rafmacalaba/datause-encoder-data huggingface dataset:
The summary statistics are shown in Appendix 3. Table 1 shows the mean differences for the female labor share between trading and non‐trading firms, while Table 2 focuses on global versus non‐global firms. Focusing on the control variables, we find consistently that output is positively correlated with the female labor share, indicating that larger firm size matters positively. By contrast, TFP is negatively associated with the female labor share, suggesting that women tend to be more employed in less‐ productive firms. The coefficient sign on capital intensity is negative, implying a higher female labor share in less capital‐intensive firms, but the result is not significant.

The results in Table 1 show that exporting is positively correlated with the female labor share, only controlling for fixed effects (column 1). Additionally controlling for a firm’s output confirms the positive relationship, although the coefficient is slightly smaller (column 2). Interestingly, additionally including capital intensity and TFP as control variables increases the coefficient on exporting (columns 3 and 4). Interpreting the coefficient in the fully specified model (column 4), the female labor share is 4.4 percentage points higher for exporting firms compared to non‐exporting firms. This is lower than the difference of 9 percentage points shown in Figure 1 and can be explained by the inclusion of control variables and fixed country‐sector, subnational region and year effects.

**Table 1: Female labor share premium of trading vs. non‐trading firms**

|Dependent<br>variable:||**_Expo_**|**_rter, exp_**|||**_Impor_**|**_ter, imp_**||
|---|---|---|---|---|---|---|---|---|
|_fem_shisrt_|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|ln_Yisrt_||0.0986|0.1665*|0.2401**||0.2987***|0.3849***|0.4884***|
|||(0.179)|(0.096)|(0.025)||(0.000)|(0.000)|(0.000)|
|ln_kisrt_|||‐0.0757|‐0.1888|||0.0030|‐0.1225|
||||(0.514)|(0.156)|||(0.981)|(0.407)|
|ln_tfpisrt_||||‐1.4452**<br>(0.019)||||‐1.8372***<br>(0.007)|
|**_tradeisrt_**|**3.6772*****|**3.6145*****|**4.3009*****|**4.3505*****|**1.8163*****|**1.7998*****|**2.1041*****|**1.9359*****|
||**(0.000)**|**(0.000)**|**(0.000)**|**(0.000)**|**(0.000)**|**(0.000)**|**(0.000)**|**(0.000)**|
|_constant_|17.8032**|0.6479|2.7873|70.3968***|28.6015|41.1258|51.8918***|65.2164***|
||(0.014)|(0.601)|(0.746)|(0.000)|(.)|(.)|(0.000)|(0.000)|
|Obs.|29,075|24,154|15,935|14,485|24,271|19,701|13,950|12,601|
|R‐squared|0.53|0.54|0.54|0.54|0.51|0.53|0.53|0.53|

Note: p*<0.1, p**<0.05, p***<0.01 (p‐values in parentheses). All regressions include country‐sector, subnational region and year fixed effects.

Focusing on importing firms, the results suggest a female labor share which is on average 1.9 percentage points higher compared to non‐importing firms, controlling for output, capital intensity, TFP and the set of fixed effects (column 8). As was the case for exporting, the female labor share premium is lower than the premium of 4.7 percentage points which was shown in Figure 1. In summary, the findings suggest that a firm’s export status explains roughly half of the difference in the female labor share (4.4 17
different environments (Gregson et al. 2001; Boerma et al. 2003). Some studies have used representative samples but these were limited to one country (Mermin et al. 2008; Reniers 2008; Ueyama and Yamauchi 2009). Bongaarts (2007) uses data from 33 nationally representative surveys, however, they examine the relationship of late marriage with risk for HIV, not remarriage specifically. To the best of our knowledge, this is the first study to use nationally representative data from so many different African countries to examine remarriage in the context of the HIV epidemic.

It has been established that divorced, separated, and widowed are at a significantly higher risk for being HIV positive than other marital groups (Naibutu et al. 1994, Nunn et al. 1994; Quigley et al. 1997; Gregson et al. 2001; Gresenguet et al. 2002; Boerma et al. 2003; Porter et al. 2004; Bongaarts 2007; Mermin et al. 2008; Reniers 2008; Boileau et al. 2009; Lopman et al. 2009). The results from this analysis confirm that divorced, separated, and widowed are at a significantly higher risk for being HIV positive than other groups.

