rafmacalaba/data-use-mentions-extended huggingface dataset:
pre-requisite for participating in global value chains and exporting to advanced economies (Bastos et al., 2018; Cadestin et al, 2018; Kummritz et al., 2017). However, producing high-quality exports remains a particular challenge for developing economies, with economic development often involving a step-change in the quality of production (Hallak and Schott, 2004; Henn et al., 2020). We contribute to this literature by examining quality upgrading effects for a broad range of developed and developing economies.

Historically in the trade literature, unit prices (trade values per unit quantity) had been used as a proxy for quality (Schott, 2004; Hallak, 2010). However, the literature has recently recognized that a range of factors other than quality itself may influence prices, such as transport costs or tariffs. We use detailed HS 10-digit level US import data to estimate export quality by employing a micro-founded measure of quality introduced by Khandelwal et al (2013). This measure has been widely used in the recent literature (e.g. Breinlich et al., 2016; Bajgar and Javorcik, 2020). Intuitively, this measure assigns a higher quality for products - conditional on prices – that have a larger market share in a given destination country (in our case, the United States).

We also contribute to the literature examining ISO quality certification – a manufacturing standard commonly linked with production quality. For example, ISO 9000 certification in Slovenian firms is found to increase firm exports along with other performance outcomes including profitability, labor productivity and wages (Javorick and Sawada, 2018). Following exchange rate devaluation Mexican firms, particularly those initially larger and more productive, are more likely to gain ISO 9000 certification, increase exports and wages (Verhoogen, 2008). ISO 9000 certification can also be a pre-requisite to join as suppliers to multinationals, which can demand continuous quality improvements in their supply chains (Javorcik and Spatareanu, 2009; Iacovone et al, 2015).

The trade literature has commonly focused on the
# **<mark>Figure 11</mark>**

Monetary Poverty in Argentina and Chile: Total, Permanent Income, and Redistribution <!-- Start of picture text --> Panel A. Argentina<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Poverty Poverty (Permanent Income) Poverty (Permanent Income and Redistribution)<br>Panel B. Chile<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Poverty Poverty (Permanent Income) Poverty (Permanent Income and Redistribution)<br>Notes: Poverty line of 5.50 dollars per person a day (2011 PPP). See Appendix B for details.<br>Sources: Authors’ estimations based on SEDLAC (CEDLAS and World Bank) and WEO (October 2018).<br>Headcount Ratio<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Headcount Ratio<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> 27
on Central, Non Central Government and Education employment are taken from Public Manaaement: OECD Country profiles, OECD 1992 and relate to 1992. Note that Education employment figure includes health workers. Primary and secondary education is managed by community-elected boards of trustees and financed by block grants provided by the central government. Public Health services are funded by general taxation through the Department of Health, which distributes them to a wide range of practitioners, administrative organizations and others providing publicly funded services. Reform of the system, however, is being contemplated.

**Norway** Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1994. Data on Data on Central Government, Non central government, Education and Health employment are taken from Public Manaaement: OECD Country profiles, OECD 1992 and relate to 1990. Public education responsibilities are shared among different levels of govemment. Primary schools and lower secondary are the responsibility of the municipalities. The central government, however, is in charge of upper secondary and higher education. Health care is run by municipalities and counties, but funded through block grants by central government, as well as through the municipalities' own revenue.

# **Portugal**

Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1993. Data on Data on Central Government, Non central government, Education and Health employment are taken from Public Management: OECD Country profiles, OECD 1992 and relate to 1989. Public education and health are the responsibility of non-central government. Military employment data include conscripts (17,600), but exclude personnel in paramilitary units, i.e., the National Republican Guard (20,900), the Public Security Police (20,000), and the Border Security Guard (8,900). Data on wages in manufacturing (monthly basis) are taken from the Intemational Labor Office's Yearbook of Labor Statistics 1995 and refer to 1993.

|**Spain
<!-- Start of picture text --> 0 .2 .4 .6 .8<br>Monetary Poverty 0 .2 .4 .6 .8<br>Monetary Poverty<br>1 1<br>.8 .8<br>resilience .6 .6<br>.4 .4<br>process legitimacy<br>.2<br>.2<br>0<br>0<br><!-- End of picture text --> Note: Fitted values and 95% confidence interval shown Source: Social Sustainability Global Database 2022.

