty isolating the lagged eects of one-o transfers over short horizons, whereas the DL specication uncovers the correct multipliers exactly (see Appendix 3.5). Other specications are reported in Appendix 3.3 and Appendix 3.4, including a specication in detrended levels, which produce broadly similar results.

# 3. Empirical Results

3.1. Graphical Evidence. Figure 1 provides a visual representation of the size and direction of the relationship between changes in transfers on the x- axis (permanent and temporary), and contemporaneous growth in per capita labor income and GDP (y-axis). The slope of relationship is a simple estimate of the size of the cross-region transfer multiplier. For the temporary transfers (Figure 1, Panels BD), each of the 50 dots represents the growth rate and transfer size in a dierent state in the quarter the transfer was paid. For permanent transfers (Figure 1, Panel A) there are too many Social Security increases to be plotted in this way. So instead, I group permanent transfer change residuals into 50 bins based on their size (across dierent quarters and states) and plot the average transfer size in each bin (x-axis) against the average growth rate residuals of per-capita labor income for that bin (y-axis). Residuals are calculated after removing time and state FEs.<sup>17</sup> .

In all four subplots in Figure 1 there is a positive and signicant relationship (line slope), indicating that states that received larger transfers tended to grow faster contemporaneously. Also note that the relationship between transfers and contemporaneous growth has a steeper slope
><!-- End of picture text --> _Source_ : Job loss and months without work are based on HFPS R1-10 for Ethiopia and Nigeria, R1-8 for Malawi, R1-11 for Nigeria and R1-5 for Uganda. Joblessness is based on the latest of these rounds per country.

In addition to work status, the HFPS collected data on income loss. In the first HFPS round, respondents were asked whether household income had increased, stayed the same, or decreased since the onset of the pandemic. Figure 7 summarizes these results and descriptive statistics are included in appendix table A3. Approximately 80% of the households in Malawi, Nigeria, and Uganda and 46% in Ethiopia, reported a decrease in their total income following the COVID-19 outbreak. Around 40% of households in Ethiopia lost income from farming, compared to 60% to 73% in the other countries. The share of households that experienced a decrease in income from non-farming family enterprises (NFE) surpassed 80% in all the countries analyzed. Finally, almost 60% of households reported a reduction in wage income in Malawi, Nigeria, and Uganda, compared to 34% in Ethiopia. These numbers clearly indicate that all types of income sources were heavily affected and that NFE income was particularly vulnerable.

While respondents were asked to compare current and pre-COVID-19 income in the first HFPS round; in subsequent rounds, they were only asked to compare their current household income to income at the time of the previous interview, but not to income before the pandemic.<sup>10</sup> Two exceptions are Nigeria’s HFPS Round 9 (in January 2021), which asked households to compare their income in the period August 2020-January 2021 to their income in the period August 2019-January 2020; and Uganda’s HFPS Round 5 (in February 2021), when households were asked to compare their current monthly income to their average

> 10 Furbush et al. (2021) show that the proportion of households reporting income loss relative to the previous HFPS
Table A2: Growing Seasons

||Growing Season|Span Calendar Years|Season Modality|
|---|---|---|---|
|Ethiopia|1 March - 30 November|no|unimodal|
|Malawi|1 October - 30 April|yes|unimodal|
|Niger|1 June - 30 November|no|unimodal|
|Nigeria|_North_: 1 May - 30 September|no|bimodal|
||_South_: 1 March - 31 August|||
|Tanzania|1 November - 30 April|yes|unimodal|
|Uganda|_North_: 1 April - 30 September<br>_South_: 1 February - 31 July|no|bimodal|

_Note_ : The table presents the growing season ranges, as defined by following FAO crop calendar for each country, respectively.

**Household Data Summary Statistics Specification and Bumpline Charts**

47
“selected”, for example if a country has a few MFG/FIRE firms and these belong to high-productivity subsectors or are politically connected. Given such issues, we use the GDP share of manufacturing and services (MFGSERV). Web Appx Fig. D.6 shows that countries with a high GDP share of MFG+FIRE are countries with a high GDP share of MFGSEFV today, thus validating this proxy (correlation of 0.78).

