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- Cocco, M. and Tuzzi, A. (2013). New data collection modes for surveys: a comparative analysis of the influence of survey mode on question-wording effects. _Quality & quantity_ , 47(6):3135–3152.

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- Cullen, W., Gulati, G., and Kelly, B. D. (2020). Mental health in the covid-19 pandemic. _QJM: An International Journal of Medicine_ , 113(5):311–312.

- Dabalen, A., Etang, A., Hoogeveen, J., Mushi, E., Schipper, Y., and von Engelhardt, J. (2016). _Mobile phone panel surveys in developing countries: a practical guide for microdata collection_ . World Bank Publications.

- De Weerdt, J., Beegle, K., Friedman, J., and Gibson, J. (2016). The challenge of measuring hunger through survey. _Economic Development and Cultural Change_ , 64(4):727–758.

- De Weerdt, J., Gibson, J., and Beegle, K. (2020). What can we learn from experimenting with survey methods? _Annual Review of Resource Economics_ , 12:431–447.

- Demombynes, G., Gubbins, P., and Romeo, A. (2013). Challenges and opportunities of mobile phone-based data collection: Evidence from south sudan. _World Bank Policy Research Working Paper_ , (6321).

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- Ding, P., Feller, A., and Miratrix, L. (2016). Randomization inference for treatment effect variation. _Journal of the Royal Statistical Society: Series B: Statistical Methodology_ , pages 655–671.

- Etang, A. and Himelein, K. (2020). Monitoring the ebola crisis using mobile phone surveys. In _Data collection in fragile states_ , pages 15–31. Palgrave Macmillan, Cham.

24
# **Does Gender Equality in Labor Participation Bring Real Equality? Evidence from Developed and Developing Countries**<sup>∗</sup>

_Federica Alfani Fabio Clementi Michele Fabiani Vasco Molini_ The World Bank University of Macerata University of Macerata The World Bank Washington DC, USA Macerata, Italy Macerata, Italy Rabat, Morocco <u>falfani@worldbank.org fabio.clementi@unimc.it m.fabiani9@unimc.it vmolini@worldbank.org</u> _Enzo Valentini_ University of Macerata Macerata, Italy <u>enzo.valentini@unimc.it</u>

**Keywords:** Female labor force participation; inequality; gender equality; developing countries. **JEL classification:** C26, E24, J13, O57

> ∗ The authors acknowledge financial support from the World Bank. We thank colleagues from the Haut Commissariat au Plan (HCP) of Morocco for excellent data preparation. We also thank Gordon Betcherman, Gladys Lopez-Acevedo, Marco Ranzani and Johannes Hoogeveen for comments and suggestions on an earlier version of the manuscript. Of course, we are the sole responsible for all possible errors the paper may contain.
# **Sensitivity Analysis of Association between CVI and social outcomes, using different ICR thresholds**

Logit Regressions, 2006-2016

|**Tab**<br>|**le B2: Unempl**<br>|**oyment Rise**|||
|---|---|---|---|---|
|Variables|Benchmark|ICR<=1.5|ICR<=2|ICR<=3|
|L. Vulnerability Index (CVI)|6.835**|5.606**|5.898***|5.737***|
||(2.673)|(2.232)|(2.146)|(1.932)|
|Constant|-3.036***|-2.981***|-3.044***|-3.154***|
||(0.979)|(0.982)|(0.993)|(1.013)|
|Macro Controls^|Yes|Yes|Yes|Yes|
|Observations|287|287|287|287|
|ROC|0.717|0.718|0.722|0.720|
|Marginal Effects at Median|1.359**|1.114**|1.174***|1.145***|

