rafmacalaba/data-use-mentions-extended huggingface dataset:
Working Conditions Survey (2005). This way, we have 51 to 95 observations on informal employees for six countries, 100 to 200 observations for eleven countries, 200 to 350 observations for five countries, and 500 to 1000 observations for four countries. Only for Sweden the number of observations (31) is insufficient and the results should be interpreted with care. The samples of formal employees and those of informal self-employed are large enough for all countries.

We find systematic differences between the formal and informal employees in terms of gender, age, educational attainment, occupation, economic sector, and establishment size. Some of these differences are country-specific or just more pronounced in some countries than in the others. Many of the comparisons are given also for informal self-employed, which appear to be quite different from informal employees in most respects. These findings might be of interest from the perspective of integrating informal labor. Moreover, some of them also contribute to the ̳exit vs. exclusion‘ literature about prevailing reasons for working informally (see Maloney, 1999; Maloney, 2004; Djankov et al., 2003; Hanousek and Palda, 2003; Perry _et al._ 2007, Williams and Renooy, 2008; Loayza, Servén and Sugawara, 2009; Pfau-Effinger _,_ 2009; Williams, 2009; Schneider, Buehn and Montenegro, 2010). Although heterogeneous nature of self-employment is now well understood in principle, the empirical base in European context remains scarce.

To further inform this debate, Section 3.2 compares formal and informal workers in terms of belonging to groups which are known to be exposed to social exclusion or discrimination (minorities, first and second generation immigrants) or are associated with past (and maybe future) work in the formal sector (past and current union membership); we also look at perceived discrimination. This analysis sheds some light on the worker mobility between formal and informal sector, thus complementing country-specific studies (Packard, 2000; Bosch et al., 2007; Bosch and Maloney, 2010
illustrate the performance of CDR-based targeting if — CDR were available from all operators in Afghanistan though it relies on the assumption that phone-owners observed on our mobile network are representative of all phone owners in Afghanistan (an assumption that is not fully satisfied, as shown in Table 1); and (ii) the _full sample_ , which includes all 2,814 households surveyed in the TUP baseline with complete asset and consumption data; this sample includes an additional 1,807 households who report

> 8The rates of overlap should be interpreted relative to the expected overlap in errors for random classifiers. Based on our selection of thresholds such that 27% of the sample is identified as ultra-poor, our three classifiers misidentify 15%-27% of the non-ultra-poor and 51%-65% of the ultra-poor. If these classifiers were random, we would expect approximately 20% overlap in inclusion errors and 55% overlap in exclusion errors.

13
we include state-level fixed effects in the regressions, denoted by γ�. A positive and significant coefficient estimate for β� presents evidence of growth acceleration. We also allow the trend coefficient to vary by state characteristics (equation 3). The characteristics we consider are the share of agriculture/non-agricultural sectors in the state

> 12 The states included in this analysis are Andhra Pradesh, Arunachal Pradesh, Assam, Bihar, Goa, Gujarat, Haryana, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Manipur, Meghalaya, Orissa, Punjab, Rajasthan, Tamil Nadu, Tripura, Uttar Pradesh and West Bengal. We construct a back casted panel on annual real gross state domestic product growth for the years 1981 to 2016.

8
along with the creation of the new capital city, Bras ́ılia (Ayres et al., 2019). This impetus was continued under subsequent governments with a series of national development plans in 1967, 1973, and 1976 emphasizing the construction of hydroelectric dams and roads improvement and highway construction (Leturcq, 2018).<sup>2</sup> The plans required a large public investment effort toward infrastructure. In the initial Targets Plan, 71 percent of public investments targeted energy, transportation, and communications (Obhara,<sup> ̄</sup> 1974). These investments were sustained in the national development plans that followed. They were undertaken at a time when electricity demand increased due to population growth and urbanization. New investments then slowed down – starting with transport followed by electricity – during the “lost decade” of the 1980s and 1990s as Brazil’s governments focused on addressing the macroeconomic crisis (Raiser et al., 2017). This led to a progressive deterioration of the transport network as investments did not cover maintenance needs (Correa and Ramos, 2010).