This study also indicates that remarried individuals (who were, at some point, part of the divorced, separated, and widowed group) are also at a significantly higher risk for HIV infection. For over half the groups studied, the HIV prevalence for remarried individuals is significantly higher when compared to the adult population overall. Remarried females in particular are at a significantly higher risk for HIV among those currently married.

It is not clear from the available data why these remarried individuals have a higher HIV prevalence rate. They may have contracted HIV during their previous marriage, either from their spouse or outside their first marriage. This could have been a reason for divorce or separation or HIV/AIDS could have caused the death of their spouse. Or they may have become HIV positive after their first marriage ended and before – or after- they remarried, when they may have reinitiated sexual activity. Causality is difficult to establish, even in multivariate regressions.

13
# **1. Introduction**

Improving access to quality education is at the core of all development strategies. It has long been recognized that education is associated with tremendous effects on economic development (Nelson & Phelps, 1966; Lee & Barro, 2001; Hanushek, 2007; Hanushek & Woessmann, 2008; Pelinescu, 2015; Angrist, et al. 2019). Given the lack of necessary resources for proper investment in education, the Millennium Development Goals (MDGs), as well as the recent Sustainable Development Goals (SDG 4), called international partners for more assistance to developing countries to achieve universal basic education as well as quality education for all. As a result, the volume of aid to education has experienced a strong improvement since the 2000s, increasing from $4.8 billion to $12.7 billion in 2017.<sup>1</sup> Developing countries were also urged to allocate more resources to the education sector.

Government expenditure on education as a share of the GDP registered a significant increase in low‐ and middle‐income countries from an average of below 3.6 % in the 2000s to above 4 % in 2015 (Figure 1). The volume of government expenditure on education also experienced a strong improvement, tripling from around $400 billion in the 2000s to nearly $1,200 billion in 2015. The school‐age population (population of pre‐primary, primary, secondary and higher education) grew from about 2.02 billion to 2.14 billion over the same period. Therefore, government expenditure per school‐age individual experienced a strong improvement over the past 15 years, increasing from an average of US$457 to US$1,110 in low‐ and middle‐income countries. Combined with the donors’ contribution to education financing, this represents significant progress for education financing.

**Figure 1: Government expenditure on education in Low‐ and Middle‐Income countries**

<!-- Start of picture text --> Education expenditure as a share of the GDP Volume of education expenditure (constant,<br>2010)<br>2000 2005 2010 2015 2000 2005 2010 2015<br>year year<br>Source: World Development Indicators<br>4.2 1.20e+12<br>4<br>1.00e+12<br>3.8<br>8.00e+11<br>3.6<br>6.00e+11<br>Government Expenditure on Education (%GDP) 3.4<br>Government Expenditure on Education constant 2010<br>4.00e+11<br><!-- End of picture text --> Despite the progress in terms of making financial resources available for education, educational outcomes did not experience similar trends. The average primary completion rate in low‐ and middle‐income countries increased from 81 % in 2000 to 89% in 2017. Most developing countries have not yet achieved universal primary education although there is some progress in terms of access to education. More and

> 1 Miningou (2019).

2
ISCO-08 occupation classifications and for the ISCO-68 classifications form the ISCO-88 classifications. With the Frey and Osborne estimated probabilities of automation, we classify occupations as being “formal sector, automation-prone” if they are more likely than not to be replaced by automation, in other words, having a probability of automation greater than or equal to 50 percent, and not in the informal agricultural sector defined below. This cut-off provides a broad definition of automation-prone because we are not interested in estimating precisely how many workers will be lose their jobs, but rather who are more likely employed in the formal, automation-substituting sector than not.

In our model, we define an informal sector that is a perfect substitute to the formal productive sector and is unaffected by automation. This captures the informal agricultural sector that comprises a large segment of employment in developing countries. Following Asian Development Bank (2015), we assume these occupations have zero risk of automation. In developing countries, these occupations are unlikely to have access to sufficient capital to adapt automation technology, and they represent default occupations. We define the informalagricultural sector by occupation classification in order not to require additional variables that some data sets may not have. For the ISCO-88 classification system, these are agriculture, fisheries, and related workers (921), subsistence agricultural and fishery workers (621), fishery workers, hunters and trappers (615), and market gardeners and crop growers (611). For the ISCO-68 classification system, these are mapped to the ISCO-88 occupations: general farmers (611), orchard, vineyard and related tree and shrub crop workers (623), nursery workers and gardeners (627), agricultural and animal husbandry workers not elsewhere classified (629), fishermen (641), and (649) fishermen, hunters and related workers not elsewhere classified.

