rafmacalaba/datause-encoder-data huggingface dataset:
# Conclusions

This analysis provides a glimpse of the richness and complexity that can be seen introducing a gender lens to poverty analysis. The findings of this study indicate that incorporating such a lens can provide important insights for policy design which has, to date, not included a systematic analysis of gender differences when it comes to policies to eradicate extreme poverty.

At the global level, four main findings appear. First, not only does a gender lens matter for poverty analysis in general it also matters in relation to the moment in the lifecycle for women and men (e.g. their marital status, presence of children and dependents in their households, when and if they join the labor market, and the responsibilities for reproductive work).

Second, children and dependents, demographically and economically, are an important vulnerability factor particularly for women during their reproductive years. Care responsibilities overlap with economically productive years (high labor market participation and best time for income generation years), making women specifically vulnerable due to multiple demands on their time.

Third, for both men and women, pooling resources, meaning having more adults working for pay in a household, appears to be a protective factor against poverty. Formal education is also a strong protective factor against poverty, especially for women.

Finally, at the household level, no one single element, whether it is self‐declared headship, adult composition, or earner composition, seems to be enough to determine household‐level poverty, so complementary approaches are required to look at household‐level poverty and its main determinants.

This paper provides important insights based on the exploration of a dataset like the GMD, and it suggests the value of continuing this type of analysis to further understand gender, and other differences within poor and vulnerable households. A clear further research agenda for evidence‐based policy decision‐ making at the country or regional level, leveraging new data being collected can build on these initial findings. Among these, expanding from income and consumption based approaches to explore the multi‐ dimensions of poverty and how these can vary between women and men is an important one.

33
# Figures

|Figure 1:|Trends in Infant Mortality and Child Mortality . . . . . .<br>.<br>55<br>in Ten DHS Countries of Sub-Saharan Africa<br>in the 1970s and 1980s|
|---|---|
|Figure 2:|Determination of the Level of Child Health<br>. . . . . . . .<br>56<br>Achievable in a Given Economlc Environment|
|Figure 3:|Differences Ln Child Health between Two . . . . . . . . . .<br>57<br>Socioeconomic Groups with Dlfferent Exposure<br>to Disease|
|Figure 4:|Differences in Child Health between Two . . . . . . . . . .<br>58<br>Income Groups Facing Equal Prices of Child<br>Health|
|Figure 5:|Infant Mortality Trends by Income Group . . . . . . . . . .<br>59<br>in Cote d'Ivoire, 1970-86 (LSMS 1985/86)|
|Figure 6:|Neonatal Mortality Trends by Income Group . . . . . . . . .<br>60<br>in Cote d'Ivoire, 1970-86 (LSMS 1985/86)|
|Figure 7:|Postneonatal Mortality Trends by Income Group . . . . . . .<br>61<br>in Cote d'Ivoire, 1970-86 (LSMS 1985/86)|
|Figure 8:|Urban and Rural Infant Mortality Trends . . . . . . . . . .<br>62<br>in Cote d'Ivoire, 1970-86 (LSMS 1985/86)|
|Figure 9:|Rural and Urban Neonatal Mortality Trends . . . . . . . . .<br>63<br>in Cote d'Ivoire, 1970-86 (LSMS 1985/86)|
|Figure 10:|Rural and Urban Postneonatal<br>Mortality Trends . . . . . . .<br>64<br>in Cote d'Ivoire, 1970-86 (LSMS 1985/86)|
|Figure 11:|Infant Mortality Trends by Savings Group<br>. . . . . . . . .<br>65<br>in Cote d'Ivoire, 1970-86 (LSMS 1985/86)|
|Figure 12:|Neonatal Mortality Trends by Savings Group<br>. . . . . . .<br>66<br>in Cote d'Ivoire, 1970-86 (LSMS 1985/86)|
|Figure 13:|Postneonatal Mortality Trends by Savings<br>. . . . . . . . .<br>67<br>Group in Cote dllvoire, 1970-86 (LSMS 1985/86)|
|Figure 14:|Infant Mortality Trends by Mother's Education . . . . . . .<br>68<br>in Cote d'Ivoire, 1970-86 (LSMS 1985/86)|
|Figure 15:|Neonatal Mortality Trends by Mother"s Education . . . . . .<br>69<br>in Cote d'Ivoire, 1970-86 (LSMS 1985/86)|
One noteworthy feature in Table 3 is that there are some systematic differences in the weights assigned to different types of sources. These are summarized in the bottom panel of the table. For each of the four types of data sources, we first report the share of each type in the total number of sources for each indicator. For example, for Control of Corruption, we rely on a total of 25 data sources, of which 7, or 28 percent, are from commercial business information providers. We also report the share of the total weights accounted for by each type of indicator. Taking the same example of Control of Corruption, these seven data sources together receive a slightly higher share of the total weight in the indicator, at 35 percent. The last column reports a simple average of these two figures across all six indicators. These show that data from commercial business information providers and data from non-governmental organizations receive weights that are somewhat higher than their proportion in the total number of data sources. NGO-based sources for example get 21 percent of the total weight in the aggregate indicators but account for 16 percent of the data sources. In contrast, survey-based indicators account for 13 percent of the weight on average, but account for 27 percent of sources; and indicators provided by public-sector organizations get almost exactly the same weight on average as their prevalence among the number of sources would suggest (25 versus 26 percent).