Social sustainability scatterplots also provide insights for redressing inequality. Certain aspects of social sustainability are associated with lower income inequality in countries, as shown in Figure 5. Higher levels of process legitimacy and, to a lesser extent, stronger social cohesion are associated with greater income equality. Put another way, rule of law, control of corruption, a fair judicial system and interpersonal and institutional trust are related with increased equality. By contrast, resilience and inclusion show a weaker, almost neutral, relationship with inequality. While we cannot claim causality, our results suggest that a highly targeted focus on improving access to services and markets and social protection (which are all captured by inclusion and resilience components) is unlikely to boost equality in a marked way. This might reflect the wide inequalities that such policies try to address in the first place, and/or the need for long-term investment in equal opportunities in education or health before results are rendered visible. All in all, while more cohesive and legitimate societies are more equal, inclusive and resilient societies can still coexist with high levels of income inequality.

**Figure 5. Societies with legitimate governments are more equal**

<!-- Start of picture text --> .2 .3 .4 .5 .6 .2 .3 .4 .5 .6<br>Gini Gini<br>1 1<
,394,924|
|_B. Effective promotion_|||||
|Warning index x 2020|-0.029|-0.028|-0.027|-0.027|
||(0.003)***|(0.003)***|(0.003)***|(0.003)***|
||[0.001]|[0.001]|[0.001]|[0.001]|
|Warning index x 2021|-0.006|-0.006|-0.007|-0.006|
||(0.003)**|(0.003)**|(0.003)**|(0.003)**|
||[0.035]|[0.054]|[0.027]|[0.035]|
|Pr(2020=2021)|0.000|0.000|0.000|0.000|
|Mean outcome|0.802|0.802|0.802|0.802|
|Adjusted R<sup>2</sup>|0.022|0.069|0.095|0.119|
|Observations|13,337,281|13,301,355|13,337,055|13,301,145|
|Controls|No|Yes|No|Yes|
|Year effects|Yes|Yes|Yes|Yes|
|Fixed effects|Municipality|Municipality|School|School|

(Continues on next page) 20
**Figure 9: Female labor share premium (percentage points), trading vs. non‐trading firms, by worker type**

<!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>Exporting vs. non‐exporting Importing vs. non‐importing GVC participants vs. non‐ FDI vs. non‐FDI firms<br>firms firms participants<br>Total workers Production workers Non‐production workers<br><!-- End of picture text --> Note: The graph shows mean differences in female labor share between trading and non‐trading firms for total, production and non‐production workers. All regressions control for output, capital intensity, TFP (all in natural logarithms) as well as country‐ sector, subnational region and year fixed effects. All results shown are significant at the 10% level. For the underlying regression results, see Appendix 4.

# **4.3 Robustness Tests**

In a next step, Table 3 splits the sample into two periods, namely 2010‐2013 and 2014‐2017, to detect if the findings hold throughout both periods. Surprisingly, the correlation between exporting and the female labor share more than twice as high for the earlier period 2010‐2013. While the female labor share premium is 5.4 percentage points relative to non‐exporting firms for the period 2010‐13, it was only 2.5 percentage points for the period 2014‐17. Moreover, the female labor share premium for importing versus non‐importing firms, GVC participants versus non‐participants and FDI firms versus non‐FDI firms is only significant over the period 2010‐2013. While the finding may be related to global GVC trade slowdown, Appendix 1 shows that the 2014‐17 surveys contain many Latin American, Sub‐Saharan African and some Middle East and North African
<!-- Start of picture text --> 8= vy a7 Sex st 8= as Educationa 95 3= 86Consumption1 quintile7 os a7<br>78 76 79 73 83 79 en tds Er}<br>2 re 70 ne nt<br>be 68 cr)<br>32 32 58 32 ‘ sal<br>283 28a 50 Fd28 so<br>3. 3 6 r)<br>od 2 e<br>FY o 2 a 2 FY . - 20<br>13<br>7<br>o Female Male o Preschool/Primary Secondary University o 1 2 3 4 5<br>(ER Piped water «I improved water<br>(EI Basic water = I) improved water on premises<br><!-- End of picture text --> <!-- Start of picture text --> =J Sex Education Consumption quintile<br>= i=]= = J<br>w~ w~ 5 wo~ 73<br>3232zB258<br>aa a<br>3 3 47 a7 3 3 3 3<br>3# 5# 42 3# 42<br>cry<br>a 26 25<br>20 a a e 19<br>1" "1 6<br>7<br>4<br>1<br>o Female Male o Preschool!Primary Secondary University o 1 2 3 4 5<br>(ER Limited and basic sanitation (I) Basic sanitation<br><!-- End of picture text --> <!-- Start of picture text --> 8 Sex 8 Education 8 Consumption quintile<br>wo~ wo~ s wo~ 73<br>w 61 PY w s<br>: 58 : 57 s7 2 58 s7<br>: 3 50 41 : 8 £8 © 43<br># #
Okedele, A. (2021). Women, quotas, and affirmative action policies in Africa. The Palgrave Handbook of African Women's Studies, 449-463.