One limitation with using mappings between SOC and ISCO classification systems is that they assume occupations in different countries involve the same tasks (Arntz, Gregory and Zierahn 2016). Our approach depends on the similarity of the task composition of jobs among mapped occupations between the different classification systems; it also relies on the similarity of the task composition of occupations across countries. For our purposes, these mappings are likely to be sufficient as we are not trying to estimate the exact proportion of workers at risk of losing their jobs to automation technology. Minor differences in the task compositions between 20
an interaction effect that arises from the possible correlation between sectoral gains and population shifts. The formula can be specified as follows: <!-- Start of picture text --> 2 1 )<br>ii2 ii1 ii1 ii2 ii1 ii1 ii2 ii1 ii2 ii1<br>II −II = ∑(II −II )nn + ∑(nn −nn )II + ∑(II −II )(nn −nn<br>Intra-sectoral Population Interaction<br>effects shift effects effects<br><!-- End of picture text --> where i is the informality rate in sector i with an employment share in year t. ii To ensure comparability, we select the years 2006, 2010 and 2016 from all surveys for the II nn decomposition, with the exception of the LFS for which there is only data for 2010-2015. We aggregate sectors into four groups: i) agriculture and mining; ii) manufacturing; iii) non-public services; and iv) public services.<sup>14</sup> The decomposition results suggest that de-industrialization does not explain the evolution of rates informal employment in the past decade. Figure 7 shows that the intra-sectoral effect dominates the change in informal employment for both 2006-2010 and 2010-2016, implying that informality rates have increased in all sectors, whereas the relative employment distribution across sectors has not changed much.<sup>15</sup> In other words, the change in informality is driven by higher incidence of informal employment across all sectors, and not by shifts in employment towards high-incidence sectors.<sup>16</sup> Alternatively, it could be argued that instead of economic activities or sectors, it is changes in demographic characteristics that explain changes in informality. For example, if women are on average more likely to be informal, an increase in informal employment could be driven by a higher share of women among the employed, or by women becoming even more likely to be

> 14 In the ESS, LFS and RLMS, public services are defined to include public administration, health and education. In the LiTS, where we are not able to identify these sectors at the disaggregated level needed, we define public services when individuals report working in the services sector and working for a public organization.

> 15 For instance, according to LFS, the rate of informal employment rose from 45.8 to 54.0 in agriculture, from 7.8 to 11.3 in industry, from 21.7 to 27.3 in non-public services and from 2.8 to 4.7 in public services, between 2010 and 2015. Qualitatively similar trends are seen in the other three surveys. In all these cases we refer to aggregate sectors where agriculture refers to agriculture and mining (i.e., primary sector), industry refers to manufacturing, utilities and construction (i.e., secondary sector) and services refers to all the other economic activities, separating into publicly and privately provided.

> 16 An additional exercise, using 12 economic activities -rather than 4 economic sectors- renders qualitatively similar results. Available from the <u>authors upon request.</u>

13/67 Informal Employment and Worker’s Wellbeing in Russia
# **Taking Power: Women’s Empowerment and Household Well-Being in Sub-Saharan Africa**

Jeannie Annan, Aletheia Donald, Markus Goldstein, Paula Gonzalez Martinez and Gayatri Koolwal<sup>*</sup>

**Keywords:** gender, power, households, health, education, Sub-Saharan Africa **JEL Codes:** C8, D1, I00, J1

> *Annan: International Rescue Committee, <u>jeannie.annan@rescue.org; Donald (corresponding author): World Bank,</u> adonald@worldbank.org; Goldstein: World Bank, <u>mgoldstein@worldbank.org;</u> Gonzalez Martinez: World Bank, <u>pgonzalezmartine@worldbank.org; Koolwal: Consultant, World Bank, koolwalg@devscience.org .</u> This paper is a product of the Gender Innovation Lab, Office of the Chief Economist, Africa Region. The authors would like to thank Alison Decker, Cheryl Doss, Naila Kabeer and Greg Seymour for valuable comments on earlier versions of this paper. We are grateful to UN Women and the World Bank Group’s Umbrella Facility for Gender Equality for financial support.
THAILAND 2. Projections **NRR-1** by 1995

