rafmacalaba/data-use-mentions-extended huggingface dataset:
<mark>The findings from this research suggest policy reforms along three dimensions: labor and parental leave policies, attitudes and social norms around the role of mothers and fathers in childrearing, and the functioning of childcare services. The promotion of paternity and shared parental leave would be an important policy step to achieve a more balanced distribution of care and work responsibilities between men and women. Mexico provides 84 days of paid maternity leave (Ley Federal del Trabajo, Art. 170) and also grants new fathers five days of paid paternity leave (Ley Federal del Trabajo, Art. 132 XXVII). However, it does not mandate paid parental leave, which can provide more time off for both mothers and fathers to care for their children (Women, Business and the Law Database 2020). Fathers’ leave has proven to be effective in stimulating fathers’ involvement in care: Huerta et al. (2013) found that in four OECD countries fathers who take leave for over two weeks were more likely to be involved in childcare during the child’s first year of life. Boll, Leppin and Reich (2014) show that father-specific parental leave led to an increase of over one hour per week spent on childcare in Germany. Incentives for uptake are also very important in this context: Those may include “daddy” quotas that are not transferable or longer shared leaves when the leave is shared (which is the case,</mark> for instance, in Iceland, Germany and Sweden).

<mark>Awareness raising activities around fathers’ involvement and the positive associated outcomes may be needed among fathers, families, the broader population and companies and employers. The findings of this research indeed call for the promotion of the positive effects from men ́s involvement when caring for children, while on the other hand, continuing to promote the economic, labor, social, and political rights of women and strengthening the mechanisms for compliance with these. Changing attitudes and beliefs that underlie the observed patterns is necessary. Information
experience, the ability to interact socially with important people like bank officers and political representatives was also mentioned. Finally, when asked about ideas for program improvement, some mentioned more training in the making of candles and incense, perhaps (though we cannot tell) because these are income generating activities that can be done at home while caring for children.

# **6. Concluding Discussion**

This paper has offered an account of self-help groups with multiple strands of activity. We offered an observational evaluation study using propensity score matching and reported evidence which shows capability indicators in several life domains to be higher for SHG members compared with non-members. Robustness was considered using subgroup analysis and evidence of missing variable bias in the propensity score model estimates. Most significantly, we also considered the impact of being in the program for more than 500 days and identified a number of capability indicators that are both significant in the main PSM model and higher for those who have been program members for longer. On that basis, the paper makes contributions to do with empirical results, methodology and the conceptualization of self-help.

In the first place, our evidence has shown that poverty alleviation and empowerment through self-help can expand the capabilities of women in several areas of significance to quality of life. The overall pattern of capability expansion documented is noteworthy for while there is robust evidence that several aspects of life quality have been enhanced, two indicators relating to the possibility of assault and discrimination are no different to those of controls. Empowerment as a comprehensive process, therefore, may require interventions that work on social environment – shaped as it is by culture 20
trend-driven change calls for truly long-term factors driving the trend component in our set of social indicators. As social indicators data are not available for time spans longer than three decades, the identification of this type of low frequency signals is more difficult. Thus, applying any sort of band-pass filter runs the risk of attaching to the trend component some relatively low frequency cyclical fluctuations. Our decomposition (for the social indicators and real GDP per capita) is thus based on the following regression: = β , where is the linear trend, is the regression coefficient, and YYtt + εεis a white noise error. The trend component is obtained tt from = β and the cyclical component is extracted from the residual: tt . ββ ββ εε TT CC TT 11 tt tt tt tt Since we use a linear trend, the cyclical and trend components are uncorrelated by construction. YY YY = YY −YY

> 11

> 12 See Appendix A for the complete list of countries used in the analysis **.**

5
and Chambers, 2017). The recommended approach to mitigate this issue is to include covariates that sufficiently explain the between-area heterogeneity in the model ( _ibid_ ). In this regard, ELL (2002) suggests the inclusion of cluster-level covariates as a way to explain location effects. Nevertheless, this approach is context specific and may not always suffice to ameliorate the issues with between-area heterogeneity. In this regard, Marhuenda et al. (2017) recommend and show that location effects should be at the same aggregation level at which estimation is desired.

