rafmacalaba/datause-encoder-data huggingface dataset:
suggest that the positive relationship between exporting or GVC participation and the female labor share is strongest for tasks that appear to be low‐skill intensive.

Interestingly, importing firms in medium‐ to high‐tech industries show the highest female labor share premium relative to non‐importing firms (3 percentage points), followed by low‐tech industries (2 percentage points), whereas FDI firms show a huge female labor share premium in medium‐tech industries of 7.4 percentage points compared to non‐FDI firms. It appears that while FDI firms overall only show a moderate female labor share premium of 1.5 percentage points, foreign ownership status in medium‐tech industries benefits the female labor share much more strongly.

# **4.6 Additional Analysis**

While the previous analysis finds a positive relationship between trading and the female labor and wage share across manufacturing firms, this section assesses the mediating role of a firm’s trading status for the link between the firm’s female labor share and average wage rates. The analysis correlates the female labor share (which now becomes the independent variable) with a firm’s average wage rates (the dependent variable). A negative correlation suggest that firms with a higher female labor share pay lower average wage rates which could imply the existence of a gender wage gap.<sup>12</sup> Appendix 10 gives futher details on the underlying model.

The results find that average wage rates are lower for firms with higher female labor shares, but also suggest that firms that trade, by exporting, importing or integrating into GVCs, show a smaller negative link than non‐trading firms (Figure 14). For firms involved in FDI, the relationship between female labor shares and average wages becomes positive. In other words, foreign firms that employ a higher share of women pay higher average wage rates which could relate to cultural norms adopted in those firms. Increasing the female labor share by 10 percentage points is associated with average wage rates of foreign firms being 2.6 percent higher. Interestingly, the correlation between the female labor share and average wages is even higher for FDI firms that offer training to their employees compared to FDI firms that do not train their workforce.<sup>13</sup>

> 12 While this type of analysis can be performed with firm‐level data only, it cannot control for worker characteristics which might also explain part of the gender wage gap.

> 13 These findings are in line with the literature on FDI spillovers, which suggests that foreign investors can help domestic firms upgrade their technological capabilities directly through sharing of production techniques and product design and assisting with technology acquisition (Paus and Gallagher, 2008), and indirectly through personnel training, advance payment, leasing of machinery, provision of inputs, help with quality assurance, and organization of product lines (Lall, 1980; Crespo and Fontoura, 2007; and Javorcik, 2008).

26
# **Political Economy Studies: Are They Actionable?**

# **Some Lessons from Zambia**

**Monica Beuran Gaël Raballand Kapil Kapoor**<sup>**1**</sup>

**Keywords:** political economy, Zambia, infrastructure, World Bank. **JEL codes:** P16, O55.

> 1 Monica Beuran, consultant, World Bank office in Zambia, Gaël Raballand, senior economist, World Bank office in Zambia, corresponding author and Kapil Kapoor, country manager, World Bank office in Zambia. The authors thank to Verena Maria Fritz and Brian Levy for helpful comments. Valuable inputs were provided by the World Bank’s Zambia country team: Barbara Mwila Kazimbaya-Senkwe, Cecil Nundwe, Indira J. Ekanayake, Jumbe Jeremiah Ngoma, Marcus J. Wishart, Patricia Palale, Pazhayannur K. Subramanian and W. Marie Sheppard.
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Uganda > Road investments > Macro results**

||||Reduction in<br>|Roads|(% variation<br>|)<br>||
|---|---|---|---|---|---|---|---|
|||||||||
|Variable|Definition|Reference|p<br>expenditure|VAT|p<br>duties|g<br>aid|<br>tax|
|Ym|Aggregate household income|875,000.00|0.16|0.18|0.19|0.21|0.19|
|EV|Equivalent variation||0.16|0.18|0.18|0.25|–0.35|
|s|Wage|1.00|0.14|0.29|0.30|0.42|0.31|
|yg|Government income|163,000.00|0.21|2.89|2.89|2.89|2.89|
|g|Total government expenditure|103,000.00|–2.15|2.09|2.09|2.09|2.09|
|ye|Firm income|113,000.00|0.29|0.10|0.09|–0.05|0.09|
|It|Total private investment|105,000.00|0.44|0.26|–0.32|–3.45|–0.07|
|GDP|GDP|834,000.00|0.21|0.21|0.21|0.20|0.21|
|e|Nominal exchange rate|1.00|0.23|0.22|–1.69|–11.68|0.05|