|Tw|PapAstamn|_".<br> ratio|Period|**Crmi**<br>rote|**ds**<br>**COF**|Cm|TR|_oinl_|**0**<br>F_mlo|In|xl00N|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1990<br>|55801<br>|S8.4<br>|1990-94<br>|1.37<br>|20.5<br>|6.7<br>|2.25<br>|64.3<br>|69.6<br>|25<br>|30<br>|
|1995<br>**2000**<br>2005|59765<br> <br>63802<br> <br>67994|52.0<br>47.2<br>45.8|1995-99<br>2000-04<br>2005-09|1.31<br>1.27<br>1.16|19.5<br>18.8<br>17.8|6.3<br>6.0<br>6.1|2.13<br>2.11<br>2.10|65.8<br>67.3<br>68.5|72.0<br>74.2<br>75.3|20<br>16<br>14|24<br>20<br>18|
|2010|n726|4S.1|201U-14|1.02|16.6|6.3|2.10|69.7|76.S|13|15|
|2015|75851|44.7|2014-19|0.90|15.5|6.5|2.09|70.9|77.8|11|13|
|2020|79336<br>|45.2|2020-24|0.80|14.8|6.7|2.08|722<br>|79.1|9|11|
|2025|**82584**|46.7|2025-49|0.52|13.5|8.4|2.07|7S.3|82.2<br>|6|7|
|2050<br>|94065<br>|63.8<br>|2050-74<br>|0.16<br>|12.5<br>|10.8<br>|2.06<br>|78.7<br>|8S.3<br>|3|5|
|2075<br>2100<br>|96035<br>100400<br>|68.0<br>71.5<br>|2075-99<br>2100-24<br>|0.10<br>0.06<br>|12.1<br>11.8<br>|11.1<br>11.2<br>|2.06<br>2.06<br>|80.4<br>8.7<br>|87.2<br>88.6<br>|3<br>2<br>|4<br>3<br>|
|2125<br>2150|101912<br>102649|72.9<br>73.9|2125-49|0.03|11.7|11.4|2.06|82.2|89.1|2|3|

# 3. **Age** structure (percent): 1990 and 2025

<!-- Start of picture text --> 75 +<br>70-74<br>65-69<br>60.64<br>55-59<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34<br>E 2232222FAW!l 25-29<br>20-24<br>15-19<br>10-14<br>5-9<br>0-4<br>10 8 6 4 2 0 2 4 6 8 10 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

# 4. Comparison of Councry with Region and Income Group, 1985-90

TFR **eO** Growth rate <!-- Start of picture text -->
*~  .<br>. l_ w _ .. _~~~~~~~io<br>Coimtr'y  leglon  Income group  Country Region Income grou.p  CutyRa  Incamgroup<br><!-- End of picture text -->
Household heads that are members of one or more organizations are less likely (51 percent are poor) to fall into poverty than their peers that are nonmembers (60 percent are poor). One explanation for this finding could be that members of organizations have more ties to other members and friends who can assist in difficult situations be it economically or emotionally.

Help from friends, perhaps related to political affiliation, may therefore substitute safety nets and financial institutions and credit to smooth economic cycles in the household. There are large differences across the nine regions in Haiti. The heads with social capital are far less likely to experience poverty in the Grand-Anse, North, South, Southeast, and West regions. There is very little difference in the Northwest and Center regions. In the Northeast and Antibonite regions household heads with social capital are more likely to experience poverty. This may be explained by large out-migrations from these regions.

The self-employed are more likely to experience poverty than employees. The incidence of poverty for the self-employed was 51 percent while that of employees was 21 percent in 2001. Work position data show that for all locations, metropolitan, rural, and urban, household heads that are self-employed are more likely to experience poverty than employees are. In the metropolitan area the likelihood of falling below the poverty line is nearly four times higher for a self-employed compared to an employee, and in rural areas the likelihood is double.