We can combine this information with the information on country coverage of data sources reported in Table 2. In particular, in the bottom panel of Table 2 we report the distribution of country-level data points, weighting each point by the weight it receives in the corresponding aggregate indicator for each country. In light of the higher weights assigned to data from commercial business information providers, we find that the weighted average share of country-level data points for this type of source rises from 43 percent (unweighted) to 59 percent (weighted). Correspondingly, the weighted share of household surveys declines somewhat from 17 percent to 8 percent, and for public sector providers from 22 percent to 13 percent.

We conclude this discussion of weighting by noting that while the weighting scheme we use has the attraction in principle of reducing the variance of the overall governance estimates, in practice this effect is relatively small, with the standard errors of the governance estimates declining by about 10 percent relative to an unweighted 15
from economies of scale in cross-border trade, and reduce distortions that result from unequal treatment of SSCBT and truck-based trade. The protagonists argue that group trade should indeed be promoted by public policy. Knowing the factors that facilitate participation in group trade is thus helpful in deciding which promotion or support activities might be most appropriate.

The empirical analysis uses a Probit model that links the participation decision in group trade to the trader’s personal traits, logistics features, and merchandise characteristics. The specification thus takes the following form: where Y _i_ is the observation whether a trader participates in group trade, _Xj,i_ is a set of _j_ different trader, logistics, and product characteristics of trader _i_ , and Φ _(.)_ is the cumulative normal distribution function.

The following factors were considered as potential determinants of group trade participation for analysis related to Equation (1): the gender of the trader (GEND), the trading experience of the trader (EXPE), membership in a traders’ association (ASSO), registration of a formal business (FORM), use of a fuel-powered vehicle for transportation (FUEL), time to assemble and transport the merchandise to the border (TIME), perishability of the products carried (PERI), and trade protection accorded to the carried products (PROT). These potential determinants are described below.