Unemployment rise: Dummy variable 1 if unemployment rate increases, 0 otherwise. Information for Macedonia not included

||**Table B3: Re**<br>|**cession**<br>|||
|---|---|---|---|---|
|Variables|Benchmark|ICR<=1.5|ICR<=2|ICR<=3|
|L. Vulnerability Index (CVI)|11.06***|9.182***|8.588**|8.837**|
||(3.816)|(3.491)|(3.369)|(3.635)|
|Constant|-4.770***|-4.509***|-4.492***|-4.680***|
||(1.320)|(1.276)|(1.236)|(1.171)|
|Macro Controls^|Yes|Yes|Yes|Yes|
|Observations|232|232|232|232|
|ROC|0.896|0.887|0.886|0.887|
|Marginal Effects at Median|1.016***|0.889***|0.833**|0.855**|

Recession: Dummy variable 1 if GDP growth is negative, 0 otherwise.

|**T**<br>|**able B4: Welfar**<br>|**e Reduction**<br>|||
|---|---|---|---|---|
|Variables|Benchmark|ICR<=1.5|ICR<=2|ICR<=3|
|L. Vulnerability Index (CVI)|5.832**|4.803**|4.712**|4.419*|
||(2.307)|(2.235)|(2.121)|(2.360)|
|Constant|-4.940***|-4.825***|-4.850***|-4.862***|
||(1.059)|(1.035)|(1.042)|(1.053)|
|Macro Controls^|Yes|Yes|Yes|Yes|
|Observations|340|340|340|340|
|ROC|0.841|0.840|0.839|0.838|
|Marginal Effects at Median|0.657***|0.549**|0.538**|0.508*|

Welfare reduction: Dummy variable 1 if GDP per capita growth is negative, 0 otherwise Unit of observation: country-year.

^ Macro controls: Current Account Balance (%GDP), General Government Balance (% GDP), Government Debt (%GDP), real GDP per capita, Inflation rate.

Weights: Sum of value common shares for all firms in sample/Stock market capitalization. Information of Stock market capitalization available for 33 countries. Calculations per country-year.

Year dummies included, errors clustered at country level. Robust standard errors in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

Source: Own calculations.

34
To test for pre-trends and the evolution of employment rates over time, I estimate an event study: where I( _year_ = _t_ ) is an indicator variable that equals 1 in year _t_ and 0 if otherwise. All else remains as previously defined.

# **_Results_**

In evaluating the effect of the power crisis on employment, I leverage two survey datasets - GLSS and Afrobarometer - spanning over the period 1998 and 2018.

Table 3 presents the DiD estimates of the effect of the power crisis on employment. For each dataset, I estimate three variant specifications to test for the robustness of the estimates to various controls. My preferred specification is column 3 (6) which is the most restrictive: including fixed effects for district, survey year, and birth-year. Individual and community characteristics such as gender, educational attainment, rural/urban status, access to road and water are also included. In addition, I control for the interaction between linear time trends and district-level characteristics such as the average homeownership and literacy rates at the baseline. These allow us to absorb trends in local economic development that may be correlated with unemployment rates across districts.

The results are stable and consistent across the two datasets. Starting with the GLSS dataset, the results in column 3 suggest that the crisis led to a 3.4 pp reduction in the probability of employment compared to a 4.7 pp reduction based on the Afrobarometer dataset (column 6). These are economically and statistically significant.

Next, I explore the trends in (un)employment rate before, during and after the crisis. These dynamics are not only important for examining parallel trends assumption, but 29
# **6 Conclusion**

We use data from the Cameroon National Surveys on Employment and Informal Sector (eesi), and the General Enterprises Census to empirically investigate the characteristics of the informal sector in Cameroon and its most salient features. Cameroon’s labor market is characterized by a large informal sector that employs around 90% of the occupied workforce, is largely concentrated in agricultural, and non-tradable services (e.g. retail trade, transportation, hotels or restaurants), and accounts for more than 30% of the gdp. Despite this substantial contribution to the gdp and the labor market the informal sector is made of micro-units, often with one worker, very low-paid jobs and low labor productivity. We document that economic activities are strongly concentrated in Yaoundé and Douala and 60% of Cameroon’s industries are geographically concentrated. Informal firms are on average less concentrated than formal firms, and cities are less specialized. Our results show that only 16 percent of these firms were highly specialized in 2009, and the most specialized cities are natural-resources based cities.