Omotilewa, O. J., Ricker‐Gilbert, J., & Ainembabazi, J. H. (2019). Subsidies for agricultural technology adoption: Evidence from a randomized experiment with improved grain storage bags in Uganda. American Journal of Agricultural Economics, 101(3), 753-772.

PASEC. (2015). _Education system performance in francophone sub-Saharan Africa: Competencies and learning factors in primary education PASEC 2014_ . Dakar: CONFEMEN “Parline – global data on national parliaments.” _Inter-Parliamentary Union_ . 2023. https://data.ipu.org/ Porter, C., & Serra, D. (2020). Gender differences in the choice of major: The importance of female role models. American Economic Journal: Applied Economics, 12(3), 226-254.

<mark>Santos Silva, M., & Klasen, S. (2021). Gender inequality as a barrier to economic growth: a review of the theoretical literature.</mark> _<mark>Review of Economics of the Household</mark>_ <mark>,</mark> _<mark>19</mark>_ <mark>, 581-614.</mark> <mark>Schomer, I., & Hammond, A. (2020). Stepping Up Women's STEM Careers in Infrastructure.</mark> Shimizutani, S., Taguchi, S., Yamada, E., & Yamada, H. (2021). The Impact of “Grow to Sell” Agricultural Extension on Smallholder Horticulture Farmers: Evidence from a Market-Oriented Approach in Kenya. Available at SSRN 3969797.

Stoeffler, Q., Carter, M., Guirkinger, C., & Gelade, W. (2022). The spillover impact of index insurance on agricultural investment by cotton farmers in
1 any of the 60+ NTMs in our data was in effect. For the specific, trade distorting NTMs discussed in Section 5.2, _NTMjt_ = 1 if that specific measure was in _TariffExposurei_ 0 _,t_ : Similar to the _NTMExposurei_ 0 _,t_ , this is the measure of tariff exposure of the firm _i_ selling product _g_ in month _t_ , where the weights of different HS-10 goods in the firm’s import bundle are determined in the initial time period. We combine the trade data with monthly data on tariffs applied by Indonesia on imports. The exposure is 15
- Singh, Prakarsh and Sandip Mitra (2017). “Incentives, information and malnutrition: Evidence from an experiment in India.” _European Economic Review_ 93, 24–46.

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**Table** **<u>3.  Impact by Decile of VAT Excluded/Exempt Products</u>**

|Deciles|Expe<br>VAT Excluded|nditure on<br>/Exempt Products|Average income<br>per decile|Subsidy relative<br>to household|
|---|---|---|---|---|
||COP(1,000)|Percent of total|COP(1,000)|income(%)|
|1|18.9|2.3|118.1|16.0|
|2|27.0|3.3|282.5|9.6|
|3|34.7|4.2|437.3|7.9|
|4|41.4|5.0|644.6|6.4|
|5|52.7|6.4|905.1|5.8|
|6|56.7|6.9|1,266.6|4.4|
|7|74.3|9.0|1,767.8|4.2|
|8|99.0|12.0|2,618.8|3.7|
|9|141.4|17.1|4,295.2|3.3|
|10|278.6|33.8|15,156.1|1.8|
|Total|824.5|100.0|||

Source: IMF (2011a) using 2003 ECV household survey data and DIAN data.