GEND: Women play a prominent role in SSCBT. The available sample of DRC importers is almost gender-balanced, with 52 percent of interviewed traders being women. Other surveys at border locations in the Great Lakes area have found an even larger majority of traders 12
||**Table 1**|**0 2SLS**|**estimati**|**on Labor**|**demand**|**and Bro**|**adband u**|**se**||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|VARIABLES|Total|Direct|Outsourced|Laborer|Professional|Production|Management|Permanent|Temporary|Women|Men|
|Broadband (1=Yes)|0.217|0.017|1.672*|0.182|‐1.217|0.166|‐0.100|‐0.011|0.607|‐0.047|0.361|
||[0.293]|[0.396]|[1.001]|[0.393]|[2.076]|[0.393]|[0.409]|[0.415]|[0.812]|[0.285]|[0.339]|
|TFP|‐0.304***|‐0.250***|‐0.698*|‐0.316***|‐0.001|‐0.280***|‐0.157***|‐0.174***|‐0.316***|‐0.213***|‐0.267***|
||[0.060]|[0.060]|[0.360]|[0.093]|[0.133]|[0.068]|[0.050]|[0.053]|[0.080]|[0.048]|[0.056]|
|Observations|44,816|44,396|11,597|43,030|24,517|43,970|42,347|38,880|29,570|43,266|44,298|
|R‐squared|0.009|0.035|‐0.668|0.012|‐0.599|0.017|0.009|0.012|‐0.077|0.021|‐0.082|
|Number of plants|8,853|8,802|2,574|8,549|5,503|8,703|8,522|7,909|6,354|8,629|8,777|
|Plant FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Sector FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|City FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Errors|||||Clu|ster Plant/St|ate|||||
|Instrument|||||Downloa|d speed at st|ate level|||||
|Weak identification test (Kleibergen‐||||||||||||
|Paap rk Wald F statistic)|12.73|12.01|4.126|13.42|2.841|12.39|12.77|11.02|9.504|13.96|13.25|
|Robust standard errors in brackets||||||||||||
|*** p<0.01, ** p<0.05, * p<0.1||||||||||||

# Robustness exercise

In this section I use a balanced panel sample to assess the degree to which sample selection could change the previous results. The balanced panel has a slightly higher share of large firms than the unbalanced panel in the main exercise, but the size distributions are not too different from one another.

**Table 11 Size distribution in the balanced panel.**

||Full sampl|e period|
|---|---|---|
|Size|FrequencyPe|rcent<br>Cum.|
|[0,50]|42,241|70.19<br>70.19|
|(50,200]|10,868|18.06<br>88.25|
|(200,1000]|5,509|9.15<br>97.41|
|>1000|1,561|2.59<br>100|
|Total|60,179|100|
||Year|2008|
|Size|FrequencyPe|rcent<br>Cum.|
|[0,50]|5,863|68.2<br>68.2|
|(50,200]|1,699|19.76<br>87.96|
|(200,1000]|802|9.33<br>97.29|
|>1000|233|2.71<br>100|
|Total|8,597|100|

Using a balanced panel does not change the main results, so there should be little concern about the effect of sample selection in the OLS regression. The fact that the balanced sample is comprised of about 70% of plants in the period is also reassuring. When I instrumented broadband on the balanced panel, the instrument loses its relevance, making the comparison between the two samples difficult. This suggests, however, that entry and exit dynamics could be related to broadband quality in a way that is systematically different for firms that remain in the sample since 2008 and those that enter or leave. Therefore, the 2SLS results in the main section should be 16
- 27 -

Since both one-year and two-year lagged foreign capital investment have positive impacts on income growth, the causal relationship is likely to be from the former to the latter, rather than the other way around.

Finally, notice that we put regional dummy variables for different regions in the equation (East Asia, South Asia, Latin America, and Sub-Saharan Africa).<sup>8</sup> The countries in the base group are non-OECD European countries (Greece, Portugal, Turkey), North African countries (Egypt, Morocco), and South Africa and Israel. Table 3 shows that only the coefficient for Sub-Saharan Region is significantly negative.

# 3.3.3 **Annual data** vs. longer **time** span

Although Table 3 results show a significant improvement than those in Table 2, there remains the problem that the estimated coefficients on the growth rate of foreign capital imports and on that of home capital investment are still too small in magnitude. Furthermore, the reported _R_<sup>_2_</sup> _'s_ are still not very high relative to the ones in other similar studies (for example, see Levine and Renelt 1992).