# **Uganda > Road investments > Sectoral results**

||||Reduction in<br>||Roads<br>|||
|---|---|---|---|---|---|---|---|
|Variable|Sector|Base|other public<br>expenditure|VAT|Import<br>duties|Foreign<br>aid|Income<br>tax|
||Crop agriculture|270,000.00|0.22|0.14|0.15|0.19|–0.07|
||Export agriculture|112,000.00|0.36|0.12|0.09|–0.23|0.04|
|Va|Mining and gas|577.00|0.09|–0.02|0.18|0.96|0.03|
|Value added or|Industries|94100.00|0.35|–0.25|–0.29|–1.29|0.07|
|output|Construction|66700.00|1.66|1.21|1.30|1.97|1.41|
||Private services|260,000.00|–0.01|0.05|0.03|0.05|0.09|
||Public services|31600.00|–2.20|1.64|1.76|3.86|1.95|
||Crop agriculture|1.00|–0.11|0.04|0.01|–0.21|–0.18|
||Export agriculture|1.00|–0.20|–0.17|–0.21|–0.65|–0.35|
||Mining and gas|1.34|0.18|0.35|1.74|–9.84|0.05|
|pq<br>Market rices|Industries|1.10|0.07|0.89|0.15|–4.52|–0.02|
|p|Construction|1.01|0.45|0.85|0.50|–1.55|0.42|
||Private services|1.02|–0.02|0.51|0.25|–1.74|0.12|
||Public services|1.00|0.05|0.44|0.32|–1.71|0.14|
||Crop agriculture|1.00|0.10|0.09|0.11|0.31|–0.30|
||Export agriculture|1.00|0.14|–0.22|–0.28|–0.81|–0.37|
|r<br>|Mining and gas|1.00|–1.34|–1.70|–0.77|2.97|–1.46|
|Rental rate of<br>capital|Industries|1.00|0.54|–0.60|–0.69|–2.69|0.10|
||Construction|1.00|2.51|1.90|2.06|3.32|2.25|
||Private services|1.00|–0.08|0.17|0.14|0.29|0.25|

45
on non-contributory social benefits went to the bottom 40 percent of the distribution in 2016. Similarly, 52 percent of the 0.6 percent of GDP spent on contributory social benefits (excluding old-age pensions) went to the bottom 40.

Figure 10. Romania. Concentration of Social Protection Programs <u>(by market income plus pensions quintiles)</u> <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Contributory Non-contributory<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>Source: World Bank estimates based on Romanian HBS 2016.<br>The distribution of these benefits Figure 11. Romania. Concentration of Social Protection<br>between urban and rural areas Programs<br>(by rural and urban areas)<br>shows that many of these programs<br>have a higher impact in rural areas 100%<br>90%<br>(Figure 11). For instance, 84 80%<br>percent of the Guaranteed 70%<br>Minimum Income program is 60%<br>50%<br>devoted to rural areas, where 81 40%<br>percent of benefits are spent on the 30%<br>poorest 20 percent of the 20%<br>10%<br>population. In contrast, most of the 0%<br>contributory programs benefit<br>urban areas, as do near-cash<br>benefits such as school supplies,<br>medicines and transportation<br>benefits.<br>As mentioned in the previous<br>section, despite the progressivity of<br>Contributory Non-contributory<br>Scholarships Heating aid<br>children<br>Unemployment benefit Child-raising + incentive benefit Temporary incapacity and maternity State allowance for Placement allowance for children Support allowance for families with children Disability benefits Guaranteed minimum income Minimum social pension Other social benefits Near-cash school related benefits Other near-cash social benefits Direct transfers<br>Unemployment benefit State allowance for children Scholarships Disability benefits Guaranteed minimum income Minimum social pension Heating aid Other social benefits Direct transfers<br> Child raising+incentive benefit Temporary work incapacity and... Support allowance for families... Other near-cash social benefits<br>Placement allowance for children Near-cash school related benefits<br><!-- End of picture text --> As mentioned in the previous section, despite the progressivity of existing social transfer programs, <u>Source: Own estimates based on the 2016 Romanian HBS.</u> 21
Table A3.3. Government budget and debt in 2013 and by simulation in 2030 (% of GDP)