To further investigate the relationship that exists between location, agglomeration, access to credit, informality and productivity across cities and industries, we develop an occupational choice model where individuals decide between formal entrepreneurship, informal entrepreneurship and non-entrepreneurial work. Our model predicts that while entrepreneurial profits increase with agglomeration effects, agglomeration has a non-monotonic (U-shaped) effect on formalization of informal firms. Using data from eesi, we provide structural estimates of the model test some of its salient implications using the generalized method of moments, disaggregated along the main cities and industries of the country. We found that certain sectors such as Manufacturing are highly constrained in capital, compared to other sectors such as Commerce. We also found that the return to capital is not uniform across sectors since some of them are more populated by informal firms compared to others.

The estimated model is used in counterfactual policy experiments to simulate the effect of infrastructure on entrepreneurship, informality and productivity. We found that increased availability of paved road, better access to finance, and tax reforms can have a substantial impact on the economy in terms of entrepreneurship, output, formalization of informal firms and productivity. This is particularly true in sectors such as Agriculture and Services which are likely to beenefit the most from such policy (e.g., improving roads has an obvious positive impact on the transportation of goods and production factors). As countries and regions strive to remain competitive in the face of globalization, local and national governments seek increasingly to support competitive cities and industries. Three key determinants of cities’ wealth are related 45
Table A2. 2 Size of public enterprise sector

||Privatization proceeds<br>as (pct of GDP)|Size of public sector<br>(0 to 6 index)*|
|---|---|---|
|1994|0.37|5.96|
|1995|0.67|5.88|
|1996|0.64|5.80|
|1997|0.33|5.76|
|1998|1.42|5.59|
|1999|4.39|5.06|
|2000|2.04|4.82|
|2001|3.35|4.41|
|2002|1.40|4.25|
|2003|1.80|4.03|
|2004|0.18|4.01|
|2005|0.25|3.98|
|2006|1.15|3.84|
|2007e|0.72|3.75|

*/ We assume full state ownership in 1993. Data for subsequent years were calculated relying on a perpetual inventory method type of approach: It= It-1 +0.2*(Pt-1), where the first It-1 is the original index (equal to zero for 1996) and Pt-1  is the privatization proceeds flow for the year.  We are grateful to Paul Conway for his help in calculating these data in a manner consistent with the overall PMR.

41
in the Chinese industrial context. Thus, they support work recommending that assembly trade and foreign entities be distinguished from ordinary trade and domestic exporters for the analysis of the structure, determinants, and consequences of Chinese export performance (Schott 2008; Jarreau and Poncet 2012). More concretely, our findings underline the distinctive functioning of the export-platform activities of foreign firms compared to the ordinary exports of domestic firms. From a policy perspective, they suggest that export promotion should concentrate on products that correspond to local domestic core competencies.

# **The role of firm-level efficiency**

We now investigate heterogeneity in the effect of product density as a function of the exporting firm’s productivity. This is an important issue. Because foreign firms have been shown to be much more productive than domestic firms in China, an alternative interpretation of the weak effect of product density for foreign firms (and in processing trade, which is dominated by foreign firms) is that how well their products conform to the local production structure is unimportant for the export performance of the most productive firms. We can test this argument by comparing high- and low-productivity firms. Another rationale for this test relates to absorptive capacity. A number of studies have identified limited absorptive capacity and the absence of appropriate technological effort as undermining spillovers (Crespo and Fontoura 2007).

Because absorptive capacity is likely to be proportional to productivity, we expect the link between density and export performance to rise with firm efficiency. We consider three alternative proxies for firm-level efficiency in the customs dataset: the number of product25
services such as bank facilities and general financial development. Barriers to Entry is measured with the barrier of access to land; we also control for competition from informal firms. For each category of the business environment, we find the relevant variables in the Enterprise Survey, _Doing Business_ , and Global Competitiveness data set, respectively. Table 1 reports the name and definition of each variable.