We suspect that the problem may arise from the use of the annual data, which contain too much noise and short term disturbances that do not reflect long-run trends, and are not captmred in the exogenous variables in the model. One way to smooth these short term disturbances is to use a longer time span. We thus divide the total period of 1965-88 into several 5-year time intervals. More specifically, we will have four observations for each country, i.e., 1970, 1975, 1980, and 1985. When t = 1985, t - 1 is 1980. All the growth rate variables are averages over the five year time span. This set-up would also reduce the serial correlation between the _JiA's._ 8The fixed-effects model does not provide estimates for regional dummies because the fixed individual country-specific intercepts already account for these individual country effects.
We set up a field experiment motivated by this thesis, in which we randomly assign male or female farmers in Malawi the task of learning about a new agricultural technique, communicating about it to other villagers, and convincing them to adopt. To measure these communicators’ performance in the field, we collect two years of follow-up data on a random sample of other nearby farmers to track how well these other farmers learned and retained information about the technology from their assigned communicator, and whether they adopted it. In addition, we administer knowledge tests to the communicators to measure how well they themselves learned about the technology and retained that information. The latter allows us to compare male and female communicators in terms of their raw innate ability to learn, while the former allows us to track how that ability translates into performance in the field when those communicators have to interact with society at large to complete the assigned task. Comparing across the two stages allows us to infer whether women under-perform on the field task due to a raw ability gap, or because of issues related to social interactions, norms and attitudes.

To shed light on the mechanisms underlying gender discrimination, we provide performance-based incentives to a randomly assigned subset of communicators. These inducements generate additional interactions with both male and female farmers, improving the accuracy of farmers’ perception of the communicators’ skills. We can thus compare whether performance on the socially mediated task continues to lag among women when the individuals they interact with are better informed about their relative knowledge.

Most real-world labor market tasks are characterized by situations where a woman has to rely on or interact with male (or female) colleagues to be successful in her job. This is true for any job that requires managing or supervising teams of workers, teaching or training others, or interacting with customers. Labor markets in the modern economy rarely offer opportunities for individuals to work and succeed in isolation. In such situations, equally able women may underperform relative to men because of overt discrimination and lack of cooperation from colleagues (Bagues and Esteve-Volart 2010; Delfgaauw et al 2013), because of social norms and attitudes (Gneezy et al 2009, Bertrand, Kamenica and Pan 2013), or because gender identities require women to behave differently when interacting with male colleagues (Akerlof and Kranton 2000).<sup>3</sup>

> 3 Hoff and Pandey (2014) provide field experimental evidence on how low-caste children in India perform worse on cognitive tasks in the presence of high-caste children, especially when caste identity is made salient.

3
_migrants’ transfers_ , which are a capital account transfer reflecting the movement of assets by a migrant from one country to another when he or she migrates (IMF 1993). The World Bank combines workers’ remittances and compensation of employees together to form the remittance measure reported in its World Development Indicators, and this has been the definition of remittances used in a number of studies in the literature (e.g. Catrinescu et al. 2009).<sup>3</sup> However, in practice it often proved difficult to separate transfers made by migrant workers from their employment income from a number of other transfers. The Balance of Payments Manual 6 (BPM6) therefore replaced the category of _workers’ remittances_ with _personal transfers_ which consist of “all current transfers in cash or in kind made or received by resident households to or from nonresident households” (IMF 2009, p. 20). Personal remittances in the World Development Indicators (and Figure 1) are then the sum of these personal transfers and compensation of employees.