All in all, infrastructure investments resulted in a significant expansion of the electricity and highway networks. Lipscomb, Mobarak, and Barham (2013) report that the transmission network in Brazil grew at an average rate of 8.9 percent per year between 1950 and 2000, resulting in a 72-fold increase from 2,359 kilometers to 167,443 kilometers over the period. As a consequence, household access to electricity rose from 48% in 1970 to 93% in 2000. The total length of highways increased from 41,189 km in 1970 (of which 3,488 km of radial highways originating from Bras ́ılia) to 90,788 km in 2000 (of which 7,440 km were radial highways originating from Bras ́ılia–calculation by authors based on DNIT data).

# **2.2 Data**

We start with creating a longitudinal dataset with four points in time (1970
smaller and often undetectable impacts on savings. Our results suggest that workers both shift resources from informal to formal vehicles and generate new savings by reducing discretionary consumption as a result of receiving direct deposit wage payments. In line with prior research, on account ownership and savings we also find evidence that our treatments improved the ability of workers to smooth consumption. Workers receiving direct deposits report fewer instances where they were unable to mitigate an unforeseen shock due to lack of resources. However, we cannot statistically distinguish these effects from the account only group.

In the second part of our analysis, we examine which characteristics shape an individual’s ability to learn how to optimally use the technology. Rather than search for treatment effect heterogeneity trait-by-trait, we use the machine learning (ML) approach proposed by Chernozhukov et al. (2018) to let the data reveal patterns of heterogeneity. We detect no heterogeneity in our main savings and consumption smoothing outcomes, which suggests equal gains through exposure for all groups. The ML procedure _does_ detect heterogeneous impacts on consumption and consumer learning. Interestingly, the ML tool partitions the sample into “savers” and “learners.” The “savers” cut

> consumers and the “Financial Inclusion Insights Survey” (CGAP, 2018), which finds that 12% of mobile money customers in Bangladesh _noticed_ that they were being overcharged. The actual incidence is likely much higher.

> 5For individuals with their own account, a person-to-person transaction requires visiting an agent to perform a cash in transaction and then executing a simple transfer on the mobile phone. An over-the-counter transaction requires visiting the same agent to make the transfer.

> 6 We use the exchange rate as of May 2014, the start date of our experiment, for all currency conversions.

3
The ES data we use was collected in different years for the various countries. Thus, we account for the annual global shocks to R&D activity by using dummy variables indicating the year the ES was conducted in the country (year fixed effects).

Next, we control for overall level of development using (log of) GDP per capita (PPP adjusted and at constant 2011 international dollars). We use lagged values (by two years) of the variable and the data source is World Development Indicators, World Bank. Following the study by Lederman and Maloney (2003), GDP per capita is used a broad proxy measure for financial depth, protection of intellectual property rights, government capacity to mobilize resources, and the quality of research institutions that may impact R&D efforts.

Our next set of baseline controls include several firm characteristics. These are firm-size proxied by the (log of) number of permanent full-time workers at the firm (three fiscal years ago, lagged), a dummy variable equal to 1 if the firm is part of a larger organization and 0 otherwise ( _Multi-firm_ ), age of the firm (logs), managerial experience proxied by the (log of) the number of years of experience the top manager has working in the industry, and a dummy variable equal to 1 if the top manager of the firm is a female and 0 otherwise. For access to finance, we use two separate variables. The first is a dummy variable equal to 1 if the firm has overdraft facility and 0 otherwise ( _Overdraft_ ). The second is how severe is (poor) access to finance as an obstacle to firms’ current operations ( _Finance Obstacle_ ). The severity level is as reported by the firms in the ES and recorded on a 0-4 scale with higher values implying a more severe obstacle.<sup>9</sup> We now aim to capture various dimensions of the business environment. Some of the controls here are defined at the firm-level and others at the country
Estimates: FIF3 + WIF3 + FIF_WIF3|1298|2135|40.110|0.066|0.002|
|P-value: FIF3 + WIF3 + FIF_WIF3=0|0.371|0.021**|0.347|0.150|0.909|
|Control Mean(BL)|7608|1026|91.95|0.084|0.012|