|||**2013**|base ser-eq+fg|ser-eq+db|ser-eq+tx md|g-eq+fg m|dg-eq+tx|
|---|---|---|---|---|---|---|---|
|Receipts|Taxes|9.7|10.8<br>10.7|10.6|12.5|10.7|16.9|
||Foreign transfers|4.0|4.0<br>5.4|4.2|4.0|8.6|4.0|
||Domestic borrowing|1.4|1.4<br>1.4|4.4|1.4|1.4|1.4|
||Foreign borrowing|1.5|1.5<br>1.5|1.5|1.5|1.4|1.5|
||Total|16.6|17.7<br>19.0|20.8|19.4|22.1|23.7|
|Spending|Education|4.0|4.5<br>4.4|4.7|4.5|4.4|4.5|
||Health|2.5|2.8<br>4.3|5.0|4.5|7.7|8.9|
||Infrastructure|2.9|2.9<br>2.9|3.0|2.9|2.8|3.0|
||Other services|4.8|5.2<br>5.1|5.2|5.1|5.0|5.0|
||Domestic transfers|1.6|1.6<br>1.6|1.7|1.6|1.5|1.6|
||Interest|0.8|0.8<br>0.8|1.2|0.8|0.7|0.8|
||Total|16.6|17.7<br>19.0|20.8|19.4|22.1|23.7|
|Debt|Domestic|20.2|19.2<br>19.0|39.7|19.2|18.5|19.2|
||Foreign|30.1|29.9<br>29.2|31.1|29.7|27.8|29.3|

Table A3.4. Balance of payments in 2013 and by simulation in 2030 (% of GDP)

|||2013|base|ser-eq+fg|ser-eq+db|ser-eq+tx md|g-eq+fg md|g-eq+tx|
|---|---|---|---|---|---|---|---|---|
|Outflows|Imports|38.2|38.5|38.4|37.5|38.0|38.1|36.8|
||Factor income to RoW|4.6|4.6|4.6|5.2|4.6|4.4|4.4|
||Net interest income to RoW|0.4|0.4|0.4|0.4|0.4|0.4|0.4|
||Total|43.2|43.5|43.3|43.1|42.9|42.9|41.7|
|Inflows|Exports|23.2|23.5|22.3|22.4|23.1|19.5|22.1|
||Private transfers from RoW|13.0|13.0|12.7|13.5|12.9|12.1|12.7|
||Official transfers from RoW|4.0|4.0|5.4|4.2|4.0|8.6|4.0|
||Government borrowing|1.5|1.5|1.5|1.5|1.5|1.4|1.5|
||FDI|1.5|1.5|1.4|1.5|1.5|1.4|1.5|
||Total|43.2|43.5|43.3|43.1|42.9|42.9|41.7|

Table A3.5. MDG indicators in 2013 and by simulation in 2030

||2013|base se|r-eq+fg se|r-eq+db se|r-eq+tx md|g-eq+fg md|g-eq+tx|
|---|---|---|---|---|---|---|---|
|MDG 1: rural poverty rate (%)|47.9|17.8|17.3|19.2|18.3|16.8|20.0|
|MDG 1: urban poverty rate (%)|27.0|8.7|8.5|9.6|9.1|8.1|10.1|
|MDG 4: rural U5MR (‰)|93.3|61.4|56.1|56.1|56.1|52.4|52.4|
|MDG 4: urban U5MR (‰)|71.9|52.4|52.4|52.4|52.4|52.1|53.1|
|MDG 7: rural water access (%)|61.5|82.5|82.9|81.0|82.0|83.4|80.4|
|MDG 7: urban water access (%)|88.4|96.6|96.8|96.1|96.5|97.0|95.9|
|MDG 7: rural sanitation access (%)|34.4|49.2|49.5|47.9|48.7|50.0|47.4|
|MDG 7: urban sanitation access(%)|50.7|69.3|69.9|67.6|68.7|70.8|66.8|

Note: Columns other than 2013 show simulated values in 2030.