The underlying data that feeds into these aggregates comes from central banks in each individual country. They typically require banks, money transfer operators and other institutions to provide reports on the transactions they process (Orozco 2006). However, in many cases the coverage of this reporting has been partial. De Luna Martínez (2005) reports on a survey of central banks in 40 developing countries that took place in 2004. He finds that 90 percent of the countries collected remittance data from commercial banks, but such data were collected in only 65 percent of the countries in which credit unions and exchange houses processed remittances, in only 38 percent of the countries in which money transfer operators processed remittances, and in only 35 percent of the countries in which post offices process remittances. He notes that this lack of coverage of financial entities was much less of a problem in the 1970s and 1980s when migrants had very few available options to send remittances, but became more of a problem as the number of channels and financial instruments available in the marketplace to transfer money overseas has increased dramatically. Even when financial institutions were reporting remittances, they did not necessarily report all remittance transactions. Orozco (2006) notes that in most cases

> 3 There has been some debate over this definition with Barajas et al. (2009) just using workers’ remittances, and Guiliano and Ruiz-Arranz (2009) using the sum of all three components, but then making adjustments on a country-by-country basis.

10
<!-- Start of picture text --> Table 2e: Bangladesh<br><!-- End of picture text --> <!-- Start of picture text --> Firm Count by Industry<br>Number of Firms Number of Firms<br>2001 2012<br>Petroleum 10 Office Equipment 13<br>Precision Instruments 32 Petroleum 19<br>Media Equipments 40 Media Equipments 136<br>Vehicles 63 Vehicles 137<br>Other Transport 110 Furniture 235<br>Electrical Machinery 115 Other Transport 276<br>Paper 127 Wood, not Furniture 298<br>Machinery 162 Machinery 461<br>Basic Metal 189 Tobacco 487<br>Tobacco 206 Electrical Machinery 738<br>Wood, not Furniture 250 Paper 902<br>Rubber & Plastics 355 Printing 904<br>Leather 368 Leather 930<br>Fabricated metal 531 Rubber & Plastics 1,036<br>Chemicals 596 Chemicals 1,057<br>Printing 848 Basic Metal 1,205<br>Furniture 1,255 Fabricated metal 1,449<br>Non-metallic metal 2,271 Non-metallic metal 4,652<br>Garments 3,654 Garments 4,670<br>Food & Bev 5,416 Food & Bev 8,805<br>Textiles 6,841 Textiles 13,297<br>0 5,000 10,000 15,000 0 5,000 10,000 15,000<br>Graphs by year Graphs by year<br><!-- End of picture text --> Table 3e: Bangladesh <!-- Start of picture text --> Firm Shares by Industry<br>Share of Firms Share of Firms<br>2001 2012<br>Petroleum 0.0 Office Equipment 0.0<br>Precision Instruments 0.1 Petroleum 0.0<br>Media Equipments 0.2 Media Equipments 0.3<br>Vehicles 0.3 Vehicles 0.3<br>Other Transport 0.5 Furniture 0.6<br>Electrical Machinery 0.5 Other Transport 0.7<br>Paper 0.5 Wood, not Furniture 0.7<br>Machinery 0.7 Machinery 1.1<br>Basic Metal 0.8 Tobacco 1.2<br>Tobacco 0.9 Electrical Machinery 1.8<br>Wood, not Furniture 1.1 Paper 2.2<br>Rubber & Plastics 1.5 Printing 2.2<br>Leather 1.6 Leather 2.2<br>Fabricated metal 2.3 Rubber & Plastics 2.5<br>Chemicals 2.5 Chemicals 2.5<br>Printing 3.6 Basic Metal 2.9<br>Furniture 5.4 Fabricated metal 3.5<br>Non-metallic metal 9.7 Non-metallic metal 11.2<br>Garments 15.6 Garments 11.2<br>Food & Bev 23.1 Food & Bev 21.1<br>Textiles 29.2 Textiles 31.9<br>0 10 20 30 0 10 20 30<br>Graphs by year Graphs by year<br><!-- End of picture text --> 42