It is also unclear how to distinguish agricultural products belonging to _R_ (those generating high rents) from agricultural products belonging to _p_<sup>_∗_</sup> _f_<sup>_Af_.</sup> In addition, productivity and yields are typically poorly measured for the latter and it is not obvious which price level to use. Since we aim to measure the fact that a country is urbanizing because it is exporting fuels and mining products or agricultural products, and given the difficulty in separating agricultural products in different categories, we aggregate them together and use as a proxy the ratio of natural resource exports (NRX) to GDP.<sup>16</sup>

> 15The sample excludes a few ex-communist countries due to the lack of pre-1989 data.

> 16NRX/GDP differs from the GDP share of natural resources, for which we have no historical data for
(Nunn and Wantchecon, 2011). The historic nature of the data on slavery provides a solid basis of the exogeneity of the instrument as there is no apparent reason for which the slavery origin of the ethnic group could affect the actual tax morale directly, except through the trust channel.

As both vertical and horizontal trust are potentially endogenous, I use two additional instruments for the identification of the equation. The latter relies on the literature on the

> 12See for instance Nunn and Wantchecon (2011), Engerman and Sokoloff (2008), Nunn (2008a), and Nunn (2008b), and Mitchener and McLean (2003).

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in the country put significant strain on national resources to provide services for the new arrivals, as well as the host community.

The Emergency Social Safety Net (ESSN) program was launched in November 2016 with the partnership of the Government of Turkey and the European Union to support the most vulnerable refugees to meet basic needs by distributing unconditional cash transfers. The program was funded by the Directorate-General for European Civil Protection and Humanitarian Aid Operations and co-implemented through a partnership of the Turkish Ministry of Family, Labor and Social Services, the World Food Programme (WFP),<sup>3</sup> and the Turkish Red Crescent (TRC). To target the most vulnerable population, the ESSN relied on eligibility criteria based on demographic characteristics at the time of application, which served as a proxy for household vulnerability. The program has rolled out nationwide and scaled-up rapidly across the country. Currently, ESSN supports 1.7 million refugees in Turkey. As of May 2020, ESSN is the largest humanitarian cash transfer for international refugees in the world.

> 1 Excluding 5.6 million Palestinian refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA).

> 2 DGMM International Protection Statistics https://en.goc.gov.tr/international-protection17.

> 3 Starting from April 2020, the International Federation of Red Cross and Red Crescent (IFRC) has taken over the coimplementation of the program from WFP.

2
daily average across hours as well as the maximum hourly value of the day as outcomes. We use data from 2020 and 2023, and we include controls for weather variables and indicator variables for day of the week and month of the year. We also study a longer time period, allowing us to better control for seasonality and overall high variance in the outcome variable. We use the satellite data and the data from regulatory-grade monitors for 2017-2020 and use the Purple Air data for 2020, 2022 and 2023.<sup>18</sup> We include results for these analyses using the longer time period at both the hourly and daily levels. For these robustness checks, we analyze both the unadjusted low-cost monitor data and the locally calibrated data, using the hourly calibration values for regressions at the hourly level and the daily calibration values for regressions at the daily level.

# **5 Results**

## **5.1 Comparison of PM2.5 Levels**

First, we compare the PM2.5 estimates generated using satellite data from MERRA-2 with the data from the regulatory-grade monitors. Figure 4 demonstrates that there is a strong alignment of values at the monthly level (Figure 4A), weekly level (Figure 4B), and daily level (Figures 4C and 4D), and that aggregating the data temporally improves alignment. The satellite data is able to capture the peaks in pollution that occur in the first two months of each calendar year (January - February), but the satellite measures also record increases in pollution in May-June for 2013-2015 that are not reflected in the data from the regulatory-grade monitors (Figure 4A). Particularly in 2019-2020, the PM2.5 estimates from the satellite data at the monthly-level are very closely aligned with the PM2.5 data from the regulatory-grade monitors (Panel 4A). This reflects the fact that in 2019-2020, the regulatory data comes primarily from a new monitor installed in 2018 in Guediawaye, which measures much higher levels of PM2.5 compared to the other
suggest that initially approximately 10 percent of the population became displaced, with a constant flow of new displaced households and returnees that were roughly equal over time (e.g., TFPM 2016; OCHA 2019). However, other sources suggest that displaced households may represent upwards of 25 percent of the population (e.g., World Bank 2017b; WFP 2019; etc.). The difference between the estimates might be due to differences in survey methodologies, where the official estimates are based on key informant interviews at the sub-district level, and other methodologies are based on household surveys. In any case, all methodologies suggest that the initial surge in forced displacement was large, with continued flows of newly displaced households and households returning 6
higher than that of male landholders. The difference is smaller than at the household-level because the gender gap in individual land size is small. Our results suggest that gender differences in tax burdens depend on whether the analysis is conducted at the level of the household or the individual—which points to the importance of data on individual ownership to reveal such differences and to ensure that policies are effectively targeted.