34
dominant issuers of international debt securities, which is in contrast to the more central role played by bank bonds in some advanced economies.

**Table 3.4a. Loans from Banks, Percent of GDP**

||Country|2002|2007|2009|2010|
|---|---|---|---|---|---|
|New Member States|Bulgaria|6|29|40|36|
||Czech Republic|14|18|15|14|
||Estonia|15|60|68|55|
||Hungary|15|32|52|50|
||Latvia|9|67|71|64|
||Lithuania|6|39|43|34|
||Poland|8|14|18|19|
||Romania|6|26|34|31|
||Slovakia|10|20|12|14|
||Slovenia|17|60|54|51|
|Candidates and Potential Candidates|Albania|1|3|6|7|
||Bosnia and Herzegovina|7|23|24|20|
||Croatia|22|49|58|54|
||Macedonia, FYR|||||
||Serbia|||||
||Turkey|14|17|19|16|
|Eastern Partnership Countries|Armenia|1|2|3|3|
||Georgia|5|4|4|3|
||Moldova|4|9|9|6|
||Ukraine|2|16|18|14|

_Source_ : BIS. Note: This table reports cross-border loans from BIS banks.

**Table 3.4b. Loans from Banks to Nonbanks, Percent of GDP**

||Country|2002|2007|2009|2010|
|---|---|---|---|---|---|
|New Member States|Bulgaria<br>|2<br>|16<br>|20<br>|20<br>|
||Czech Republic|6|9|9|8|
||Estonia|7|14|12|11|
||Hungary|6|13|23|21|
||Latvia|2|13|15|15|
||Lithuania|3|9|9|6|
||Poland|4|6|7|7|
||Romania|5|10|15|13|
||Slovakia|6|7|9|9|
||Slovenia|9|19|22|23|
|Candidates and Potential Candidates|Albania|0|1|5|6|
||Bosnia and Herzegovina|6|6|8|7|
||Croatia|10|30|34|34|
||Macedonia, FYR|||||
||Serbia|||||
||Turkey|11|12|13|10|
|Eastern Partnership Countries|Armenia|0|1|2|1|
||Georgia|3|1|2|3|
||Moldova|2|2|2|2|
||Ukraine|1|5|6|5|

_Source_ : BIS. Note: This table reports cross-border loans from BIS banks to nonbanks.

Table 3.4e shows the consolidated liabilities to BIS-reporting banks. The consolidated data take into account local lending by the domestic affiliates of foreign-owned banks, while netting out any cross-border loans by the foreign affiliates of domestically-owned banks. Taken in tandem with the locational data reported in Table 3.4f, these data show that local lending by foreign-owned affiliates are a substantial component of the total exposure of foreign banks in these countries. As indicated in Section 2, a high level of foreign ownership in the banking 13
aged 6-17 years old and out of school and inactive persons above the age of 60). This selection of variables was largely based on findings from the literature on emerging economies and from our own screening of possible predictors of FLP.

The HBS data used for comparing results with LFS data is the 2007 National Living Standards Survey covering 7,200 households. The survey focused on household expenditure and revenues and was principally administered to measure poverty, inequality and other dimensions of living standards but contains modules on employment and education and uses the same employment and educational categories used by the LFS. It was therefore possible to reproduce with precision the set of variables used with LFS data.

# **Results**

# **FLP trends**

The period covered by this study (1999-2012) was a period characterized by sustained growth, averaging four percent a year, and by a very rich set of macroeconomic and social reforms that transformed the economy. For most of the period considered, macroeconomic stability reigned in Morocco and GDP growth trickled down effectively to the population so that the country was able to deliver in terms of households income and poverty reduction. The overall economic performance of Morocco during the period considered was good by macroeconomic and microeconomic standards (see Douidich et al, 2013 and Verme et al., 2014 for a full description of reforms and economic performance).