If the location effect is specified at the same level where estimation is desired, then the difference between ELL (2003) and MR (2010) reduces to differences in how estimates are obtained and the addition of Empirical Best (EB) prediction by MR (2010). The EB method from MR (2010) conditions on the survey sample data and thus makes more efficient use of the information at hand, whereas ELL does not include this component. In essence, under ELL, for any given area present in the sample the ELL estimator of the census area mean _y_ ̄ _c_ is obtained by averaging across _M_ simulated censuses and is given by _y_ ̄ _c_<sup>_∗_(</sup><sup>_m_)</sup> ≈ _X_<sup> ̄</sup> _c_<sup>_′β_+</sup><sup>_η_</sup> _c_<sup>_∗_(</sup><sup>_m_)</sup> + ̄ _e_<sup>_∗_</sup> _c_<sup>(</sup><sup>_m_)</sup> , _m_ = 1 _, . . . , M_ , where _E_ [ _ηc_<sup>_∗_]=0</sup> <u>1</u> _M_ and _E_ [ _e_<sup>_∗_</sup> _ch_<
Imbs, J. and Wacziarg, R. 2003. “Stages of diversification.” _The American Economic Review_ . 93(1): 63-86.

Jones, Ronald W. 2000. Globalization and the Theory of Input Trade. MIT Press: Cambridge, MA.

Lall, S. 1998. “Exports of manufactures by developing countries: Emerging patterns of trade and location.” _Oxford Review of Economic Policy_ . 14(2): 54-73.

Lall, S. 2000. “The technological structure and performance of developing country manufactured exports, 1985-1998.” _Oxford Development Studies_ . 28(3): 337-369.

Leamer, E.E. 1984. Sources of Comparative Advantage: Theory and Evidence. Cambridge MA: MIT Press.

Lederman, Daniel and Laura Saenz. 2003. “Innovation around the World: A Cross-Country Data Base of Innovation Indicators.” Mimeographed. Office of the Chief Economist for Latin America and the Caribbean, World Bank, Washington DC.

Mayer, W. 1984. “The infant-export industry argument.” _Canadian Journal of Economics_ . 17(2): 249-269.

Neary, J. Peter. 2003. “Competitive versus comparative advantage.” _The World Economy_ . 26(4): 457-470.

UNCTAD. 2003. “UNCTAD handbook of statistics.” TD/STAT.28. Geneva: UNCTAD.

Vettas, Nikolaos. 2000. “Investment dynamics in markets with endogenous demand.” _The Journal of Industrial Economics_ . 48(2): 189-203.

World Bank. 2004. “Doing Business in 2004: Understanding Regulation.” Rapid Response Unit, World Bank, Washington D.C.

41
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schooling, direct costs of training account for about 75% of the total costs of training (foregone productivity only accounts for 25%). Therefore, it is not possible to read the return to firm investments in human capital from the coefficient on training in a regression of productivity on training. Data on direct costs is essential for computing meaningful estimates of the internal rate of return to these investments.

our estimates of the internal rate of return to training vary across firms. While investments in human capital have on average negative returns for those firms which do not provide training, we estimate that the returns for firms providing training are quite high, our lower bound being of 17% and our preferred estimate being 24%. Such high returns suggest that company job training is a sound investment for firms and for the economy as a whole, possibly yielding higher returns than either investments in physical capital or investments in schooling. Therefore, it is puzzling why these firms train on average such a small proportion of the total hours of work (less than 1%). We suggest three possible explanations: 1) coordination failures between employers and employees; 2) uncertainty in the returns to training; 3) credit constraints. Unfortunately we cannot assess the empirical importance (if any) of each of these hypotheses.

21
4 percent. In order to test the impact of having a population with zero income on shared prosperity we have run two scenarios in this sub‐section: (1) keeping people with zero income; (2) excluding people with zero income per capita. Composite figure 9 shows the annualized growth rates for the bottom 40 percent. The differences in estimates for selected scenarios are reported in percentage points in two sub‐figures in the bottom part of figure 9. We also plot sum of shares of the population with zero income in two periods for all countries.