For Enterprise Survey (ES) indicators, the logic of constructing the variables is the same as in Reyes et al. (2017). We measure an aspect of the business environment for a firm with the mean of the response of other firms in the same city and in the same industry category. Specifically, (1) Government Protection is measured with “Corruption Obstacle” (the extent to which corruption is viewed as an obstacle by firms in the city-industry cell) and “Security Costs” (the city-industry average of a firm’s expenditure on security as a share of its sales). (2) Infrastructure is measured with “Power Outage” (the city-industry share of firms that experienced a power outage in the survey year) and “Web” (the city-industry share of firms that use websites to conduct business). (3) Human Capital is captured by “Skill Labor Obstacle” (the extent to which a shortage of skilled labor is viewed as an obstacle by firms in a cityindustry cell). (4) Access to Finance is reflected by “Overdraft” (the city-industry share of firms with an overdraft facility from a financial institution). (5) Barriers to Entry is measured by “Land Access Obstacle” (the extent to which access to land is viewed as an obstacle by firms in a city-industry cell). For all the variables based on subjective perceptions of the degree of obstacles (Corruption Obstacle, Skill Labor Obstacle, Land Access Obstacle),<sup>6</sup> we transform the ranking to a dummy variable indicating moderate or more serious obstacles. In total, we have seven Enterprise Survey indicators to measure the business environment. It is worth noting that even though some of the indicators are binary variables at the firm level, when aggregated to the city-industry level, all these ES indicators are continuous.

When constructing _Doing Business_ indicators and Global Competitiveness indicators, we find the most relevant variables for each BE category. Two points are worth noting. First, when more than one indicator measures the same aspect of the BE, we choose only one variable among the candidates according to the number of non-missing observations and the correlation with the corresponding Enterprise Survey indicator. Second, although _Doing Business_ indicators and Global Competitiveness indicators overlap to some extent with Enterprise Survey indicators, not all BE categories in Enterprise Survey indicators can be matched to a

> 6 The original ranking ranges from zero (no obstacle) to 4 (very severe obstacle).

9
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where _f_ ( β<sup>ˆ</sup> ’ _Xij_ ) is the imputed probability of selection of untrained farmer _i_ in village _j_ . If _Dij_ is very close to zero, the untrained farmer _i_ of village _j_ is very similar to the trained group from that village in the attributes that reflect capacity for opinion leadership. If _Dij_ is positive, the group of trained farmers of village _j_ is “better” (has on average a higher capacity for opinion leadership) than the untrained farmer _i_ of the same village.

Using the variable _Dij_ as a measure of dissimilarity (socio-economic “distance”) between the untrained farmers and the trained farmers in their villages enables the testing of the three alternative hypotheses formulated in Section II: If untrained farmers are more likely to seek and learn IPM knowledge from opinion leaders who are similar to them (hypothesis _(a)_ ), then gains in IPM knowledge by any farmer _i_ from village _j_ during the period 1991-1998 covered by the two surveys will be negatively related to the absolute value of _Dij_ (or to transformations of _Dij_ which increase in value the larger is the absolute value of _Dij_ , such as the square of _Dij_ ). If untrained farmers are more likely to seek advice and learn from farmers who have strong opinion leadership characteristics and who are of higher socio-economic standing (hypothesis _(b)_ ), then the gains in untrained farmers’ IPM knowledge between 1991 and 1998 would be positively related to _Dij_ . If untrained farmers tend to learn from opinion leaders who are better than themselves, but not too much better (hypothesis _(c)_ ), then the relationship between gains in IPM knowledge and _Dij_ will follow a nonlinear pattern such as a quadratic, where the linear term is positive, but the square term is negative (implying that at the margin, as the opinion leaders become too dissimilar to the followers, their impact on knowledge diffusion diminishes). Because hypothesis _(b)_ is nested within hypothesis _(c)_ , the empirical formulation will test both hypotheses simultaneously.

More formally, if we denote the level of IPM knowledge in 1991 and 1999 as K(0) and K(1), respectively, then the following multivariate equations will facilitate the testing of hypothesis 13
most skewed gender wage gap and is among the few regions where gender labor force participation is both large and growing.