29. **While the system of VAT exemptions reduces income inequality by 0.6 Gini points it does so at a cost of 1.54 percent of GDP.** To reach this conclusion, a household data simulation exercise was undertaken
The differential effect of the ad campaign on recorded malaria incidence across urban and rural settlements aligns with the survey-based evidence. According to the 2019-2021 India Demographic and Health Survey (India IIPS, 2022), solid dwellings represent almost 85% of the total housing units in urban areas, while they are less than 48% in rural areas. Evidence presented above showed significant increases in bed net adoption and decreases of malaria incidence among respondents and households residing in solid houses, with no impacts on households residing in non-solid dwellings. Hence, one may expect that recorded malaria cases are more likely to decrease in localities with a larger proportion of solid dwellers.

Figure 5: Trends in malaria incidence rates (N. of monthly cases/1M people) <!-- Start of picture text --> Urban incidence (cases/1M people): treated vs. control subdistricts<br>Campaign starts<br>0.00<br>-1.42<br>-9.13**<br>Rural incidence (cases/1M people): treated vs. control subdistricts<br>Campaign starts<br>0.00<br>-3.16 -2.31<br>Apr-Jun 2020 Jul-Aug 2020 Sep 2020-May 2021<br>(omitted)<br>10<br>0<br>-10<br>-20<br>10<br>0<br>-10<br>-20<br><!-- End of picture text --> Notes: Each point reports the coefficient on the interaction term Treatment _×_ Period, where Period is a dummy variable equal to 1 at each period on the graph (i.e., Apr-Jun 2020, Jul-Aug 2020 and Sep 2020-May 2021). The omitted period is Jul-Aug 2020. Two separate regressions are estimated for rural and urban incidence rates. Regressions include subdistrict and period fixed-effects, as well as district-level controls interacted with period dummies. Standard errors
<!-- Start of picture text --> Total income decreased (vs. prepandemic)<br>80—<br>=<br>2=£fe) 60-<br>o<br>3<br>3 40-<br>=fe)<br>&»i<br>wo 20-<br>o-<br>HICs UMICs LMICs LICs<br>GB Apr-Jun20.) Jul-Dec20. =) 2021<br>HICs: 8; UMICs: 22; LMICs: 16; LICs: 5.<br><!-- End of picture text --> <!-- Start of picture text --> Total income decreased (vs. prepandemic)<br>80—<br>&<br>S 60-<br>£°o<br>a<br>3 40-<br>£<br>fo)<br>@<br>© 20-<br>norat<br>o-<br>EastAsiaand Europeand Latin America Middle East and Sub-Saharan<br>Pacific CentralAsia and Caribbean North Africa Africa<br>GB Apr-Jun20.) Jul-Dec20. =) 2021<br>EAP: 8; ECA: 8; LCA: 24; MNA: 2; SSA: 9.<br><!-- End of picture text -->
panel sample not only differs in terms of self-reporting but also in interviewing the respondents separately in absence of other household members, as explained above.

Table 2 shows that in the IHS4, all activities have the same or lower participation rates as compared to the IHPS, with the exception of activities in agriculture (any crop work — as well as men’s crop work strictly for household consumption). The distinction between crop production intended mainly for sale — versus all for household consumption/subsistence agriculture — was added to better understand how the survey approach affects the measurement of crop employment under the

> 13 Additional results including zeroes for the non-employed (i.e. unconditional sample) are available upon request. There were more significant differences in the unconditional sample owing to differences in participation across the two surveys. For weekly hours, we assume that no one can work more than 12 hours per day during the past seven days such that weekly hours are capped at 84 hours. We replace missing values with the median value of weekly hours within the enumeration area (EA) and take the logarithm conditional on participation. Annual hours are calculated based on the average number of hours per week spent on wage employment multiplied with the average number of weeks per month and the total number of months during the last 12 months. For _ganyu_ employment, we do not have data on the number of hours per week, so we assume an average of six hours per day multiplied with the number of days per week spent on _ganyu_ . We replace unrealistically high values with a maximum of 4,368 hours as we assume that no one can work more than 12 hours per day all year round. Annual earnings represent net income and are derived from all cash and in-kind remuneration that a worker received during the past 12 months. We winsorize earnings at top 1 percent; replace missing values with the median value within the enumeration area; and take the logarithm conditional on employment.