In absolute terms (though not strictly comparable), the impact of excluding zero income is stronger than the choice of grouped versus microdata, but weaker than the impact of excluding negative income. Thus, the largest gap for excluding zero income is 0.38 percentage points in absolute terms (Brazil) compared to the highest 0.7 percentage points obtained from excluding negative income (table 5) and highest 0.14 percentage points obtained from grouped versus microdata test (figure 5).

16
foreign<br>evelopment: Funds and|
|**SOURCE**: R&D and sales data from N<br>_Development: 2005, Data Tables_. Avai<br>***Food is the proxy for grain mlling; ch<br>steel and metalproducts; forglass and ce|ational Science Foundation, Division of<br>lable at: http://www.nsf.gov/statistics/n<br>emicals is the proxy for fertilizers and<br>ment we use an average ofplastics, rub|Science Resources Statistics,_Survey of I_<br>sf10319/content.cfm?pub_id=3750&id<br>pesticides; primary and fabricated metals<br>ber, mineral and woodproducts.|_ndustrial Research and_<br>=3.<br>are the proxies for iron,|

74
it is less than 5 percent of the top country in absolute R&D 5 expenditures (the United States, US$ 369 billion). Russia’s 2008 R&D 4 expenditures as a share of GDP (1.03 percent) is certainly higher than some Source: OECD Science and Technology Indicators 2011. Eastern European countries (e.g., Poland, 0.6 percent or Romania, 0.54 percent) and comparable to some BRICs (Brazil 1.02 percent or China 1.4 percent). Yet, it is less than half the EU Lisbon Agenda target of 3 percent and much lower than that of economies at the technological frontier (e.g., the United States 2.7 percent, Sweden 3.7 percent, and Israel 4.7 percent).

In parallel to declining scientific performance, the proportion of researchers in the workforce has been diminishing. While in the mid-1990s, the proportion of researchers in overall employment in Russia was 1.5 times the OECD average – almost twice the share in EU-27 countries and more than double the EU15 country ratio – in 2008 it had become lower than in both (Figure 1). At the same time, Russia’s 7
sup>34</sup> Sectoral institutions also influence the elasticity of supply of producers in as much as they impact the supply-side constraints that producers have to deal with, among which, chiefly, difficulties to access inputs.

To grow cotton, farmers indeed need costly inputs but face strong cash constraints as credit markets are quasi non-existent in most of African rural areas. As a result, production overwhelmingly occurs through interlinked transactions whereby inputs are provided on

> 29 According to him, whereas the average elasticity in Mali between 1990 and 2004 is slightly higher than between 1969 and 1989, it has nonetheless been trending downwards sharply since 1996 and the results are even starker once improvements in technology are netted out.

> 30 The elasticity of 0.5 is maintained for the rest of the world.

> 31 The model is calibrated with price, production and subsidy data averaged over the 1999-2002 crop seasons.

> 32 The adaptation of the Goreux Model to multiple supply elasticities is given in Shepherd and Delpeuch (2007), Appendix 2, page 8.

> 33 This section heavily draws on Delpeuch and Leblois (2011).

> 34 The exchange rate between local currencies and the dollar, in which cotton is traded worldwide; transport costs and the efficiency of the companies which transform seed cotton into cotton lint (the ginners) also affect producer prices. See Baffes (2011) on the exchange rate and Delpeuch et al. (2011) as well as Tschirley et al. (2009) on transport and ginning costs.

11
_the study countries_ - access to capital, and difficulty in finding and keeping customers in this highly competitive sector.