# 2.2.1 Bangladesh

Bangladesh is the top-ranked country in the South Asia region, having a better female labor participation rate and improved gender wage gap compared to other South Asian economies. Bangladesh's female labor force participation has increased over the last one and half decades, rising from 16 percent in 1996 to 30 percent by 2010 mainly fueled by employment in rural agricultural areas and certain specific sectors such as garment industries.<sup>10</sup> Amin (2005) shows that female labor force participation has increased mainly in Bangladesh owing to better payment of home-based economic activities. Although the number of women participating in employment has been increasing, they are still outnumbered by their male counterparts in terms of numbers in majority of the employment sectors.

Bangladesh's female labor force participation was 29 percent in 2013 as shown in Table 1. Taking a detailed look at sectoral distribution of the female workers, it can be observed that agriculture, manufacturing and community and personal services are the significant contributors of women’s employment in Bangladesh. In terms of the sectoral composition of employment, the significant feature of Bangladesh is that when compared to other South Asian countries in general, a small proportion of women work in agriculture. In 2013, the share of women employees in agriculture, hunting, animal production and related activities was just 35.6 percent of total employees in the sector. However, as compared to men (34.5 percent) a large majority of women (65.7 percent) are employed in agriculture.

During 2010 to 2013, the female labor force participation grew rapidly in manufacturing, wholesale trade, health and other community services. The share of women in manufacturing almost doubled and became much higher than that of men during this period. The same is true for wholesale trade and other community services sectors as well. However, women’s labor participation has a much lower share in the services sector. This is mainly due to women’s low participation in transport and retail trade in Bangladesh.<sup>11</sup> Overall, Bangladesh has a much higher proportion of women workers in the manufacturing sector and agriculture sector.

> 10 The daily Star, Women workforce growing fast October 11, 2015 accessed online at <u>http://www.thedailystar.net/frontpage/women-workforce-growing-fast-155149.</u> 11 Asian Development Bank & International Labor Organization (2016).

11
Our model includes two independent distance variables to capture the role of trade costs. To measure export distance ( _XDij_ ), we use the arc distance between the Chinese port closest to province _i_ and the destination country _j_ . To measure import distance ( _MDit_ ), we need to take into account that multiple inputs from various economies are used in the production of a specific export good. As a consequence, we measure import distance using the following formula: where _Mijt_ is province _i_ ’s imports from country _j_ in period _t_ ; and _XDij_ is the arc distance between the Chinese port closest to province _i_ and the source country _j_ .

To analyze if China’s processing exports to East Asian economies are more sensitive to export distance and less sensitive to import distance than its processing exports to Western countries, we introduce a dummy variable, _Eastj_ , that equals 1 if the economy of destination is an East Asian economy and 0 if the destination market is a non-Asian OECD country. We then introduce interaction terms between _Eastj_ and our two distance variables ln _XDij_ and ln _MDit_ as independent variables in our model.

Hypothesis 1 will be confirmed if ln _XDij_ and ln _MDit_ both have a negative effect on processing exports. Hypothesis 2 will be validated if (i) the coefficient on the interaction term between _Eastj_ and ln _XDij_ is significantly negative and (ii) the coefficient on the interaction term between _Eastj_ and ln _MDit_ is significantly positive.

We estimate the effect of distance on processing exports using a standard set of controls. Specifically, we use data from, respectively, _China’s Statistical Yearbook_ to include controls for GDP per capita and population size for Chinese provinces. We also use data from _China’s Statistical Yearbook_ to add a control for Chinese provincial wages.

Anderson and van Wincoop (2003, 2004) highlighted the importance of controlling for multilateral resistance in gravity models, i.e. the fact that bilateral flows not only depend on bilateral trade barriers but also on trade barriers across all trading partners. To construct a specification that captures multilateral resistance, we follow Rose and van Wincoop (2001) and Feenstra (2004) by adding both exporter-specific and importerspecific fixed effects. Ideally these fixed effects should be time-varying since the 20