<!-- Start of picture text --> Figure 6.3: Headcount Poverty and Work Sector by Location in Haiti, 2001<br><!-- End of picture text --> <!-- Start of picture text --> 70.0<br>Agriculture<br>60.0<br>Industry<br>Service<br>50.0 Public/other<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>Total Haiti Metropolitan Urban Rural<br><!-- End of picture text --> _<u>Source:</u>_ <u>Own calculations based on HLCS 2001.</u> Those who work in agriculture are far more likely to be poor than others. This suggests that productivity in agriculture is lower than in services or industry. The P0 is 58 percent in agriculture, but 43 percent among industrial workers, and 34 percent among service workers. Public sector workers experienced the lowest poverty incidence of 23 35
- 41 -

# **Appendix** B. **RAIL** COSTS, **PROFITABILITY, AND INFRASTRUCTURE** CHANGES

Most of the statistical and econometric studies estimating rail costs and production functions suffer from two fundamental weaknesses. First, they generally fail to differentiate between way-and-structures capital, which is a measure of the quantity and quality of the capital utilized in the roadbed, and track, which in addition to being a proxy for the roadbed capital is also a measure of common carrier obligations to haul commodities. Second, they generally fail to take into account the effect upon costs of the route network and differentiate between high-density, fully utilized track and light-density, underutilized track.'<sup>9</sup> Way-and-structures (WS) capital is a measure of the capital utilized in the roadbed and as such should be treated as a conventional factor of production. An increase in the fixed factor, WS capital, should lead to a reduction in other factors and hence reduction in variable costs. In contrast, track and low-density track should be treated as technological variables that affect the costs of the railroad firm in a way that is not necessarily associated with conventional production theory. An increase in low-density route-miles or total track represents an increase in common carrier obligations and should therefore be associated with increases in expenditures on other factors of production.

A ceteris paribus reduction in WS capital will reduce the quality of the existing track and hence lead to cost increases by requiring increased amounts of variable factors--more money must be spent on equipment maintenance and train crews as the quality of the roadbed deteriorates and speeds are reduced. Similarly, a ceteris paribus reduction in track will not only be correlated with reduction in common carrier obligations and improvements in the quality of the existing track, but also with increases in its utilization. The first two considerations will tend to reduce costs, while the latter will tend to increase them, making the impact of reduced track somewhat ambiguous. Reduction in low-density track, on the other hand, will reduce common carrier obligations and their associated costs and will therefore tend to generate cost savings.

# **B.1 Railroad Costs and Infrastructure Variables**

To assess the possible savings that would accrue from policies aimed at changing the railroad infrastructure, it is important-to quantify the impact of changing the three main infrastructure variables--the amount of WS capital, general track, and low-density track--upon rail costs.

Ceteris paribus increases in WS capital will raise the amount of capital embodied in each mile of track and thus lead to reductions in variable costs. Indeed, econometric estimates (from the United States)

> 19 See Friedlaender and Spady (1980).
3 quarter, to one percent of the value of goods inspected.<sup>5</sup> Proponents argue (see Mowbray (1988), Dornbusch (1987), or Societe General de Surveillance (1989) that the service is cost effective since preshipment inspection prevents price gouging by sellers and false invoicing to avoid tariff and tax liabilities or effect capital flight, combats shipment of substandard goods or items that otherwise fail to meet contractual requirements, and can be used to verify that excessive freight charges are not levied on imports (see Societe General de Surveillance (1988)).

In spite of the importance of these problems, and the claims concerning the utility of PSI for dealing with them, there appear to have been no comprehensive anElyses aimed at evaluating the performance and results of reshipment inspection. The present study provides some relevant information by analyzing Madagascar's relative import prxces befcre and after PSI requirements were adopted. In particular, an attempt is made to determine if Madagascar paid "inflated" prices for some goods and, if so, how effect.vpreshipment inspection was in countering this problem. Also, statistics relating SThe PSI companies focus almost exclusively on imports, although they have been employed for some export products, especially when subsidies and other incentives are offered. While the actual services performed differ from country to country, the normal PSI contract covers the following 14 basic points: (1) the purpose of the contract; (2) the nature and scope of the inspection services to be rendered; (3) obligations regarding comparison of prices; (4) obligations of the contracting government; (5) identification of the goods subject to inspection and those to be exempt; (6) special procedures regarding inspections of goods from certain countries; (7) exempt transactions; (8) reporting requirements; (9) obligations of the inspection company and vendora; (10) fees and other charges; (11) method of payment; (12) liability; (13) resolution of disputes between the contractor and government; and (14) term of the contract. Regarding point (6), exports from the (former) socialist countries of Europe and Asia and other developing countries are generally exempt from inspection, although some Eastern European countries are covered by Madagascar's contract.