# **What determines HE earnings?**

HE owners have a range of personal characteristics, but which ones matter most for earnings? To gauge this question, we ran a simple OLS regression analysis on owners reported gross earnings per hour (e.g. reported gross profits per hour worked by the owner), using age, level of education and training, gender, hours of work, and location as explanatory variables. This simple analysis has several weaknesses. First, we could not control for capital so returns to assets are included as owner‟s earnings. Second, we did not have variables commonly used to control for the unobserved selection that led a person to start a HE (e.g. know how, networks, business skills, etc.)<sup>.9</sup> Since we have only HE owners in the sample, many of these unmeasured personal characteristics will be present at a similar level in, for example, most urban HE owners. Other characteristics will vary across the sample, but may be correlated with education, possibly causing this variable to be overstated.

HE owners‟ education is highly correlated with earnings. The higher level of education the higher earnings. Consistent with that finding that those with completed primary education had the highest propensity to be HE owners, standardized regressions<sup>10</sup> of hourly earnings for Tanzania, Rwanda and Ghana show that HEs owners do have positive and increasing returns to education if they complete primary, but education below this level does not add significantly to returns in two out of three countries. (Annex Table 2). In both Tanzania and Rwanda, there is no return to having started, but not completed primary education. This is striking given that the majority of HE owners in Rwanda are in this category. But these results may also reflect the tendency of the education variable to pick
in the benchmark model. As data for real effective exchange rates are not available for certain countries, this model is estimated individually for 29 EMDEs by using quarterly data.

# **3.3. Disentangling oil price shocks: Demand versus supply**

The earlier literature (with the exception of Kilian et al., 2009) generally treated oil price shocks as the same regardless of the source of the shock. More recently, there is growing recognition that the macroeconomic effects of supply and demand shocks in the crude oil market are different, depending on whether the oil price increase is caused by disruption in oil production, demand from global economic activity, or precautionary demand (Gnimassoun et al., 2017). There is empirical evidence that commodity prices in general, and oil prices in particular, are determined not only by supply side factors but also by demand-side factors related to the global business cycle (for example, Hamilton, 2009; Kilian, 2009; Alquist et al., 2013; Kilian and Murphy, 2014). Therefore, in this section, we examine the impact of oil price shocks on the current account while accounting for the source of the oil price fluctuations.

We distinguish between the effects of oil demand and oil supply shocks on current account balances by using data on both global oil prices and global oil production, where oil demand and oil supply shocks result in impulse responses of oil prices and oil production with different signs.<sup>6</sup> In technical terms, we use the algorithm introduced by Arias, Rubio-Ramirez, and Waggoner (2018) to search for 1,000 successful draws satisfying our sign restrictions. To allow an appropriate comparison with the models with recursive identification, the shock sizes (of oil demand and oil supply) are normalized such that they result in a 1 percent increase in real crude oil prices.

> 5 According to <u>https://www.worldstopexports.com/, the worldwide values of crude oil, natural gas
baseline were included in the random assignment to the different treatment groups and revisited in 2013.

In this study we use demographic, location, and household asset data collected in the baseline survey, and daily health diary data on children collected during the 2013 follow-up. The unit of randomization is the compound. A compound may house more than one household,

> 8We analyze effects of the two interventions on preventive care and longer-term health in a separate paper. We find no impact on illness incidence in the 2013 data used here, and measured chlorination levels in 2014 do not significantly differ between treatment groups. For more detail, see section 5 below and Dean and Sautmann (2023).

10
calculated from the microdata or grouped data. Creating grouped data and estimating a Lorenz curve using grouped data seems unnecessary and some might even argue that the additional steps add non‐negligible estimation errors (Azevedo and Mitra, forthcoming). The grouped data approach was adopted for the monitoring of extreme poverty because the World Bank could not access household unit record data for most countries when the monitoring started. Even though PovcalNet has started using microdata for estimating extreme poverty for most countries since 2015, there are still several large countries such as China, Iran and Algeria that do not share household unit record data with the Bank. As a result, PovcalNet needs to use the grouped data approach for these countries to calculate poverty and shared prosperity indicators. This is currently the case for 15 out of 94 countries.