By contrast, the labor market performance has been disappointing. The unemployment rate declined marginally in recent years but only for men and the Labor Force Participation Rate (LFPR) and the Employment Rate experienced little changes, despite evident employment and job mobility, and females experienced overall declines in both rates (Verme et al, 2014). Quarterly data (Figure 1) show that: 1) the FPR in both urban and rural areas declines over time; 2) the FPR rate is higher in rural areas as compared to urban areas; 3) The urban-rural gap increases over time and 4) the rural FPR has been more volatile than the urban FPR (as one should expect because of seasonality effects).

It is remarkable that both urban and rural female participation rates declined during a period of sustained growth and it is also difficult to observe a rural-urban trade-off, with shrinking FPR in rural agricultural areas and rising FPR in urban industrialized areas. This is striking also because female enrollment and completion rates have increased remarkably during the same period and for all levels of education while fertility rates continued to decline and marriage age increased.

Declining female participation rates have been explained by a combination of mainly three factors. The first factor points to a poor performance of women relatively to men. Males’ participation rates between 1999 and 2012 have increased and women show a worse performance than men on all aspects of labor mobility including job and occupational mobility (Verme et al., 2014). The second factor relates to culture and gender norms and is signaled by an exit of women 8
households. Moving from indoors to an open kitchen lowers the PM10 concentration in cooking and living areas by almost the same magnitude as switching from firewood to kerosene. Switching from mud walls to other materials lowers PM10 far more than any other factor, and use of thatch roofs also has a large effect.

(5) In a representative sample of households, how serious is the indoor air quality problem for poor families in Bangladesh? For our assessment, we adopt the Indian rural PM10 exposure standard: a 24-hour average of 100 ug/m<sup>3</sup> . Our results for six Bangladeshi regions suggest that indoor PM10 concentrations are quite high for many poor families. For all rural families with per capita incomes below $1.00/day, we estimate a mean PM10 concentration of 275 ug/m<sup>3</sup> for kitchen spaces -- nearly three times the Indian standard. This falls somewhat in peri-urban and urban areas (to 226 and 193 ug/m<sup>3</sup> , respectively), but remains much higher than the standard.

(6) Are there significant geographic variations in indoor air quality? Our results suggest great geographic variation, even for households in the same per capita income group. This variation reflects local differences in fuel use and, more significantly, construction practices that affect ventilation. For the poorest households, rural PM10 concentrations vary from 410 ug/m<sup>3</sup> in Cox’s Bazar to 202 ug/m<sup>3</sup> in Faridpur. Even in urban areas, concentrations differ by almost 100 ug/m<sup>3</sup> between the highest areas, Jessore and Rajshahi, and the lowest, Sylhet. The poorest households in Rangpur face the same mean indoor concentration (198 ug/m<sup>3</sup> ) as the highest-income households in Cox’s Bazar.

(7) What are the prospects for clean stoves and clean fuels?

Our survey suggests that limited information may be a significant impediment to adoption of improved stoves. Only 15% of our sample households regard improved 39
and Fernández, 2013; Attanasio et al. 2013), and Uruguay (Berlinski et al, 2008).<sup>1</sup> Outside the Latin American context, the causal evidence on the effectiveness of preschool and its viability as a cost-effective model for improving child development outcomes is scarcer (see for example Bouguen et al, 2013; Arbour et al, 2016; Ozler et al, 2016; Behrman, Cheng and Todd, 2004; Rosero and OOsterbeek, 2011).