Note: Analysis of Covariance (ANCOVA) regression specification is estimated. Standard errors are clustered at the kebele level. The estimated equations include baseline values of outcome indicators as well as baseline sociodemographic characteristics such as sex of the household head, household size, age of the household head, number of students in the household, and region. The values of large livestock, small ruminant and poultry are winsorized at the top one percent. ***, **, and * indicate significance at the one, five and 10 percent critical level.

# **_Off-farm Income_**

For our final economic activity measure, we look at off-farm income (see Table 6). Although both interventions are designed to strengthen agricultural and livestock productivity in agricultural households, we found positive impacts on off-farm activities when either spouse is targeted. Targeting the female spouse in round 3 resulted in a significant positive effect on income from businesses that are managed or run by a woman. Income from these businesses increased by Br 974, which is 2.4 times the mean value of the same category of income for control households at baseline. Meanwhile, targeting either the male or the female spouse results in an increase in household wage income by the endline.

Targeting both spouses undoes both the business and wage impacts from targeting either spouse, and results in a significant negative interaction effect on income from businesses with female involvement. One reason for this could be that as the male spouse is also targeted, he may have 23
particularly in lower-income countries. We use the most recent measurement reported for each country. In our sample, the average number of hospital beds per 1,000 people is 0.6, 1.6, 3, and 4.15 in low-, lower-middle, upper-middle, and high-income countries, respectively.

**Non-COVID-19 mortality** Baseline mortality rates _π_  ̄ _n_ 1, _π_  ̄ _n_ 2 and _π_  ̄ _n_ 3 are computed from country-specific life table data obtained from the Global Health Observatory Data Repository of the World Health organization.<sup>22</sup>

In terms of elevated mortality due to shortfalls in aggregate income, several papers have estimated the relation between economic shocks and infant or young child mortality (Baird et al., 2011; Bhalotra, 2010; Cruces et al., 2012; Friedman and Schady, 2013; Maruthappu et al., 2017). For low and middle-income countries, the population groups most vulnerable to declines in aggregate income are young children and, perhaps, the elderly (Cutler et al., 2002). We focus on mortality impacts among children under-5 as this population group has been the most extensively studied. We estimate the effect of short-term aggregate income shocks on mortality following the methodology of Baird et al. (2011). We use data on GDP per capita from the World Development Indicators. The values are adjusted for purchasing power parity, corresponding to 2011 US dollars. Data on infant and child mortality are taken from retrospective birth histories as reported in the Demographic and Health Surveys (DHS) conducted in 83 low- and middle-income countries between 1985 and 2017. The combined sample is of 5.2 million births in low- and middle-income countries. We run regressions of the following form: where _Dict_ is a binary indicator that takes the value 1 if child _i_ in country _c_ died in year _t_ , log GDP is the natural logarithm of per capita GDP, _fc_ ( _t_ ) is a country-specific flexible time trend, _δc_ is the
& Higgins, S. (2018). The CEQ Assessment: Measuring the Impact of Fiscal Policy on Inequality and Poverty. Commitment to Equity Handbook: Estimating the Impact of Fiscal Policy on Inequality and Poverty, 219.

<mark>MacDonald, J. M., & Nelson Jr, P. E. (1991). Do the poor still pay more? Food price variations in large metropolitan areas.</mark> _<mark>Journal of Urban Economics</mark>_ <mark>,</mark> _<mark>30</mark>_ <mark>(3), 344-359.</mark> Marinho, Alexandre (2009). A economia das filas no sistema único de saúde (SUS) brasileiro. Texto para Discussão, No. 1390, Instituto de Pesquisa Econômica Aplicada (IPEA), Brasília.