The last technical issue is associated with the use of income for measuring shared prosperity. There is a clear preference of using consumption data for monitoring extreme poverty, because it has several advantages over income data in terms of welfare measurement (Deaton, 1997). Nevertheless, many middle income countries in LAC and ECA use income data for their official poverty measurement. For the 2014 and 2015 update of the GDSP, the GPWG decided to increase the number of countries for which income, rather than consumption, is used to estimate shared prosperity.

However, the distribution of income is quite different from that of consumption expenditure and thus additional problems, which consumption data did not have, may occur by moving from consumption to income. For example, income data are usually more volatile than consumption. Of particular concern is income with negative or zero values. Such outliers can affect inequality statistics and means of the poorest 40 percent, resulting in a “lumpy” movement of the shared prosperity indicator.

Empirically testing the impact of different methodological choices in this section is based on a different set of countries depending on the type of test conducted. Comparing grouped versus microdata is based on 20 countries representing all regions of the World. Sixteen countries
surveys identify key biases that raises concern about measurement of poverty and inequality using CPHS data with reported weights. For this reason, we undertake a systematic reweighting exercise with the objective to transform the CPHS into a nationally representative survey (and thereby correct for these biases). Following recent literature (Wittenberg, 2009; Tack and Ubilava, 2013), we adopt the 13Not all households are interviewed in all three waves in a year, due to households being unavailable, locked at the time of survey or other reasons. For households that are visited more than once a year, we choose one visit at random.

> 14If the fertility rate falls to below replacement level, it signals that the population is stabilizing.https://indianexpress.com/article/india/fertility-rate-falls-to-below-replacementlevel-signals-population-is-stabilising-7639986/

> 15The individual level weights that are bundled in CPHS survey dataset are based on population projections from the Census. As these projections can become dated overtime, we observe the household size captured in CPHS’ survey roster and calculate individual weights as the product of CPHS’ reported household weight and its size.

> 16Note that the non-response adjusted household weights are still based on census’ household level projections.

11
2006-2019), and Senegal (2000-2019). To merge the NTM panel data with the firm-level trade data at the HS6 product level, we convert the HS6 product codes in both the firm-level trade data and the NTMs data into a consolidated version, following the procedure outlined in Fernandes et al. (2016).

## **3.2.1** **_Stylized facts on firm-level exports_**

We observe a total of 16,021 unique firms throughout the sample used in the study. Of these, 1,579 firms are located in Burkina Faso, 8,015 firms are located in Malawi, and 6,428 firms are located in Senegal (Table 1). About 70% of the firms are active exporters in the manufacturing sector with the remaining 30% exporting agricultural products. The number of firms we observe increased a lot over time from 320 firms in 2000 to 2,407 firms in 2019. The same is true for the number of products exported by the firms and the number of destinations that they serve. We also observe that the number of high-income destinations has risen over time, increasing from 23 in 2000 to 63 in 2019. However, most firms only serve a few destinations or export a few products (see Figure A3). The average firm only serves three destinations and exports 29 products, with differences across origin countries and sectors. The average 13We followed the procedure outlined in Fernandes et al. (2016) to clean the firm level data.

8
0.20)|(0.51)|
|Socialist legal origins (=1)<br>1.58|0.02|0.27***|0.42***|0.28**|0.14|
|(2.11)|(0.06)|(0.07)|(0.12)|(0.13)|(0.53)|
|Panel A of Table 7: Other country-level risk factors||||||
|Political Risk (PRS Group)<br>2.74|0.00|-0.09|-0.11|-0.52**|-1.16**|
|(2.82)|(0.09)|(0.08)|(0.13)|(0.26)|(0.51)|
|Corruption Perception (Transparency Int’l)<br>-0.66|-0.00|0.09|0.19|0.52**|0.97***|
|(0.88)|(0.15)|(0.15)|(0.15)|(0.26)|(0.23)|
|Economic Freedom (Heritage Foundation)<br>-1.98|-0.01|0.05|-0.11|-0.32*|-0.54*|
|(2.63|(0.08)|(0.07)|(0.13)|(0.18)|(0.32)|
|Panel A of Table 8: Capital market openness and develo|pment|||||
|De-jure fnancial openness index<br>-0.18|0.00|0.03|