As such, the potential benefits as well as the costs and feasibility of investing in preschool across multiple communities and under conditions of extreme poverty remain largely open questions. This study helps fill this gap by conducting one of the first large-scale randomized experiments of preschool in an African context. We analyze the effects of a community based preschool intervention randomly assigned to 30 of 76 eligible rural communities in the Gaza province of Mozambique. At a cost of US$3.09 per child per month, the program provided up to three preschool classrooms per community, community mobilization activities, learning materials, instructor training, and monthly parenting meetings. We collected a baseline survey of 2,000 households with preschool aged children just prior to the roll-out of the program in 2008. We additionally measured primary school performance on a sample of first graders and interviewed local community leaders. Two years later, we conducted a follow-up survey on the original sample of children plus cross sections of first graders and community leaders in the 76 evaluation communities.

We measure the effects of preschool on key dimensions of children’s development and school readiness, including the cognitive (numeracy, working memory), linguistic (receptive language, use of gestures, sounds and movements), psycho-social and behavioral (personal and social) and physical (fine and gross motor skills, health and nutrition) domains. We then analyze the effects of preschool attendance on subsequent primary school enrollment and a teacher assessment of performance in first grade. To shed light on potential causal pathways, we explore heterogeneity analysis by the child’s initial conditions, treatment duration, and analyze the effects of the program on child health and parenting practices. Additionally, given that preschools may free up caregiver time at home, we explore potential spill-over effects of the program on older siblings and caregivers.

> 1 Existing preschool evaluations in Latin America have relied exclusively on quasi-experimental identification strategies (Leroy et al, 2012). Other preschool studies compare participating and non-participating children without establishing causal attribution, for example Mwaura et al., 2008 for Kenya, Uganda, and Zanzibar, and Rao et al., 2012 for Cambodia.

3
**Figure 1: Distribution of real GDP per capita of prefecture-level cities in Guangdong (20002013)**

<!-- Start of picture text -->
* * * * Guangzhou<br>* * * * Shenzhen<br>* * * * Zhuhai<br>0 30000 60000 90000 120000<br>Real GDP per capita (yuan)<br>2000 2006 2010 2013<br>.00006<br>.00004<br>Kernel density<br>.00002<br>0<br><!-- End of picture text -->

Data source: Guangdong Bureau of Statistics, Guangdong Statistical Yearbooks (several editions), China Statistical Publishing House, Beijing. Note: The three rows of stars show the levels of real GDP per capita of Guangzhou, Shenzhen, and Zhuhai respectively, from top to bottom, with the color indicating the years of 2000 (red), 2006 (purple), 2010 (black) and 2013 (blue).

Figure 1 shows that Shenzhen, Guangzhou, and (to a lesser extent) Zhuhai are more developed than the rest of the economy in a significant manner. Figure 2 shows the Gini coefficient of the gross regional product per capita in the 21 prefecture-level cities rose from 0.35 in 2000 to almost 0.40 in 2005-2006 before steadily declining to 0.34 in the recent three years. Figure 3 shows, during the period of relatively slow growth after the Asian financial crisis in 2000-2003, the richer cities grew faster and inequality increased; during the period of high growth performance in 2003-2009, still the richer cities grew faster and inequality continued to increase; while in the recent period of 2009-2013, when growth slowed down (partly related to the global economic crisis and the restructuring), the poorer cities grew faster than the richer ones and inequality started to decline.

4
including R&D, education, health care, some infrastructure, and certain environmental protection investments, and others. Also, those studies that have performed analyses over time generally find that the rates of return show no tendency to decline over time. Recent surveys of this literature, cited earlier in the text, find an amazing degree of consensus on the estimated rates of return obtained in many studies that evaluate rates of return to public goods around the world. The fact that the studies reviewed have used different approaches and diverse data bases and have been applied to a large number of countries around the world means that this consensus should be taken seriously. These extremely high and non-declining rates of return suggest that there are important investment opportunities in public goods that are not being fully exploited. That is, it suggests under-spending in public goods.

Second, many firm-level studies around the world (some cited earlier in the text) have found that subsidies aimed at promoting investment, employment, and growth do not achieve these objectives. In some cases they are counterproductive to these goals. Thus, while the rates of return to public goods are very high, the rates of return to private goods or subsidies are very low or even negative. Yet governments tend to spend a large share of their resources on the goods that have low returns while at the same time foregoing investments with very large rates of return. Switching spending priorities can obtain massive dividends.

25