Mendes, M. (2016). O teto de gastos e a proteção aos pobres. _Folha de S. Paulo: September, 25, 2016_ . <u>https://www1.folha.uol.com.br/ilustrissima/2016/09/1815978-o-teto-de-gastos-e-a-protecao-aospobres.shtml</u> Mejia-Mantilla, Carolina, Johanna Fajardo-Gonzalez, Maya, Goldman, Jon, Jellema and Haley Renda (2019). Impact of Fiscal Policy on Poverty and Inequality in Uganda: fiscal incidence analysis using the UNHS 2016/17. Policy Research Working Paper 9051, World Bank, Washington, DC. Available at: <u>https://documents1.worldbank.org/curated/en/647301572456138632/pdf/Impact-of-Fiscal-Policy-onPoverty-and-Inequality-in-Uganda-Fiscal-Incidence-Analysis-Using-the-UNHS-2016-17.pdf</u> <mark>Organisation for Economic Co-operation and Development (OECD) (2021).</mark> _<mark>COVID-19 emergency government support and ensuring a level playing field on the road to recovery</mark>_ <mark>. Available at:</mark> <u><mark>https://read.oecd-ilibrary.org/
2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30<br><!-- End of picture text --> Percent <!-- Start of picture text --> Share of countries Share of GDP<br>100<br>80<br>60<br>40<br>20<br>0<br>2011-21 2022-30 2011-21 2022-30 2011-21 2022-30<br>MNA SAR SSA<br><!-- End of picture text -->

- E. Contributions to regional potential growth

F. Contributions to regional potential growth <!-- Start of picture text --> Percentage point<br>10<br>TFP Capital Labor Potential growth<br>8<br>6<br>4<br>2<br>0<br>-2<br>MNA SAR SSA<br>2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30<br><!-- End of picture text --> Sources: Penn World Tables, World Bank.

Note: EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa. A. B. E. F. GDP-weighted arithmetic averages using potential growth estimate based on production function approach. C. D. Number of economies and their share of GDP in the region among 61 EMDEs with potential growth in each region.
the magnitudes equivalent to a 43 percent increase in profits and a 40 percent increase in sales, both relative to the control mean.

There is no difference in the rates of business ownership by rounds 4 and 5 of the survey, but our analysis has shown that both treatments led to women with lower Raven scores starting businesses and the cash plus training treatment led to women with lower wealth starting businesses. We would expect this selection to bias the OLS estimates downwards, since lower ability and poorer individuals might be expected to earn lower profits. In this sense, the estimates in panel A might be considered lower bounds for the treatment effect.

How sensitive our results are to this selection? To provide some indication, we use the sample of business owners to estimate a multinomial logit for the probability of being in each of our three treatment groups. We use the set of variables that were used in Table 2 to predict training take-up, including wealth, ability, risk attitudes, interest in attending training, interest in 23
Data on female labor force participation and fertility are sourced from the World Bank’s World Development indicators, while information on female educational attainment comes from the BarroLee database. Controls – PPP-adjusted per capita income and overall trade openness – come from the Penn World Tables 6.3 (Heston et al., 2002).

Table 2 reports some summary statistics for the female content of exports for the OECD and nonOECD country groups. We can see that for the OECD, these averages are slightly lower, at about 0.25, and stable across decades. For the non-OECD countries, the female content of exports is higher, between 0.27 and 0.30, and, if anything, rising over time. Notably, the dispersion among the non-OECD countries is both much larger than among the OECD, and increasing over time. While the OECD sample, the standard deviation is stable at 0.03-0.04, for the non-OECD is rises monotonically from 0.08 to 0.12 between the 1960s and the 2000s.