|**Table 15: Exploring the Presen**|**ce of Omitted Variable**<br>**Category of Addi**|**Bias in Base OLS R**<br>**tional Covariates In**<br>|**egression Results Und**<br>**tegrated to the Base Re**|**erlying the Mean De**<br>**gression**<br>|**composition Nort**<br>|**h, Male**<br>|
|---|---|---|---|---|---|---|
|_VARIABLES_|Base North|Manager<br>characteristics|Plot characteristics|Geospatial<br>characteristics|Crop fixed<br>effects|Community<br>characteristics|
|Female manager=1|||||||
|Age|-0.0148|-0.0402**|-0.0159|-0.0229|-0.0245*|-0.00798|
||(0.0142)|(0.0168)|(0.0142)|(0.0152)|(0.0133)|(0.0141)|
|Age Squared|0.000124|0.000369**|0.000134|0.000192|0.000216*|3.97e-05|
||(0.000137)|(0.000164)|(0.000137)|(0.000148)|(0.000125)|(0.000138)|
|Years of schooling|-0.00735|-0.00683|-0.00767|-0.00816|-0.00115|-0.00708|
||(0.00601)|(0.00613)|(0.00604)|(0.00634)|(0.00585)|(0.00638)|
|Manager is nonmuslim†|<br>-0.00958|<br>0.00230|<br>-0.00484|<br>-0.0164|<br>-0.0928|<br>-2.88e-05|
||(0.0955)|(0.0966)|(0.0943)|(0.119)|(0.0920)|(0.0960)|
|Manager has nonfarm activity†|-0.0848|-0.101|-0.0852|-0.133**|-0.0707|-0.0539|
||(0.0604)|(0.0615)|(0.0602)|(0.0669)|(0.0575)|(0.0634)|
|Household female adult size (age 12-60)|<br>0.0384*|<br>0.0547|<br>0.0392*|<br>0.0555**|<br>0.0262|<br>0.0251|
||(0.0227)|(0.0434)|(0.0227)|(0.0253)|(0.0216)|(0.0234)|
|Household male adult size (age 12-60)|<br>0.0679***|<br>0.0940**|<br>0.0663***|<br>0.0561**|<br>0.0737***|<br>0.0447*|
||(0.0245)|(0.0471)|(0.0246)|(0.0265)|(0.0235)|(0.0257)|
|Child dependency ratio|<br>-0.00448|<br>0.0144|<br>-0.00270|<br>0.0136|<br>0.00850|<br>-0.0116|
||(0.0421)|(0.0552)|(0.0421)|(0.0456)|(0.0414)|(0.0445)|
|Log [GPS-Land Area (HA)]|<br>-0.704***|<br>-0.711***|<br>-0.706***|<br>-0.696***|<br>-0.728***|<br>-0.673***|
||(0.0425)|(0.0422)|(0.0425)|(0.0471)|(0.0398)|(0.0473)|
|Log [GPS-Land Area (HA) Squared]|<br>-0.0517***|<br>-0.0529***|<br>-0.0518***|<br>-0.0529***|<br>-0.0549***|<br>-0.0638***|
||(0.0160)|(0.0158)|(0.0161)|(0.0173)|(0.0156)|(0.0183)|
|Plot distance to household|0.000304|0.000293|0.000309|0.000384**|0.000266|0.000611*|
||(0.000227)|(0.000223)|(0.000231)|(0.000166)|(0.000300)|(0.000315)|
|Access to extension†|<br>0.131|<br>0.154|<br>0.120|<br>0.0979|<br>0.145|<br>0.117|
||(0.0950)|(0.0942)|(0.0930)|(0.109)|(0.0967)|(0.0861)|
|Grows cash crop†|<br>0.0559|<br>0.0600|<br>0.0546|<br>0.0211|<br>0.394|<br>0.0661|
||(0.0728)|(0.0742)|(0.0733)|(0.0788)|(0.471)|(0.0791)|
|Only one crop on plot †|<br>-0.472***|<br>-0.464***|<br>-0.469***|<br>-0.528***|<br>-0.0657|<br>-0.451***|
|<br>Robust standard errors in parentheses. *** p<0|(0.0647)<br>.01, ** p<0.05, * p<0.1.|(0.0653)<br>State fixed effects no|(0.0640)<br>t displayed. † indicates|(0.0692)<br>dummy variable|(0.0700)|(0.0681)|

50