8
as a source of exogenous variation of competition in local banking markets to identify the causal eect of bank competition. The authors nd that a reduction in bank competition results in a signicant increase in lending spreads and a decrease in loan volume. They also show that bank competition has real eects. A 1 percent increase in spreads leads to a 0.2 percent decline in employment. Moreover, they show that if Brazilian spreads fell to world levels, output would increase by approximately 5 percent.

In addition to a high average level, there is a large dispersion in credit spreads to Brazilian rms. Cavalcanti et al. (2021) develop a quantitative dynamic general equilibrium model and calibrate it to the Brazilian data for the period 2005-2016. They use data from the Brazilian credit registry, a condential loan level data set covering all the credit operations in Brazil and containing information on loan characteristics and interest rates. They merge these data with Brazil's linked employer-employee administrative dataset to examine how interest

> 16See Cavalcanti et al. (2021).

> 17See Joaquim and Van Doornik (2019). They report that during this same time span the share of assets held by the 5-largest banks in the U.S. increased from 30 percent to more than 45 percent. Averaging across countries, the share of assets held by the 5 largest banks is 78 percent.

16
[0.060]|[0.074]|[0.100]|[0.086]|
|Constant|0.04<br>[0.006]***|0.01<br>[0.002]***|||
|Observations|808|432|801|418|
|R-squared|0.050|0.092|||
|Number of Countries|40|18|40|18|
|Country Fixed Effect|Yes|Yes|Yes|Yes|
|Adjusted R-squared|||-0.202|0.028|
|**First Stage IV Estimates**|||**First Stage **|**First Stage **|
|Trade Weighted Growth * Exp|||1.41<br>[.2]***|1.33<br>[.08]***|
|Trade Weighted Growth * Rec|||1.77<br>[.29]***|1.36<br>[.15]***|
|**Underidentification - Weak Identification - We**|**ak Instruments**|**Tests**|||
|LM test statistic for underidentification|||44.93|29.74|
|p-value of underidentification LM statistic (Ander|son or Kleiberge|n-Paap)|0|4.95e-08|
|F statistic for weak identification (Cragg-Donald o|r Kleibergen-Paa|p)|23.97|47.36|
|Anderson-Rubin chi-sqared test of significance of<br>p-value of Anderson-Rubin chi-sqared test of endo|endogenous regr<br> genous regressor|essors<br>s|15.45<br>0.000442|28.69<br>5.89e-07|

Source: Authors’ calculations based on World Economic Outlook database 2019.

Robust standard errors in brackets
The remarkable increase in their number between 2001 and 2011 means that a large number of settlements were reclassified from rural to urban, because of the change in their demographic and workforce characteristics.

It is important to note that there are two dimensions to the distinction between urban and rural spaces in India: a statistical one, and an administrative one. The statistical dimension is measured through the criteria mentioned above and used by the census. The administrative dimension distinguishes between: (i) Statutory Towns, which are administered by different types of Urban Local Bodies (ULBs); and (ii) Villages, which are administered by village councils (Gram Panchayats). The two categories of local governments function under different principles outlined in the 73<sup>rd</sup> and 74<sup>th</sup> Constitutional Amendment Acts which were enacted in 1992 to introduce local governments as the third constitutional tier of government. Therefore, among settlements, CT is a specific subgroup defined as urban by the Census of India but under rural governance.

Importantly, of the total urban growth of approximately 90 million people, about 44% is due to natural population growth in urban areas that existed in 2001, 21% is due to migration from urban to rural areas and the remaining, i.e., more than a third is because of re-classification of rural settlements as urban areas due to the change in the character of settlements, i.e., mostly new CTs - with a small proportion due to villages being reclassified as urban local bodies (Pradhan, 2013). As such, the ‘morphing’ of places is more important in urban growth in India than the ‘movement’ of people. A recent World Bank report on “Leveraging Urbanization in South Asia” (Ellis and

> 1 Large cities are defined as settlements of more than 50,000 persons, similar to the Metropolitan Statistical Area in the United States (Uchida and Nelson, 2010).

2