Tables 3a to 3c report, for the different time periods, the countries with highest and lowest _FLNX_ values. Typically, countries with the highest values of female content of exports are those that export mostly textiles and wearing apparel, while countries with the lowest _FLNX_ are natural resource exporters. Equally important for our empirical strategy are changes over time. Tables 4a and 4b report the countries with the largest positive and negative changes in _FLNX_ between the 1960s and today. We can see that relative to the cross-sectional variation, the time variation is also considerable. Table 5 presents the summary statistics of the country-level variables of interest and controls. Our final dataset contains country-level variables on up to 146 countries.

# **4 Regression Results**

Table 6 reports the results of estimating the impact of the gender gap on comparative advantage in trade, i.e. eq. (21). The top panel presents the OLS results, the bottom panel the two-stage least squares results in which the
. Other countries have special laws, which can take the form of specific anti-usury laws, such as those in El Salvador, Italy or Uruguay, or could be part of a more general consumer protection act, such as the ones enacted in Finland, Korea (Republic of) or San Marino. Restrictions on deposit rates additionally rely on administrative regulation, which typically takes the form of central bank directives. For example, the deposit rate floor prevalent in WAEMU countries is based on a decree of the Council of Ministers and the Board of the Central Bank of West African States. Finally, one notable exception where the basis of IRC regulation is a court ruling is the British dependency of Guernsey, where part of the legal system is based on customary law.

Fixed or absolute limits are the typical methodology employed for setting IRCs, regardless of the restriction type. The methodology for determining the level at which interest rates are limited is a critical feature of the interest rate regime. Countries that set a fixed maximum (or minimum) rate introduce an additional rigidity that makes them less responsive to changes in the macroeconomic environment. However, relative rate limits come in different flavors, ranging from those set relative to a benchmark market rate to others set as a spread over an administrative rate, with discretion for the government to determine the latter (turning many of these into quasi-fixed frameworks). Rates set relative to central banks’ policy rates deserve a separate mention, as they introduce some flexibility at the cost of distorting the monetary transmission channels, as it became manifest in Kenya’s failed attempt to control interest rates. Figure 3 shows that countries that decide to impose IRCs often choose a hard ceiling (or floor). However, some countries opt for flexibility when imposing caps on rates. Surprisingly, data on lending rate ceilings show that low and middle income economies are as likely as their high income peers to choose a benchmark market rate (both at 27 percent of those with IRCs). As far as caps on deposit
cities in the 15-country sample.<sup>30</sup> Figure 9 shows all roads and primate routes for Tanzania. Descriptive statistics are in Table 2.

> 26Countries such as Canada, Australia, and Botswana have low road density relative to their economic peers. But their road systems are not particularly inadequate. In each case, a contiguous region containing half or more of total land area has very low population density, so that the marginal benefit of an additional road there is very low. The chief problem with the percentage of roads paved is that the denominator is affected by the coverage of national roads data systems, which can vary substantially. The World Development Indicators (WDI) reports both of these measures. But of the 120 annual changes in road density available for the 255 country-years in the present sample, 66 are zero, while another 8 are, implausibly, over 10 percent in absolute value. When data are missing for a period of one or more years for a given country, the annual growth rates implied by the values before and after the data gap are even more implausible. Similar statements can be made about the percent paved data. These large changes are likely due to reclassification rather than actual road construction. 27The most comprehensive previous spatial database, Vector Map Level 0 (VMAP0, formerly known as Digital Chart of the World, DCW), is a declassified US military product combining data of unknown quality from 4 decades, with little metadata. In some countries, there are clear gaps in coverage. Most strikingly, the most densely populated areas of Bangladesh, surrounding the capital Dhaka, have essentially no roads.

> 28I am unaware of any systematic time-varying data on road surface in the countries under study. Burgess et al. (2011) use time-varying data for Kenya.

> 29Of the 43 countries in Sub-Saharan Africa, only Djibouti, Equatorial Guinea, Guinea-Bissau, and Somalia do not have data.

> 30The remaining 12 cities include 4 on islands or in exclaves. See Appendix for