There could be several reasons for the horizontal gender tax inequity. First, while tax liabilities for equal-sized farms are the same, area-based land taxation results in a heavier tax burden on households with lower productivity and consumption. This is confirmed when tax incidence is disaggregated by land area classes (Table 9), where the tax burden on female-only households with less than 0.5 hectare of land is 47 percent higher than for dual-adult households. Researchers have found a gender productivity gap (Aguilar et al. 2015<sup>22</sup> ; Ghebru and Holden 2015) and lower sharecropper yields on women-owned than on men-owned farms in Ethiopia (Ghebru and Holden 2015). Households with no male adults have to sharecrop their land because of taboos against women working in agriculture (Bezabih et al. 2016). These factors would lower consumption for female-only and female-headed households. Second, for most female-only and female-headed households, the landholding size is less than 0.5 hectare, compared to about 40 percent for dual-adult and male-headed households. These smallholders face the highest per hectare tax rate.

From a gender perspective, land taxes seem to reinforce existing gender inequities because they place a heavier burden on female-headed- and female-only households, who already face several areas of disadvantage in agriculture and consumption.

> 22 Although the analysis of Aguilar et al. (2015) focuses on female farm managers, not female-headed households, 95 percent of female managers are in fact heads of households.

15
have contributed significantly toward convergence in aggregate labor productivity. Interestingly, productivity growth in agriculture was the main driver of aggregate convergence. Nevertheless, agricultural productivity in low-income countries still markedly lags and is weakly converging relative to advanced countries. As for manufacturing, they find evidence in support of unconditional convergence consistent with Rodrik (2013) albeit with a slower convergence rate. In line with Bakker et al. (2020), their findings also imply addressing barriers to sectoral reallocation through human capital development, good governance, and a favorable business environment would boost productivity growth and accelerate convergence.

# **_Regional convergence_**

Studies have also examined the patterns of regional inequalities and convergence within countries. Gennaioli et al. (2014), based on regional data from 83 countries, show that rich countries experienced faster regional convergence than poor countries, mainly attributed to better capital markets in the former and limited factor mobility in the latter. Their results further show that the pace of convergence across regions within countries is relatively faster than the one observed across countries.Yet, the regional convergence rate is generally slow, as indicated by lower resource mobility. This is despite regions being more likely to exhibit homogeneity in technology, productivity, and institutions compared to countries.

Examining the determinants of regional growth, Gennaioli et al. (2014) show that geography (e.g., resource endowment, temperature, proximity to the ocean) and human capital (e.g., educational attainment) are important factors but not culture (e.g., ethnic heterogeneity, trust) and institutions (e.g., business environment) factors. More importantly, they highlight the crucial role of human capital through the mechanisms of worker education, entrepreneurial/managerial education, and externalities while noting the ease with which firms, workers, and entrepreneurs move across regions than countries.

Coming to country-specific studies, Kinfemichael and Morshed (2019b) argue that the slowdown of labor productivity convergence in recent years in the United States is possibly due to
which foreign currency proceeds are required to be brought into<br>Thailand,as well as relaxingthe related reportingrequirement|
|Thailand|2010 I<br>T|CC|Tax on Inflows|Withholding tax of 15% to be imposed on interest income and capital gains receivable by non-resident corporate and individual<br>investors on bonds issued bythegovernment,state enterprises and the Bank of Thailand.|
|Turkey|2010 I<br>E|CC|Tax on Inflows|Reduction in withholdingtax rate on bonds issued abroad byTurkish firms with a maturityof over 3years.|

a/See Pasricha (2011) for further analysis and sources. The following abbreviations used: I/O – Inflow/outflow; T/E – Tightening/easing; Type – CC – capital control; CM – Currency based measure; OM – other measure; Source: IMF AREAER 2002 - 2009, Reinhart and Reinhart 2008, national sources and news articles.

37
In our baseline analysis, we employ daily data of emerging market sovereign spreads over Treasuries on U.S. dollar-denominated debt for 58 countries included in the J.P. Morgan Emerging Markets Bond Index Global (EMBI Global). Table A2 presents our sample of countries, and the sample period covered is January 2, 2019 through May 27, 2022. The global factor used in our analysis is the EMBI Global index. Corporate spreads are captured by the J.P Morgan CEMBI IG+ index. To construct the regional factor, we adopt an approach similar to that used by Daehler et al. (2021). First, countries are grouped into geographic buckets according to the seven regional classifications defined by the World Bank’s World Development Indicators database.<sup>14</sup> The regional factor for a given country is then calculated as the daily average logged spread for the country’s regional grouping, excluding the country itself.

The country-specific economic indicators used as controls include the total external debt stock, primary balance (% of GDP), GDP per capita growth, and annual changes in consumer prices, all lagged by one year. Data for the external debt stock and primary balance is sourced from the spring 2022 vintage of the cross-country database of fiscal space created by Kose et al. (2022). GDP per capita growth is sourced from the IMF WEO database, and annual inflation data comes from the IMF as well.

Our policy-related variables are included to control for the influence of monetary policy decisions and pandemicinduced lockdowns and restrictions. These controls include dummy variables indicating dates of Federal Reserve and ECB announcements through the pandemic timeframe, and daily log changes in the Oxford COVID-19 Government Response Tracker Stringency Index. The Stringency Index, ranging from 0 to 100 (with 100 indicating the strictest response), is a composite of nine metrics measuring the stringency of school and workplace closures, stay-at-home requirements, and other government-mandated restrictions. Use of the Stringency Index serves as a control for variation in governments
on the total household income variable, we generate a new total household income by summing all the net incomes reported by household members (Appendix A, Table A.10). Yet, the estimated scale parameters of 0.3 for adults and 0.06 for children obtained from the BUC

> 21 The full regression results are shown in Table 1.

> 22 The adult scale parameter is still high when using satisfaction with economic conditions (0.8), but the child scale parameter decreases to 0.02.  The POL model also similarly provides a higher elasticity for adults (0.8), as well as for children (0.1) (Table 5, row 3).

19
if employment growth (instead of decrease) causes an increase in the likelihood of leaving the program.

**Informality** Second, we aim to disentangle the effect for areas with historically high informality rates and areas with historically low informality rates. We classify municipalities by using the census data from 2010 into municipalities that have informality rates above and below

> 32The STATA command _ssaggregate_ by Borusyak _et al._ (2022) is used. The command converts the shift-share IV dataset – in our case, at the municipality level – into a dataset of weighted shock-level aggregates. In other words, the outcome and treatment variables are regressed on exposure weights and a set of control variables to get to the industry-level aggregates.

> 33As outlined above we are not trying to isolate a specific factor that affects employment but rather look at general employment trends. This is confirmed by Adao _et al._ (2019), who find that in their application, the first stage differs less when looking at the application of Bartik (1991) since “the Bartik IV absorbs the bulk of the shift-share covariates that affect the change in the employment rate and wages across commuting zones.”

21
if a larger share of their coworkers is eligible for paternity leave. This suggests that paternity leave may negatively affect fathers’ earnings by causing them to lose out on high-wage positions to competing coworkers who do not take leave.

It is worth noting that much of the evidence on family leave policies is based on data from high-income economies. However, a small number of papers use data from developing economies; given their importance in a global study of the evolution of leave polices, we highlight them here. A study of maternity leave policies in 121 low- and middle-income economies by Fallon, Mazar and Swiss (2017) finds no evidence of a positive impact on female labor supply; however, the authors find a positive association with a range of development outcomes, including reduced infant and child mortality. Using firm-level data from 66 mostly developing economies, Amin and Islam (2019) find a positive association between maternity leave provisions and female labor supply at the firm level. They highlight that the results are much stronger in countries where maternity benefits are entirely funded by the government. Similar conclusions are reached by the authors in an update to their previous analysis that extends the country coverage to 111 countries and looks at maternity leave policies in greater depth (Amin and Islam, 2022). Again, looking specifically at the relationship between paid maternity leave and female employment in the private sector, the authors find a large and significant positive correlation between the length of maternity leave and the share of female workers in a firm. The authors confirm their previous finding that context matters, as the positive relationship is much stronger when the maternity benefits are paid for by the government, and where the share of the female population at childbearing age is larger.

The only paper identified in this research that considered the impacts of paternity leave policies in developing economies is by Amin, Islam and Sakhonchik (2016). Using data from 53 developing economies, the authors find a strong positive association between the provision of paternity leave and female employment at the firm level